Science, Faculty of
DSpace
UBCV
Barclay, Charlotte
Master of Science - MSc
University of British Columbia
The transition to multicellularity is widely considered a crucial evolutionary process, yet the precise mechanisms are still being uncovered. Advancements in bioinformatic techniques and high-throughput DNA sequencing provide increasing opportunities to better understand complex phenomena like multicellularity, through the study of early branching model organisms. However, to fully leverage the available tools, high quality reference genomes are required. As a result an increasing number of studies combine long read and HiC sequence data to generate a high quality, chromosome scale assembly with high contiguity. This study utilises PacBio's novel long and accurate HiFi reads, in conjunction with Hi-C sequencing technology, to produce a chromosome-scale assembly for the ctenophore Mnemiopsis leidyi. The final assembly demonstrates remarkable contiguity, with an N50 value of 15.6MB and a computationally derived karyotype of n=13. These findings demonstrate an improvement of greater than two orders of magnitude when compared to the previous short read assembly for this species. Further structural genomic organisation was identified, using Arrowhead as part of the Juicer pipeline, to provide the first evidence of topologically associating domains (TADs) and loops in this organism. These findings highlight the importance of utilizing advanced sequencing technologies to improve existing assemblies in our understanding of genomic organisation and structure, and the considerations that should be taken when comparing results derived from different bioinformatic tools. Mnemiopsis leidyi is phylogenetically well placed to answer evolutionary questions surrounding the origins of multicellularity and the addition of a high-quality reference assembly will prove imperative to future molecular and genomics research.
3D GENOMIC INSIGHTS OF AN EARLY BRANCHING METAZOAN: A CHROMOSOME SCALE ASSEMBLY OF MNEMIOPSIS LEIDYI AND DIFFERENTIAL EXPRESSION IN EARLY DEVELOPMENT by Charlotte Barclay MSci (Hons), The University of Southampton, 2013 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2023 © Charlotte Barclay, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: 3D genomic insights of an early branching metazoan: a chromosome scale assembly of Mnemiopsis leidyi and differential expression in early development submitted by Charlotte Barclay in partial fulfilment of the requirements for the degree of Master of Science in Bioinformatics Examining Committee: Dr Steven Plotkin, Professor, Physics and Astronomy, UBC Supervisor Dr Wyeth Wasserman, Professor, Medical Genetics, UBC Supervisory Committee Member Dr Sheila Teves, Assistant Professor, Biochemistry, UBC Supervisory Committee Member iii Abstract The transition to multicellularity is widely considered a crucial evolutionary process, yet the precise mechanisms are still being uncovered. Advancements in bioinformatic techniques and high-throughput DNA sequencing provide increasing opportunities to better understand complex phenomena like multicellularity, through the study of early branching model organisms. However, to fully leverage the available tools, high quality reference genomes are required. As a result an increasing number of studies combine long read and HiC sequence data to generate a high quality, chromosome scale assembly with high contiguity. This study utilises PacBio's novel long and accurate HiFi reads, in conjunction with Hi-C sequencing technology, to produce a chromosome-scale assembly for the ctenophore Mnemiopsis leidyi. The final assembly demonstrates remarkable contiguity, with an N50 value of 15.6MB and a computationally derived karyotype of n=13. These findings demonstrate an improvement of greater than two orders of magnitude when compared to the previous short read assembly for this species. Further structural genomic organisation was identified, using Arrowhead as part of the Juicer pipeline, to provide the first evidence of topologically associating domains (TADs) and loops in this organism. These findings highlight the importance of utilizing advanced sequencing technologies to improve existing assemblies in our understanding of genomic organisation and structure, and the considerations that should be taken when comparing results derived from different bioinformatic tools. Mnemiopsis leidyi is phylogenetically well placed to answer evolutionary questions surrounding the origins of multicellularity and the addition of a high-quality reference assembly will prove imperative to future molecular and genomics research. iv Lay Summary Life's transition to multicellularity has happened multiple times across the tree of life, however the exact mechanisms of this process still elude us. Recent advances in DNA analysis techniques are unravelling the mysteries of this process at the molecular level. Researchers have focused on early organisms like ctenophores (comb jellies) and porifera (sponges) to learn more, relying on accurate genetic blueprints known as reference genomes. This study used advanced sequencing to create a detailed reference genome of the ctenophore Mnemiopsis leidyi. This genome consists of 13 chromosomes, each divided into smaller sections called topologically associating domains (TADs). TADs are distinct regions where nearby DNA interacts more than distant DNA. This genome organization is now seen in these ctenophores for the first time. This refined genome reference of Mnemiopsis leidyi is vital for future research into evolution and multicellularity mechanisms. v Preface The work in this thesis was carried out by myself under the guidance of Dr Steve Plotkin at the Department of Physics and Astronomy at the University of British Columbia. The chromosome scale assembly was the result of a collaborative project between myself and Pranav Garg. While Pranav performed the long read library preparation (section 2.2.2.2), I actively participated in the collection of Mnemiopsis leidyi larval samples for long read sequencing (section 2.2.1) and the sequencing was outsourced to the QB3 team (section 2.2.2.2). Sample collection and cross-linking for the Hi-C library was conducted by myself, with library preparation and sequencing conducted by collaborators at Phase Genomics (section 2.2.2.1). The initial long read assembly using metaFlye (section 2.3) was conducted by Pranav, with contributions by myself and Gabriel Dall’Alba to the pipeline order and filtering steps. The alignment steps, removal of PCR duplicates and comparative scaffolding to produce a final chromosome scale assembly were conducted by me. Gene model prediction which was ultimately used in the differential expression analysis was conducted by Gabriel Dall’Alba. All other bioinformatic analysis including topological domain calling and differential expression analysis were completed by me. The chromosome scale assembly which was the result of a collaboration, is currently being prepared as a manuscript for journal submission. It is my sincere hope that this research will contribute to the ever-growing body of knowledge of Mnemiopsis leidyi and its role as a model organism and inspire future explorations in the field. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v List of Tables ................................................................................................................................ ix List of Figures .................................................................................................................................x Glossary ........................................................................................................................................ xi Acknowledgements ..................................................................................................................... xii Dedication ................................................................................................................................... xiii Chapter 1: Introduction ................................................................................................................1 1.1 The origins of multicellularity ........................................................................................ 1 1.2 Gene expression facilitates cell differentiation in early development ............................ 1 1.3 Structural organisation of the genome ............................................................................ 3 1.4 Genome assembly ........................................................................................................... 5 1.4.1 Genome sequencing and the power of a chromosome scale assembly ....................... 5 1.4.2 Long read and Hi-C sequencing for reference genomes ............................................. 6 1.5 Mnemiopsis leidyi as a model organism ......................................................................... 8 1.5.1 Early development in Mnemiopsis leidyi .................................................................. 10 1.5.2 Changes in transcriptome through development in Mnemiopsis leidyi .................... 10 1.6 Aims and objectives ...................................................................................................... 11 1.6.1 Knowledge gap ......................................................................................................... 11 1.6.2 Objectives ................................................................................................................. 12 Chapter 2: Methods .....................................................................................................................14 2.1 Culture and maintenance of Mnemiopsis leidyi ............................................................ 14 2.1.1 Pseudo kreisel arrangement and maintenance .......................................................... 14 2.1.2 Feeding and rotifer cultures ...................................................................................... 15 2.2 Sample collection and whole genome sequencing........................................................ 16 2.2.1 Larval collection ....................................................................................................... 16 2.2.2 Library preparation and sequencing.......................................................................... 17 2.2.2.1 Hi-C library ....................................................................................................... 17 vii 2.2.2.2 PacBio HiFi library ........................................................................................... 18 2.3 Chromosome scale assembly ........................................................................................ 19 2.3.1 SALSA2 .................................................................................................................... 20 2.3.2 YaHS ......................................................................................................................... 22 2.3.3 Quality checks and manual curation ......................................................................... 22 2.4 Structural organisation of the genome .......................................................................... 23 2.5 Genome annotation ....................................................................................................... 24 2.6 Gene regulation across development ............................................................................ 24 2.6.1 Generating gene counts ............................................................................................. 25 2.6.2 Quality checks and normalization ............................................................................. 25 2.6.3 Differential expression using DeSeq2 ...................................................................... 26 Chapter 3: Results........................................................................................................................27 3.1 Genome assembly ......................................................................................................... 27 3.1.1 SALSA2 vs YaHS scaffolding.................................................................................. 28 3.1.2 13 chromosomes in Mnemiopsis leidyi ..................................................................... 31 3.2 Structural organisation of the genome .......................................................................... 33 3.3 Differential expression in early development ............................................................... 36 3.3.1 Quality check and normalization of count data ........................................................ 36 3.3.2 The maternal to zygotic transition ............................................................................ 38 3.3.3 Gastrulation ............................................................................................................... 41 3.3.4 Cydippid emergence ................................................................................................. 44 Chapter 4: Discussion ..................................................................................................................47 4.1 Chromosome scale assembly ........................................................................................ 48 4.2 Structural organisation .................................................................................................. 50 4.3 Differential expression at key developmental stages .................................................... 53 4.4 Future work ................................................................................................................... 55 4.5 Concluding Remarks ..................................................................................................... 56 Bibliography .................................................................................................................................58 Appendix A ............................................................................................................................... 67 A.1 FastQC quality check for raw HiC reads .................................................................. 67 viii Appendix B ............................................................................................................................... 68 B.1 Metadata of RNA-seq samples ................................................................................. 68 B.2 Quality assurance of all time points .......................................................................... 75 B.3 Hierachical clustering shows sample similarity of the maternal to zygotic subset ... 76 B.4 Dispersal estimates for differentially expressed genes at the maternal to zygotic transition ............................................................................................................................... 77 B.5 Top 25 Differentially expressed genes at zygotic genome activation ...................... 78 B.6 Hierachical clustering shows sample similarity of the gastrulation subset ............... 79 ............................................................................................................................................... 79 B.7 Dispersal estimates for differentially expressed genes of the gastrulation subset .... 79 B.8 Top 25 Differentially expressed genes across gastrulation....................................... 80 B.9 Hierachical clustering of the cydippid subset ........................................................... 81 B.10 Dispersal estimates of genes for the cydippid subset................................................ 81 B.11 Top 25 Differentially expressed genes across onset of cydippid emergence ........... 82 ix List of Tables Table 2.1 Sample collection dates for PacBio long read and Hi-C material ................................ 19 Table 3.1 A summary of sequencing data for PacBio HiFi and Phase Genomics Hi-C .............. 27 Table 3.2 Comparison scaffolding statistics for YaHS vs SALSA2 ............................................ 29 x List of Figures Figure 1.1 An adult Mnemiopsis leidyi (A) and phylogenetic position (B) ................................... 9 Figure 2.1 Representation of the lab set up .................................................................................. 15 Figure 2.2 Pipeline for chromosome scale assembly ................................................................... 21 Figure 3.1 Length profiles of the two scaffolded assemblies with Ya HS and SALSA2 compared to the original short read Ryan (2013) assembly. ........................................................................ 30 Figure 3.2 A dot plot somparing similarity between SALSA2 vs the YaHS assemblies ............. 31 Figure 3.3 Hi-C heatmap of the final assembly ............................................................................ 32 Figure 3.4 Comparison of TAD size and number called by Arrowhead at variable resolution . 34 Figure 3.5 Hi-C contact map shows evidence of TADs and loops .............................................. 35 Figure 3.6 Cluster analysis on the combined GSE60478 and GSE111748 transcriptomic data .. 37 Figure 3.7 PCA of sample expression at the maternal to zygotic transition ................................ 39 Figure 3.8 Log fold change of differentially expressed genes at maternal to zygotic transition 39 Figure 3.9 Heatmap of top 25 differentially expressed genes at maternal to zygotic transition . 40 Figure 3.10 PCA of sample expression at the gastrulation .......................................................... 42 Figure 3.11 Log fold change of differentially expressed genes at gastrulation .......................... 42 Figure 3.12 Heatmap of top 25 differentially expressed genes at gastrulation ........................... 43 Figure 3.13 PCA of sample expression at the cydippid emergence ............................................. 45 Figure 3.14 Log fold change of differentially expressed genes at cydippid emergence ............. 45 Figure 3.15 Heatmap of top 25 differentially expressed genes at cydippid emergence ............... 46 xi Glossary ASW Artificial sea water DEG Differentially expressed genes gDNA genomic DNA GO Gene ontology HiFi high fidelity reads hpf Hours post fertilisation LFC Log fold change MZT Maternal to zygotic transition NGS Next generation sequencing PCA Principal components analysis SMRT Single-molecule real time sequencing ZGA Zygotic genome activation xii Acknowledgements Thanks to my supervisor Dr. Plotkin for introducing me to this topic and thanks to my lab mates over the years Gabriel and Anna who introduced me to UBC and showed me around. Thanks as well to Santanu, Jason and Pranav. Thank you to our collaborators at Phase Genomics who helped us with the HiC library. Thanks to my committee, I appreciate the support and guidance you’ve provided and the safe space you created for me to ask the ‘dumb’ questions. Most importantly, a huge thanks to my family for supporting me through this. Returning to grad school after many years out was always going to be a challenge but the last year has thrown some obstacles and I could not have got through it without you. To my husband Sam, I would be lost without you. xiii Dedication This is for Sam and our future rainbow. 1 Chapter 1: Introduction 1.1 The origins of multicellularity The transition from unicellular to multicellular organisms has long fascinated the scientific community particularly the progression towards complex organisms featuring differentiated cell types observed today. This evolutionary jump represents a significant transition, with it’s persistence supporting arguments about its necessity (Grosberg & Strathmann, 2007; Niklas & Newman, 2013, 2020). Metazoan evolution is characterised by genomic mechanisms resulting in specialised cell functions; however understanding of the evolution of cell types and regulatory programs resulting in diverse animal body plans, remains limited (Levin et al., 2016; Sebé-Pedrós et al., 2018). By examining the genetic modifications across early development of model organisms, we can infer the genomic mechanisms necessary for the transition from unicellular ancestors to multicellularity. Genetic processes in early development demonstrate how cells differentiate, communicate, and organize into tissues, mirroring key steps in multicellular evolution. Model organisms that are early branching can offer valuable perspectives due to their proximity to the evolutionary jump and provide insights for future study. 1.2 Gene expression facilitates cell differentiation in early development The process of gene expression is a crucial mechanism that underlies cell fate decisions and cell differentiation during early embryonic development across Metazoa. Gene expression controls the activation or deactivation of specific genes, thereby affecting the behaviour and characteristics of 2 cells and ultimately determining their fate. The diversity of animal body plans and cell types is governed by differences in gene expression that allow for differentiated cell types from pluripotent embryonic cells (Levin et al., 2016). These gene expression patterns are regulated by a variety of regulatory DNA molecules, such as transcription factors, silencers, and insulators, as well as epigenetic factors. These mechanisms tightly regulate the gene expression process by controlling the accessibility of DNA to the transcriptional machinery. Together, these elements form gene regulatory networks (GRNs) that interact to facilitate complex gene expression patterns that determine cell fate, establishing distinct cell lineages and differentiation pathways during early embryonic development (Levine & Davidson, 2005). The precise expression of genes in a specific spatial and temporal pattern is critical in defining the developmental program of an embryo and therefore is of interest in understanding the mechanisms of multicellularity. The comprehension of pluripotency preservation and attenuation is intricately linked with the exploration of multicellularity. Recent studies have identified a specific set of genes, Oct3/4, Sox2, Klf4, and c-Myc – denoted the OSKM network, that can induce pluripotency when over-expressed (Okita et al., 2007; Takahashi et al., 2007, 2014; Takahashi & Yamanaka, 2006). The result, induced pluripotent stem cells (iPSCs) have become increasingly popular in medical research, specifically in regenerative medicine and cell therapy based strategies as the cell types closely resemble embryonic stem cells. Since the initial discovery, iPSCs have been considered a valuable resource to study cell fate decisions (Moradi et al., 2019; Takahashi et al., 2007; Takahashi & Yamanaka, 2006). Understanding how these factors interact with each other, whether they are in proximity and whether this network is conserved across the tree of life could uncover further insights into the loss of pluripotency and ultimately multicellularity. 3 1.3 Structural organisation of the genome The regulation of gene expression resulting in differentiation operates not linearly but in a three-dimensional context. The genome is a complex and tightly folded structure and in humans this presents as over 2m of DNA compacted into 23 pairs of chromosomes in a nucleus of 10 um diameter (Sun et al., 2000). However, the exact role genome structure has in gene regulation and bringing together elements such as promoters and enhancers, is still not fully characterised. At the upmost level, the nuclear organisation of the genome is in chromosome territories, which are further sub-divided into interacting domains. The discovery of these interacting domains, widely known as topologically associating domains (TADs) led to a understanding of the structure and organisation of the genome (Dixon et al., 2012). First discovered through advances in chromatin conformation capture techniques, specifically Hi-C sequencing, TADs are regions of the genome that exhibit increased interactions between sequences that reside within a TAD and diminished interactions between sequences adjacent (Dixon et al., 2012; Rao et al., 2014). Evidence suggests cis-regulatory elements frequently interact within their respective domains to regulate gene expression and as such TADs and wider chromatin confirmation are considered to be tightly linked to gene expression (Symmons et al., 2014; van Mierlo et al., 2023). However, this is contended, with studies suggesting an uncoupling of TADs and gene expression (Dixon et al., 2012; Ghavi-Helm et al., 2019; Nora et al., 2012). Despite this, TADs are widely considered fundamental units of structural organisation and have been confirmed in a variety of different animal models including mice, the fruit fly (Drosophila melanogaster) and zebrafish (Danio rerio) (Dixon et al., 2012; Franke et al., 2021; Rao et al., 2014; Sexton et al., 2012). Although domains are conserved across cell types and even species, the functions of TADs are still not fully defined 4 and their widely debated role in gene expression highlights the need for further study (Tena & Santos-Pereira, 2021). Recent evidence has suggested that while boundaries are consistent across cell types, the boundary strengths can vary, with changes in the boundary strengths having implications in disease (Gong et al., 2018). The identification of TADs or TAD calling, can be done through different computational methods. Genomic interactions derived from the Hi-C data are divided into chunks (bins) of different base pair lengths known as the resolution and a matrix of these interactions is generated. The results of TAD calling is widely dependant on a number of factors, namely sequencing coverage, quality of sequencing data, genome size and complexity and arguably the quality of the reference genome (Zufferey et al., 2018). In general, a higher sequencing coverage is preferential, as it improves the resolution and accuracy of the results. However, there is no fixed threshold for sequencing coverage that applies universally across all organisms. Instead, it depends on the specific characteristics of the genome being studied. Prior TAD calling studies have required a coverage of at least 20 to 30x of the genome, however this may be higher for larger or more complex genomes (Bonev & Cavalli, 2016; Rao et al., 2014; Sefer, 2022). Additionally, it's important to note that the sequencing coverage alone is not sufficient for accurate TAD calling, and other factors such as the quality of the sequencing data, the quality of the genome assembly, and the choice of TAD-calling algorithm will also affect the results (Rao et al., 2014; Sefer, 2022; Zufferey et al., 2018). 5 1.4 Genome assembly A good quality reference genome is fast becoming a vital commodity for much scientific research, providing a critical foundation for further work by providing a complete and accurate representation of the genetic material of a species. High quality reference genomes facilitate the identification and analysis of genetic variations, annotation of functional elements, and enables the comparison of genomic data across different samples, species, and studies - including those on the genetic regulation of multicellularity. Despite a wide resource of published chromosome scale reference genomes for vertebrates and other bilaterians, there are a lot fewer for non-bilaterians; namely Ephydatia muelleri (Phylum Porifera, Class Demospongiae) (Kenny et al., 2020), Rhopilema esculetum (Phylum Cnidaria, Class Scyphozoa) (Y. Li et al., 2020), Nematostella vectensis (Phylum Cnidaria, Class Anthozoa) and Hormiphora californensis (Phylum Ctenophora, Class Tentaculata) as well as an increasing number of chromosome genomes being made available through the Darwin Tree of Life Project (Blaxter, 2022; Kenny et al., 2020; Y. Li et al., 2020; Schultz et al., 2021). In part, this is considered to be partially due to the challenge of isolating genomic DNA and other nucleic acids from nonbilaterians, as well as their highly heterozygous genomes, which complicates genome assembly (Ohdera et al., 2019; Simister et al., 2011). However, recent advances in long-read sequencing and the use of Hi-C data have made chromosome-scale assemblies for nonbilaterians possible. 1.4.1 Genome sequencing and the power of a chromosome scale assembly ‘Knowledge of sequences can contribute much to our understanding of living matter’ – Frederick Sanger (Heather & Chain, 2016). 6 Next generation sequencing (NGS), revolutionised the understanding of the genome and has had a huge impact across the fields of science including health, genetics and evolution. The parallelisation compared to Sanger sequencing has allowed for improved speed with which both DNA and RNA can be sequenced, resulting in an increase in the cost effectiveness, with the first release of NGS technology estimated to reduce cost from $100M to $1-2000 per genome (Goodwin et al., 2016). However, despite the advantages that this technology provides – short reads are often still insufficient to adequately describe some of the complexities of the genome, particularly with regards to organisation. Long repeat rich regions, structural variations such as large inversions and deletions, can be difficult to identify with short read technologies. Reconstruction by way of assembly can therefore, prove problematic (Amarasinghe et al., 2020; Heather & Chain, 2016). Due to their ability to capture more of the genome in a single pass, long reads can help generate higher contiguity assemblies. More recently, long reads with chromatin interaction data obtained from Hi-C sequencing have been used to generate high quality genomes in contiguous sequences on the scale of whole chromosomes (Burton et al., 2013). A chromosome scale reference can provide better understanding of the structural organisation of the genome, the interaction between genes and their regulatory elements and how changes in higher level chromatin can impact cellular function and potentially contribute to disease. 1.4.2 Long read and Hi-C sequencing for reference genomes Long-read sequencing, or third-generation sequencing, has revolutionised the field of genomics by generating read lengths in the range of kilobases – an order of magnitude greater than the short 7 read counterparts. Pioneering this technology are Oxford Nanopore Technologies and Pacific Biosciences (PacBio). The principle of nanopore technology involves threading a solitary DNA strand through a nanopore equipped with an enzyme, detecting electrical signal variations and yielding reads in the range of 10 – 100’s Kb in length with an initial accuracy of 70 -80%, since enhanced to 94% (Wang et al., 2021). Alternatively PacBio use single-molecule real-time (SMRT) sequencing to achieve long reads, with the first iterations of this technology producing reads between 20 – 100Kb with an error rate of 11% (Pollard et al., 2018). Recent developments by PacBio have incorporated circularising the DNA and conducting multiple passes to create a consensus known as HiFi reads, which are significantly more accurate than earlier long reads with an accuracy of 99.9% (Wenger et al., 2019). Ultimately, these increases in accuracy in long-read sequencing has resulted in a significant advantage over short-read sequencing for de novo genome assembly, particularly in the .ability to span difficult-to-sequence repetitive regions. In recent years, Hi-C data has been used in conjunction with long read to generate chromosome scale assemblies, with long range interaction data enabling scaffolding and correcting misorientation or mis-joins (Burton et al., 2013; Gallardo-Escárate et al., 2021; Nagy et al., 2022; Schultz et al., 2021). Hi-C sequencing was initially developed to study this three-dimensional structure of chromatin in the nucleus. This technique identifies chromatin interaction via proximity ligation, where upon interacting elements of the genome are cross-linked prior to ligation (Lieberman-Aiden et al., 2009). Cross linked regions are cut with restriction enzymes and ‘sticky ends’ are biotinylated prior to ligation. After shearing, the DNA is purified, using streptavidin beads to pull down biotinylated reads only, generating a library of predominantly interacting elements that are chimeric in nature (Lieberman-Aiden et al., 2009).The arrangement of chromatin 8 within the nucleus plays a critical role in regulating biological function at both the gene and global nuclear levels. Examining how chromatin is packaged and organized within the nucleus can provide valuable insight into the spatial dynamics of gene regulation, chromosome structure, genome stability, and transmission (Belton et al., 2012; Lieberman-Aiden et al., 2009). 1.5 Mnemiopsis leidyi as a model organism Ctenophores (Phylum Ctenophora), commonly known as comb jellies, are phylogenetically well-placed to offer insights into the mechanisms that govern multicellularity, due to their positioning as a candidate for the earliest branching metazoan lineage (Figure 1.1). Evidence suggests that ctenophores diverged from bilaterians 600 million years ago, however there is still frequent debate as to whether ctenophores (Dunn et al., 2008; Hejnol et al., 2009; Moreland et al., 2014; Ryan et al., 2013) or porifera (Philippe et al., 2009; Pick et al., 2010) are the earliest branching group, with one phylogenetic study producing multiple viable tree constructions dependent on the subset of genes and taxonomic abstracts selected (Nosenko et al., 2013). While initial studies classified ctenophores as eumetazoans, the sister group of cnidaria and bilaterians, next generation sequencing of both M. leidyi (Ryan et al., 2013) and the ctenophore Pleurobrachia bachei (Moroz et al., 2014) challenged this classification suggesting ctenophores were the sister group to all other metazoans, a position typically associated with sponges. A chromosome scale genome for Hormiphora californensis further supported this (Schultz et al., 2021). Regardless of whether ctenophores or sponges occupy the position of the earliest branching organism, studying ctenophores as an early branching group provides researchers with a unique opportunity to explore conserved genetic elements that may shed light on the molecular mechanisms necessary for the 9 emergence of multicellularity. By investigating the genomic characteristics and developmental processes of ctenophores, scientists can gain valuable insights into the evolutionary history and genetic innovations that led to the development of complex multicellular organisms. Mnemiopsis leidyi has particular use as a model organism and is well-suited for a broad range of experimental genetic, embryological and developmental studies due to both its rapid generation time of around three weeks from larvae to adult, as well as the ability to spawn healthy adults from a lab culture daily (Presnell et al., 2022). There also exists well defined protocols, specifically microscopy data of early cleavage cycles, transcriptomic data for developmental time series and for regeneration as well as immunostaining protocols (Davidson et al., 2017; Pang et al., 2010; Pang & Martindale, 2008a, 2008b; Presnell et al., 2022; Salinas-Saavedra et al., 2018). Previous genomic studies have elucidated the presence of various cell types, including ciliated cells, secretory cells, and interestingly muscular systems unlike their other basal metazoan counterparts. Furthermore, recent investigations have identified the presence of a neural net and Figure 1.1: An adult Mnemiopsis leidyi B Schematic for phylogenetic position of M. leidyi. Adapted from Ryan et al. (2013). 10 the absence of several classes of homeodomains that are missing from the existing M. leidyi genome (Moroz et al., 2014; Ryan et al., 2010). This ever increasing evidence demonstrates that this organism presents a valuable avenue for elucidating the evolution of multicellularity, underscoring the significance of further research on this intriguing species. 1.5.1 Early development in Mnemiopsis leidyi Ctenophore development is renowned for its remarkable stereotypy (Fischer et al., 2014; Martindale & Henry, 1997, 1998, 1999). Fertilization in these animals occurs promptly after spawning, leading to rapid progression towards free-swimming embryonic stages. In particular, the early developmental events in the ctenophore M. leidyi have been extensively documented from the initial cleavage cycle within 30 minutes of fertilisation, followed by successive cleavages occurring approximately every 20 minutes subsequently until the hatching of the cydippid larvae from the chorion at approximately 18 – 20 hours post fertilisation (hpf) (Fischer et al., 2014). Notably, the micromere sub-lineages from early development have also been identified in M. leidyi. Furthermore, this species undergoes the maternal-to-zygotic transition during early development, where maternal factors are gradually replaced by zygotic gene expression, driving subsequent developmental events. 1.5.2 Changes in transcriptome through development in Mnemiopsis leidyi Existing studies have categorised transcription profiles of M. leidyi through early development, with the period of zygotic genome activation (ZGA) otherwise known as the maternal to zygotic transition (MZT). This important transition has been described as occuring between 1.5 – 2 hpf with differential expression of 1,908 contigs between single and eight cell stages (Davidson et al., 11 2017) and 3 hpf where actinomycin D transcription inhibition affected development only after the 60-cell stage (Fischer et al., 2014). Overlap in expression domains has been previously seen for a subset of germline genes in regions of high cell proliferation, suggesting that these genes play a role in stem cell specification and maintenance in M. leidyi (Reitzel et al., 2016). Although differential expression analysis has been performed on the single and eight-cell stages of M. leidyi, it is of scientific interest to further investigate the extent of variation in expression profiles by incorporating an updated gene model and leveraging the additional transcriptomic data from complementary stages, which has been subsequently amassed since the original analysis. The remarkable advancements in sequencing technologies subsequent to the initial gene annotation, coupled with the increased availability of transcriptomic and expressed sequence tag (EST) data, present an opportunity to refine and enhance the fragmented gene models in question. Furthermore, while it is widely understood that cell differentiation is governed by changes in gene expression, the role that chromatin plays in expression patterns and ultimately cell fate decisions has been largely uncharacterised in this organism. Changes in chromatin accessibility are not measured in this study, however the overall 3D structure is defined and preliminary optimisation of protocols for DNA extraction at these early stages is well underway. 1.6 Aims and objectives 1.6.1 Knowledge gap Ctenophores are phylogenetically well placed to provide insights into the mechanisms that govern multicellularity. However, before more complicated studies such as gene manipulation can occur, it is vital to have a high quality reference genome. While there have been previous efforts to 12 characterise genomes for the Ctenophora, these studies have been limited by the technology available at the time and specifically for M. leidyi there still lacks a high contiguity reference genome. The present reference genome spans 156 Mb and comprises 5,100 scaffolds, having been generated through the utilisation of next-generation sequencing, specifically employing the short-read Roche 454 sequencing technology (Ryan et al., 2013). More recently, the Hormiphora californensis genome has been characterised to a chromosome-scale through advances in sequencing technology and bioinformatic tools (Schultz et al., 2021). An improvement to the current short read assembly, could be advantageous in providing better genomic resolution for future experimental studies. In addition, there is a lack of information of structural genomic organisation of this genome, with the chromosomal territories and particularly with reference to the presence and absence of topological domains. Existing studies have identified the presence of domains in another ctenophore lineage Hormiphora californensis, however currently these remain undefined in M. leidyi (Schultz et al., 2021). Furthermore there still exists debate around the conservation and role of TADs in the structural organisation of the genome and whether this is a necessity for more complex life forms. 1.6.2 Objectives This thesis aims to utilise these technological advances in long read and Hi-C sequencing along with bioinformatic tools, to improve the contiguity of the M. leidyi genome compared to the existing short read assembly and provide updated differential expression analysis of key developmental timepoints. This thesis has the following objectives: 13 • To define a chromosome-scale reference assembly of M. leidyi This study will leverage recent advancements in sequencing technology and bioinformatic methodologies to establish a highly contiguous and chromosome-scale assembly for the model organism M. leidyi. This will be achieved by the de novo assembly of long-read HiFi sequencing data obtained from PacBio sequencing platform with Hi-C contact data. • To identify the structural organisation of the genome of M. leidyi This study will use the Hi-C data to generate a Hi-C heatmap to visualise the chromatin interactions and identify potential chromosome domains and utilise bioinformatic tools to identify the presence or absence of other structural elements in this organism. • To determine differentially expressed genes at key stages of early development in M. leidyi This study will perform differential expression analysis on transcript data for M. leidyi at the maternal to zygotic transition (: 0-3 hours post fertilization ) and cydippid emergence (17-20 hours hpf), which refers to the larval hatching process from the chorion. The transcript data, is obtained from NCBI series GSE60478 and GSE111748. 14 Chapter 2: Methods 2.1 Culture and maintenance of Mnemiopsis leidyi The DNA libraries that were generated for the genome assembly have been extracted from spawns of adult Mnemiopsis leidyi laboratory cultures. Ctenophores are pelagic organisms that are susceptible to water flow and quality. Mnemiopsis leidyi is a relatively hardy coastal species compared to deep water and open ocean counterparts, they are still sensitive to poor water quality and rely on environmental cues and a healthy planktonic food supply for survival and plentiful spawns (Presnell et al., 2022). Wild northern and southern Atlantic populations, require 6 to 9 and 3 to 5 hours respectively of darkness to trigger gamete release, known as spawning (Martindale, 1987). 2.1.1 Pseudo kreisel arrangement and maintenance To mimic the natural environment, M. leidyi cultures are maintained in two 90 gallon pseudo kreisels containing artificial sea water (ASW), at an optimised flow rate of approximately 1.0 RPM, promoting feeding mobility while minimising the risk of damage. Temperature is maintained at 19 ͦC by an in-line chiller (Tradewind IL-I5-S) and salinity is stabilized at 30 ppt. Daily diurnal cycles to trigger gamete release are mimicked with timer controlled full-spectrum visible light LED (MARS aqua, MZAQ-300-100LED). The cultures are kept in a light-insulated room, with the LED light source set to 8 hours within a 24 hour period, generating a 8:16 light: dark cycle. The LED light is activated at midnight and turns off at 8am and as such spawning is induced 4 hours post darkness at approximately noon, with embryo’s collected for library preparation post 1 pm. Figure 2.1 describes the pseudo kriesel and autofeeding set up. 15 2.1.2 Feeding and rotifer cultures Mnemiopsis leidyi specimens are provided with daily feedings comprising live cultures of rotifers (Brachionus rotundiformis) and copepods (Apocyclops panamensis). The rotifers are administered automatically via a pump, delivering approximately 2.4 - 3 litres per day in regular doses, at a concentration of 300 - 400 individuals per ml. On the other hand, copepods are manually fed to Mnemiopsis leidyi once a day. Additionally, periodic supplementation of brine shrimp (Artemia spp.) is implemented to enhance the nutritional value of the rotifer feed and improve the production of spawns in preparation for experimental procedures. Figure 2.1: Representation of the lab set up. Rotifer cultures are the primary food source with pumps used to feed rotifer buckets with algal concentrate (RG). Autodosing pumps also control the volume of rotifers harvested and fed to the ctenophore cultures in the pseudo kriesel. A full spectrum visible light LED is on a 8:16 light:dark cycle to induce spawning. Created with biorender. 16 2.2 Sample collection and whole genome sequencing Samples for library preparation comprised cydippid larvae collected immediately post hatching from the chorion, approximately 18 – 24 hours post fertilisation. DNA extraction and library prep face challenges due to mesoglea-rich adult stages and the small genome size. Using a pooled sample of densely packed larvae optimizes DNA yield relative to sample weight. Adults were removed prior to spawn, which is onset 4 hours after darkness. To increase fecundity, artemia were fed to the adult cultures 24 hours prior to spawning. 2.2.1 Larval collection A Pyrex dish with dimensions of 100x50mm (3140-100) is utilized to extract adult ctenophores from the pseudo kreisel and transfer them to a static aquarium. The aquarium is filled with a 50:50 mixture of filtered water from the pseudo kreisel and sterile ASW of the same salinity to minimise macro-biological contaminants. Adult ctenophores were transferred a final time to a larger Pyrex dish (150x75mm) coated with a thin layer of commercial water repellent, RainX, to reduce adhesion. After approximately two hours, adults are returned to the pseudo kreisels with minimal water transfer. Fertilized eggs descend to the bottom of dish, where they are left overnight to mature at room temperature. The dish is covered to prevent evaporation and an increase in salinity which could result in the dehydration of developing embryos. At 18 hpf, cydippids from the chorion and are harvested using a light microscope. Cydippids were collected using a 20 μL pipette with the tip cut to widen the pore, into a 60mm-wide glass petri dishes of fresh sterile 30 ppt ASW. 17 2.2.2 Library preparation and sequencing 2.2.2.1 Hi-C library The HiC library was extracted from the cydippid larval spawn of 20 lobate adults, immediately post hatching at 18 – 24 hpf. Adults were removed the day prior and spawned as described in section 2.2.1. Multiple spawns were conducted over consecutive days to collect >10,000 larvae to an estimate cell population of 10 million, as recommended by industry collaborators Phase Genomics. Larval samples were transferred to a 1.5ml Eppendorf tube and spun down at 1000g following which excess ASW was aspirated. Paraformaldehyde at 1% concentration was added to each sample at 10 X the sample volume to cross link. The sample was incubated at room temperature for no more than 20 minutes, with periodic mixing and vortexing. After incubation, powdered glycine was added directly to the sample, to a final concentration of 1g/100ml (125mM) to quench cross linking. An additional 15 minutes incubation with periodic vortexing following which the sample was spun down at 1000g for 1 minute and the supernatant aspirated. The fixed sample was then rinsed with PBS, before a final spin and aspiration step, then shipped on ice to collaborators Phase Genomics. The library was prepared by Phase Genomics in accordance with the manufacturers protocol using Proximo Animal (v4) technology. Upon receipt of samples, cross linked cells were lysed and fragmented with a cocktail of restriction enzymes HinfI, MseI, DdeI and DpnII. The restriction fragments were ligated with biotinylated adapters, before capture on streptavidin-coated magnetic beads in a proximity ligation step. Fragments were sheared and DNA purified and PCR amplified prior to sequencing. The HiC library was sequenced on the Illumina Novaseq 6000 instrument. 18 2.2.2.2 PacBio HiFi library High molecular weight genomic DNA was also extracted from cydippid larvae at a corresponding timepoint. Thirty nine lobate adults were removed and spawned as described in section 2.2.1. Approximately 2600 larvae were collected in a single spawning event (at >1000 cells per larvae) into a 2ml Eppendorf tube. The gDNA library was prepared using the Circulomics Nanobind Big DNA kits v1 (NB-900-701-01). The sample was spun down for 10 min at 9800 g, after which the supernatant was aspirated and 750 μL of buffer CT was immediately added. The sample was first homogenised then spun at ~3000g for 5 minutes before aspirating the supernatant and proceeding to cell lysis. Sample was centrifuged and supernatant transferred before binding with the nanobind disks. Three wash steps were performed before final elution, in accordance with manufacturers protocol. Date Library Sample Name # Larvae 20/08/2021 Hi-C MleSample1 2000 21/08/2021 Hi-C MleSample2 2000 23/08/2021 Hi-C MleSample3 >4500 25/08/2021 Hi-C MleSample4 600 27/08/2021 Hi-C MleSample5 800 31/08/2021 Hi-C MleSample6 2100 08/04/2022 PacBio HiFiSample 2600 Table 2.1: Sample collection dates for PacBio long read and Hi-C material. Multiple days of spawing were conducted for Hi-C samples and ultimately combined to provide enough material for Hi-C library prep. 19 Sequencing was performed by QB3 genomics facility at the University of California Berkeley. A SMRT library was prepared for circular consensus sequencing using the SMRTbell express template prep kit 2.0, with an extension to the ligation step where adaptors were run overnight. Exonuclease digestion was also performed. The remainder of the protocol was run according to the manufacturer instructions. The circularised library was sequenced on a SMRT cell 8M for 30 hours on a Sequel II. Full details of the library preparation can be found in the manufacturers guidelines and P. Garg (2023) thesis. 2.3 Chromosome scale assembly A comparative analysis of chromosome-scale scaffolding was performed using two leading tools for chromosome scale scaffolding SALSA2 v2.3 (Ghurye et al., 2017, 2019) and YaHS v1.1 (Zhou et al., 2023). To generate the chromosome-scale assembly, contact information derived from the Hi-C reads was used to correct and scaffold a preliminary assembly constructed from PacBio’s long (HiFi) reads. Circular consensus reads were obtained from raw reads using the PacBio’s software pbccs v6.4.0 with --all, after which reads were filtered to retain only high quality reads with a quality score >= 0.99 using bamtools. The assembler metaFlye v2.9.1-b1780 (Kolmogorov et al., 2020) was run in metagenomics mode to account for the pooled sample using the --meta --scaffold --no-alt-contigs --iterations 2 –pacbio-hifi parameters. Blobtools v1.1 (Laetsch et al., 2017) was used to filter out non metazoan content, using the information provided from the tool for taxonomy and coverage. The assembled long read output 20 was then parsed into the scaffolding pipeline. Full details of the long read assembly and filtering can be found in Garg (2023) thesis. The initial long read assembly was used as the starting reference for the chromosome scale scaffolding. The assembly was indexed and Hi-C alignment performed using the updated Burrows-Wheeler Alignment (BWA) tool, bwa-mem2 (H. Li & Durbin, 2009, 2010; Vasimuddin et al., 2019). Alignment was conducted with bwa-mem2 v.2.2.1 with the HiC specific parameters -5SPM for filtering and scoring to account for the chimeric nature of the reads. The -SP parameter allows for alignment with each mate separately, while still maintaining the appropriate formatting for paired end reads, while -5 is used to report the 5’ portion of the primary alignment and -M is used to annotate the secondary/supplementary clipped reads as secondary, for compatibility with Picard MarkDuplicates. The raw sequencing reads were filtered to remove adapter sequences and low-quality bases. Figure 3 presents an overview of the computational pipelines that were run. The assembly and scaffolding steps were run on UBC’s High Performance Computing environment Sockeye 2.3.1 SALSA2 Post alignment of the Hi-C reads to the long read assembly, PCR duplicates were identified using samblaster v0.1.26 (Faust & Hall, 2014) and the mapped SAM file was converted to BAM format and PCR duplicates removed using SAMtools v1.15.1 (Danecek et al., 2021). SALSA2 scaffolding was using information from the gfa graph to aid scaffolding and the multiple restriction enzymes -m yes -g <.gfa> -e GANTC,TTAA,CTNAG,GATC. The SALSA2 package was installed21 Figure 2.2: The YaHS vs SALSA2 scaffolding pipeline. 22 using a conda environment through the bioconda channel, source code available at https://github.com/marbl/SALSA. The complete process including pre-processing alignment, filtering and iterative scaffolding has been added to a pipeline and shared on github at https://github.com/cbarcl01/SALSAscaffold. To check the quality of the assembly and visualise any potential misjoins, the convert.sh script available at the SALSA2 github repository at https://github.com/marbl/SALSA was utilised to generate a contact map .hic file which was loaded to Juicebox. 2.3.2 YaHS Hi- reads were first mapped to the long read assembly, with the same parameters as in SALSA2 to maintain reliability of comparison between tools. The PCR duplicates were identified using samblaster as previously and the mapped SAM file was converted to BAM format and PCR duplicates removed using samtools view -S -h -b -F 2316. The scaffolding tool YaHS (v1.1) was run using default parameters and -e GANTC,TTAA,CTNAG,GATC to include information from restriction enzymes. The software was downloaded directly from the github repo https://github.com/c-zhou/yahs. 2.3.3 Quality checks and manual curation After the first round of scaffolding with YaHS gaps were filled using the bioinformatic tool gapless (v1.0.0) available from github at https://github.com/schmeing/gapless. PacBio HiFi reads were used as the read type with parameter – pb_hifi for three passes. A final scaffolding step with YaHS was performed, and a HiC contact map was generated using the YaHS juicer integration 23 juicer pre, utilising pre and hic-straw v1.3.1 components of the Juicer package (Durand, Robinson, et al., 2016; Durand, Shamim, et al., 2016), with parameter -a to generate editable output files for manual curation. The assembly was manually curated in Juicebox, an interactive genomic and contact map viewer application (Durand, Robinson, et al., 2016). 2.4 Structural organisation of the genome After manual curation a final Hi-C contact map was generated using the juicer pre script in the YaHS tool. The output HiC contact map was loaded with the final assembly into Juicebox, and the annotation tool utilised to identify potential chromosome territories. Topological domains were called by applying the Arrowhead algorithm as part of the Juicer pipeline to the output HiC matrix. The arrowhead transformation calculates a matrix comparing the observed vs expected contact density (Rao et al., 2014). Arrowhead was called at varying resolutions 5Kb, 10Kb, 50Kb and 100Kb using the -r parameter and with sliding windows at 100, 250, 500, 1Kb, 2Kb (default) and 10Kb using -m. In addition to adjusting for these parameters, domains were called using two normalisation methods Knight-Ruiz the recommended normalisation for the Juicer pipeline and Vanilla Coverage using -k KR or VC. Potential loops were identified using the HiCCUPS algorithm with default parameters normalisation -k KR and resolution set to -r 5Kb, 10Kb. The resultant TAD and loop files were loaded into Juicebox as a 2D annotation to the HiC contact map. 24 2.5 Genome annotation Gene model prediction and subsequent annotation were performed by G. Dall’ Alba as part of a pipeline developed in a masters study that is currently unpublished. In this pipeline, first repeats within the assembled genome were identified using RepeatModeler v2.0.3 (Flynn et al., 2020) and the Extensive de novo TE Annotator (EDTA) v2.0.1 (Ou et al., 2019). Subsequently, the genome was softmasked using RepeatMasker v4.1.1 (Tarailo-Graovac & Chen, 2009), utilizing the libraries provided by RepeatModeler and EDTA. In order to run the BRAKER2 pipeline, available M. leidyi RNA-seq reads were first aligned to the softmasked genome using HISAT2 v2.2.1 (Kim et al., 2019). The RNA-seq datasets utilised for the annotation process included publicly available NCBI datasets GSE60478, GSE111748, GSE93977 and PRJEB28334. The aligned RNA-seq data and the masked genome were then input to BRAKER2 v2.1.5 (Hoff et al., 2019) pipeline in the genome and RNA mode, with default parameters for identification of gene models and annotation. 2.6 Gene regulation across development Differential expression analysis was performed on count data obtained from M. leidyi across different developmental time points. Transcriptomic data and metadata was acquired from NCBI GSE60478 and GSE111748, previously utilised in a developmental time course study (Levin et al., 2016). Gene counts were determined from the predicted gene models generated using the chromosome scale assembly of the present study (refer to section 2.6.1 for gene count and section 2.5 for the gene model annotation). 25 2.6.1 Generating gene counts Counts were calculated for the predicted gene models using the tool featureCounts (v2.0.6) (Liao et al., 2014). To quantify counts for gene models, transcripts were first aligned to the newly created reference genome using the tool STAR with default parameters and removal of low quality reads. The alignment files along with the annotation file for reference genome are used in the featureCounts pipeline with the parameters -T 8 -S 0 -t gene -g geneiD -Q 10 -a annotation_file . 2.6.2 Quality checks and normalization Gene count data for both studies GSE60478 and GSE111748 were combined into one count file. The mean for each gene was calculated and lowly expressed genes (where mean across all samples <2) were removed. Count data was normalised to account for variability across expression levels of different genes by applying a median of ratios normalisation in the DESeq2 package. Variance stabilising transformation, from the DeSeq2 R package v 1.38.3, was used to adjust for library size between the different samples with blind=T (Love et al., 2014). Outliers were identified through, unsupervised clustering analyses to assess sample similarity. Hierarchical clustering was performed using pheatmap v1.0.12, where clustering method was average linkage and annotated with corresponding developmental stage. Additionally, principal component analysis (PCA) was performed to assess variance and detect clustering patterns among the samples. Identified outliers were excluded from further processing. 26 2.6.3 Differential expression using DeSeq2 Normalised gene count data was subset into three smaller datasets across developmental timepoints of interest, namely the maternal to zygotic transition (MZT), gastrulation and cydippid emergence. Differential expression (DE) analysis was performed for the three subset datasets, with the earliest stage always acting as the reference for DE analysis. Differential expression was performed using DESeq2, with batch accounted for by including the SRA study in the design. Differential expression was assessed with a p value of < 0.05. The predicted gene sequence fasta files for the differentially expressed genes were parsed into the web interface for eggNOGmapper with default parameters to get Gene Ontology terms. 27 Chapter 3: Results 3.1 Genome assembly The Hi-C library was sequenced on a NovaSeq 6000 resulting in approximately 70 M Hi-C paired end reads at 150 bp length, which were received in October 2021. The PacBio raw reads were acquired in July 2022 subsequent to sequencing on a Sequel II platform. Employing PacBio’s circular consensus (CCS) approach, an approximate count of 2.5 million circularized high-fidelity (HiFi) reads of diverse lengths were obtained (see Table 3.1). A notable proportion of the post-CCS reads were observed to fall within the 10 kilobase (Kb) to 17 Kb size range. A high coverage was achieved for both the Hi-C chromatin conformation capture at 104X coverage, with HiFi reads at 166X coverage (Table 3.1). GC content was consistent between the two read types. A high quality was assured for the Hi-C reads with an average Phred score of 36, obtained through the FastQC tool (Andrews, 2010). Library Size Number of reads Read length Estimated coverage GC content Hi-C 21 Gb 69,789,521 150 bp 104X 39% PacBio 33.1 Gb 2,555,857 112 to 33 Kb 166X 39% Table 3.1: Sequencing data for PacBio HiFi reads and Phase Genomics Hi-C. 28 3.1.1 SALSA2 vs YaHS scaffolding The comparative scaffolding was conducted after the initial long read assembly from the PacBio HiFi reads, using Flye and Blobtools to remove environmental contaminants. Both scaffolding approaches significantly improved contiguity, resulting in an approximately two-order-of-magnitude enhancement compared to the existing short read assembly. Moreover, the genome size increased by approximately 45 Mb. However, notable differences were observed between the two scaffolders. Scaffolding with SALSA2 produced an assembly of 200.95 Mb, represented by 85 scaffolds. Comparatively, the YaHS scaffolding pipeline yielded a reduced number of scaffolds (64 scaffolds) and a slightly larger genome size of 201.15 Mb (Table 3.2). In addition to a reduced number of scaffolds, YaHS demonstrated superior contiguity compared to SALSA2. This was evident through a higher N50 value of 15.6 Mb achieved by YaHS, surpassing the N50 value of 12.3 Mb observed in the assembly scaffolded with SALSA2. Furthermore, YaHS achieved this enhanced contiguity by consolidating a larger portion of the assembly into fewer scaffolds (Figure 3.1). The GC content of both assemblies exhibited consistency, with similar proportions of bases observed in both the scaffolded assemblies. Specifically, the base composition consisted of 30% A, 20% C, 20% G, and 30% T in both assemblies. Slight variations in the distribution of bases can be attributed to the presence of unknown bases or "gaps" in the genome, represented by N's. The SALSA2 assembler had a 0.03% proportion of N's, whereas the YaHS assembler had a lower percentage of 0.01%. In contrast, the previous short read assembly exhibited a significantly higher proportion of N's, at 3.55% (Table 3.2). 29 Regarding scaffold lengths, SALSA2's assembly had eight scaffolds larger than 10 Mb and 27 scaffolds larger than 1 Mb. The YaHS pipeline however had 13 scaffolds surpassing 10 Mb in length, representing 98.5% of the genome, indicative of chromosome scale, with a longest scaffold of 22 Mb. This represents a significant improvement compared to the existing short read assembly, which had only one scaffold larger than 1 Mb. Statistic YaHS SALSA2 Ryan 2014 Number of scaffolds 64 85 5,100 Total genome size 201,150,574 200,953,794 155,865,547 Longest scaffold (bp) 22,014,687 21,109,345 1,222,598 Shortest scaffold (bp) 575 575 987 Number of scaffolds > 10Kb 61 83 1,253 Number of scaffolds > 100Kb 22 53 509 Number of scaffolds > 1Mb 13 27 1 Number of scaffolds > 10Mb 13 8 0 N50 (bp) 15,562,203 12,253,698 187,314 L50 6 7 242 %A 30.42 30.42 29.47 %C 19.55 19.55 18.75 %G 19.58 19.56 18.74 %T 30.44 30.43 29.49 Total Number of Ns 27,700 57,600 5,527,301 %N 0.01 0.03 3.55 Table 3.2: Comparison of statistics for fasta outputs after scaffolding with YaHS versus SALSA2 30 Figure 3.2 demonstrates that the two assemblies produced are largely in agreement, with concordance between the two sequence files. The dot plot highlights that the scaffolds are consistently larger in YaHS, with the SALSA2 assembly noisier, with multiple SALSA scaffolds often mapping to a single YaHS scaffold. Figure 3.1: Length profiles of the two scaffolded assemblies with Ya HS and SALSA2 compared to the original short read Ryan (2013) assembly. 31 3.1.2 13 chromosomes in Mnemiopsis leidyi Minor gaps were filled using Gapless with a second round of scaffolding with YaHS and manual curation in Juicebox resulting in a final assembly of 13 large chromosome scale scaffolds (Figure 3.3) representative of 99.14% of the 200 Mb genome, with the final N50 15.7 Mb and the total number of N’s reduced to 24,400. The longest scaffold had reduced minimally to 21.9 Mb. Figure 3.2: Dotplot comparing the scaffolded assembly using YaHS (x-axis) vs SALSA2 (y-axis). Fasta sequence files of the two scaffolded assemblies were aligned against each other and ordered along the YaHS assembly from smallest to largest scaffold. YaHS scaffolded assembly SALSA2 scaffolded assembly Genome (Mb) Identity 32 Figure 3.3 Heatmap of Hi-C data demonstrates that Mnemiopsis leidyi is organised into 13 super scaffolds (computationally derived and shown in blue), which reflect the 13 chromosomes of the organism. 33 3.2 Structural organisation of the genome The genome's higher level organisation appears to consist of 13 chromosome territories (Figure 3.3), with evidence of smaller compartments like topological domains (TADs) and loops. Topological domains were called at varying resolutions at 1Kb, 5Kb, 10 Kb, 50 Kb and 100 Kb across two normalisation types VC and KR. Normalisation made little difference to the number of TADs called or the average size, therefore the following analysis is done with KR only. The average size of identified TADs increased as the resolution (r) decreased. This pattern was consistent regardless of the window size (m) used with the Arrowhead algorithm. Resolution had a greater impact on the number and average size of TADs compared to the sliding window (m). The highest TAD count occurred at a resolution (r) of 10Kb and a sliding window (m) of 250 bp. The 10Kb resolution consistently produced the highest count across all tested sliding window sizes (Figure 3.4). TADs called at 5Kb resolution and 2Kb sliding window averaged 74Kb, while at 10Kb resolution, the average size was 160Kb (Figure 3.4). Notably, when visualising the domains in Juicebox, the 100Kb resolution appeared to effectively capture large-scale structural elements such as chromosome territories, whereas resolutions of 5Kb and 10Kb exhibited smaller structural elements. These smaller structural elements, visually appear more consistent with the expectations for TADs, and they are often accompanied by loops, however there is also evidence of TADs not being identified by the Arrowhead algorithm, with loops presenting near increased contact densities (Figure 3.5). The Arrowhead algorithm was unable to consistently identify TADs at the higher resolutions, 1Kb and 2.5Kb across all window sizes, which could have impacted the calling of some TADs. 34 Figure 3.4 (A) Average number of TADs identified by Arrowhead at each resolution, where average is taken from TADs identified at sliding window (m) 100, 250, 500, 1000 and 2000, (B) average TAD size called by Arrowhead for Mnemiopsis leidyi with respect to resolution at sliding window (m) 2000. The box and whisker demonstrates the variation, with the tails depicting the minimum and maximum values. A B35 Figure 3.5 Hi-C contact map with nested TADs as determined by Arrowhead at (A) 100Kb resolution, (B) 10 kb resolution and (C) 5kb resolution with loops called by HiCCUPS in yellow. TADs show evidence of nesting with domains called by larger resolutions encompassing smaller domains. Red indicates number of contacts. 100 Kb resolution 0 18 Mb 10 Kb resolution 13 Mb 18 Mb 15 Mb 18 Mb 5 Kb resolution 200 25 15 BA C 36 3.3 Differential expression in early development 3.3.1 Quality check and normalization of count data Following the generation of gene counts using featureCounts (refer to section 2.6 for methods), hierarchical clustering was employed across all 90 samples to evaluate sample similarities and detect any potential outliers. The analysis indicated that the primary source of variation in gene expression was associated with the developmental time (i.e. hours post fertilisation). However, three samples, exhibited distinct clustering patterns and did not display similarities with other samples and were subsequently excluded from downstream analyses (Figure 3.6). Additionally, hierarchical clustering was run on the smaller subsets, identifying SRR682885 as a potential outlier in the maternal to zygotic transition dataset (samples taken between 0 - 3 hpf, see appendix B.3), samples SRR1552896 and SRR6828890 at gastrulation (samples taken between 3 – 6 hpf, see appendix B.6) and SRR6828901 in the cydippid emergence subset (samples taken at 17 and 20 hpf, see appendix B.9). 37 Figure 3.6: Hierachical clustering analysis on normalised expression data of all 90 samples from the combined GSE60478 and GSE111748 transcriptomic data. The samples mostly appear to group by hours post fertilisation, however there are a three outliers SRR682882, SRR1552884, and SRR6828923 which are indicative of anomalous samples, with only 50% similarity to other samples. 38 3.3.2 The maternal to zygotic transition A subset of the dataset corresponding to timepoints 0 - 3 hours post fertilisation (hpf) was selected. This subset was further categorized into two stages; maternally loaded RNA (0 and 1 hpf) versus zygotic genome activation (2 – 3 hpf). The application of Principal Component Analysis (PCA) to the gene expression data revealed indications of grouping based on the developmental stage along the first principal component (PC1), which accounted for 48% of the total variance (Figure 3.7). Maternal loading was used as the reference for DE analysis which identified 1279 DEGs (p<0.05) of which 669 where upregulated and 610 were downregulated (Figure 3.8) out of 18152 genes with a non-zero read count. Of these, 33 were identified as potential outliers. The top 25 DEGs, were annotated with the Gene Ontology (GO) terms; activity of protein serine/threonine kinases (GO:0004674), chromatin organisation (GO:0006325) and chromatin remodelling (GO:0006338). Notably, these gene models were consistently upregulated during the 2 - 3 hour post-fertilization (hpf) stage, corresponding to the onset of zygotic genome activation. Cluster analysis of the top 25 DEG depicted a marked change in expression from the first group at 0 -1 hpf and the second group 2-3hpf (Figure 3.9). 39 Figure 3.7: PCA plot across the maternal to zygotic transition for gene count data at early development when RNA is maternally loaded ( 0 – 1 hpf) and at zygotic genome activation ( 2 – 3 hpf). Figure 3.8: Log fold change of gene expression across the maternal to zygotic transition. Differentially expressed genes highlighted in blue (p < 0.05). 40 Figure 3.9: Heatmap of the top 25 differentially expressed genes across the maternal to zygotic transition. Annotations suggest gene cluster by stage, with evidence of maternally loaded RNA being degraded at 0 – 1 hpf and zygotic genome activation occurring by 2 – 3 hpf. Upregulated genes showed in dark red with yellow depicting downregulated genes (p < 0.05). 41 3.3.3 Gastrulation A subset of the dataset corresponding to timepoints 3 – 6 hours post fertilisation (hpf) was selected. This subset was further categorized into three distinct stages of gastrulation: early (3 - 4 hpf), mid (4.5 – 5 hpf), and late (5.5 – 6 hpf) gastrulation. The application of Principal Component Analysis (PCA) to the gene expression data revealed indications of grouping based on the developmental stage along the first principal component (PC1), which accounted for 44% of the total variance (Figure 3.10). Although there was evidence of distinct clustering within each developmental stage, some degree of overlap was observed among samples near the boundaries of adjacent groups, particularly between the timepoints of 4 and 4.5 hpf. The earliest stage was used as the reference for differential expression analysis which identified 590 DEGs of which 317 where upregulated and 273 were downregulated (Figure 3.11) out of 18337 genes with a non-zero read count. Of these, 113 were identified as potential outliers. EggNOGmapper annotations revealed that within the top 25 DEGs, the most recurrent GO terms related to cysteine peptidase activity (GO:0008234 and carbonate dehydratase activity (GO:0004089). Gene models associated with wnt signalling were also upregulated across gastrulation. Visualising the top 25 DEGs in a heatmap also provides evidence of clustering by stage, with one outlier, a mid gastrulation sample SRR1552895 clustering with other late gastrulation timepoints (Figure 3.12). Metadata for this sample shows that this was collected at 5 hpf, close to the boundary for late gastrulation points. 42 Figure 3.10: PCA plot across gastrulation for gene count data at early (3 – 4 hpf), mid (4.5 – 5 hpf) and late (5.5 – 6 hpf) gastrulation. Figure 3.11: Log fold change of gene expression across gastrulation. Differentially expressed genes highlighted in blue (p < 0.05).. 43 Figure 3.12: Heatmap of the top 25 differentially expressed genes across gastrulation. Annotations shows gene cluster by stage, with one mid gastrulation sample SRR1552895 clustering with other late gastrulation timepoints. Upregulated genes showed in dark red with yellow depicting downregulated genes (p < 0.05). 44 3.3.4 Cydippid emergence A subset of the dataset corresponding to timepoints 17 and 20 hpf was selected to represent the stage prior to cydippid emergence (17 hpf) and onset of cydippid emergence (20 hpf). The PCA identified that PC1 only accounted for 23% of the total variance (Figure 3.13). There was significant overlap between the two groups and little evidence that variation was linked to developmental time. The earliest stage was used as the reference for differential expression analysis which identified only 67 DEGs of which 52 where upregulated and 15 were downregulated (Figure 3.14) with 211 identified as potential outliers. Visualising the top 25 DEGs in a heatmap suggests there is clustering by stage, however this is not supported by the PCA (Figure 3.15). Annotated GO terms in the DEGs pertains to cellular transport processes (GO:0006810), vesicle-mediated transport (GO:0016192) and autophagosome organisation (GO:1905037). 45 Figure 3.13: : PCA plot at for pre cydippid stage (17 hpf) and at the onset of cyddipid emergence (20 hpf). Figure 3.14: Log fold change of gene expression at the onset of cydippid emergence. Differentially expressed genes highlighted in blue (p < 0.05).. 46 Figure 3.15: Heatmap of the top 25 differentially expressed genes at the onset of cydippid emergence. Annotations shows gene cluster by stage. Upregulated genes showed in dark red with yellow depicting downregulated genes (p < 0.05). 47 Chapter 4: Discussion The field of omics is advancing biological understanding by enabling researchers to study biological systems at a genome wide scale. Omics refers to the large-scale analysis of biological molecules, such as DNA, RNA, proteins, and metabolites, in order to gain a comprehensive understanding of biological processes. By studying these molecules, researchers can identify genes that are involved in specific biological processes, characterize the proteins that are produced by these genes, and examine the metabolic pathways involved. This information can be used to further understanding across a variety of biological disciplines including disease, environmental monitoring, the development of new biotechnologies (such as therapies for disease, or drug development) and, as in the interest of this study, to further understanding on the complexities surrounding multicellularity and evolution. By utilising these advancements in techniques to investigate early branching organisms such as ctenophores, we can further our understanding of complex evolutionary processes. However, in order to maximise the ever increasing suite of bioinformatic tools and draw confident conclusions from genomic analysis, it is vital to have high quality baseline data, namely in the form of a reference genome. What is considered a high quality genomic assembly has changed in recent years with the advancement of sequencing technologies, and therefore existing assemblies are required to be updated. This study has produced a high contiguity chromosome scale assembly for Mnemiopsis leidyi, demonstrating a hundred fold increase in contiguity compared to the existing genome assembly derived from short read sequencing, specifically utilising Roche 454 technology. In addition, the analysis in this thesis marks the first attempts at identifying structural genomic 48 elements, specifically topological domains and loops in this organism. Updated gene models as a result of the improved assembly have been used to repeat differential expression analysis across the maternal to zygotic transition and conduct preliminary analysis across gastrulation and cydippid emergence. 4.1 Chromosome scale assembly Two chromosome scale assembly pipelines were tested in this study, utilising outputs of an initial long read assembly generated with metaFlye, an assembler designed for long read sequencing data including PacBio (Kolmogorov et al., 2020). The two pipelines incorporated two scaffolding tools, SALSA2 which utilises a graph based approach to iteratively merge the scaffolds based on information provided in the HiC reads (Ghurye et al., 2017, 2019) and Yet another HiC Scaffolder or YaHS, which employs a hierarchical scaffolding algorithm to incorporate HiC data and is currently being used and maintained for a large multi coalition project the Darwin Tree of Life (Zhou et al., 2023). To maintain consistency across the pipelines, the same mapping and filtering procedures for Hi-C reads were applied. This ensured that both SALSA2 and YaHS utilised comparable input data, allowing for a fair comparison of their scaffolding performances. Both SALSA2 and YaHS scaffolders exhibited remarkable genome contiguity, as indicated by their respective N50 scores of 12.3 Mb and 15.6 Mb. These values represent a significant improvement, spanning two orders of magnitude, compared to the 187 Kb N50 in 5100 scaffolds achieved in the previous short read (Roche 454) assembly (Ryan et al., 2013). The results obtained from both scaffolders were largely comparable, providing additional confidence in the results, however the tool YaHS, which is maintained and used by the Sanger Institute, marginally 49 outperforms SALSA2 with respect to contiguity and the number of gaps. Additionally YaHS has the benefit of integration with other tools including the Juicer pipeline and Juicebox integrated genomics viewer which aids manual curation (Durand, Robinson, et al., 2016; Durand, Shamim, et al., 2016; Zhou et al., 2023). This study supports the use of YaHS as a scaffolder for future non-traditional model organisms. Following gap closing with Asset and manual curation, 99.14% of the genome assembly was successfully encompassed within the 13 largest scaffolds. These scaffolds, all surpassing the 10Mb threshold, exhibit dimensions that qualify them as chromosome-scale structures, providing evidence that the genome of this species is orchestrated into 13 distinct chromosome territories. This marks the first suggestion of a karyotype n=13 for this species, with microscopic efforts are currently underway to corroborate these computational efforts. High quality chromosome scale reference genomes serve as a crucial basis for downstream analysis, as the increased contiguity can provide improved accuracy in gene models and annotation, mutations, structural variants and phylogenetic analysis. In particular chromosome scale assemblies can serve as a reliable foundation for subsequent investigations for model organisms, particularly those of lesser known groups, facilitating the identification of genes or genomic region of interest and genomic analysis under experimental conditions. Gene identification and annotation can pose a challenge for short read assemblies, as these highly fragmented assemblies often result in genes being distributed across multiple contigs or scaffolds (Pollard et al., 2018; Zhang et al., 2022). By achieving higher contiguity in this study for M. leidyi we gain improved capacity to correctly characterise genes, their associated regulatory elements and interactions and their structural organisation within the genome. This is particularly powerful in the study of the genetic mechanisms of multicellularity, where accurate gene models are vital. 50 Moreover, recent high-quality genomic assemblies were published for Hormiphora californensis, a ctenophore species from the Pleurobrachiidae family (Schultz et al., 2021) and Pleurobrachia bachei (Hoencamp et al., 2021) with a consistent karyotype of n=13, despite the significantly smaller size of the H. californensis genome at 110Mb. A consistent karyotype across an early branching phylum could hold significant biological implications, suggesting the presence of a stable and successful genetic framework in this group. This stability could indicate the preservation of essential genomic elements or functions critical for the survival and adaptation of organisms within this phylum. Our study contributes to the consistency if the karyotype and provides further evidence that more wide scale genomic studies are warranted. 4.2 Structural organisation This study provides the first evidence for the presence of topologically associating domains (TADs) and loops as fundamental components of chromatin structure in M. leidyi. To identify TADs the Arrowhead algorithm was employed while loops were detected through hiCCUPS analysis, both of which are integrated within the Juicer package (Durand, Shamim, et al., 2016). Topological domains are considered to be important structural regions within a chromosome which help to facilitate gene expression and regulation (Gorkin et al., 2014; Hoencamp et al., 2021; Pombo & Dillon, 2015; Rao et al., 2014; Szabo et al., 2019). To have an effect on transcription and by design gene expression, regulatory DNA elements such as enhancers and promoters must be in close spatial proximity to the genomic region of intended effect, with TADs able to bring into close spatial proximity these regulatory elements. Understanding whether this mechanism is conserved across the tree of life is of interest to evolutionary development scientists and 51 identifying loops and domains in this early branching organisms adds to the increasing evidence of the importance of these domains and argues for their necessity in forming multicellular life. The genomic locations of these structural elements was subjected to examination using Juicebox, a visualisation platform that integrates with the Juicer pipeline. Visualising this data suggested that TADs in this species are more prominently associated with loops, indicating that TAD formation in M. leidyi is potentially driven by the establishment of chromatin loops. TAD boundaries and the associated loops are considered conserved across species, and the presence in this early branching organism indicates their functional importance. This conservation suggests that the proper organization of the genome into TADs and the maintenance of boundary loops are critical in complex life and may have role in multicellularity. However, the precise conformation and underlying mechanisms of loop extrusion in this organism still remain unknown. Interestingly, loops were detected across the entire genome, encompassing regions characterized by higher contact density that could potentially represent TADs, but that had not been identified accordingly by the Arrowhead algorithm, implying the existence of additional undiscovered TADs in the genome. Insufficient Hi-C coverage may be a contributing factor in the limitation of the Arrowhead algorithms ability to adequately call TADs in M. leidyi and as such further study is warranted to comprehensively unravel the intricate architecture of TADs and loops, as well as mechanistic details and regulatory factors governing TAD formation in this species. Further work will undoubtedly contribute to the broader understanding of chromatin structure and gene regulation in early branching organisms. Comparative studies have provided evidence that both the number of TADs identified as well as the size can vary significantly dependent on both the caller used and the resolutions at which the 52 TADs are determined (Sefer, 2022; Zufferey et al., 2018). The results found in this study support this previous work with an increase in TAD size seen at resolutions of 50Kb and 100Kb, compared to 5Kb. Additionally, the sliding window (m) size affected the size of the domains, with a larger m correlating with an increased size, however the impact of was less significant than resolution. The highest frequency of TADs were consistently identified at 10Kb resolution, with a mean of 73Kb, comparable to the H. californensis genome with TADs an average of 60Kb. Unlike in humans where TADs can vary from hundreds of kilobases to megabases with an average of 1.15Mb (Lanzuolo et al., 2019; Lupiáñez et al., 2016; Rao et al., 2014), the size of these compartments were relatively consistent. Interestingly, this is similar to the TADs seen in Drosophila (Sexton et al., 2012). The reasons for these differences in TAD size are not yet fully understood, but they may be related to differences in genome organization, gene expression regulation, and other factors. While the evidence of TADs and potential presence of loops marks a good primary step for further work in understanding chromatin structure of this organism, as previously described, there is evidence of TADs that are not being statistically identified using the bioinformatic algorithm Arrowhead. Visual investigation of Hi-C maps and the presence of loops at regions where there is a noticeable increase in contact signal suggests that increased coverage or further study may be required to elucidate these. Prior studies have suggested that off diagonal signals such as inversions can confound TAD calling and despite this lineage being inbred to close to homozygosity this can cause potential downstream implications (Schultz et al., 2021). Furthermore, comparisons with other species to identify conserved TADs should be carefully considered. Recent studies have highlighted that across species TAD analysis is challenging, and many comparative studies of 53 TAD structures, did not fully consider the experimental design and downstream processing that could impact and firm conclusions of confirmation of conservation (Eres & Gilad, 2021). 4.3 Differential expression at key developmental stages Differential analysis conducted on the timepoints 0, 1, 2, and 3 hours post fertilisation revealed the upregulation of 669 genes and downregulation of 610 genes (p = 0.05) when using maternal loading (0 and 1 hpf) as the baseline. The clustering of samples based on developmental stage strongly suggests that the observed changes are attributable to temporal progression. A distinct change in gene expression patterns provide evidence of zygotic gene activation by 2 hpf, supporting evidence in the Davidson (2017) study that the MZT actually begins earlier than previously thought, at around 1.5 hpf rather than 3 hpf as suggested in an earlier study (Fischer et al., 2014). The observed upregulation of gene models associated with serine/threonine kinase activity and chromatin order and remodeling at 2-3 hours post-fertilization (hpf) corroborates findings from a prior study, emphasizing the significance of chromatin restructuring during the maternal-to-zygotic transition (MZT) (Davidson et al., 2017). Enhancers can help aid transcription by providing a binding site for transcription factors which can act on genes which are spatially close, but further apart in linear space through chromatin looping (Bouwman & de Laat, 2015; Ptashne, 1986). Upregulation of gene models predicted to be involved in this process further supports the importance of chromatin restructuring in zygotic genome activation. Conducting future investigations that specifically examine chromatin structure preceding and following the MZT would be of interest to advancing our understanding in this area. 54 The principle component analysis and hierarchical cluster analysis in the gastrulation subset indicate that the majority of the variance can be attributed to the developmental stage, however there is some overlap which highlights the need for further data for these time points. Differential gene expression analysis revealed that gene models associated with Wnt signaling were identified among the Differentially Expressed Genes (DEGs), exhibiting upregulation during the later stages of gastrulation. This observation aligns with previous expression profiles, as a prior investigation of the Wnt signaling pathway reported heightened expression of Wnt genes towards the conclusion and subsequent to gastrulation related to the formation of the tentacle bulb (Pang et al., 2010). Subsequent differential analysis performed on the timepoints 17 and 20 hours post fertilisation, which correspond to cydippid emergence, demonstrated the upregulation of 52 genes and downregulation of 15 genes (p = 0.05) when employing 17 hpf as the baseline. However, there was significant overlap in the principle component analysis indicating that the variation is not solely attributed to developmental time. There may be potential influences from batch effects or other contributing factors and the reliability of this data is questionable. Further RNA seq data prior to and post cydippid emergence is required to elucidate the mechanisms underlying ctenophore cydippid emergence and shed light on the regulatory pathways governing this developmental event. 55 4.4 Future work Further insight into the genome structure of M. leidyi may be achieved through advanced sequencing techniques, by increasing the coverage of the Hi-C and collecting additional supporting sequencing data. Advancements in bioinformatics tools and high performance computing could also provide further insights through the utilisation of machine learning tools. In recent years, machine learning tools such as Super-Resolution HiC (SRHiC), a deep learning model to predict high-resolution Hi-C data from low-coverage sequencing data (Z. Li & Dai, 2020) and a TAD recognition tool which utilises neural networks (Yang & Chang, 2021) have been suggested as alternate ways to identify TADs when coverage is low. This study further exemplifies the existence of variations in the quantity and average dimensions of topologically associated domains (TADs) across different domain callers, accompanied by inconsistent results at varying resolutions (Zufferey et al., 2018). Such inconsistencies directly impact the reliability and robustness of TADs and other structural elements for future research. Hence, it underscores the imperative need for future work to develop additional standardized operating procedures and best practices in TAD calling to address these challenges effectively, particularly when using the existing techniques for non-standard model organisms such as ctenophores. In order to truly understand the link between differential expression at key developmental timepoints and the structural organisation of the genome we need to quantify the structure at time points which support the existing available data. Although Hi-C data can capture chromatin conformation across the genome, it is not ideal for identifying specific regions, CTCF elements, and has drawbacks with the large amount of starting material and the complexity of the protocol. 56 An alternative could be ATAC sequencing, the assay for transposase accessible chromatin, which utilises a hyperactive Tn5 to unbiasedly ligate DNA at regions of open and accessible chromatin (Buenrostro et al., 2015). One of the benefits of ATAC sequencing is the requirement for less starting material than Hi-C, and a simplified protocol, however as of the time of writing protocol optimisation for ctenophore species has yet to be published. At the time of writing work is ongoing to identify an ideal and robust protocol, but preliminary efforts show that an extended lysis step is required to fully break apart nuclear steps. Additionally a dechorionation protocol similar to those practised with zebrafish appears to be beneficial to aid the lysis step as the chorion appears to slow down penetration with the lysis buffer. Once a baseline for chromatin accessibility has been established and protocols optimised, utilising newly emerging techniques such as multi omics approaches at the single cellular level, would be interest to define processes that govern cell fate decisions. Studies using these techniques have had insightful results already in understanding the immune response in COVID, cancer evolution, drug therapy and plant research (Nam et al., 2021; Stephenson et al., 2021; Vandereyken et al., 2023; Zielinski et al., 2021). 4.5 Concluding Remarks Genomic data and a comprehensive understanding of overall genomic structure play a crucial role in determining the relationship and phylogenetic position of non-bilaterian organisms and thereby shedding light on the the evolution of multicellularity. While the genomes of cnidaria Nematostella vectenis (Zimmermann et al., 2020) and Rhopilema esculentum (Y. Li et al., 2020), as well as the the Placozoa Trichoplax adhaerens (Srivastava et al., 2008) and sponges Amphimedon queenslandica (Srivastava et al., 2010) Ephydatia muelleri (Kenny et al., 2020), have provided 57 valuable insights into the genomic composition of early metazoans, further investigations spanning across diverse phyla are imperative. Ctenophores are a candidate for the sister group to all other metazoans, however the currently available data for ctenophore species such as M. leidyi and P. bachei has been insufficient to definitively determine the placement of this lineage or conduct comprehensive comparisons of structural similarities and differences, such as synteny. To comprehensively explore the relationship between this lineage and multicellularity, as well as its significance as a member of the potential sister group of all animals, good quality baseline genomic data in the form of a high-quality reference genome is essential. This study provides important foundational information for future molecular investigations on this organism through the proviso of a chromosome-scale reference assembly. Furthermore it provides the first attempts of identifying structural elements including topological domains and loops in this organism, with computational evidence that highest structural order of the genome is into 13 chromosomal territories. This study also emphasizes the need for further quantification of chromatin organization across these developmental timepoints, as well as acquisition of additional RNA-seq data to unravel the molecular mechanisms governing cell differentiation. Currently, protocols for ATAC sequencing are being optimised with the intention to assess variation in heterochromatin and euchromatin across development while considering gene expression, which as of yet has not been well characterised in any ctenophore species. By understanding how different regions of the genome undergo structural changes and comparing them to existing transcriptome data, we can gain a deeper understanding of the role chromatin accessibility plays in development and multicellularity. 58 Bibliography Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. (2020). Opportunities and challenges in long-read sequencing data analysis. Genome Biology, 21(1), 1–16. https://doi.org/10.1186/s13059-020-1935-5 Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics, Babraham Institute, Cambridge, United Kingdom. Belton, J. M., McCord, R. P., Gibcus, J. H., Naumova, N., Zhan, Y., & Dekker, J. (2012). Hi–C: A comprehensive technique to capture the conformation of genomes. Methods, 58(3), 268–276. https://doi.org/10.1016/J.YMETH.2012.05.001 Blaxter, M. L. (2022). Sequence locally, think globally: The Darwin Tree of Life Project. Proceedings of the National Academy of Sciences of the United States of America, 119(4), 1–7. https://doi.org/10.1073/pnas.2115642118 Bonev, B., & Cavalli, G. (2016). Organization and function of the 3D genome. Nature Reviews Genetics, 17(11), 661–678. https://doi.org/10.1038/nrg.2016.112 Bouwman, B. A. M., & de Laat, W. (2015). Getting the genome in shape: The formation of loops, domains and compartments. Genome Biology, 16(1), 1–9. https://doi.org/10.1186/s13059-015-0730-1 Buenrostro, J. D., Wu, B., Chang, H. Y., & Greenleaf, W. J. (2015). ATAC-seq: A method for assaying chromatin accessibility genome-wide. Current Protocols in Molecular Biology, 2015, 21.29.1-21.29.9. https://doi.org/10.1002/0471142727.mb2129s109 Burton, J. N., Adey, A., Patwardhan, R. P., Qiu, R., Kitzman, J. O., & Shendure, J. (2013). Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nature Biotechnology, 31(12), 1119–1125. https://doi.org/10.1038/nbt.2727 Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M., & Li, H. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2), giab008. https://doi.org/10.1093/gigascience/giab008 Davidson, P. L., Koch, B. J., Schnitzler, C. E., Henry, J. Q., Martindale, M. Q., Baxevanis, A. D., Browne, W. E., Conceptualization, W. E. B. ;, Methodology, P. L. D. K., Formal, W. E. B. ;, Investigation, W. E. B. ;, Writing -Original, W. E. B. ;, Draft, P. L. D., Writing -Review, W. E. B. ;, Visualization, W. E. B. ;, & Resources, W. E. B. ; (2017). The maternal-zygotic transition and zygotic activation of the Mnemiopsis leidyi genome occurs within the first three cleavage cycles HHS Public Access. Mol Reprod Dev, 84(11), 1218–1229. https://doi.org/10.1002/mrd.22926 Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012). Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 485(7398), 376–380. https://doi.org/10.1038/nature11082 Dunn, C. W., Hejnol, A., Matus, D. Q., Pang, K., Browne, W. E., Smith, S. A., Seaver, E., Rouse, G. W., Obst, M., Edgecombe, G. D., Sørensen, M. V, Haddock, S. H. D., Schmidt-Rhaesa, A., Okusu, A., Kristensen, R. M., Wheeler, W. C., Martindale, M. Q., & Giribet, G. (2008). Broad phylogenomic sampling improves resolution of the animal tree of life. Nature, 452(7188), 745–749. https://doi.org/10.1038/nature06614 59 Durand, N. C., Robinson, J. T., Shamim, M. S., Machol, I., Mesirov, J. P., Lander, E. S., & Aiden, E. L. (2016). Juicebox Provides a Visualization System for Hi-C Contact Maps with Unlimited Zoom. Cell Systems, 3(1), 99–101. https://doi.org/10.1016/J.CELS.2015.07.012 Durand, N. C., Shamim, M. S., Machol, I., Rao, S. S. P., Huntley, M. H., Lander, E. S., & Aiden, E. L. (2016). Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments. Cell Systems, 3(1), 95–98. https://doi.org/10.1016/J.CELS.2016.07.002 Eres, I. E., & Gilad, Y. (2021). A TAD Skeptic: Is 3D Genome Topology Conserved? Trends in Genetics, 37(3), 216–223. https://doi.org/10.1016/J.TIG.2020.10.009 Faust, G. G., & Hall, I. M. (2014). SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics, 30(17), 2503–2505. https://doi.org/10.1093/bioinformatics/btu314 Fischer, A. H. L., Pang, K., Henry, J. Q., & Martindale, M. Q. (2014). A cleavage clock regulates features of lineage-specific differentiation in the development of a basal branching metazoan, the ctenophore Mnemiopsis leidyi. EvoDevo, 5(1), 0–19. https://doi.org/10.1186/2041-9139-5-4 Flynn, J. M., Hubley, R., Goubert, C., Rosen, J., Clark, A. G., Feschotte, C., & Smit, A. F. (2020). RepeatModeler2 for automated genomic discovery of transposable element families. Proceedings of the National Academy of Sciences, 117(17), 9451–9457. https://doi.org/10.1073/pnas.1921046117 Franke, M., De la Calle-Mustienes, E., Neto, A., Almuedo-Castillo, M., Irastorza-Azcarate, I., Acemel, R. D., Tena, J. J., Santos-Pereira, J. M., & Gómez-Skarmeta, J. L. (2021). CTCF knockout in zebrafish induces alterations in regulatory landscapes and developmental gene expression. Nature Communications, 12(1), 5415. https://doi.org/10.1038/s41467-021-25604-5 Gallardo-Escárate, C., Valenzuela-Muñoz, V., Nuñez-Acuña, G., Valenzuela-Miranda, D., Gonçalves, A. T., Escobar-Sepulveda, H., Liachko, I., Nelson, B., Roberts, S., & Warren, W. (2021). Chromosome-scale genome assembly of the sea louse Caligus rogercresseyi by SMRT sequencing and Hi-C analysis. Scientific Data, 8(1), 1–12. https://doi.org/10.1038/s41597-021-00842-w Ghavi-Helm, Y., Jankowski, A., Meiers, S., Viales, R. R., Korbel, J. O., & Furlong, E. E. M. (2019). Highly rearranged chromosomes reveal uncoupling between genome topology and gene expression. Nature Genetics, 51(8), 1272–1282. https://doi.org/10.1038/s41588-019-0462-3 Ghurye, J., Pop, M., Koren, S., Bickhart, D., & Chin, C. S. (2017). Scaffolding of long read assemblies using long range contact information. BMC Genomics, 18(1), 1–11. https://doi.org/10.1186/s12864-017-3879-z Ghurye, J., Rhie, A., Walenz, B. P., Schmitt, A., Selvaraj, S., Pop, M., Phillippy, A. M., & Koren, S. (2019). Integrating Hi-C links with assembly graphs for chromosome-scale assembly. PLoS Computational Biology, 15(8), 1–19. https://doi.org/10.1371/journal.pcbi.1007273 Gong, Y., Lazaris, C., Sakellaropoulos, T., Lozano, A., Kambadur, P., Ntziachristos, P., Aifantis, I., & Tsirigos, A. (2018). Stratification of TAD boundaries reveals preferential insulation of super-enhancers by strong boundaries. Nature Communications, 9(542). 60 https://doi.org/10.1038/s41467-018-03017-1 Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016). Coming of age: Ten years of next-generation sequencing technologies. Nature Reviews Genetics, 17(6), 333–351. https://doi.org/10.1038/nrg.2016.49 Gorkin, D. U., Leung, D., & Ren, B. (2014). The 3D genome in transcriptional regulation and pluripotency. Cell Stem Cell, 14(6), 762–775. https://doi.org/10.1016/j.stem.2014.05.017 Grosberg, R. K., & Strathmann, R. R. (2007). The Evolution of Multicellularity: A Minor Major Transition? Annual Review of Ecology, Evolution, and Systematics, 38(1), 621–654. https://doi.org/10.1146/annurev.ecolsys.36.102403.114735 Heather, J. M., & Chain, B. (2016). The sequence of sequencers: The history of sequencing DNA. Genomics, 107(1), 1–8. https://doi.org/10.1016/j.ygeno.2015.11.003 Hejnol, A., Obst, M., Stamatakis, A., Ott, M., Rouse, G. W., Edgecombe, G. D., Martinez, P., Baguñà, J., Bailly, X., Jondelius, U., Wiens, M., Müller, W. E. G., Seaver, E., Wheeler, W. C., Martindale, M. Q., Giribet, G., & Dunn, C. W. (2009). Assessing the root of bilaterian animals with scalable phylogenomic methods. Proceedings of the Royal Society B: Biological Sciences, 276(1677), 4261–4270. https://doi.org/10.1098/rspb.2009.0896 Hoencamp, C., Dudchenko, O., Elbatsh, A. M. O., Brahmachari, S., Raaijmakers, J. A., van Schaik, T., Cacciatore, Á. S., Contessoto, V. G., van Heesbeen, R. G. H. P., van den Broek, B., Mhaskar, A. N., Teunissen, H., St Hilaire, B. G., Weisz, D., Omer, A. D., Pham, M., Colaric, Z., Yang, Z., Rao, S. S. P., … Rowland, B. D. (2021). 3D genomics across the tree of life reveals condensin II as a determinant of architecture type. Science, 372(6545), 984–989. https://doi.org/10.1126/science.abe2218 Hoff, K. J., Lomsadze, A., Borodovsky, M., & Stanke, M. (2019). Whole-Genome Annotation with BRAKER. Methods in Molecular Biology, 1962, 65–95. https://doi.org/10.1007/978-1-4939-9173-0_5 Kenny, N. J., Francis, W. R., Rivera-Vicéns, R. E., Juravel, K., de Mendoza, A., Díez-Vives, C., Lister, R., Bezares-Calderón, L. A., Grombacher, L., Roller, M., Barlow, L. D., Camilli, S., Ryan, J. F., Wörheide, G., Hill, A. L., Riesgo, A., & Leys, S. P. (2020). Tracing animal genomic evolution with the chromosomal-level assembly of the freshwater sponge Ephydatia muelleri. Nature Communications, 11(1), 1–11. https://doi.org/10.1038/s41467-020-17397-w Kim, D., Paggi, J. M., Park, C., Bennett, C., & Salzberg, S. L. (2019). Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology, 37(8), 907–915. https://doi.org/10.1038/s41587-019-0201-4 Kolmogorov, M., Bickhart, D. M., Behsaz, B., Gurevich, A., Rayko, M., Shin, S. B., Kuhn, K., Yuan, J., Polevikov, E., Smith, T. P. L., & Pevzner, P. A. (2020). metaFlye: scalable long-read metagenome assembly using repeat graphs. Nature Methods, 17(11), 1103–1110. https://doi.org/10.1038/s41592-020-00971-x Laetsch, D. R., Blaxter, M. L., & Leggett, R. M. (2017). BlobTools : Interrogation of genome assemblies. F1000Research, 6(1287), 1–16. Lanzuolo, C., Youngson, N., Vaquerizas, J. M., Schirmer, E. C., Sivakumar, A., & De Las Heras, J. I. (2019). Spatial Genome Organization: From Development to Disease. https://doi.org/10.3389/fcell.2019.00018 61 Levin, M., Anavy, L., Cole, A. G., Winter, E., Mostov, N., Khair, S., Senderovich, N., Kovalev, E., Silver, D. H., Feder, M., Fernandez-Valverde, S. L., Nakanishi, N., Simmons, D., Simakov, O., Larsson, T., Liu, S. Y., Jerafi-Vider, A., Yaniv, K., Ryan, J. F., … Yanai, I. (2016). The mid-developmental transition and the evolution of animal body plans. Nature, 531(7596), 637–641. https://doi.org/10.1038/nature16994 Levine, M., & Davidson, E. H. (2005). Gene regulatory networks for development. Proceedings of the National Academy of Sciences of the United States of America, 102(14), 4936–4942. https://doi.org/10.1073/pnas.0408031102 Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754–1760. https://doi.org/10.1093/bioinformatics/btp324 Li, H., & Durbin, R. (2010). Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics, 26(5), 589–595. https://doi.org/10.1093/bioinformatics/btp698 Li, Y., Gao, L., Pan, Y., Tian, M., Li, Y., He, C., Dong, Y., Sun, Y., & Zhou, Z. (2020). Chromosome-level reference genome of the jellyfish Rhopilema esculentum. GigaScience, 9(4), 1–10. https://doi.org/10.1093/GIGASCIENCE/GIAA036 Li, Z., & Dai, Z. (2020). SRHiC: A Deep Learning Model to Enhance the Resolution of Hi-C Data. Frontiers in Genetics, 11(April), 1–9. https://doi.org/10.3389/fgene.2020.00353 Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923–930. https://doi.org/10.1093/bioinformatics/btt656 Lieberman-Aiden, E., Van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., Sandstrom, R., Bernstein, B., Bender, M. A., Groudine, M., Gnirke, A., Stamatoyannopoulos, J., Mirny, L. A., Lander, E. S., & Dekker, J. (2009). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 326(5950), 289–293. https://doi.org/10.1126/science.1181369 Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 1–21. https://doi.org/10.1186/s13059-014-0550-8 Lupiáñez, D. G., Spielmann, M., & Mundlos, S. (2016). Breaking TADs: How Alterations of Chromatin Domains Result in Disease. Trends in Genetics, 32(4), 225–237. https://doi.org/10.1016/j.tig.2016.01.003 Martindale, M. Q. (1987). Marine Biology Larval reproduction in the ctenophore Mnemiopsis mccradyi. Marine Biology, 94, 409–414. Martindale, M. Q., & Henry, J. Q. (1997). Reassessing embryogenesis in the Ctenophora: The inductive role of e1 micromeres in organizing ctene row formation in the “mosaic” embryo, Mnemiopsis leidyi. Development, 124(10), 1999–2006. https://doi.org/10.1242/dev.124.10.1999 Martindale, M. Q., & Henry, J. Q. (1998). The development of radial and biradial symmetry: The evolution of bilaterality. American Zoologist, 38(4), 672–684. https://doi.org/10.1093/icb/38.4.672 62 Martindale, M. Q., & Henry, J. Q. (1999). Intracellular fate mapping in a basal metazoan, the ctenophore Mnemiopsis leidyi, reveals the origins of mesoderm and the existence of indeterminate cell lineages. Developmental Biology, 214(2), 243–257. https://doi.org/10.1006/dbio.1999.9427 Moradi, S., Mahdizadeh, H., Šarić, T., Kim, J., Harati, J., Shahsavarani, H., Greber, B., & Moore, J. B. (2019). Research and therapy with induced pluripotent stem cells (iPSCs): Social, legal, and ethical considerations. Stem Cell Research and Therapy, 10(1), 1–13. https://doi.org/10.1186/s13287-019-1455-y Moreland, R. T., Nguyen, A. D., Ryan, J. F., Schnitzler, C. E., Koch, B. J., Siewert, K., Wolfsberg, T. G., & Baxevanis, A. D. (2014). A customized Web portal for the genome of the ctenophore Mnemiopsis leidyi. BMC Genomics, 15(1), 1–13. https://doi.org/10.1186/1471-2164-15-316 Moroz, L. L., Kocot, K. M., Citarella, M. R., Dosung, S., Norekian, T. P., Povolotskaya, I. S., Grigorenko, A. P., Dailey, C., Berezikov, E., Buckley, K. M., Ptitsyn, A., Reshetov, D., Mukherjee, K., Moroz, T. P., Bobkova, Y., Yu, F., Kapitonov, V. V., Jurka, J., Bobkov, Y. V., … Kohn, A. B. (2014). The ctenophore genome and the evolutionary origins of neural systems. Nature, 510(7503), 109–114. https://doi.org/10.1038/nature13400 Nagy, I., Veeckman, E., Liu, C., Bel, M. Van, Vandepoele, K., Jensen, C. S., Ruttink, T., & Asp, T. (2022). Chromosome-scale assembly and annotation of the perennial ryegrass genome. BMC Genomics, 23(1), 1–20. https://doi.org/10.1186/s12864-022-08697-0 Nam, A. S., Chaligne, R., & Landau, D. A. (2021). Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nature Reviews Genetics, 22(1), 3–18. https://doi.org/10.1038/s41576-020-0265-5 Niklas, K. J., & Newman, S. A. (2013). The origins of multicellular organisms. Evolution and Development, 15(1), 41–52. https://doi.org/10.1111/ede.12013 Niklas, K. J., & Newman, S. A. (2020). The many roads to and from multicellularity. Journal of Experimental Botany, 71(11), 3247–3253. https://doi.org/10.1093/jxb/erz547 Nora, E. P., Lajoie, B. R., Schulz, E. G., Giorgetti, L., Okamoto, I., Servant, N., Piolot, T., Van Berkum, N. L., Meisig, J., Sedat, J., Gribnau, J., Barillot, E., Blüthgen, N., Dekker, J., & Heard, E. (2012). Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature, 485(7398), 381–385. https://doi.org/10.1038/nature11049 Nosenko, T., Schreiber, F., Adamska, M., Adamski, M., Eitel, M., Hammel, J., Maldonado, M., Müller, W. E. G., Nickel, M., Schierwater, B., Vacelet, J., Wiens, M., & Wörheide, G. (2013). Deep metazoan phylogeny: When different genes tell different stories. Molecular Phylogenetics and Evolution, 67(1), 223–233. https://doi.org/10.1016/J.YMPEV.2013.01.010 Ohdera, A., Ames, C. L., Dikow, R. B., Kayal, E., Chiodin, M., Busby, B., La, S., Pirro, S., Collins, A. G., Medina, M., & Ryan, J. F. (2019). Box, stalked, and upside-down? Draft genomes from diverse jellyfish (Cnidaria, Acraspeda) lineages: Alatina alata (Cubozoa), Calvadosia cruxmelitensis (Staurozoa), and Cassiopea xamachana (Scyphozoa). GigaScience, 8(7), giz069. https://doi.org/10.1093/gigascience/giz069 Okita, K., Ichisaka, T., & Yamanaka, S. (2007). Generation of germline-competent induced pluripotent stem cells. Nature, 448(7151), 313–317. https://doi.org/10.1038/nature05934 63 Ou, S., Su, W., Liao, Y., Chougule, K., Agda, J. R. A., Hellinga, A. J., Lugo, C. S. B., Elliott, T. A., Ware, D., Peterson, T., Jiang, N., Hirsch, C. N., & Hufford, M. B. (2019). Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline. Genome Biology, 20(1), 275. https://doi.org/10.1186/s13059-019-1905-y Pang, K., & Martindale, M. Q. (2008a). Developmental expression of homeobox genes in the ctenophore Mnemiopsis leidyi. Development Genes and Evolution, 218(6), 307–319. https://doi.org/10.1007/s00427-008-0222-3 Pang, K., & Martindale, M. Q. (2008b). Mnemiopsis leidyi Spawning and Embryo Collection. CSH Protocols, 2008, pdb.prot5085. https://doi.org/10.1101/pdb.prot5085 Pang, K., Ryan, J. F., Mullikin, J. C., Baxevanis, A. D., & Martindale, M. Q. (2010). Genomic insights into Wnt signaling in an early diverging metazoan, the ctenophore Mnemiopsis leidyi. EvoDevo, 1(1), 1–15. https://doi.org/10.1186/2041-9139-1-10 Philippe, H., Derelle, R., Lopez, P., Pick, K., Borchiellini, C., Boury-Esnault, N., Vacelet, J., Renard, E., Houliston, E., Quéinnec, E., Da Silva, C., Wincker, P., Le Guyader, H., Leys, S., Jackson, D. J., Schreiber, F., Erpenbeck, D., Morgenstern, B., Wörheide, G., & Manuel, M. (2009). Phylogenomics Revives Traditional Views on Deep Animal Relationships. Current Biology, 19(8), 706–712. https://doi.org/10.1016/j.cub.2009.02.052 Pick, K. S., Philippe, H., Schreiber, F., Erpenbeck, D., Jackson, D. J., Wrede, P., Wiens, M., Alié, A., Morgenstern, B., Manuel, M., & Wörheide, G. (2010). Improved phylogenomic taxon sampling noticeably affects nonbilaterian relationships. Molecular Biology and Evolution, 27(9), 1983–1987. https://doi.org/10.1093/molbev/msq089 Pollard, M. O., Gurdasani, D., Mentzer, A. J., Porter, T., & Sandhu, M. S. (2018). Long reads: Their purpose and place. Human Molecular Genetics, 27(R2), R234–R241. https://doi.org/10.1093/hmg/ddy177 Pombo, A., & Dillon, N. (2015). Three-dimensional genome architecture: Players and mechanisms. Nature Reviews Molecular Cell Biology, 16(4), 245–257. https://doi.org/10.1038/nrm3965 Presnell, J. S., Bubel, M., Knowles, T., Patry, W., & Browne, W. E. (2022). Multigenerational laboratory culture of pelagic ctenophores and CRISPR–Cas9 genome editing in the lobate Mnemiopsis leidyi. Nature Protocols, 17(8), 1868–1900. https://doi.org/10.1038/s41596-022-00702-w Ptashne, M. (1986). Gene regulation by proteins acting nearby and at a distance. Nature, 322(6081), 697–701. https://doi.org/10.1038/322697a0 Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., & Aiden, E. L. (2014). A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell, 159(7), 1665–1680. https://doi.org/10.1016/j.cell.2014.11.021 Reitzel, A. M., Pang, K., & Martindale, M. Q. (2016). Developmental expression of “germline”- and “sex determination”-related genes in the ctenophore Mnemiopsis leidyi. EvoDevo, 7(1), 1–16. https://doi.org/10.1186/s13227-016-0051-9 Ryan, J. F., Pang, K., Mullikin, J. C., Martindale, M. Q., & Baxevanis, A. D. (2010). The homeodomain complement of the ctenophore Mnemiopsis leidyi suggests that Ctenophora 64 and Porifera diverged prior to the ParaHoxozoa. EvoDevo, 1(1), 1–18. https://doi.org/10.1186/2041-9139-1-9 Ryan, J. F., Pang, K., Schnitzler, C. E., Nguyen, A. D., Moreland, R. T., Simmons, D. K., Koch, B. J., Francis, W. R., Havlak, P., Smith, S. A., Putnam, N. H., Haddock, S. H. D., Dunn, C. W., Wolfsberg, T. G., C.Mullikin, J., Martindale, M. Q., & Baxevanis, A. D. (2013). The genome of the ctenophore Mnemiopsis leidyi and its implications for cell type evolution. Science, 342(6164). https://doi.org/10.1126/science.1242592 Salinas-Saavedra, M., Salinas-Saavedra, M., & Martindale, M. Q. (2018). Improved protocol for spawning and immunostaining embryos and juvenile stages of the ctenophore Mnemiopsis leidyi. Protocol Exchange, 1–11. https://doi.org/10.1038/protex.2018.092 Schultz, D. T., Francis, W. R., McBroome, J. D., Christianson, L. M., Haddock, S. H. D., & Green, R. E. (2021). A chromosome-scale genome assembly and karyotype of the ctenophore Hormiphora californensis . G3 Genes|Genomes|Genetics. https://doi.org/10.1093/g3journal/jkab302 Sebé-Pedrós, A., Chomsky, E., Pang, K., Lara-Astiaso, D., Gaiti, F., Mukamel, Z., Amit, I., Hejnol, A., Degnan, B. M., & Tanay, A. (2018). Early metazoan cell type diversity and the evolution of multicellular gene regulation. Nature Ecology and Evolution, 2(7), 1176–1188. https://doi.org/10.1038/s41559-018-0575-6 Sefer, E. (2022). A comparison of topologically associating domain callers over mammals at high resolution. In BMC Bioinformatics (Vol. 23, Issue 1). BioMed Central. https://doi.org/10.1186/s12859-022-04674-2 Sexton, T., Yaffe, E., Kenigsberg, E., Bantignies, F., Leblanc, B., Hoichman, M., Parrinello, H., Tanay, A., & Cavalli, G. (2012). Three-Dimensional Folding and Functional Organization Principles of the Drosophila Genome. Cell, 148(3), 458–472. https://doi.org/10.1016/J.CELL.2012.01.010 Simister, R. L., Schmitt, S., & Taylor, M. W. (2011). Evaluating methods for the preservation and extraction of DNA and RNA for analysis of microbial communities in marine sponges. Journal of Experimental Marine Biology and Ecology, 397(1), 38–43. https://doi.org/https://doi.org/10.1016/j.jembe.2010.11.004 Srivastava, M., Begovic, E., Chapman, J., Putnam, N. H., Hellsten, U., Kawashima, T., Kuo, A., Mitros, T., Salamov, A., Carpenter, M. L., Signorovitch, A. Y., Moreno, M. A., Kamm, K., Grimwood, J., Schmutz, J., Shapiro, H., Grigoriev, I. V, Buss, L. W., Schierwater, B., … Rokhsar, D. S. (2008). The Trichoplax genome and the nature of placozoans. Nature, 454(7207), 955–960. https://doi.org/10.1038/nature07191 Srivastava, M., Simakov, O., Chapman, J., Fahey, B., Gauthier, M. E. A., Mitros, T., Richards, G. S., Conaco, C., Dacre, M., Hellsten, U., Larroux, C., Putnam, N. H., Stanke, M., Adamska, M., Darling, A., Degnan, S. M., Oakley, T. H., Plachetzki, D. C., Zhai, Y., … Rokhsar, D. S. (2010). The Amphimedon queenslandica genome and the evolution of animal complexity. Nature, 466(7307), 720–726. https://doi.org/10.1038/nature09201 Stephenson, E., Reynolds, G., Botting, R. A., Calero-Nieto, F. J., Morgan, M. D., Tuong, Z. K., Bach, K., Sungnak, W., Worlock, K. B., Yoshida, M., Kumasaka, N., Kania, K., Engelbert, J., Olabi, B., Spegarova, J. S., Wilson, N. K., Mende, N., Jardine, L., Gardner, L. C. S., … Collaboration, C. I. of T. I. and I. D.-N. I. of H. R. (CITIID-N. C.-19 B. (2021). Single-cell 65 multi-omics analysis of the immune response in COVID-19. Nature Medicine, 27(5), 904–916. https://doi.org/10.1038/s41591-021-01329-2 Sun, H. Bin, Shen, J., & Yokota, H. (2000). Size-Dependent Positioning of Human Chromosomes in Interphase Nuclei. Biophysical Journal, 79(1), 184–190. https://doi.org/10.1016/S0006-3495(00)76282-5 Symmons, O., Uslu, V. V., Tsujimura, T., Ruf, S., Nassari, S., Schwarzer, W., Ettwiller, L., & Spitz, F. (2014). Functional and topological characteristics of mammalian regulatory domains. Genome Research, 24(3), 390–400. https://doi.org/10.1101/gr.163519.113 Szabo, Q., Bantignies, F., & Cavalli, G. (2019). Principles of genome folding into topologically associating domains. Science Advances, 5(4). https://doi.org/10.1126/sciadv.aaw1668 Takahashi, K., Tanabe, K., Ohnuki, M., Narita, M., Ichisaka, T., Tomoda, K., & Yamanaka, S. (2007). Induction of Pluripotent Stem Cells from Adult Human Fibroblasts by Defined Factors. Cell, 131(5), 861–872. https://doi.org/10.1016/j.cell.2007.11.019 Takahashi, K., Tanabe, K., Ohnuki, M., Narita, M., Sasaki, A., Yamamoto, M., Nakamura, M., Sutou, K., Osafune, K., & Yamanaka, S. (2014). Induction of pluripotency in human somatic cells via a transient state resembling primitive streak-like mesendoderm. Nature Communications, 5(1), 3678. https://doi.org/10.1038/ncomms4678 Takahashi, K., & Yamanaka, S. (2006). Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell, 126(4), 663–676. https://doi.org/10.1016/j.cell.2006.07.024 Tarailo-Graovac, M., & Chen, N. (2009). Using RepeatMasker to Identify Repetitive Elements in Genomic Sequences. Current Protocols in Bioinformatics, 25(1), 4.10.1-4.10.14. https://doi.org/https://doi.org/10.1002/0471250953.bi0410s25 Tena, J. J., & Santos-Pereira, J. M. (2021). Topologically Associating Domains and Regulatory Landscapes in Development, Evolution and Disease. Frontiers in Cell and Developmental Biology, 9(July), 1–10. https://doi.org/10.3389/fcell.2021.702787 van Mierlo, G., Pushkarev, O., Kribelbauer, J. F., & Deplancke, B. (2023). Chromatin modules and their implication in genomic organization and gene regulation. Trends in Genetics, 39(2), 140–153. https://doi.org/10.1016/j.tig.2022.11.003 Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023). Methods and applications for single-cell and spatial multi-omics. Nature Reviews Genetics. https://doi.org/10.1038/s41576-023-00580-2 Vasimuddin, M., Misra, S., Li, H., & Aluru, S. (2019). Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems. 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 314–324. https://doi.org/10.1109/IPDPS.2019.00041 Wang, Y., Zhao, Y., Bollas, A., Wang, Y., & Au, K. F. (2021). Nanopore sequencing technology, bioinformatics and applications. Nature Biotechnology, 39(11), 1348–1365. https://doi.org/10.1038/s41587-021-01108-x Wenger, A. M., Peluso, P., Rowell, W. J., Chang, P. C., Hall, R. J., Concepcion, G. T., Ebler, J., Fungtammasan, A., Kolesnikov, A., Olson, N. D., Töpfer, A., Alonge, M., Mahmoud, M., Qian, Y., Chin, C. S., Phillippy, A. M., Schatz, M. C., Myers, G., DePristo, M. A., … 66 Hunkapiller, M. W. (2019). Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nature Biotechnology, 37(10), 1155–1162. https://doi.org/10.1038/s41587-019-0217-9 Yang, J. Y., & Chang, J. M. (2021). Pattern recognition of topologically associating domains using deep learning. BMC Bioinformatics, 22, 1–14. https://doi.org/10.1186/s12859-022-05075-1 Zhang, T., Zhou, J., Gao, W., Jia, Y., Wei, Y., & Wang, G. (2022). Complex genome assembly based on long-read sequencing. Briefings in Bioinformatics, 23(5), bbac305. https://doi.org/10.1093/bib/bbac305 Zhou, C., McCarthy, S. A., & Durbin, R. (2023). YaHS: yet another Hi-C scaffolding tool. Bioinformatics (Oxford, England), 39(1), 10–12. https://doi.org/10.1093/bioinformatics/btac808 Zielinski, J. M., Luke, J. J., Guglietta, S., & Krieg, C. (2021). High Throughput Multi-Omics Approaches for Clinical Trial Evaluation and Drug Discovery. Frontiers in Immunology, 12(March), 1–10. https://doi.org/10.3389/fimmu.2021.590742 Zimmermann, B., Robb, S. M. C., Genikhovich, G., Fropf, W. J., Weilguny, L., He, S., Chen, S., Lovegrove-Walsh, J., Hill, E. M., Ragkousi, K., Praher, D., Fredman, D., Moran, Y., Gibson, M. C., & Technau, U. (2020). Sea anemone genomes reveal ancestral metazoan chromosomal macrosynteny. BioRxiv, 2020.10.30.359448. https://www.biorxiv.org/content/10.1101/2020.10.30.359448v1%0Ahttps://www.biorxiv.org/content/10.1101/2020.10.30.359448v1.abstract Zufferey, M., Tavernari, D., Oricchio, E., & Ciriello, G. (2018). Comparison of computational methods for the identification of topologically associating domains. Genome Biology, 19(1), 1–18. https://doi.org/10.1186/s13059-018-1596-9 67 Appendices Appendix A Appendix data relating to the chromosome scale assembly of Mnemiopsis leidyi. A.1 FastQC quality check for raw HiC reads 68 Appendix B Additional information relating to the differential expression analysis of combined datasets GSE60478 and GSE111748. B.1 Metadata of RNA-seq samples Run Assay Type Bases BioSample Experiment Release Date Sample Name Source_name hpf Stage SRA Study SRR1552869 RNA-Seq 191277310 SAMN02996836 SRX682394 17/02/2016 GSM1480754 single embryo 1 0 1. Early development SRP045566 SRR1552870 RNA-Seq 310945040 SAMN02996837 SRX682395 17/02/2016 GSM1480755 single embryo 2 1 1. Early development SRP045566 SRR1552871 RNA-Seq 373829365 SAMN02996838 SRX682396 17/02/2016 GSM1480756 single embryo 3 2 2. Zygotic genome activation SRP045566 SRR1552872 RNA-Seq 314972070 SAMN02996839 SRX682397 17/02/2016 GSM1480757 single embryo 4 3 3. 60 cell stage SRP045566 SRR1552873 RNA-Seq 68309710 SAMN02996840 SRX682398 17/02/2016 GSM1480758 single embryo 5 4 4. Early gastrulation SRP045566 SRR1552874 RNA-Seq 220846290 SAMN02996841 SRX682399 17/02/2016 GSM1480759 single embryo 6 4.5 4. Early gastrulation SRP045566 SRR1552875 RNA-Seq 123221945 SAMN02996842 SRX682400 17/02/2016 GSM1480760 single embryo 7 5 5. Late gastrulation SRP045566 SRR1552876 RNA-Seq 160546680 SAMN02996843 SRX682401 17/02/2016 GSM1480761 single embryo 8 5.5 5. Late gastrulation SRP045566 69 SRR1552877 RNA-Seq 236251925 SAMN02996844 SRX682402 17/02/2016 GSM1480762 single embryo 9 6 6. Post gastrulation SRP045566 SRR1552878 RNA-Seq 298374825 SAMN02996845 SRX682403 17/02/2016 GSM1480763 single embryo 10 6.5 6. Post gastrulation SRP045566 SRR1552879 RNA-Seq 155235115 SAMN02996846 SRX682404 17/02/2016 GSM1480764 single embryo 11 7 7. Pharynx invagination SRP045566 SRR1552880 RNA-Seq 167467965 SAMN02996847 SRX682405 17/02/2016 GSM1480765 single embryo 12 7.5 7. Pharynx invagination SRP045566 SRR1552881 RNA-Seq 190627150 SAMN02996848 SRX682406 17/02/2016 GSM1480766 single embryo 13 8 8. Later development SRP045566 SRR1552882 RNA-Seq 282885435 SAMN02996849 SRX682407 17/02/2016 GSM1480767 single embryo 14 8.5 8. Later development SRP045566 SRR1552883 RNA-Seq 156387525 SAMN02996850 SRX682408 17/02/2016 GSM1480768 single embryo 15 9 8. Later development SRP045566 SRR1552884 RNA-Seq 713230 SAMN02996851 SRX682409 17/02/2016 GSM1480769 single embryo 16 10 9. Bioluminescence SRP045566 SRR1552885 RNA-Seq 406994105 SAMN02996852 SRX682410 17/02/2016 GSM1480770 single embryo 17 11 10. Later development SRP045566 SRR1552886 RNA-Seq 363022555 SAMN02996853 SRX682411 17/02/2016 GSM1480771 single embryo 18 12 10. Later development SRP045566 SRR1552887 RNA-Seq 182778190 SAMN02996854 SRX682412 17/02/2016 GSM1480772 single embryo 19 13 10. Later development SRP045566 SRR1552888 RNA-Seq 160935355 SAMN02996855 SRX682413 17/02/2016 GSM1480773 single embryo 20 20 13. Larval stage SRP045566 SRR1552889 RNA-Seq 269810590 SAMN02996856 SRX682414 17/02/2016 GSM1480774 single embryo 21 0 1. Early development SRP045566 SRR1552890 RNA-Seq 211178870 SAMN02996857 SRX682415 17/02/2016 GSM1480775 single embryo 22 1 1. Early development SRP045566 SRR1552891 RNA-Seq 180787705 SAMN02996858 SRX682416 17/02/2016 GSM1480776 single embryo 23 2 2. Zygotic genome activation SRP045566 SRR1552892 RNA-Seq 228658640 SAMN02996859 SRX682417 17/02/2016 GSM1480777 single embryo 24 3 3. 60 cell stage SRP045566 70 SRR1552893 RNA-Seq 174538210 SAMN02996860 SRX682418 17/02/2016 GSM1480778 single embryo 25 4 4. Early gastrulation SRP045566 SRR1552894 RNA-Seq 193968355 SAMN02996861 SRX682419 17/02/2016 GSM1480779 single embryo 26 4.5 4. Early gastrulation SRP045566 SRR1552895 RNA-Seq 247487940 SAMN02996862 SRX682420 17/02/2016 GSM1480780 single embryo 27 5 5. Late gastrulation SRP045566 SRR1552896 RNA-Seq 29601425 SAMN02996863 SRX682421 17/02/2016 GSM1480781 single embryo 28 5.5 5. Late gastrulation SRP045566 SRR1552897 RNA-Seq 256670190 SAMN02996864 SRX682422 17/02/2016 GSM1480782 single embryo 29 6 6. Post gastrulation SRP045566 SRR1552898 RNA-Seq 147556640 SAMN02996865 SRX682423 17/02/2016 GSM1480783 single embryo 30 6.5 6. Post gastrulation SRP045566 SRR1552899 RNA-Seq 486489360 SAMN02996866 SRX682424 17/02/2016 GSM1480784 single embryo 31 7 7. Pharynx invagination SRP045566 SRR1552900 RNA-Seq 194268795 SAMN02996867 SRX682425 17/02/2016 GSM1480785 single embryo 32 7.5 7. Pharynx invagination SRP045566 SRR1552901 RNA-Seq 326269930 SAMN02996868 SRX682426 17/02/2016 GSM1480786 single embryo 33 8 8. Later development SRP045566 SRR1552902 RNA-Seq 45671815 SAMN02996869 SRX682427 17/02/2016 GSM1480787 single embryo 34 8.5 8. Later development SRP045566 SRR1552903 RNA-Seq 105200760 SAMN02996870 SRX682428 17/02/2016 GSM1480788 single embryo 35 9 8. Later development SRP045566 SRR1552904 RNA-Seq 220468710 SAMN02996871 SRX682429 17/02/2016 GSM1480789 single embryo 36 10 9. Bioluminescence SRP045566 SRR1552905 RNA-Seq 232719340 SAMN02996872 SRX682430 17/02/2016 GSM1480790 single embryo 37 11 10. Later development SRP045566 SRR1552906 RNA-Seq 207217360 SAMN02996873 SRX682431 17/02/2016 GSM1480791 single embryo 38 12 10. Later development SRP045566 SRR1552907 RNA-Seq 235860660 SAMN02996874 SRX682432 17/02/2016 GSM1480792 single embryo 39 13 10. Later development SRP045566 SRR1552908 RNA-Seq 166488700 SAMN02996875 SRX682433 17/02/2016 GSM1480793 single embryo 40 20 13. Larval stage SRP045566 71 SRR6828882 RNA-Seq 561540 SAMN08689821 SRX3784971 14/03/2018 GSM3039179 single embryo 41 0 1. Early development SRP135247 SRR6828883 RNA-Seq 133581560 SAMN08689820 SRX3784972 14/03/2018 GSM3039180 single embryo 42 1 1. Early development SRP135247 SRR6828884 RNA-Seq 44050615 SAMN08689819 SRX3784973 14/03/2018 GSM3039181 single embryo 43 2 2. Zygotic genome activation SRP135247 SRR6828885 RNA-Seq 96324585 SAMN08689818 SRX3784974 14/03/2018 GSM3039182 single embryo 44 3 3. 60 cell stage SRP135247 SRR6828886 RNA-Seq 61311495 SAMN08689817 SRX3784975 14/03/2018 GSM3039183 single embryo 45 4 4. Early gastrulation SRP135247 SRR6828887 RNA-Seq 105194810 SAMN08689816 SRX3784976 14/03/2018 GSM3039184 single embryo 46 4.5 4. Early gastrulation SRP135247 SRR6828888 RNA-Seq 75520795 SAMN08689815 SRX3784977 14/03/2018 GSM3039185 single embryo 47 5 5. Late gastrulation SRP135247 SRR6828889 RNA-Seq 15125915 SAMN08689814 SRX3784978 14/03/2018 GSM3039186 single embryo 48 5.5 5. Late gastrulation SRP135247 SRR6828890 RNA-Seq 95290545 SAMN08689813 SRX3784979 14/03/2018 GSM3039187 single embryo 49 6 6. Post gastrulation SRP135247 SRR6828891 RNA-Seq 97561520 SAMN08689812 SRX3784980 14/03/2018 GSM3039188 single embryo 50 6.5 6. Post gastrulation SRP135247 SRR6828892 RNA-Seq 53192405 SAMN08689811 SRX3784981 14/03/2018 GSM3039189 single embryo 51 7 7. Pharynx invagination SRP135247 SRR6828893 RNA-Seq 99500240 SAMN08689810 SRX3784982 14/03/2018 GSM3039190 single embryo 52 7.5 7. Pharynx invagination SRP135247 SRR6828894 RNA-Seq 54178040 SAMN08689809 SRX3784983 14/03/2018 GSM3039191 single embryo 53 8 8. Later development SRP135247 SRR6828895 RNA-Seq 90120940 SAMN08689808 SRX3784984 14/03/2018 GSM3039192 single embryo 54 8.5 8. Later development SRP135247 SRR6828896 RNA-Seq 93205035 SAMN08689807 SRX3784985 14/03/2018 GSM3039193 single embryo 55 9 8. Later development SRP135247 SRR6828897 RNA-Seq 54516070 SAMN08689806 SRX3784986 14/03/2018 GSM3039194 single embryo 56 10 9. Bioluminescence SRP135247 72 SRR6828898 RNA-Seq 82183080 SAMN08689805 SRX3784987 14/03/2018 GSM3039195 single embryo 57 11 10. Later development SRP135247 SRR6828899 RNA-Seq 145611795 SAMN08689804 SRX3784988 14/03/2018 GSM3039196 single embryo 58 12 10. Later development SRP135247 SRR6828900 RNA-Seq 46654720 SAMN08689803 SRX3784989 14/03/2018 GSM3039197 single embryo 59 13 10. Later development SRP135247 SRR6828901 RNA-Seq 70423955 SAMN08689802 SRX3784990 14/03/2018 GSM3039198 single embryo 60 20 13. Larval stage SRP135247 SRR6828902 RNA-Seq 172099970 SAMN08689801 SRX3784991 14/03/2018 GSM3039199 single embryo 61 13 10. Later development SRP135247 SRR6828903 RNA-Seq 191808925 SAMN08689792 SRX3784992 14/03/2018 GSM3039200 single embryo 62 14 10. Later development SRP135247 SRR6828904 RNA-Seq 160596415 SAMN08689791 SRX3784993 14/03/2018 GSM3039201 single embryo 63 15 11. Pre cydippid emergence SRP135247 SRR6828905 RNA-Seq 183984745 SAMN08689790 SRX3784994 14/03/2018 GSM3039202 single embryo 64 16 11. Pre cydippid emergence SRP135247 SRR6828906 RNA-Seq 132273435 SAMN08689789 SRX3784995 14/03/2018 GSM3039203 single embryo 65 17 11. Pre cydippid emergence SRP135247 SRR6828907 RNA-Seq 97524070 SAMN08689844 SRX3784996 14/03/2018 GSM3039204 single embryo 66 18 12. Cydippid emergence SRP135247 SRR6828908 RNA-Seq 54910730 SAMN08689827 SRX3784997 14/03/2018 GSM3039205 single embryo 67 19 12. Cydippid emergence SRP135247 SRR6828909 RNA-Seq 146565475 SAMN08689828 SRX3784998 14/03/2018 GSM3039206 single embryo 68 20 13. Larval stage SRP135247 SRR6828910 RNA-Seq 288119650 SAMN08689830 SRX3784999 14/03/2018 GSM3039207 single embryo 69 13 10. Later development SRP135247 SRR6828911 RNA-Seq 259948255 SAMN08689787 SRX3785000 14/03/2018 GSM3039208 single embryo 70 14 10. Later development SRP135247 SRR6828912 RNA-Seq 247659720 SAMN08689788 SRX3785001 14/03/2018 GSM3039209 single embryo 71 15 11. Pre cydippid emergence SRP135247 SRR6828913 RNA-Seq 193100530 SAMN08689829 SRX3785002 14/03/2018 GSM3039210 single embryo 72 16 11. Pre cydippid emergence SRP135247 73 SRR6828914 RNA-Seq 134191260 SAMN08689842 SRX3785003 14/03/2018 GSM3039211 single embryo 73 17 11. Pre cydippid emergence SRP135247 SRR6828915 RNA-Seq 149161775 SAMN08689823 SRX3785004 14/03/2018 GSM3039212 single embryo 74 18 12. Cydippid emergence SRP135247 SRR6828916 RNA-Seq 49762895 SAMN08689822 SRX3785005 14/03/2018 GSM3039213 single embryo 75 19 12. Cydippid emergence SRP135247 SRR6828917 RNA-Seq 85594670 SAMN08689824 SRX3785006 14/03/2018 GSM3039214 single embryo 76 13 10. Later development SRP135247 SRR6828918 RNA-Seq 363395830 SAMN08689825 SRX3785007 14/03/2018 GSM3039215 single embryo 77 14 10. Later development SRP135247 SRR6828919 RNA-Seq 4702425 SAMN08689826 SRX3785008 14/03/2018 GSM3039216 single embryo 78 15 11. Pre cydippid emergence SRP135247 SRR6828920 RNA-Seq 170662975 SAMN08689843 SRX3785009 14/03/2018 GSM3039217 single embryo 79 16 11. Pre cydippid emergence SRP135247 SRR6828921 RNA-Seq 152875485 SAMN08689841 SRX3785010 14/03/2018 GSM3039218 single embryo 80 17 11. Pre cydippid emergence SRP135247 SRR6828922 RNA-Seq 279071835 SAMN08689840 SRX3785011 14/03/2018 GSM3039219 single embryo 81 18 12. Cydippid emergence SRP135247 SRR6828923 RNA-Seq 40078465 SAMN08689839 SRX3785012 14/03/2018 GSM3039220 single embryo 82 19 12. Cydippid emergence SRP135247 SRR6828924 RNA-Seq 108737090 SAMN08689838 SRX3785013 14/03/2018 GSM3039221 single embryo 83 20 13. Larval stage SRP135247 SRR6828925 RNA-Seq 214480945 SAMN08689837 SRX3785014 14/03/2018 GSM3039222 single embryo 84 14 10. Later development SRP135247 SRR6828926 RNA-Seq 177107840 SAMN08689835 SRX3785015 14/03/2018 GSM3039223 single embryo 85 15 11. Pre cydippid emergence SRP135247 SRR6828927 RNA-Seq 173088405 SAMN08689834 SRX3785016 14/03/2018 GSM3039224 single embryo 86 16 11. Pre cydippid emergence SRP135247 SRR6828928 RNA-Seq 191028110 SAMN08689833 SRX3785017 14/03/2018 GSM3039225 single embryo 87 17 11. Pre cydippid emergence SRP135247 SRR6828929 RNA-Seq 139155940 SAMN08689832 SRX3785018 14/03/2018 GSM3039226 single embryo 88 18 12. Cydippid emergence SRP135247 74 SRR6828930 RNA-Seq 120382885 SAMN08689831 SRX3785019 14/03/2018 GSM3039227 single embryo 89 19 12. Cydippid emergence SRP135247 SRR6828931 RNA-Seq 56253085 SAMN08689836 SRX3785020 14/03/2018 GSM3039228 single embryo 90 20 13. Larval stage SRP135247 75 B.2 Quality assurance of all time points This is the principle component analysis across all 90 samples which may be anomalies and require removal from downstream analysis. 76 B.3 Hierachical clustering shows sample similarity of the maternal to zygotic subset 77 B.4 Dispersal estimates for differentially expressed genes at the maternal to zygotic transition 78 B.5 Top 25 Differentially expressed genes at zygotic genome activation baseMean log2FoldChange lfcSE stat pvalue padj 67571-Mle_Anno_final.03g099060 43.668 1.871 0.206 9.099 9.15E-20 1.66E-15 67571-Mle_Anno_final.03g099040 58.387 1.716 0.190 9.016 1.95E-19 1.76E-15 67571-Mle_Anno_final.02g133840 30.755 1.523 0.174 8.769 1.81E-18 1.09E-14 67571-Mle_Anno_final.04g087670 55.897 2.285 0.262 8.724 2.68E-18 1.21E-14 67571-Mle_Anno_final.02g130660 171.591 -1.857 0.216 -8.599 8.03E-18 2.77E-14 67571-Mle_Anno_final.11g199960 77.212 1.734 0.202 8.584 9.17E-18 2.77E-14 67571-Mle_Anno_final.03g096260 80.369 0.951 0.117 8.161 3.32E-16 8.59E-13 67571-Mle_Anno_final.02g140310 107.982 -1.281 0.157 -8.133 4.18E-16 9.48E-13 67571-Mle_Anno_final.04g087680 34.831 1.811 0.224 8.069 7.07E-16 1.42E-12 67571-Mle_Anno_final.04g087660 57.165 2.128 0.265 8.019 1.06E-15 1.93E-12 67571-Mle_Anno_final.02g149940 29.608 1.718 0.222 7.730 1.07E-14 1.76E-11 67571-Mle_Anno_final.05g057520 64.434 2.265 0.300 7.552 4.28E-14 6.47E-11 67571-Mle_Anno_final.02g142600 387.037 -1.842 0.245 -7.523 5.34E-14 7.44E-11 67571-Mle_Anno_final.03g096250 63.243 1.178 0.159 7.406 1.31E-13 1.69E-10 67571-Mle_Anno_final.08g042070 220.958 -1.541 0.211 -7.313 2.62E-13 3.17E-10 67571-Mle_Anno_final.02g139260 53.804 -1.326 0.184 -7.227 4.93E-13 5.59E-10 67571-Mle_Anno_final.04g087650 76.238 2.098 0.292 7.183 6.80E-13 7.25E-10 67571-Mle_Anno_final.03g095410 138.489 -1.832 0.263 -6.960 3.41E-12 3.43E-09 67571-Mle_Anno_final.07g152920 62.757 1.186 0.171 6.950 3.66E-12 3.49E-09 67571-Mle_Anno_final.10g117410 88.850 1.735 0.251 6.918 4.59E-12 4.16E-09 67571-Mle_Anno_final.05g072900 58.924 -1.318 0.192 -6.878 6.07E-12 5.24E-09 67571-Mle_Anno_final.08g050540 19.340 1.869 0.273 6.841 7.85E-12 6.47E-09 67571-Mle_Anno_final.02g147270 33.615 1.873 0.276 6.798 1.06E-11 8.34E-09 67571-Mle_Anno_final.04g087970 156.528 0.793 0.117 6.766 1.32E-11 9.96E-09 67571-Mle_Anno_final.04g085520 41.331 1.687 0.251 6.723 1.78E-11 1.29E-08 79 B.6 Hierachical clustering shows sample similarity of the gastrulation subset B.7 Dispersal estimates for differentially expressed genes of the gastrulation subset 80 B.8 Top 25 Differentially expressed genes across gastrulation baseMean log2FoldChange lfcSE stat pvalue padj 67571-Mle_Anno_final.07g152560 150.538 1.789 0.169 10.607 2.75E-26 4.43E-22 67571-Mle_Anno_final.01g184750 686.082 1.280 0.125 10.235 1.39E-24 1.12E-20 67571-Mle_Anno_final.10g114400 170.163 1.044 0.108 9.653 4.78E-22 2.57E-18 67571-Mle_Anno_final.06g009340 89.566 1.857 0.204 9.104 8.70E-20 3.50E-16 67571-Mle_Anno_final.02g147040 584.640 1.913 0.222 8.601 7.89E-18 2.54E-14 67571-Mle_Anno_final.10g113020 265.939 2.106 0.249 8.444 3.08E-17 8.25E-14 67571-Mle_Anno_final.02g147030 98.519 1.865 0.224 8.328 8.23E-17 1.89E-13 67571-Mle_Anno_final.02g133840 29.001 -1.197 0.144 -8.289 1.15E-16 2.05E-13 67571-Mle_Anno_final.05g061140 557.703 1.562 0.188 8.297 1.07E-16 2.05E-13 67571-Mle_Anno_final.09g040120 170.436 2.197 0.271 8.112 4.98E-16 8.02E-13 67571-Mle_Anno_final.10g113000 171.146 2.022 0.251 8.055 7.92E-16 1.16E-12 67571-Mle_Anno_final.05g061230 286.615 2.096 0.262 8.007 1.18E-15 1.58E-12 67571-Mle_Anno_final.04g083510 63.553 1.771 0.229 7.729 1.08E-14 1.34E-11 67571-Mle_Anno_final.01g182900 103.093 1.553 0.201 7.716 1.20E-14 1.38E-11 67571-Mle_Anno_final.03g098250 65.885 1.540 0.202 7.604 2.87E-14 3.08E-11 67571-Mle_Anno_final.12g026830 162.465 1.108 0.148 7.500 6.36E-14 6.40E-11 67571-Mle_Anno_final.02g150950 88.690 -0.492 0.066 -7.450 9.36E-14 8.86E-11 67571-Mle_Anno_final.04g084120 200.323 2.402 0.324 7.423 1.15E-13 1.02E-10 67571-Mle_Anno_final.04g088860 92.391 2.071 0.281 7.365 1.77E-13 1.24E-10 67571-Mle_Anno_final.04g088900 92.391 2.071 0.281 7.365 1.77E-13 1.24E-10 67571-Mle_Anno_final.04g088910 92.391 2.071 0.281 7.365 1.77E-13 1.24E-10 67571-Mle_Anno_final.04g088920 86.846 2.055 0.278 7.386 1.51E-13 1.24E-10 67571-Mle_Anno_final.09g033650 79.418 1.788 0.243 7.370 1.70E-13 1.24E-10 67571-Mle_Anno_final.04g088940 99.498 1.964 0.268 7.318 2.52E-13 1.69E-10 67571-Mle_Anno_final.04g088840 86.501 2.059 0.284 7.244 4.35E-13 2.69E-10 81 B.9 Hierachical clustering of the cydippid subset B.10 Dispersal estimates of genes for the cydippid subset 82 B.11 Top 25 Differentially expressed genes across onset of cydippid emergence baseMean log2FoldChange lfcSE stat pvalue padj 67571-Mle_Anno_final.04g082670 74.798 0.909 0.151 6.031 1.63E-09 2.39E-05 67571-Mle_Anno_final.01g173250 1830.381 -0.459 0.078 -5.881 4.07E-09 2.99E-05 67571-Mle_Anno_final.05g059930 656.974 0.621 0.115 5.419 5.98E-08 0.000181 67571-Mle_Anno_final.09g028390 2183.987 -0.572 0.106 -5.414 6.15E-08 0.000181 67571-Mle_Anno_final.10g114090 47.427 0.707 0.130 5.441 5.29E-08 0.000181 67571-Mle_Anno_final.08g054160 98.087 0.647 0.121 5.365 8.11E-08 0.000198 67571-Mle_Anno_final.01g175150 1677.676 -0.476 0.090 -5.283 1.27E-07 0.000233 67571-Mle_Anno_final.04g078110 123.778 0.630 0.119 5.299 1.16E-07 0.000233 67571-Mle_Anno_final.08g044660 800.228 0.510 0.097 5.237 1.63E-07 0.00024 67571-Mle_Anno_final.08g048880 364.108 0.526 0.100 5.242 1.59E-07 0.00024 67571-Mle_Anno_final.01g173540 1870.536 -0.440 0.085 -5.174 2.29E-07 0.000306 67571-Mle_Anno_final.02g146840 363.231 0.446 0.087 5.113 3.18E-07 0.000389 67571-Mle_Anno_final.11g196450 71.605 -0.508 0.100 -5.073 3.92E-07 0.000443 67571-Mle_Anno_final.11g193970 260.669 0.636 0.126 5.028 4.96E-07 0.00052 67571-Mle_Anno_final.03g091850 64.111 0.612 0.123 4.955 7.22E-07 0.000687 67571-Mle_Anno_final.04g086800 184.695 0.713 0.144 4.948 7.49E-07 0.000687 67571-Mle_Anno_final.11g196440 99.773 -0.472 0.096 -4.911 9.04E-07 0.000781 67571-Mle_Anno_final.04g083300 331.250 0.428 0.088 4.865 1.15E-06 0.000935 67571-Mle_Anno_final.08g053290 59.224 0.466 0.097 4.811 1.50E-06 0.00116 67571-Mle_Anno_final.05g073250 858.615 0.860 0.180 4.779 1.76E-06 0.001291 67571-Mle_Anno_final.05g062670 54.127 0.592 0.124 4.758 1.96E-06 0.001367 67571-Mle_Anno_final.06g003050 173.156 0.793 0.171 4.628 3.69E-06 0.002461 67571-Mle_Anno_final.11g194390 279.487 0.548 0.119 4.615 3.94E-06 0.002512 67571-Mle_Anno_final.11g199620 189.697 0.503 0.110 4.575 4.77E-06 0.002916 67571-Mle_Anno_final.01g180140 542.605 0.533 0.118 4.498 6.85E-06 0.004019
Thesis/Dissertation
eng
Bioinformatics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Plotkin, Steven
3D genomic insights of an early branching metazoan : a chromosome scale assembly of Mnemiopsis leidyi and differential expression in early development
Text