Science, Faculty of
DSpace
UBCV
Leung, Tiffany Ying
Master of Science - MSc
University of British Columbia
X chromosome inactivation (XCI) is the process in which one copy of the X chromosomes in females (XX) is randomly silenced to achieve dosage compensation of gene expression from X chromosome of males (XY). XCI is known to be incomplete, resulting in a subset of genes on the inactive X (Xi) being expressed. Genes along the X chromosome are classified into three categories based upon their expression from the Xi: subject, escape and variable. Escape genes, corresponding to 15% of X-linked genes, are generally expressed from the Xi at a substantially lower level compared to the active X (Xa). The underlying mechanisms in controlling escape from the heterochromatic state of Xi has been a long-standing question. While there is evidence that supports a role for intrinsic DNA elements in the escape XCI, they have not yet been identified. With increasing amounts of data for transcription factor (TF) binding sites, the objective of this thesis was to identify the regulatory TFs facilitating the ability of genes to escape XCI via a bioinformatics approach using empirical data. ChIP-seq peaks of 155 TFs from the ReMap database were assessed for enrichment at regulatory regions of 55 escape genes. 19 TFs were identified via enrichment analysis in the transcription start site regions of escape genes. Co-binding of pairs of enriched TFs were characterized by gene set similarity between the target genes. Of the 155 TFs examined, ZFP36, an RNA binding protein that alters RNA stability, showed importance in both enrichment analysis and co-binding analysis. An initial exploration of methods to compare the structures of Xi and Xa were undertaken using ChIA-PET data, providing insights into the limitations of current data resources and opportunities for future studies to inform the topographical organization of escape genes within Xi. The results of this thesis refined our knowledge on cis-regulatory elements and trans-acting factors potentially involved in escape of XCI. This list of enriched TF may be useful in future analyses, experimental or computational, to further determine their sufficiency and necessity in the escape of XCI.
IDENTIFICATION OF REGULATORY MECHANISMS IN GOVERNINGGENE EXPRESSION FROM THE INACTIVATED X CHROMOSOMEbyTiffany Ying LeungB.Sc., The University of British Columbia, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Bioinformatics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)October 2022© Tiffany Ying Leung, 2022The following individuals certify that they have read, and recommend to the Faculty of Graduateand Postdoctoral Studies for acceptance, the thesis entitled:Identification of regulatory mechanisms in governing gene expression from the inactivated Xchromosomesubmitted by Tiffany Ying Leung in partial fulfillment of the requirements forthe degree of Master of Sciencein BioinformaticsExamining Committee:Dr. Wyeth Wasserman, Professor, Medical Genetics, UBCSupervisorDr. Carl de Boer, Assistant Professor, Biomedical Engineering, UBCSupervisory Committee MemberDr. Gabriela Cohen Freue, Associate Professor, Statistics, UBCSupervisory Committee MemberiiAbstractX chromosome inactivation (XCI) is the process in which one copy of the X chromosomes infemales (XX) is randomly silenced to achieve dosage compensation of gene expression from Xchromosome of males (XY). XCI is known to be incomplete, resulting in a subset of genes onthe inactive X (Xi) being expressed. Genes along the X chromosome are classified into threecategories based upon their expression from the Xi: subject, escape and variable. Escape genes,corresponding to 15% of X-linked genes, are generally expressed from the Xi at a substantiallylower level compared to the active X (Xa). The underlying mechanisms in controlling escapefrom the heterochromatic state of Xi has been a long-standing question. While there is evidencethat supports a role for intrinsic DNA elements in the escape XCI, they have not yet beenidentified.With increasing amounts of data for transcription factor (TF) binding sites, the objective of thisthesis was to identify the regulatory TFs facilitating the ability of genes to escape XCI via abioinformatics approach using empirical data. ChIP-seq peaks of 155 TFs from the ReMapdatabase were assessed for enrichment at regulatory regions of 55 escape genes. 19 TFs wereidentified via enrichment analysis in the transcription start site regions of escape genes.Co-binding of pairs of enriched TFs were characterized by gene set similarity between the targetgenes. Of the 155 TFs examined, ZFP36, an RNA binding protein that alters RNA stability,showed importance in both enrichment analysis and co-binding analysis.iiiAn initial exploration of methods to compare the structures of Xi and Xa were undertaken usingChIA-PET data, providing insights into the limitations of current data resources andopportunities for future studies to inform the topographical organization of escape genes withinXi.The results of this thesis refined our knowledge on cis-regulatory elements and trans-actingfactors potentially involved in escape of XCI. This list of enriched TF may be useful in futureanalyses, experimental or computational, to further determine their sufficiency and necessity inthe escape of XCI.ivLay SummaryIn human, sex is determined by the pair of sex chromosomes, where females have two Xchromosomes and males have one X and one Y chromosome. Potentially because differences inthe number of copies of genes can have large effects, a process exists in females to turn off onecopy of the X chromosome. This process has been extensively studied. However, on the Xchromosome that is turned off, a few genes remain on. How these genes “escape” being turnedoff is unknown. In this thesis, data from experiments that show where proteins stick in thegenome is used to explore how genes escape. Proteins that stick to escape genes more than toother genes may be important to the escape process. The findings suggest, but do not prove, thata protein called ZFP36 that sticks to RNA rather than DNA may be important and will motivatefuture research.vPrefaceThis idea of this project is developed from grant project collaboration between Dr. CarolynBrown and my supervisor, Dr. Wyeth Wasserman. Under the supervision of Dr. Wasserman, Iperformed all the bioinformatic analyses to answer the research question and wrote the entiretyof this thesis.At the time of writing and thesis submission, none of the results of this thesis have beenpublished. However, the enrichment analysis component is under preparation for inclusion in amanuscript to be submitted to a peer reviewed scientific journal.viTable of ContentsAbstract ………………………………………………………………………………………… iiiLay Summary…………………………………………………………………………………… vPreface…………………………………………………………………………………………... viTable of Contents ……………………………………………………………………………… viiList of Tables …………………………………………………………………………………… ixList of Figures …………………………………………………………………………………... xList of Abbreviations …………………………………………………………………………... xiList of Genes or Genetic Factors ……………………………………………………………... xiiAcknowledgements …………………………………………………………………………… xiiiChapter 1: Introduction ………………………………………………………………………... 11.1 Thesis Overview …………………………………………………………………………. 11.2 X-chromosome inactivation……………………………………………………………… 11.2.1 X-Inactive Specific Transcript (XIST) …………………………………………….. 31.3 Xi structure - Barr body ………………………………………………………………….. 31.3.1 Epigenetic features of Xi …………………………………………………………... 41.4 Escape from XCI ………………………………………………………………………… 51.4.1 Escape from XCI in mouse………………………………………………………… 71.5 Cis-regulatory elements in XCI ………………………………………………………….. 91.5.1 Waystations ………………………………………………………………………… 91.5.2 Escape elements ………………………………………………………………….. 101.5.3 Boundary elements ……………………………………………………………….. 101.6 Bioinformatics approach ……………………………………………………………….. 121.6.1 Motif enrichment analysis ………………………………………………………... 131.6.2 Prediction of chromosome structure ……………………………………………… 131.7 Thesis objectives………………………………………………………………………... 14Chapter 2: Methods …………………………………………………………………………… 152.1 Inactivation status of X-linked genes…………………………………………………… 152.2 Transcription factor ChIP-seq peaks……………………………………………………. 192.3 Regions of interest (ROI)……………………………………………………………….. 262.4 Data preparation………………………………………………………………………… 272.4.1 GC composition ………………………………………………………………….. 27vii2.5 Enrichment analysis…………………………………………………………………….. 272.5.1 Iterations ………………………………………………………………………….. 282.5.2 Number of escape genes ………………………………………………………….. 282.5.3 Threshold for enrichment for human …………………………………………….. 292.5.4 Determination of enrichment for mouse………………………………………….. 292.6 Co-binding: Dice similarity coefficient ………………………………………………… 302.7 Variant data ……………………………………………………………………………... 302.8 Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) …………. 31Chapter 3: Results …………………………………………………………………………….. 323.1 Transcription factor binding density compared between escape and subject genes ……. 323.2 Enrichment analysis…………………………………………………………………….. 333.2.1 TSS regions……………………………………………………………………….. 343.2.2 Upshell & Downshell …………………………………………………………….. 363.2.3 TF characteristics in mouse TSS regions…………………………………………. 363.3 Co-binding: TF-pairs …………………………………………………………………… 383.4 ChIA-PET analysis ……………………………………………………………………... 423.4.1 Similarity of processed samples ………………………………………………….. 423.4.2 Structural models …………………………………………………………………. 44Chapter 4: Discussion …………………………………………………………………………. 464.1 Overview of findings …………………………………………………………………… 464.2 Limitations ……………………………………………………………………………… 484.3 Outstanding questions ………………………………………………………………….. 494.4 Future directions ……………………………………………………………………….. 504.5 Conclusion ……………………………………………………………………………… 51References……………………………………………………………………………………… 52viiiList of TablesTable 2.1 Metadata of human escape genes…………………………………………………….. 16Table 2.2 Metadata of mouse escape genes …………………………………………………….. 18Table 2.3 Metadata of TFs in ReMap ………………………………………………………….. 20Table 3.1 Enriched co-binding TF pairs in TSS regions of human escape genes ……………… 40ixList of FiguresFigure 1.1 Dosage compensation of human sex chromosomes ………………………………….. 2Figure 1.2 Distribution of inactivation status in the human and mouse Xi …………………….... 8Figure 1.3 Theoretical cis-regulatory factors in determining the escape of inactivation ………. 11Figure 2.1 Scheme of regions analyzed ………………………………………………………… 26Figure 3.1 Distribution of the number of binding TFs at the TSS region of genes …………….. 33Figure 3.2 Summary of transcription factors enriched in TSS regions of escape genes ……….. 35Figure 3.3 Summary of transcription factors enriched in shell regions of escape genes……….. 37Figure 3.4 Escape-over-background ratios of the DSCs for enriched TFs in the TSS regions … 39Figure 3.5 Similarity scores of ChIA-PET data used for analysis……………………………… 43Figure 3.6 Structural models of X chromosomes from ChIA-PET data ……………………….. 45xList of Abbreviations3D Three dimensionalbp BasepairBAC Bacterial artificial chromosomeChIA-PET Chromatin interaction analysis with paired-end tag sequencingChIP-seq Chromatin immunoprecipitation assay with sequencingDNA Deoxyribonucleic acidDSC Dice similarity coefficientFDR False discovery rateFISH Fluorescence in situ hybridizationkb KilobaseLINE Long interspersed nuclear elementlncRNA Long non-coding RNAMAPQ Mapping qualityMb MegabasePAR Pseudoautosomal regionPET Paired-end tagsPRC Polycomb repressive complexRNA Ribonucleic acidROI Region of interestTF Transcription factorTSS Transcription start siteXa Active XXIC X inactivation centreXCI X-chromosome inactivationXi Inactive XxiList of Genes or Genetic FactorsARID3A AT-Rich Interaction Domain 3ABATF3 Basic Leucine Zipper ATF-Like Transcription Factor 3CREM CAMP Responsive Element ModulatorCTCF CCCTC-binding factorHDAC2 Histone Deacetylase 2HSF1 Heat Shock Transcription Factor 1IRF5 Interferon Regulatory Factor 5MAFF MAF BZIP Transcription Factor FMYB MYB Proto-Oncogene, Transcription FactorNFATC1 Nuclear Factor Of Activated T Cells 1NIPBL NIPBL Cohesin Loading FactorRAD51 RAD51 RecombinaseRBBP5 RB Binding Protein 5SREBF1 Sterol Regulatory Element Binding Transcription Factor 1STAT1 Signal Transducer And Activator Of Transcription 1TBP TATA-Box Binding ProteinXIST X-active specific transcriptYY1 YY1 Transcription FactorxiiAcknowledgementsI would like to express my sincere appreciation to many people who have helped and supportedme for the past 2 years, especially during the past few months writing this thesis. First andforemost, I would like to thank my supervisor, Dr. Wyeth Wasserman, for his full support,guidance and patience throughout this whole project. Thank you for funding and believing in me,none of this would have been possible without your help.I extend my appreciation to all the members of the Wasserman Lab for their continuous supportand positivity, especially during the hard times of COVID. Special thanks to Alejandro Aguirre,Brittany Hewitson, Meltem Omur and Solenne Correard for their friendship and providing asupportive environment. Thank you for making the life of a graduate student more manageablewith all the laughter, games, snacks and drinks.Many thanks to Dr. Carolyn Brown for her guidance and passion throughout this project. I wouldlike to extend my gratitude to my committee members including Dr. Carl de Boer and Dr.Gabriela Cohen Freue for their time and feedback.Lastly, my deepest thanks go to my family. I thank my brother Lukie for his advice andencouragement, and my parents, Idy and Sammy, for their endless love and understanding, whichhelped me to achieve this milestone in my life.xiiiChapter 1: Introduction1.1 Thesis OverviewX-chromosome inactivation (XCI) is an epigenetic phenomenon in which one copy of the Xchromosomes in female mammalian cells is silenced to allow dosage compensation to maleswith a single copy of X chromosome. However, XCI is incomplete which allows some genes onthe inactive X (Xi) chromosome to escape the silencing mechanism and be expressed, althoughat a substantially lower level than genes on the active X (Xa) chromosome. Escape genes caneither be constitutively or variably escape; variable escape genes are selectively expressed fromthe Xi in specific individuals or tissue types. The mechanism of XCI and the identification ofescape genes on the X chromosome has been extensively studied, however, the mechanism orelements contributing to the escape from inactivation status of X-linked genes is still unknown.This thesis focuses on the application of computational methods to gain insights intomechanisms enabling expression of genes from Xi.1.2 X-chromosome inactivationThe determination of biological sex in eutherian mammals lies in the pair of sex chromosomes,where female have XX and males have XY (Figure 1.1). The X and Y chromosomes are a resultof evolutionary divergence of a homologous pair of autosomes. The two sex chromosomes nowshare homologues in the pseudoautosomal regions (PAR) located at either end of the sexchromosomes (Lahn & Page, 1999). The PARs allow the X and Y chromosomes to pair formeiosis and for recombination to occur, hence these regions exhibit greater conservation,1whereas most genes located outside the PARs have diverged functionally (Graves, 2016). The Ychromosome lost many of the ancestral genes shared with the X chromosome and accumulatedgenes involved in sex-determination (SRY gene - sex-determining region Y) and spermatogenesis(Skaletsky et al., 2003). The evolution of the Y chromosome results in the X chromosomecarrying approximately 10 times the number of protein-coding genes (Bachtrog, 2013). With XYmales only having a single copy of the X chromosome, X-linked gene expression between thetwo sexes would be expected to be unbalanced, hence there’s a need for dosage compensation. In1949, Dr. Lyon proposed that XX individuals have developed a mechanism now known as Xchromosome inactivation (XCI), in which one copy of the X chromosome is randomlyinactivated in early development (Figure 1.1) (Lyon, 1962). Figure 1.1 Dosage compensation of human sex chromosomes.In humans, inactivation of a single X chromosome in females is the mechanism for dosagecompensation between sexes. Figure is adapted from Balaton, B. P., & Brown, C. J. (2016).Escape Artists of the X Chromosome. Trends in Genetics, 32(6), 348–359.https://doi.org/10.1016/j.tig.2016.03.00721.2.1 X-Inactive Specific Transcript (XIST)The search for the molecular mechanisms controlling X inactivation highlighted a uniquemechanism as being central to the regulation process. XIST is a 17-kb long non-coding RNA(lncRNA), polyadenylated and spliced, expressed exclusively from the one of the X in females(Brown et al., 1992). XCI is initiated when XIST is expressed from the X inactivation centre(XIC), a gene locus containing several elements involved in inactivation, of the Xi (Brown et al.,1991). XIST spreads in a cis-manner, therefore determining Xi as the X chromosome from whichit is transcibed. The transcribed XIST RNA localizes within the Xi territory, termed XIST cloud,and the other XIST allele remains inactive on the Xa (Sunwoo et al., 2017). After coating Xi,XIST establishes asynchronous replication, and recruits chromatin proteins and silencingregulators to create a stable heterochromatin structure (Avner & Heard, 2001). 1.3 Xi structure - Barr bodyChromosomes in the human genome do not exist as a linear entity within the nuclei, instead theyform three-dimensional (3D) structures through which the accessibility of different regions isdetermined by spatial organization. In 1949, a dense nuclear structure was reported in femaleneural cells only, later termed as the Barr body, after its discoverer, contributing to the forming ofDr. Lyon’s hypothesis of XCI (Barr & Bertram, 1949; Lyon, 1962). The X chromosomes dodiffer substantially in shape and surface area, with Xi organized into a round shape with smoothsurface, whereas, Xa forms a flat shape with irregular surface (Eils et al., 1996). However, it wasrevealed in a FISH analysis that Xi and Xa have similar compaction and volumes, despite theobvious appearance of Xi as a solid mass under the light microscopy and the difference in DAPI3staining intensity between the Xs (Eils et al., 1996; Rego et al., 2008). Based on in-depthanalysis on the Xi structure, the facultative domains possess a condensed ultrastructure formedby tightly folded chromatin fibres in a compact volume, which is distinct from the structure of itssurrounding euchromatin and constitutive heterochromatin (Rego et al., 2008). 1.3.1 Epigenetic features of XiEpigenetic regulation refers to the alterations of gene expression and genome organization thatare not a result of changes in the DNA sequence (Handy et al., 2011). Within the nucleus, DNAwraps around histone proteins, specifically two H3-H4 dimers and two H2A-H2B dimers, andpackages into structures known as nucleosomes (Handy et al., 2011). The distance betweennucleosomes is regulated by the modifications on the protruding histone tails. Histonemodifications typically occur at lysine residues (K) in exposed protein tails and are widelyrecognized as genome regulators as they play a role in determining chromatin structure,heterochromatin or euchromatin, and transcription status in different cell stages. There are manyforms of histone modifications, including, but not limited to, methylation, acetylation,phosphorylation and ubiquitination, which are mediated by their own family of histonemodifying enzymes and are readily reversible (Handy et al., 2011).Following the initiation of XCI by XIST, multiple epigenetic modifications are required tomaintain the Xi in a heterochromatic, transcriptionally inert state. The accumulation of repressivehistone modifications on the Xi influences its spatial reorganization into a Barr body (Hall &Lawrence, 2010). The earliest histone modifications associated with XCI are the depletion ofactive chromatin marks at promoters and enhancers; primarily the deacetylation of H3K27 and,to a lesser extent, the deacetylation of H4, H3K9 and the demethylation of H3K4me3 and4H3K4me1 (Żylicz et al., 2019). Subsequently, polycomb repressive complexes (PRCs), PRC1and PRC2, are recruited by XIST after transcription repression has begun, ubiquitinatingH2AK119 at intergenic regions and methylating H3K27 within gene bodies on the Xi,respectively (Żylicz et al., 2019). Cytosine methylation is another epigenetic modification that influences transcriptional activity inXCI by interfering with TF binding at CpG islands and inducing chromatin conformationalchanges (Weber et al., 2007). CpG islands are commonly defined as DNA sequences greater than200bp, with more than 50% GC content and a CpG observed to expected ratio greater than 0.6(Gardiner-Garden & Frommer, 1987). The relationship between promoter methylation andtranscriptional activity is most apparent for promoters with CpG islands. CpG island promoterson X chromosomes that undergo XCI have higher methylation as compared to CpG islands onautosomes, whereas, CpG island promoters of escape genes do not show differential DNAmethylation when compared to autosomes (Weber et al., 2007). 1.4 Escape from XCIDespite the heterochromatic silencing of Xi, some genes manage to escape inactivation and areactively expressed at a low (compared to the corresponding Xa alleles) but detectable level.Escape genes can be conceptually divided into two categories: constitutive and facultative.Constitutive escape genes, commonly termed escape, consistently escape XCI, whereasfacultative escape genes, commonly termed variable escape, escape XCI in a individual-specific,tissue-specific, population-specific or study-specific manner (Balaton et al., 2015). Typically, agene is defined as escaping when its expression level from Xi is at least 10% of its corresponding5allele’s expression level from Xa. In addition to expression levels, escape from XCI can bepredicted by epigenetic markers, most commonly studied via DNA methylation at promoterregions. As the assigned XCI status of a gene can be influenced by study-specific details such asthe specimens used or the method(s) of detection, a consensus XCI status of 639 X-linked geneswas assigned by aggregating different types of data, including analyses of allelic expression fromhuman-mouse hybrid cell lines, allelic ratio of single nucleotide variants in fibroblast cell linesand microarray data, and the difference in CpG island methylation between females and males inseveral tissues (Balaton et al., 2015). In humans, approximately 55 X-linked genes constitutively escape inactivation and another 47genes variably escape inactivation (Balaton et al., 2015). The majority of the escape genes inhumans are located on the short arm of the X chromosome, and are situated within clusterscorrelated with topologically associating domain regions (Figure 1.2) (Marks et al., 2015; Miller& Willard, 1998). In fact, all genes located in the PAR1, at the tip of the short arm of Xchromosome, are classified as escaping from XCI as they all have an expressed homolog on theY chromosome and thus do not require dosage compensation; while some genes on the PAR2,located at the tip of the long arm of X chromosome, are subject to inactivation (Ross et al.,2005). The difference in inactivation status of genes for the two PARs may reflect theevolutionary ages of the regions and the origin of the genes that reside within them. Genes inPAR1, on the younger short arm of X chromosome, are descended from materials added to andshared by the sex chromosomes, whereas, genes in PAR2, on the older long arm of Xchromosome, resulted from duplication of X chromosome DNA onto the Y chromosome (Lahn& Page, 1999; Ross et al., 2005). 61.4.1 Escape from XCI in mouseMouse X chromosomes also undergo XCI, however, there are fewer escape genes in mice, withonly about 13 genes classified as constitutively escaping and about 19 genes variably escaping(Barros de Andrade e Sousa et al., 2019). Unlike humans, mouse escape genes appear to besituated as individual genes rather than in clusters (Figure 1.2), with a few cell lines appearing tobe exceptions (Marks et al., 2015). The number of variably escaping genes in mice will likelyincrease with further studies, as many studies of XCI in mice have used a few common crosses.Some mouse escape genes also escape in humans, suggesting some conservation pressure in XCI(Marks et al., 2015). In fact, from a study of XCI across several species, 75% of genes haveconserved XCI status in mammals, though with the rarer escape genes being less concordantcompared to subject genes (Balaton et al., 2021). 7Figure 1.2 Distribution of inactivation status in the human and mouse Xi.Genes subject to XCI are labeled in blue and escape genes are labeled in orange. Figure is fromBerletch, J. B., Yang, F., & Disteche, C. M. (2010). Escape from X inactivation in mice andhumans. Genome Biology, 11(6), 213. https://doi.org/10.1186/gb-2010-11-6-21381.5 Cis-regulatory elements in XCIThe mechanism governing the escape of XCI by genes on the Xi has yet to be fully understood,though multiple models have been proposed for how X-linked activation and escape occurs. Aspreviously mentioned, neither the genomic location of the gene nor the need of dosagecompensation for a homologous gene on the Y chromosome is sufficient in determining theinactivation status of a gene. There are indications that intrinsic elements in the surroundingDNA sequence of a gene may contain signals or features that influence its escape capability, withconceptual models that can be divided into three categories: waystations, escape elements andboundary elements (Figure 1.3). 1.5.1 WaystationsIn 1983, Gartler and Riggs hypothesized the idea of “waystations”, regions of X with the abilityto facilitate the spread of XCI by stabilizing and amplifying the condensation signal (Figure1.3a) that originates from the XIC (Gartler & Riggs, 1983). Studies of X:autosome translocation,introduction of autosomal material to Xi, and Xist transgenes (i.e. the introduction of inducibleXist to autosomal loci), have demonstrated that XCI is capable of spreading across autosomalregions, though less efficiently as compared to the Xi (Cotton et al., 2014; Loda et al., 2017).Under the waystation model, the results of these studies suggest that waystations are regions withspecific characteristics that are present on autosomes and more abundantly on the Xchromosomes. The mechanisms are conserved over mammalian evolution, as studies performedin a murine model with human X sequence indicate that escape mechanisms persist as thesequences exhibit human-like status in the mouse (Peeters et al., 2018). A leading waystationmodel involves long interspersed nuclear elements (LINEs), which are particularly enriched onthe X chromosome compared to autosomes (Boyle et al., 1990; Lyon, 1998). The density of9LINEs is positively correlated with the presence of subject genes and the efficiency of spread ofXCI to autosomal regions, and negatively correlated with the presence of escape genes (Bailey etal., 2000; Loda et al., 2017). In addition to LINEs, pre-existing heterochromatin marks areproposed as features of waystations since they are enriched at transgenic autosomal genessilenced by XCI, including H3K27me3 and EZH2, both of which are PRC2-related (Cotton etal., 2014; Loda et al., 2017).1.5.2 Escape elementsIn contrast to waystations, “escape elements” are proposed to facilitate the escape of inactivationof proximal genes (Figure 1.3b). Bacterial artificial chromosome (BAC) transgenes demonstratedthe retention of inactivation status for the introduced human sequences, with escape genescontinuing to express and subject genes remaining inactivated, when inserted into a normallyX-inactivated region, suggesting that escape elements are intrinsic properties of escape gene loci(N. Li & Carrel, 2008). ALU repeat elements are depleted near subject genes but are enriched inthe vicinity of escape genes (Cotton et al., 2014). The transcription factor YY1 has beenproposed as an escape factor because its binding motif and ChIP-seq peaks are overrepresentedat transcription start sites (TSSs) of escape genes, and it has a significant Xi-biased occupancy atimportant Xi superloop loci, including XIST (Chen et al., 2016).1.5.3 Boundary elementsThere are several subject and escape genes domains annotated on the human X. Boundaryelements are hypothesized to localize at the edges of such chromosomal compartments to preventthe spread of heterochromatin or euchromatin into nearby regions (Figure 1.3). The truncation ofa transgenically studied BAC is believed to have removed a XCI boundary within the construct,10leading to a disruption of the segregation between escape and subject domains. Regions aroundthe integration site of the BAC inappropriately escape inactivation, while genes within the BACmaintain their inactivation status, suggesting the boundary region prevents the spread of escapeto adjacent regions (Horvath et al., 2013). CCCTC-binding factor (CTCF) is the most studiedand most promising candidate of boundary elements, with its binding site present at transitionregions between subject and escape, but depleted within large gene domains of the sameinactivation status (Filippova et al., 2005). However, CTCF alone is not sufficient in insulatingthe silencing from spreading to adjacent domains (Ciavatta et al., 2006). Figure 1.3 Theoretical cis-regulatory factors in determining the escape of inactivation.a) Model for XCI where genes are silenced due to the presence of waystations, in red. b) Modelfor escape where genes are expressed due to the presence of an escape element, in green.Boundary elements, in yellow, prevent the spread of silencing by waystations or escaping byescape elements. Figure is from Yang, C., Chapman, A. G., Kelsey, A. D., Minks, J., Cotton, A.M., & Brown, C. J. (2011). X-chromosome inactivation: Molecular mechanisms from the humanperspective. Human Genetics, 130(2), 175–185. https://doi.org/10.1007/s00439-011-0994-9111.6 Bioinformatics approachThe use of bioinformatic study to search for potential intrinsic elements associated with escapefrom XCI has been previously conducted. Motif-oriented analysis, a method that focuses on thetarget sequences for sequence-specific DNA binding transcription factors (TFs), has beenpredominantly used; however, this approach has several limitations. A motif-oriented approachdoes not consider true (experimentally supported) binding events at motif incidences. The highlikelihood of false predictions of binding events will increase with the length of the regionconsidered (Boeva, 2016). Additionally, some TFs (a larger class inclusive of DNA binding TFs)do not have a determined DNA binding motif or may act via cooperation with other factors tobind to DNA. Hence searching for individual motifs may overlook contributing factors.With the growth in genomics data and supporting database resources, the limitations impactingprevious studies can be reduced through consideration of empirical data, specifically ChIP-seq, amethod to identify interactions between DNA and proteins. ChIP-seq data is more suitable indetermining potential intrinsic elements as they provide true binding events of TFs at escaperegions. In addition, ChIP-seq allows detection of both direct (sequence specific DNA binding)and indirect (DNA proximity) interactions. Major databases, such as ENCODE (Luo et al.,2020), the UCSC Genome Browser (Kent et al., 2002) and ReMap (Hammal et al., 2022),provide access to rich data resources. In particular, ReMap and ENCODE provide curated anduniformly processed ChIP-seq data, providing both ease of access and analysis-ready data.ReMap is a curated database of TFs peaks obtained from analyses of DNA-binding experimentsfrom public sources for several species, including Homo sapiens and Mus musculus (Hammal et12al., 2022), and will be the primary resource for experimentally determined protein-DNAinteractions in this study.1.6.1 Motif enrichment analysisMotif enrichment analysis has been long established within the field of bioinformatics, forinstance the oPOSSUM (Ho Sui et al., 2005) and HOMER (Heinz et al., 2010) services. Itfocuses on enrichment of known motifs from motif databases, as opposed to de novo patterndetection, which allows us to immediately associate detected features with biochemicalmechanisms. The methods generally use one or both of two approaches, both involving thecomparison of a foreground dataset to a background dataset (Hashim et al., 2019). Determinationof the appropriate background dataset is important, as the properties of a genome vary acrossfunctional roles of sequences, and sequence composition differences can readily influence theenrichment of motifs. The first approach counts the number of regions containing a consensussequence (e.g. a TF binding site) and determines if the count in the foreground is significantlydifferent from the number of regions with the feature in the background (Hashim et al., 2019). Inthe second approach, the density of features (e.g. features per bp) is compared, thus indicatingwhether the number of observed features was different between the datasets.1.6.2 Prediction of chromosome structureMethods for predicting 3D chromosome structures have been advanced by high-throughput,next-generation chromosome conformation capture technologies. Chromatin interactionsexperiments, such as Hi-C and ChIA-PET, allow for the investigation and visualization of 3Dchromatin structures and provide information pertinent to gene regulation, e.g., genes that aremore accessible to transcription factors in the chromatin structure are expected to be more highly13expressed. The capture methods allow for the determination of regions of chromosomes that arein close proximity. Interaction frequency on an all-versus-all basis can be extracted as atwo-dimensional contact matrix, which can then be subsequently used to model genome foldingvia algorithms, including distance-based, contact-based and probability based (Oluwadare et al.,2019). Such methods for constructing the 3D chromosome are exemplified by TADBit(distance-based), MOGEN (contact-based) and PASTIS (probability-based) (Oluwadare et al.,2019). Previously, models have been generated for X chromosomes over the course ofdevelopment to visualize its change in conformation during X-inactivation (Zhu et al., 2019) andfrom X chromosome Hi-C data for Xa and Xi were not obviously distinct in the level ofcondensation (Zhu & Wang, 2019). The early work has been limited by available data, asallele-specific properties essential for comparing Xa against Xi has been limited. In the absenceof ideal data, a comparison between models generated with females and males X chromosomescould assist in understanding how the chromosome structures compare.1.7 Thesis objectivesThe objectives of this thesis are to (1) identify candidate TFs, defined as proteins that regulatetranscription, involved in governing the escape from Xi by performing enrichment analysis ofChIP-seq data, (2) identify TF interactions of candidate elements that are enriched in escapegenes via gene set similarity analysis and (3) create 3-dimensional models of Xa and Xi tounderstand the relationship between spatial distribution of genes and their inactivation status.Overall, this thesis aims to advance the understanding of the mechanism of escape from XCIwith a focus on the TSS regions of escape genes.14Chapter 2: Methods2.1 Inactivation status of X-linked genesThe inactivation status of X-linked genes was obtained from Balaton et al. (2015), in whichinactivation status categories are assigned to genes based on expression level and methylationstatus. We combined categories as follows. There are 22 assigned PAR genes, limited to geneslocated within the PAR1 region. There are 55 escape genes, combining 29 “E” and 26 “MostlyE” genes (Table 2.1). Subject genes consist of 331 “S” and 131 “Mostly S” genes, for a total of462 genes. The 47 variable genes include 37 “VE” and 10 “Mostly VE” genes.The inactivation status of X-linked genes for mice was obtained from Barros de Andrade e Sousaet al. (2019), with additional support based on results from Balaton et al. (2021). There are 13confident escape genes, defined as a) more than 3 sources supporting escape status in Barros deAndrade e Sousa et al. (2019) or b) 3 sources in Barros de Andrade e Sousa et al. (2019) andsupported as escape in Balaton et al. (2021) (Table 2.2).15Table 2.1 Metadata of human escape genes.Gene Name TSS (hg38) Strand CpG islandoverlap?Bp of TSS regionoverlapped by CpGIslandBalatonconsensuscallGYG2 2828929 + True 917 Mostly EARSD 2929339 - True 631 Mostly EARSH 3006545 + False 0 ECXorf28 3271819 NaN False 0 EMXRA5 3346652 - True 1000 Mostly EPRKX 3713649 - True 772 ENLGN4X 6228867 - False 0 Mostly EHDHD1 7048919 NaN False 0 ESTS 7147711 + True 559 EPNPLA4 7927413 - True 531 EKAL1 8528873 NaN False 0 Mostly EFAM9C 13044620 - False 0 ETCEANC 13653140 + True 520 Mostly ERAB9A 13689127 + True 383 Mostly ETRAPPC2 13734620 - True 798 Mostly EOFD1 13734747 + True 895 Mostly EGPM6B 13817093 - False 0 Mostly EGEMIN8 14029892 - True 566 Mostly EACE2 15600960 - False 0 ECA5BP1 15674915 NaN True 434 ECA5B 15738269 + True 585 Mostly EZRSR2 15790483 + True 634 EAP1S2 15854813 - True 1000 Mostly ECTPS2 16712669 - True 646 Mostly ES100G 16650157 + False 0 ESYAP1 16719611 + True 358 ETXLNG 16786465 + True 1000 E16RBBP7 16870362 - True 1000 Mostly EEIF1AX 20141838 - True 827 EEIF2S3 24054955 + True 563 EZFX 24149728 + True 1000 EVENTXP1 26558336 NaN True 219 ELOC347411 40623565 NaN True 839 ECXorf38 40647561 - True 669 EUSP9X 41085444 + True 1000 Mostly EDDX3X 41334161 + True 1000 EFUNDC1 44542859 - False 0 EKDM6A 44873187 + True 1000 Mostly EUBA1 47193828 + True 502 EINE1 47204847 NaN False 0 ECDK16 47218680 + True 860 Mostly EKDM5C 53225207 - True 937 Mostly EIQSEC2 53321350 - True 816 Mostly ESMC1A 53422654 - True 669 Mostly ERPS4X 72277248 - True 527 Mostly EJPX 73944323 NaN False 0 ENAP1L3 93673578 - True 240 Mostly ETAF7L 101291334 - True 487 ENXF5 101857577 - False 0 EAK026512 103917656 NaN False 0 Mostly EZCCHC16 112083024 NaN False 0 EHTR2C 114584085 + True 179 Mostly EGPR112 136300962 NaN True 41 EVGLL1 136532214 + False 0 EL1CAM 153886173 - True 1000 Mostly E17Table 2.2 Metadata of mouse escape genes.Highlighted genes also escape in humans.Gene Name TSS Strand# of escapesources inBarros deAndrade eSousa et al.mRNAseqvalidated inBarros deAndrade eSousa et al.escape inmouse inBalaton et al.1810030O07Rik 12673668 - 4 NaN FalseUsp9x 13071497 + 5 NaN FalseDdx3x 13280969 + 5 NaN TrueKdm6a 18162574 + 6 Yes TrueNkap 37126794 + 4 Yes FalseUtp14a 48256861 + 4 NaN FalseFirre 50635321 - 5 NaN FalseEif2s3x 94212862 - 6 NaN FalseTaf1 101532733 + 4 NaN FalseOgt 101640059 + 4 NaN False5530601H04Rik 105079609 - 3 Yes TruePbdc1 105079755 + 6 NaN TrueKdm5c 152233019 + 7 NaN True182.2 Transcription factor ChIP-seq peaksThe non-redundant ChIP-seq peaks are obtained from ReMap, 2022 release (Hammal et al.,2022). This set of non-redundant peaks uses clusters of duplicate peaks to determine an averagestart, end and summits coordinates. Peaks are then trimmed to the median size of peaks for eachspecific TF (Hammal et al., 2022). For humans, peaks are obtained for all 155 TFs characterizedin GM12878 cells (Table 2.3).For enrichment analysis for mouse TFs, peaks for 46 TFs are obtained from 11 different celllines. The set of mouse TFs were determined based upon the results of the enrichment analysis inhumans. The set of TFs enriched in the TSS regions were searched in mouse cell lines withinReMap, and for each TF, the cell line providing the most number of ChIP-seq peaks in the Xchromosome was chosen. Subsequently, for each enriched TF, 2 additional TFs with the mostsimilar number of ChIP-seq peaks in the respective cell line were chosen for comparison.19Table 2.3 Metadata of TFs in ReMap.TF EntrezID Protein Class# of peaks onX in ReMap2022ARID3A 1820 DNA-binding transcription factor 614ARNT 405 basic helix-loop-helix transcription factor 357ASH2L 9070 histone modifying enzyme 786ATF2 1386 basic leucine zipper transcription factor 1775ATF3 467 basic leucine zipper transcription factor 87ATF7 11016 basic leucine zipper transcription factor 1919BACH1 571 C2H2 zinc finger transcription factor 605BATF 10538 basic leucine zipper transcription factor 280BATF3 55509 basic leucine zipper transcription factor 223BCL11A 53335 C2H2 zinc finger transcription factor 1308BCL3 602 membrane traffic protein 857BCLAF1 9774 DNA metabolism protein 24BHLHE40 8553 basic helix-loop-helix transcription factor 1838BMI1 648 304BRCA1 672 ubiquitin-protein ligase 18CBFB 865 DNA-binding transcription factor 789CBX5 23468 362CEBPB 1051 basic leucine zipper transcription factor 157CEBPZ 10153 DNA-binding transcription factor 47CHD1 1105 chromatin/chromatin-binding, or -regulatory protein 26CHD2 1106 chromatin/chromatin-binding, or -regulatory protein 217CHD4 1108 chromatin/chromatin-binding, or -regulatory protein 76CREB1 1385 1096CREM 1390 121620CTCF 10664 C2H2 zinc finger transcription factor 2095CUX1 1523 homeodomain transcription factor 8DPF2 5977 1409DRAP1 10589 DNA-binding transcription factor 295E2F4 1874 general transcription factor 23E2F8 79733 general transcription factor 461E4F1 1877 C2H2 zinc finger transcription factor 140EBF1 1879 DNA-binding transcription factor 617EGR1 1958 C2H2 zinc finger transcription factor 603ELF1 1997 winged helix/forkhead transcription factor 1817ELK1 2002 winged helix/forkhead transcription factor 10EP300 2033 histone modifying enzyme 382ESRRA 2101 C4 zinc finger nuclear receptor 34ETS1 2113 winged helix/forkhead transcription factor 205ETV6 2120 winged helix/forkhead transcription factor 1035EZH2 2146 histone modifying enzyme 0FOS 2353 basic leucine zipper transcription factor 57FOXK2 3607 82FOXM1 2305 605GABPA 2551 winged helix/forkhead transcription factor 394GATAD2B 57459 DNA-binding transcription factor 827GTF2F1 2962 general transcription factor 318HCFC1 3054 750HDAC2 3066 histone modifying enzyme 70HDAC6 10013 histone modifying enzyme 144HSF1 3297 winged helix/forkhead transcription factor 124IKZF1 10320 C2H2 zinc finger transcription factor 2521IKZF2 22807 C2H2 zinc finger transcription factor 1592IRF3 3661 winged helix/forkhead transcription factor 79IRF4 3662 winged helix/forkhead transcription factor 1344IRF5 3663 winged helix/forkhead transcription factor 36JUNB 3726 basic leucine zipper transcription factor 1316JUND 3727 basic leucine zipper transcription factor 203KLF5 688 C2H2 zinc finger transcription factor 171MAFF 23764 basic leucine zipper transcription factor 164MAFK 7975 basic leucine zipper transcription factor 56MAX 4149 basic helix-loop-helix transcription factor 46MAZ 4150 C2H2 zinc finger transcription factor 161MED1 5469 general transcription factor 1064MEF2A 4205 MADS box transcription factor 909MEF2B 100271849 MADS box transcription factor 1017MEF2C 4208 MADS box transcription factor 272MLLT1 4298 1598MTA2 9219 homeodomain transcription factor 1680MTA3 57504 homeodomain transcription factor 626MXI1 4601 basic helix-loop-helix transcription factor 78MYB 4602 199MYC 4609 basic helix-loop-helix transcription factor 118NBN 4683 damaged DNA-binding protein 1265NFATC1 4772 Rel homology transcription factor 761NFATC3 4775 Rel homology transcription factor 1257NFE2 4778 basic leucine zipper transcription factor 7NFIC 4782 DNA-binding transcription factor 1216NFXL1 152518 zinc finger transcription factor 2122NFYA 4800 DNA-binding transcription factor 27NFYB 4801 DNA-binding transcription factor 265NIPBL 25836 chromatin/chromatin-binding, or -regulatory protein 662NR2C1 7181 C4 zinc finger nuclear receptor 187NR2C2 7182 C4 zinc finger nuclear receptor 7NR2F1 7025 C4 zinc finger nuclear receptor 980NR3C1 2908 C4 zinc finger nuclear receptor 350NRF1 4899 DNA-binding transcription factor 116PAX5 5079 homeodomain transcription factor 2154PAX8 7849 homeodomain transcription factor 33PBX3 5090 homeodomain transcription factor 950PKNOX1 5316 homeodomain transcription factor 1315PML 5371 ubiquitin-protein ligase 552RAD21 5885 2066RAD51 5888 DNA metabolism protein 683RB1 5925 chromatin/chromatin-binding, or -regulatory protein 796RBBP5 5929 21RCOR1 23186 chromatin/chromatin-binding, or -regulatory protein 11RELA 5970 Rel homology transcription factor 796RELB 5971 Rel homology transcription factor 1799REST 5978 C2H2 zinc finger transcription factor 473RFX5 5993 winged helix/forkhead transcription factor 31RNF2 6045 ubiquitin-protein ligase 326RXRA 6256 C4 zinc finger nuclear receptor 270SIN3A 25942 chromatin/chromatin-binding, or -regulatory protein 16SIX5 147912 homeodomain transcription factor 228SKIL 6498 115523SMAD1 4086 DNA-binding transcription factor 334SMAD5 4090 DNA-binding transcription factor 389SMARCA5 8467 DNA helicase 975SMC3 9126 381SP1 6667 C2H2 zinc finger transcription factor 1021SREBF1 6720 33SREBF2 6721 8SRF 6722 665STAT1 6772 DNA-binding transcription factor 70STAT2 6773 DNA-binding transcription factor 63STAT3 6774 DNA-binding transcription factor 2STAT5A 6776 DNA-binding transcription factor 365SUZ12 23512 chromatin/chromatin-binding, or -regulatory protein 174TAF1 6872 787TARDBP 23435 RNA metabolism protein 1808TBL1XR1 79718 47TBP 6908 general transcription factor 178TBX21 30009 Rel homology transcription factor 1759TCF12 6938 basic helix-loop-helix transcription factor 1746TCF3 6929 basic helix-loop-helix transcription factor 1370TCF7 6932 245TP73 7161 P53-like transcription factor 29TRIM22 10346 ubiquitin-protein ligase 2097TSC22D4 81628 71UBTF 7343 DNA-binding transcription factor 46USF1 7391 215USF2 7392 85524WRNIP1 56897 DNA helicase 1YBX1 4904 80YY1 7528 C2H2 zinc finger transcription factor 42ZBED1 9189 DNA metabolism protein 377ZBTB2 57621 C2H2 zinc finger transcription factor 2253ZBTB33 10009 C2H2 zinc finger transcription factor 332ZBTB40 9923 C2H2 zinc finger transcription factor 632ZEB1 6935 C2H2 zinc finger transcription factor 974ZFP36 7538 RNA metabolism protein 539ZMYM3 9203 zinc finger transcription factor 250ZNF143 7702 C2H2 zinc finger transcription factor 1211ZNF18 7566 C2H2 zinc finger transcription factor 123ZNF207 7756 DNA metabolism protein 390ZNF217 7764 C2H2 zinc finger transcription factor 477ZNF24 7572 C2H2 zinc finger transcription factor 400ZNF274 10782 C2H2 zinc finger transcription factor 8ZNF384 171017 C2H2 zinc finger transcription factor 145ZNF592 9640 147ZNF597 146434 C2H2 zinc finger transcription factor 140ZNF622 90441 RNA metabolism protein 2ZNF687 57592 684ZSCAN29 146050 C2H2 zinc finger transcription factor 55ZZZ3 26009 chromatin/chromatin-binding, or -regulatory protein 6252.3 Regions of interest (ROI)The TSS of human genes were obtained from the NCBI RefSeq Select, which provides a singlerepresentative transcript for every protein-coding gene (Navarro Gonzalez et al., 2021). Forgenes not annotated in NCBI RefSeq Select, the TSS coordinates were obtained from Balaton etal. (2015) and then converted from hg19 to hg38 build via LiftOver (Hinrichs et al., 2006). TheTSS of mouse genes were as defined in Barros de Andrade e Sousa et al. (2019).The TSS region of each gene was denoted as the 500bp upstream and downstream of the TSS.The “upshell” region is the 10kb upstream of the TSS region (i.e. opposite of the direction oftranscription), whereas the “downshell” region is the 10kb downstream of the TSS region (Figure2.1).Figure 2.1 Scheme of regions analyzed.The TSS region constitutes the 1kb surrounding the TSS of a gene. Upshell and downshellregions constitute the 10kb upstream and downstream of the TSS region.262.4 Data preparationEnrichment analysis is a comparison of instances of overlaps with features of interest between aquery set and a background set, therefore, a suitable background is important for relevant results.Several steps of data wrangling were required to ensure an appropriate dataset was input to allowfor a proper comparison of enrichment of ChIP-seq peaks in escape genic regions againstnon-escape and non-PAR genic regions.2.4.1 GC compositionAs GC composition varies regionally in genomes and is correlated with functionality, in order toreduce confounding influence by the GC frequency on the enrichment analysis we matched GCcomposition for the ROIs. To create a proper background, each escape gene was matched with 5random non-escape genes with similar GC composition, within 3%, in the ROI to create a fulldataset to be used for enrichment analysis. Furthermore, to allow proper comparison within theset of annotated ROI and avoid counting multiple overlaps of ChIP-seq peaks within a singleregion, the ChIP-seq peaks are intersected with the GC-matched full dataset to retain only peaksthat lie within the set of ROI and only a single instance of overlap per TF per region.2.5 Enrichment analysisEnrichment analyses are performed by GIGGLE (Layer et al., 2018), version 0.6.3 obtained fromhttps://github.com/ryanlayer/giggle. GIGGLE is a package that identifies and ranks thesignificance of overlaps between provided genomic regions of a query and features of interest.Background sets were processed via the GIGGLE index command with default parameters. For27enrichment analysis, GIGGLE was executed with additional options: -s -g <background size>. -swas added to output significance per feature analyzed and -g was added to provide a moreaccurate genome size for significance testing, and the inputted genome size is calculated as thesize of background multiplied by the length of TSS region or shells, 1000 or 10000, respectively.GIGGLE outputs several metrics for the enrichment analysis, including odds ratio, Fisher’s tailp-values and a GIGGLE score. The GIGGLE score is the product of −log10(p-value) andlog2(odds ratio), which can help with interpretation, as the two measures reflect different relatedbut complementary properties (Layer et al., 2018). Overlap ratio, the number of overlapped TFpeaks divided by the number of TFs in the background set, is calculated for each TF.2.5.1 IterationsAs the GC-matched non-escape genes are randomly chosen and enrichment analysis is highlydependent on the background provided, 20 iterations of GC-matching and enrichment analysiswere performed. The multiple iterations allow escape gene regions to be compared to a largediversity of background sets to increase reliability by focusing on recurrently observed results.2.5.2 Number of escape genesTo increase stringency, the number of escape genes overlapped by a TF was considered. Thenumber of escape genes overlapped is represented by a percentage, calculated out of 55 escapegenes used in the analysis.282.5.3 Threshold for enrichment for humanThe thresholds defined for enrichment of a TF in escape gene regions based on metrics output byGIGGLE are as follows: (a) odds ratio greater than 1, (b) top 15% of TFs based on Fisher’s lefttail p-value rank in an ascending manner, (c) top 15% of TFs based on GIGGLE score rank in adescending manner and (d) overlap ratio greater than 0.167 (⅙ as the ratio of number of escapegenes within the background set). Additionally, TFs were considered enriched if they weredetermined to be enriched in at least 50% of the iterations, or 10 iterations.More stringently, the threshold for determination of enrichment was increased to 15 iterations,75% of the iterations, and the TF had to overlap at least 11 escape genes, which is 20% of theescape genes on X chromosome.2.5.4 Determination of enrichment for mouseEnrichment analysis was performed using mouse TFs at TSS regions of mouse escape genes for20 iterations. Determination of enrichment did not involve thresholds for metrics output byGIGGLE. Instead, TFs were ranked within their respective cell line by the odds ratio and overlapratio in a descending manner to determine if the homologous TFs to the set of human enrichedTFs are typically ranked higher than other TFs, which were restricted to those TFs with a similarnumber of experimentally detected peaks.292.6 Co-binding: Dice similarity coefficientTo measure the co-binding of two TFs in a region, the Dice similarity coefficient (DSC) wasused to compare the agreement between the gene set overlapped by the TFs. The DSC formula isdefined as (Dice, 1945):2 𝑥 | 𝐴 ∩ 𝐵 |𝐴 + 𝐵where A and B each represent a single set of genes overlapped by a TF. To determine TF pairsthat are specifically enriched in escape regions, the DSCs obtained for the overlapped escapegene set were divided by the DSCs obtained for the overlapped background gene set, similar tothe concept of an observed-over-expected ratio. Only TF pairs with a DSCs ratio over 1.5 wereconsidered. Fisher's exact test was performed to include the measure of significance in theunderstanding of co-binding. The p-value obtained from the Fisher’s exact test were correctedfor multiple testing by the Benjamini-Hochberg method for a false discovery rate (FDR) of 0.10.2.7 Variant dataVCF files of GM12878, GM12891 and GM12892 were acquired from the European NucleotideArchive at EMBL-EBI (Leinonen et al., 2011), under the accession number ERZ022005,ERZ021996 and ERZ022000 respectively. The variants and converted from hg19 to hg38 buildvia LiftOver (Hinrichs et al., 2006).302.8 Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET)RAD21-mediated ChIA-PET data was obtained for GM12878, GM12891 and GM12892 celllines from Konstantin et al. (2020). ChIA-PET2 pipeline (G. Li et al., 2017), version 0.9.3, wasobtained from https://github.com/GuipengLi/ChIA-PET2. Pipeline was executed with defaultparameters except for -d 1 -Q 5 to consider short reads and a lower mapping quality (MAPQ)threshold to retain more paired-end tags (PETs). Similarity scores between the different sampleswas determined by cLoops2 (Cao et al., 2021), version 0.0.3, obtained fromhttps://github.com/YaqiangCao/cLoops2.PETs were then phased using the allelic specific pipeline of ChIA-PET2, in which PETs wereassigned to a specific allele as long as one of the pairs overlapped a SNV. Chromatin structureswere modeled via TADbit (Serra et al., 2017), version 1.0.1, using the allelic matrices with binsizes of 2Mb. 4 parameters (upfreq_range, lowfreq_range, maxdist_range, dcutoff_range) formodeling were determined through an optimization step. 100 models were generated for eachsample, but only 10 models were kept as result.31Chapter 3: Results3.1 Transcription factor binding density compared between escape and subject genesUnderstanding the differences of binding of TFs between escape and subject genes may provideinsight into the mechanisms enabling escape. GM12878 is selected as the cell line of focus forthe thesis to study transcription factor binding in human, as it is well characterized with a largenumber of experimental profiles generated in studies, and more importantly, it has severeX-inactivation skewing towards the paternal allele (Kucera et al., 2011).The distribution of the number of observed bound TFs within the TSS regions for the escape andsubject genes reveals bimodal shape for both classes (Figure 3.1), indicating two classes of geneswithin each category - frequently bound (potentially those with more accessible chromatin) andinfrequently bound. The distribution for the escape genes indicates a greater portion of geneswithin the frequently bound class compared to subject genes, and a peak that is shifted towards ahigher number of TFs. While there are genes with no or few TF interactions for both categories,it is important to note that the available data is sparse (only 155 characterized TFs), as there arein excess of 1,200 sequence specific DNA binding TFs and an unknown number of indirectrelationships to consider.32Figure 3.1 Distribution of the number of binding TFs at the TSS region of genes.Density curves for subject (blue) and escape (orange) genes. As the number of genes for subject(n=462) and escape (n=55), density was transformed from histograms of counts of binding TFsper gene to allow comparison of the shape of their distribution.3.2 Enrichment analysisEnrichment analysis is a method used to identify DNA elements that are present in regions ofinterest more than expected by chance, such as escape regions. All human enrichment analysesperformed were done with the available ChIP-seq peaks of TFs for GM12878 in the ReMapdatabase.Using the ChIP-seq peaks of TFs available for GM12878 and inactivation status of genes derivedfrom Balaton et al (2016), elements that may be involved in escape from inactivation wereidentified using metrics including odds ratio, Fisher’s left tail p-value, GIGGLE score, number of33iterations with detection and overlapped escape genes. The one-sided Fisher’s exact test is givenfocus, as it provides a measure of statistical significance. The Fisher’s threshold is intended toreduce the number of TFs that arise in the enrichment analysis that have extremely low bindingevents throughout the X chromosome. Of the 155 TFs available in ReMap for GM12878, asubset of 43 TFs were enriched in at least one of the ROI (TSS region, upshell or downshell).3.2.1 TSS regionsBased on the enrichment results using TSS regions of escape genes compared to a background ofnon-escape genes, 19 candidate TFs (Figure 3.2a) were identified computationally. Notably, noneof the 19 TFs bind to all 55 escape genes, in fact, 11 of the enriched TFs bind to less than 20% ofthe escape genes. Although some TFs bind to a higher percentage of escape genes, such asARID3A and CREM, due to their high binding frequency in background genes they were notenriched. This observation indicates that the enrichment analysis is not biased towards TFs thathave many binding sites within the ROI.To focus on a shorter list of candidate TFs, additional criteria and more stringent thresholds wereapplied, resulting in 5 TFs: HSF1, MYB, NIPBL, STAT1 and ZFP36. These 5 TFs, which are thefocus of further analysis below, are each from different structural classes of proteins (Table 2.3).The specific genes overlapped were explored to determine if the 5 enriched TFs have a preferredregion for binding within the X chromosome. Only 4 escape genes overlap binding for all 5 TFs,and these are not situated within a specific region of the X chromosome (Figure 3.2b). 31 of the55 escape genes do not overlap any of the 5 TFs. In fact, consistent with Figure 3.1, it isobserved that 8 and 13 escape genes are bound by only a single TF or not bound by TFs, at theirTSS regions, respectively (Figure 3.2c).34Figure 3.2 Summary of transcription factors enriched in TSS regions of escape genes.a) The x-axis includes all TFs that were considered enriched within at least one of the regions ofinterest analyzed. The y-axis of the bar graphs indicates the percentage of escape genes that thespecific TF overlaps in the ROI. Enrichment analysis was performed for 20 iterations, the size ofthe circle indicates the number of iterations that a specific TF was considered enriched. EnrichedTFs highlighted in purple, have met enrichment thresholds (odds ratio ≥ 1, top 15% ranked by onFisher’s left tail p-value, top 15% ranked on GIGGLE score, overlap ratio ≥ 0.167) in at least50% of the iterations. A subset of 5 enriched TFs, highlighted in green, met more stringentcriteria, defined as being enriched in at least 75% of the iterations and overlapping at least 20%of escape genes. b) Binding of the stringent set of TFs at escape genes. Green indicates a bindingevent. c) The number of TFs binding (n=155 total TFs) at each of the 55 escape genes.353.2.2 Upshell & DownshellIn addition to the TSS region, regions immediately upstream and downstream may be enrichedfor other regulatory regions, such as enhancers. The enrichment analysis of the upshell region ofescape genes identified 18 TFs (Figure 3.3a) as enriched when compared to non-escape upshellregions on the X chromosome. For downshell regions, 19 enriched TFs were detected (Figure3.3b). Between the two shell regions, 3 enriched TFs were shared: BATF3, NFATC1, RAD51.Interestingly, all 3 of these TFs were not determined as enriched within the TSS region.6 of the enriched TFs in the upshell were shared with the TSS region, including HDAC2, IRF5,RBBP5, SREBF1, STAT1 and ZFP36. For downshell regions, 4 of enriched TFs were alsoenriched within the TSS region: MAFF, MYB, NIPBL, TBP. Although only a small number ofTFs were shared between the TSS region and shells, 4 (STAT1 and ZFP36 for upstream; MYBand NIPBL for downstream) of the 5 TFs within the stringent set of TFs in the TSS region wereincluded within the shared list, another indication of their potential relevance to XCI escape.3.2.3 TF characteristics in mouse TSS regionsAs escape from XCI occurs in both humans and mice, it may be informative to considerproperties in the binding of mouse TFs. However, the available experimental data is more sparse,necessitating the use of data spanning more cell lines. Out of the 19 enriched TFs for the humanTSS regions, only 15 have ChIP-seq data available in a mouse cell line within ReMap;unfortunately, ZFP36 does not have available data.As the data of these TFs are from different cell lines, they cannot be analyzed together.Therefore, meeting all thresholds specified for the human enrichment analysis were not36applicable for the mouse enrichment analysis. Instead, and not ideally, for each TF of interest, wecompared its binding properties to other TFs characterized in the same cell lines. The “other”TFs were selected to match the number of peaks observed for the TF of interest in eachcomparison. Out of the 15 TFs analyzed, only MYB ranked higher than the “other” TFs, meetingthresholds of an odds ratio over 1 and overlap ratio over 0.167.Figure 3.3 Summary of transcription factors enriched in shell regions of escape genes.Refer to legend of Figure 3.2a. Enrichment results for a) upshell and b) downshell.373.3 Co-binding: TF-pairsIn the previous section, the TFs were analyzed as individual entities, however, TFs do nottypically work alone, they act (directly or indirectly) with or form complexes with other TFs toperform their functions. To explore if pairs of TFs play a role in the escape from XCI, pairwisesimilarity of the set of genes overlapped at the TSS region was calculated for each pair of TFs.The similarity coefficient is a value between 0 and 1, with 1 indicating the highest similaritybetween the two sets of queries being compared. There are several well-known set-similaritymeasures, including the Dice similarity coefficient (DSC), which is chosen for the purpose ofthis thesis because the calculation is more intuitive. However, DSC doesn’t account for genesthat are not overlapped by either TFs nor does it provide a measure of significance, whichmotivated the additional inclusion of Fisher’s exact test in this analysis. It is noted that thedistribution of TF binding which indicates two classes of genes, those with large number of TFsbound and those with few, leads to an expectation of a higher number of pairs to consider.Focusing on TF pairs where both are enriched within TSS regions of escape genes, 55 TFs pairsmet the imposed thresholds set on escape-over-background ratio of DSCs and the adjustedp-values. The top two TFs participating in the identified pairs are NIPBL and TBP, which bothparticipated in more than 20% of the pairs identified (Figure 3.4). The third most prominent TFin the enriched pairs is ZFP36, occurring in 10 out of 55 of the pairs. Subsequently, restrictingresults to pairs with at least one of the 5 stringent TSS enriched TFs, the number of pairs is 36.Upon ranking these pairs based on their escape-over-background ratio of DSC, 6 of the top 10ranked pairs involved ZFP36 (Table 3.1).38Figure 3.4 Escape-over-background ratios of the DSCs for enriched TFs in the TSS regions.TFs labeled in the heatmaps are the 19 enriched TFs within the TSS region of human escapegenes. a) Heatmap of DSC scores of TF pairs related to the escape gene set. b) Heatmap of DSCscores of TF pairs related to the background gene set. c) Heatmap of DSCescape-over-background ratio, obtained by dividing DSC in escape by DSC in background.39Table 3.1 Enriched co-binding TF pairs in TSS regions of human escape genes.At least one of the TFs within the TF pairs listed below are one of the 5 TFs that passed thestringent thresholds. 36 TF pairs were determined as enriched by meeting thresholds ofescape-over-background ratio of similarity score ≥ 1.5 and the adjusted p-values with a FDR of0.10. Highlighted top 10 TFs pairs that involved ZFP36.TF1 TF2 DSC escape-over-background ratioBH correctedp-values for escapeIRF5 ZFP36 2.38 0.045231HSF1 ZFP36 2.17 0.016504BRCA1 ZFP36 2.14 0.026219MYB TBP 2.10 0.017707MAFF ZFP36 1.96 0.027473BRCA1 NIPBL 1.95 0.023141STAT1 ZFP36 1.85 0.015605NIPBL RBBP5 1.83 0.030589HSF1 ZBED1 1.81 0.017213UBTF ZFP36 1.80 0.032046MAFF NIPBL 1.79 0.012818MYB MYC 1.78 0.043293STAT1 TBP 1.76 0.017707BRCA1 HSF1 1.75 0.03235MYB UBTF 1.74 0.023952IRF5 NIPBL 1.74 0.023141MAFF MYB 1.71 0.021507NIPBL ZFP36 1.68 0.027802NIPBL ZNF18 1.67 0.020735IRF5 MYB 1.67 0.03792340ZFP36 ZNF18 1.65 0.037834MYC STAT1 1.65 0.030019NIPBL TBL1XR1 1.65 0.040519HSF1 IRF5 1.61 0.018075HSF1 NIPBL 1.60 0.006852NIPBL TBP 1.60 0.00822TBP ZFP36 1.59 0.012717MYB TBL1XR1 1.56 0.038835MYC NIPBL 1.56 0.024066NIPBL STAT1 1.56 0.018531HSF1 RBBP5 1.55 0.045573NIPBL UBTF 1.54 0.03943ATF2 ZFP36 1.53 0.021685STAT1 UBTF 1.52 0.008296BRCA1 MYB 1.51 0.037923MYB STAT1 1.50 0.036048413.4 ChIA-PET analysisPerhaps a predecessor to the presence of TFs at regulatory regions of escape genes, structuraldifferences between the Xa and Xi may affect the accessibility of regions for transcription. WhileHi-C data was sought and processed (data not shown), we were unable to access data ofsufficient size and experimental design to enable allele-specific comparison of Xi and Xa. Thelack of a male sample of Hi-C data for comparison with GM12878 precluded success with theavailable Hi-C data. Therefore, ChIA-PET data was used to perform a preliminary structuralanalysis of the Xi and Xa chromosomes. ChIA-PET data was obtained for the HapMap CEU trio,GM12878 GM12891 and GM12892. GM12891 and GM12892 are the father and mother ofGM12878, respectively. The use of the paternal sample can provide us with informationrestricted to an Xa, which can then be compared with GM12878, hence potentially highlightingdifferences between Xa and Xi. Unfortunately but important to note, the GM12878 is skewedtowards inactivation of the paternal X (Kucera et al., 2011). The purpose of the maternal sampleis to act as a female standard for GM12878, as they should share similarity since they bothpossess Xa and Xi.3.4.1 Similarity of processed samplesTo ensure the observed differences between the X chromosome ChIA-PET data of GM12878 andGM12891 pertain to differences between Xa and Xi rather than sample preparation, similaritymeasures of the datasets were obtained and compared for chromosome 1 and chromosome X.The similarity between the datasets was higher for chromosome 1 than for chromosome X(Figure 3.5), which is expected since autosomal structures between males and females should besimilar. When looking at individual replicates, samples were not separated by cell lines forchromosome 1 (Figure 3.5a), but GM12891 (the male sample) was clustered on its own for42chromosome X (Figure 3.5b). For the full dataset, for chromosome 1, combining replicates, thesamples were not divided into clusters (Figure 3.5c), whereas, for chromosome X, GM12878 andGM12892 were clustered, although still sharing high similarity measures with GM12891 (Figure3.5d).Figure 3.5 Similarity scores of ChIA-PET data used for analysis.Similarity scores and clustering output by cLoops2 for a) chromosome 1 for all replicates; b)chromosome X for all replicates; c) chromosome 1 from full dataset (combined of replicates); d)chromosome X from full dataset.433.4.2 Structural models31 million, 47 million and 36 million valid paired-end tags (PETs), were extracted byChIA-PET2 for GM12878, GM12891 and GM12892, respectively. However, less than 1% of thevalid PETs were retained after phasing by their variant data into alleles 1 and 2. For GM12891,one of the alleles has a notably lower number of PETs compared to other alleles, which isexpected as males only have one X chromosome, therefore, only one allele is retained as the Xa.Although limited by data, we generated chromatin models from each of the allelic data usingTADBit to visualize the structural differences between the Xa and Xi (Figure 3.6).Based solely on the appearance of the models, there are distinct structures of X chromosomesobserved for both female samples, GM12878 and GM12892. Of the two X chromosomestructures in the female samples, one is visibly less condensed and more comparable to the Xchromosome structure of GM12891, which is believed to be Xa. Although distinct structureswere obtained using allelic data, it was not possible to determine if there were confoundingfactors, for example, if the lack of PETs in the assigned Xa is due to actual structural aspect orthe lack of overlaps with SNVs.44Figure 3.6 Structural models of X chromosomes from ChIA-PET data.Models of X chromosome structures generated by TADBit using ChIA-PET data fromGM12878, GM12891 and GM12892. Resolution used was 2Mb. The assignments of Xa and Xiwere based on the appearance of the structures.45Chapter 4: Discussion4.1 Overview of findingsThe goals of this thesis were to identify DNA elements that contribute to escape from Xchromosome inactivation, and assess how structural conformations of X chromosomes influenceor correlate with the inactivation status of escape genes. Towards the primary goal, similarstudies using motif enrichment analysis highlighted transcription factor YY1 as enriched at TSSregions of escape genes (Chen et al., 2016). Due to several drawbacks of studying motifenrichment and the new databases with experimentally determined annotations, this thesisfocused on the study of TFs associated with escape of XCI using empirical data.Using up-to-date annotations of gene escape status and uniformly processed TF ChIP-seq data,the number of enriched TFs determined in three regions (TSS region, upshell region anddownshell region) of escape genes relative to other X chromosome genes were determined (19,18 and 19, respectively) (Figure 3.2, Figure 3.3). Focusing on the immediate promoter region(TSS region), more stringent criteria highlighted 5 TFs, of which 4 also were elevated eitherdownstream or upstream. None of the enriched TFs overlaps all annotated escape genes,suggesting multiple TFs may be involved in escape.Potential functional cooperation between pairs of TFs was assessed based on overlaps betweengene sets bound by individual TFs to determine pairs that have enriched co-binding incidences inescape TSS regions. Using pairwise similarity analysis, NIPBL, TBP were highlighted as TFsthat are most frequently engaged in co-binding incidences. A body of literature addresses the roleof CTCF and TAD formations in XCI. Like CTCF, NIPBL has a role in cohesin loading, which46suggests it could play a role in structural properties of the escape regions on Xi. As TBP binds toTATA-like regions in gene promoters and is key to transcription initiation, a functional linkbetween the two factors may contribute to escape gene expression.Both enrichment and co-binding analysis highlighted that ZFP36 has a strong presence at escaperegions. For enrichment analysis, ZFP36 was one of the 5 TFs that passed the stringentthresholds, while in the co-binding analysis, pairs that involve ZFP36 were observed more oftenat escape regions than other combinations. The presence of ZFP36 is one of the most intriguingaspects of the research findings in this thesis. ZFP36, also called tristetraprolin (TTP), is aRNA-binding protein that interacts with AU-rich elements (Brooks & Blackshear, 2013). TheAU-rich elements are considered instability elements that are targets of RNA binding proteinsand recruit mRNA degradation elements (Brooks & Blackshear, 2013). As ZFP36 has the abilityto bind and subsequently degrade mRNA, this leads to a hypothesis that ZFP36 can bind to XISTor other lncRNA as they approach escape genes and direct their local degradation. Thedegradation of XIST or other RNA could allow a region to remain more accessible totranscription machinery and hence escape from XCI.While the analysis of empirical TF binding data led to a novel insight, the analysis ofchromosome structure was limited by the available data. Structural models were generated withChIA-PET data for a family trio of the daughter, mother and father. For each of the females, twodistinct structures were modeled, with one being less condensed. The less condensed models infemales were comparable to the model generated from father, which represents the Xa. Thesestructures were able to highlight the conformational differences between Xa and Xi.474.2 LimitationsAs a purely computational study, the thesis is highly dependent upon the data available. For theenrichment analysis, only TFs available in ReMap for GM12878 cells were considered. The listof 155 TFs encompasses less than 10% of TFs in the human genome, hence the analysis ismissing important information regarding TFs that regulate the escape from XCI. Additionally,ChIP-seq data for some available TFs are sparse (i.e. only few peaks observed). Such data is aconstraint for enrichment analysis. For example, YY1 ChIP-seq data in ReMap 2022 only has 42peaks over the whole X chromosome, and only 5 peaks within TSS regions of focus in this study.This lack of ChIP-seq peaks results in an inability to assess YY1 enrichment, despite the motifhaving been highlighted in previous studies of escape (Chen et al., 2016).The use of ChIP-seq data, as compared to motif-based studies, also suggests futurebioinformatics methods research. The construction of background sets based on sequence GCcomposition could be explored further. Such an approach was inspired by previous motif-basedstudies and published observations of biased GC compositions in ChIP-seq datasets, but it isunclear if the empirical data used in this thesis is similarly influenced by GC composition for theanalysis of the X chromosome. Future studies across a wider variety of ChIP-seq data couldexplore whether GC composition matching in the background is necessary.Additionally, the GIGGLE package was used in this thesis for detection of ChIP-seq peakenrichment in escape genes, but could be adjusted in the future to take statistical considerationsfurther into account. First, the iterative use of different background sets could allow for acombined statistical score to be generated rather than the counting approach for detection.48Second, the current approach treats each distinct ChIP-seq dataset in isolation and does notaccount for multiple testing. As the statistical measures are used as ranking metrics rather thanfor the significance, it does not impact the findings in this thesis. However, a rigorous handlingof multiple testing could contribute to a future approach that is more statistically driven.Another limitation was the inability to determine if the binding of TFs at the ROI is directlyenabling escape of XCI or reflects the downstream recruitment of TFs after escape wasestablished by other factors. Similarly, analysis of the TF pairs was solely to discover potentialco-bindings incidences but is incapable of determining if the TF pairs functionally cooperate topromote transcription. In addition, the co-binding analysis only considers 2 TFs in eachcomparison, while TF can partner in larger sets.For the structural analysis, the use of ChIA-PET data may not be as ideal as Hi-C data, which isbelieved to provide high resolution based on the restriction enzyme used for digesting thegenome. Additionally, due to the quality of the ChIA-PET data obtained, we lowered thethreshold for MAPQ in the pipeline to allow a larger number of interactions for consideration.By selecting a more permissive threshold, the data may be noisier, such as considering reads thatmap to multiple locations in the genome.4.3 Outstanding questionsAlthough this thesis was able to identify TFs that may have a role in the escape of XCI based onenrichment of binding events, outstanding questions remain. Since not all escape genes interactwith the identified set of TFs and the TF data is sparse, are there additional TFs that contribute in49functionally important ways to escape? Alternatively, the focus of this thesis was placed uponTFs. Given the ZNP36 factor that arose, it seems appropriate for future focus to explore the roleof RNA binding proteins and the presence or exclusion of lncRNA at escape regions.Additionally, while this thesis is able to provide information about TFs that may be involved inescape of XCI, the precise mechanisms by which escape is achieved remains to be determined.4.4 Future directionsBuilding on the bioinformatics focus of this thesis, experimental studies will be important.Specifically, the enriched TFs should be tested for sufficiency and necessity in the escape ofXCI. If a TF is sufficient for the escape of XCI, the presence of its binding region or motif withinthe normally silenced transgene construct should allow the transgene to escape XCI onceintegrated into the target genome. Whereas, the test for necessity should be a loss-of-functionexperiment, where the removal of the TF binding region from the transgenic construct willsilence the escape gene once it’s integrated into the target genome. Such an experiment wouldrefine the list of enriched TFs by any that have entered the list due to their high presence atescape genes, likely recruited for transcription, but do not contribute to the escape mechanism. Ina similar concept, the list of TF pairs obtained from the co-binding analysis would need to betested and refined.The potential role of ZFP36 and its known contribution to RNA stability suggests paths forexploration. A related mechanism to consider is genomic imprinting on autosomes. Imprinting isthe phenomenon in which epigenetics marks are deposited on specific loci and lead to theexpression from only a single copy of the chromosome (Autuoro et al., 2014). A subset of50imprinted genes have been tied to lncRNA, such as Airn that regulates the Igf2r loci, acting in cisto recruit chromatin remodelling proteins (Autuoro et al., 2014). Since, imprinted regions havemany similarities to XCI, it may be possible to disrupt imprinting by targeting ZFP36 to theregions to assess its contributions to escape.4.5 ConclusionThis research of this thesis aimed to identify relevant TFs that participate in the mechanism toescape from XCI. With the availability of processed empirical data in databases, the opportunityto determine individual TF involvement by analyzing their true-binding events was created. Ourresults highlighted 5 TFs, based on enrichment and co-binding analyzes, as preferentiallyengaged with regions within escape genes. Especially ZFP36, a RNA-binding protein, whichsuggests future research to consider how the regulation of lncRNA molecule levels near escapegenes contributes to escape from XCI.51ReferencesAutuoro, J. M., Pirnie, S. 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Thesis/Dissertation
eng
Bioinformatics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Wasserman, Wyeth W.
Identification of regulatory mechanisms in governing gene expression from the inactivated X chromosome
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