UBC Faculty Research and Publications

The western painted turtle genome, a model for the evolution of extreme physiological adaptations in… Bradley Shaffer, H; Minx, Patrick; Warren, Daniel E; Shedlock, Andrew M; Thomson, Robert C; Valenzuela, Nicole; Abramyan, John; Amemiya, Chris T; Badenhorst, Daleen; Biggar, Kyle K; Borchert, Glen M; Botka, Christopher W; Bowden, Rachel M; Braun, Edward L; Bronikowski, Anne M; Bruneau, Benoit G; Buck, Leslie T; Capel, Blanche; Castoe, Todd A; Czerwinski, Mike; Delehaunty, Kim D; Edwards, Scott V; Fronick, Catrina C; Fujita, Matthew K; Fulton, Lucinda; Graves, Tina A; Green, Richard E; Haerty, Wilfried; Hariharan, Ramkumar; Hernandez, Omar; Hillier, LaDeana W; Holloway, Alisha K; Janes, Daniel; Janzen, Fredric J; Kandoth, Cyriac; Kong, Lesheng; de Koning, AP J; Li, Yang; Literman, Robert; McGaugh, Suzanne E; Mork, Lindsey; O’Laughlin, Michelle; Paitz, Ryan T; Pollock, David D; Ponting, Chris P; Radhakrishnan, Srihari; Raney, Brian J; Richman, Joy M; St John, John; Schwartz, Tonia; Sethuraman, Arun; Spinks, Phillip Q; Storey, Kenneth B; Thane, Nay; Vinar, Tomas; Zimmerman, Laura M; Warren, Wesley C; Mardis, Elaine R; Wilson, Richard K Mar 28, 2013

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata


52383-13059_2012_Article_3061.pdf [ 744.01kB ]
JSON: 52383-1.0132642.json
JSON-LD: 52383-1.0132642-ld.json
RDF/XML (Pretty): 52383-1.0132642-rdf.xml
RDF/JSON: 52383-1.0132642-rdf.json
Turtle: 52383-1.0132642-turtle.txt
N-Triples: 52383-1.0132642-rdf-ntriples.txt
Original Record: 52383-1.0132642-source.json
Full Text

Full Text

The western painted turtle genome, a model forthe evolution of extreme physiologicaladaptations in a slowly evolving lineageBradley Shaffer et al.Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28 (28 March 2013)RESEARCH Open AccessThe western painted turtle genome, a model forthe evolution of extreme physiologicaladaptations in a slowly evolving lineageH Bradley Shaffer1,2*, Patrick Minx3*, Daniel E Warren4, Andrew M Shedlock5,6, Robert C Thomson7, Nicole Valenzuela8,John Abramyan9, Chris T Amemiya10, Daleen Badenhorst8, Kyle K Biggar11, Glen M Borchert12,13, Christopher W Botka14,Rachel M Bowden12, Edward L Braun15, Anne M Bronikowski8, Benoit G Bruneau16,17, Leslie T Buck18, Blanche Capel19,Todd A Castoe20,21, Mike Czerwinski19, Kim D Delehaunty3, Scott V Edwards22, Catrina C Fronick3, Matthew K Fujita21,23,Lucinda Fulton3, Tina A Graves3, Richard E Green24, Wilfried Haerty25, Ramkumar Hariharan26, Omar Hernandez27,LaDeana W Hillier3, Alisha K Holloway16, Daniel Janes8, Fredric J Janzen8, Cyriac Kandoth3, Lesheng Kong25,AP Jason de Koning20, Yang Li25, Robert Literman8, Suzanne E McGaugh28, Lindsey Mork19, Michelle O’Laughlin3,Ryan T Paitz12, David D Pollock20, Chris P Ponting25, Srihari Radhakrishnan8,29, Brian J Raney30, Joy M Richman9,John St John24, Tonia Schwartz8,29, Arun Sethuraman8,29, Phillip Q Spinks1,2, Kenneth B Storey11, Nay Thane3,Tomas Vinar31, Laura M Zimmerman12, Wesley C Warren3, Elaine R Mardis3 and Richard K Wilson3AbstractBackground: We describe the genome of the western painted turtle, Chrysemys picta bellii, one of the mostwidespread, abundant, and well-studied turtles. We place the genome into a comparative evolutionary context,and focus on genomic features associated with tooth loss, immune function, longevity, sex differentiation anddetermination, and the species’ physiological capacities to withstand extreme anoxia and tissue freezing.Results: Our phylogenetic analyses confirm that turtles are the sister group to living archosaurs, and demonstratean extraordinarily slow rate of sequence evolution in the painted turtle. The ability of the painted turtle towithstand complete anoxia and partial freezing appears to be associated with common vertebrate gene networks,and we identify candidate genes for future functional analyses. Tooth loss shares a common pattern ofpseudogenization and degradation of tooth-specific genes with birds, although the rate of accumulation ofmutations is much slower in the painted turtle. Genes associated with sex differentiation generally reflectphylogeny rather than convergence in sex determination functionality. Among gene families that demonstrateexceptional expansions or show signatures of strong natural selection, immune function and musculoskeletalpatterning genes are consistently over-represented.Conclusions: Our comparative genomic analyses indicate that common vertebrate regulatory networks, some ofwhich have analogs in human diseases, are often involved in the western painted turtle’s extraordinaryphysiological capacities. As these regulatory pathways are analyzed at the functional level, the painted turtle mayoffer important insights into the management of a number of human health disorders.Keywords: Amniote phylogeny, anoxia tolerance, chelonian, freeze tolerance, genomics, longevity, phylogenomics,physiology, turtle, evolutionary rates* Correspondence: brad.shaffer@ucla.edu; pminx@genome.wustl.edu1Department of Ecology and Evolutionary Biology, University of California,Los Angeles, Los Angeles, CA 90095-1606, USA3The Genome Institute, Washington University School of Medicine, CampusBox 8501, 4444 Forest Park Avenue, St Louis, MO 63108, USAFull list of author information is available at the end of the articleBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28© 2013 Shaffer et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.BackgroundTurtles (also known as chelonians or Testudines) are anenigma. As the vertebrate paleontologist Alfred Romernoted half a century ago, ‘The chelonians are the mostbizarre, and yet in many respects the most conservative, ofreptilian groups. Because they are still living, turtles arecommonplace objects to us; were they entirely extinct,[they] would be a cause for wonder’ [1]. From the Triassicto the present, turtles have been morphologically conserva-tive, and even the earliest turtles [2] are instantly recogniz-able. The living crown group of turtles extends back atleast 210 million years [3] and is characterized by a numberof unique morphological and physiological features. Besidestheir distinctive shell, turtles have extremely long lifespans,are often reproductively active at very advanced ages, oftendetermine sex by the temperature at which eggs incubate,are the most anoxia-tolerant tetrapods known, and havethe capacity in some species to freeze nearly solid, thaw,and survive with negligible tissue damage. The westernpainted turtle genome harbors a wealth of information onthe genetic basis of these and other adaptations that char-acterize this unique vertebrate lineage.Two of the great physiological challenges to vertebratesurvival are hypoxia and cold tolerance. Particularly fortemperate ectotherms like the western painted turtle,the two are closely linked, because winter hibernationoften occurs underwater in ice-locked ponds, andinvolves long periods with limited access to oxygen. Thewestern painted turtle is capable of surviving, with noloss of physiological function, 4 months under condi-tions of exceptionally low oxygen availability at 3°C [4]and at least 30 h at 20ºC [5]. This anoxia tolerance,when combined with the ability to survive freezing of50% body water [6], allows hatchling painted turtles toendure long winters in their nests across the northernpart of their range in North America. It also provides anunprecedented model to study natural mechanisms thatprotect the heart and brain from hypoxia-induced injury.Cardiac infarct and cerebral stroke are the first andthird leading causes of death in the United States [7],and while conventional therapies continue to extendhuman lifespan, progress in improving outcomes fromthese conditions has been limited. Our genomic analysesindicate that painted turtles frequently achieve theirextreme physiological capacities, at least in part, usingconserved amniote molecular pathways; functional ana-lyses of these pathways across turtles with varying phy-siological capacities thus may provide important insightsfor human disease prevention.Results and discussionReference genomeWe sequenced the nuclear genome of a single femalewestern painted turtle, Chrysemys p. bellii, that we field-collected from southern Washington, using a combina-tion of next-generation whole genome shotgun andSanger-based BAC end reads (see Materials and Methods,Sequencing and Assembly, Additional file 1, Tables S1, S2).The assembly averages 18-fold coverage across 2.59 Gbwith an N50 scaffold size of 5.2 Mb, and represents atleast 93% of the genome. By all available measures, theassembled sequences have sufficient nucleotide and struc-tural accuracy to provide a suitable template for initialanalysis (see Materials and Methods, Assembly Qualityand Coverage Assessments).Genome annotationAfter soft masking the C. p. bellii genome with Repeat-Masker [8], gene annotation was performed using thehomology-based pipeline GPIPE [9-11] using a non-redundant protein set from human (Ensembl release66), chicken (Ensembl release 66) and green anole(Ensembl release 66) as template. Based on the qualityof the alignments with the template proteins, the con-servation of exon boundaries and the absence of frameshifts and premature stop codons, we predicted a totalof 21,796 protein-coding gene models in C. p. bellii,including 144,670 exons (average 6.63 exons per gene),and an average transcript size of 1,023 nucleotides(median 743 nucleotides). Using cDNAs obtainedthrough 454 sequencing of libraries derived from brain,testes, ovaries, and trunk, we identified a total of 40,091exons within 7,961 gene models to which cDNAs couldbe mapped.Repeat structureApproximately 10% of the C. p. bellii assembly containsan abundance of transposable elements (TEs) thatinclude nearly 80 distinct lineages of RNA-derived retro-transposons and DNA transposons, suggesting a longand dynamic history of clade-specific genomic diversifi-cation (see Additional file 1, Table S3, Additional file 2,Figures S1-S4). The western painted turtle exhibits inter-mediate TE copy number relative to birds and the lizardAnolis, and is rich in LTR elements including endogen-ous retroviruses, LINEs in the CR1 and RTE families,predominantly MIR-like SINEs, and DNA transposons(see Additional file 2, Figures S1-S3). These transposonsinclude 385 SPIN elements in the hAT-Charlie family notpreviously detected by slot blot hybridization assays forseven turtle and four crocodilian species [12]. Consistentwith the close evolutionary relationship between turtlesand archosaurs (birds and crocodilians, see below), theseelements and the overall genome have a GC content of43% that is more similar to birds than Anolis [9,13](see Materials and Methods, Repeat Structure, Additionalfile 1, Table S3, Additional file 2, Figures S1-S6). Chrys-emys p. bellii also exhibits a moderate density of tandemBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 2 of 22repeats (1% genomic sequence coverage with an averagedensity of 111 repeats per MB) with length and frequencydistributions more similar to birds than to Anolis [13].Overall, the repetitive landscape of C. p. bellii exhibits asubstantial amount of lineage-specific evolution that dis-tinguishes turtles from other major amniote taxa butexhibits some similarities to archosaurs, in keeping withtheir sister group relationship. Long generation timesand a slow rate of molecular evolution may have facili-tated the diversification of turtle repeats, potentiallyimpacting both genomic stability and dynamics of tran-scriptome function [14-17].Isochore structureThe presence of GC-rich isochores is a well-known featureof birds and mammals, but is a minor component of geno-mic structure in the lizard Anolis. The western paintedturtle genome has an average GC proportion of 0.43,which is consistent with other amniotes (see Additionalfile 2, Figure S7). At a 3-kb scale, the standard deviation ofGC content is 0.059, which is also intermediate amongvertebrate genomes (see Additional file 2, Figure S7). Thestandard deviation of GC content in the western paintedturtle is intermediate between those of the lizard Anolisand mammals/birds for sliding window sizes ranging from5 kb to 320 kb (Figure 1), suggesting that the gene-richisochores that characterize the endothermic birds andmammals are not as prominent a feature of the westernpainted turtle genome (see Materials and Methods,Isochores, Additional file 1, Table S4, Additional file 2,Figures S7-S9). For the western painted turtle, we found aweak but significant correlation between the GC contentof protein-coding genes and their flanking sequence, indi-cating a slight, but potentially important relationshipbetween genomic environment and the nucleotide compo-sition of genes (see Additional file 2, Figure S8). We alsofound a slight negative relationship between the GC con-tent and the length of intergenic sequences in the westernpainted turtle (not shown); thus, GC-rich regions tend tobe more gene dense. This is a strong relationship in mam-mals and birds, but is non-existent in Anolis.To examine the evolution of GC content in the contextof the vertebrate phylogeny, we quantified GC content atthird codon positions (GC3) using the 2,366 simpleorthologs (1:1) identified from the OPTIC pipelineorthology predictions for zebrafish, pufferfish, chicken,zebrafinch, western painted turtle, green anole, platypus,mouse, and human (see Materials and Methods, Identifi-cation of gene family expansion/contraction for methodson determining gene homology). We used the programNHML (with default parameters) to estimate: (1) ances-tral GC content; and (2) GC3*, the equilibrium GC con-tent, which can be interpreted as the GC content towardwhich a lineage is evolving. Our results are consistentwith trends from previous phylogenetic analyses of GCcontent [18,19], with the exception that chicken seems tobe in equilibrium with regard to GC3. The westernpainted turtle shows a striking decrease in GC3 from itscurrent value of 46.74% to a GC3* value of 38.90%, indi-cating an erosion of GC content that is also seen inAnolis (see Additional file 2, Figure S9) [18].One mechanism that can contribute to this erosion ishomogenization of recombination. Recombination is corre-lated with several evolutionary processes and genomic fea-tures. For instance, regions with higher recombinationactivity experience more efficient selection as well as higherGC content in mammals and birds. It stands to reason thatgenes with higher GC3 will have a lower lineage-specificdN/dS; that is, genes with higher GC content will experi-ence more efficient selection. To test this, we divided upthe genes from human, chicken, and western painted turtleinto ‘high GC3’ and ‘low GC3’ genes based on the GC3values of genes for each taxon. We then examined the dis-tribution of dN/dS values between these two groups foreach taxon. We expected, if recombination has a landscapesimilar to mammals and birds, that the ‘high GC3’ geneswill have a lower lineage-specific dN/dS and ‘low GC3’genes will have greater dN/dS values. We found this to bethe case in human and chicken [18], indicating a heteroge-neous recombination landscape (see Additional file 1,Table S4). In the painted turtle, we found that there is aneven greater disparity in dN/dS between ‘high GC3’ and‘low GC3’ genes than in human and chicken, indicatingthat an even more heterogeneous landscape exists in turtle.This may indicate that rather than a recombination-basedmechanism driving GC content in turtle (for example, GC-biased gene conversion), mutational biases are playing animportant role in the trajectory of GC3.Phylogeny and evolutionary ratesThe phylogenetic position of turtles has remained one ofthe last unresolved problems in vertebrate evolutionaryhistory, with recent hypotheses suggesting widely dispa-rate placements [20,21]. Our phylogenetic analysis of1,955 sets of rigorously screened gene orthologs (seeMaterials and Methods, Multiple alignments and geneorthologs) for eight vertebrate species (human, platypus,chicken, zebrafinch, anole, turtle, python, and alligator),analyzed separately or as a concatenated dataset, concurwith two recent phylogenomic analyses [20,22] in placingturtles as the sister group to Archosauria with strong sta-tistical support (Figure 2). Thus, based on independent,genome-scale analyses, the phylogenetic placement ofturtles as well-nested within diapsid amniotes appears tobe relatively secure.We also estimated the relative rate of substitution in asmaller dataset that was designed to minimize missingdata. This dataset comprised 309 orthologs that wereBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 3 of 22identified in all eight species. Our analyses indicate thatthe turtle lineage has undergone a remarkable substitu-tion-rate slowdown relative to other amniotes (Figure 2).Estimates of relative evolutionary rates under a relaxedmolecular clock suggest that turtles have the slowest rateof substitution among the eight representative amniotelineages analyzed. Turtle genomes evolve at about one-third the rate seen in humans, and roughly one-fifth therate of the fastest-evolving python lineage (see Materialsand Methods, Phylogeny and substitution rate, Additionalfile 1, Tables S5-S6). Given the long generation time thatcharacterizes turtles, our comparative analysis is consistentwith the negative relationship between generation timeand rate of molecular evolution found in reptiles [23] andother amniotes [24], although the observed slowdown inarchosaurs and turtles may also suggest a broad, lineage-specific effect.Extreme anoxia tolerance in the painted turtleAlthough all turtles can withstand anoxia for a few hourswith no discernable tissue damage, the painted turtle is acandidate for the most extreme anoxia-tolerant tetrapodknown. To explore the transcriptomic basis of thisextreme anoxia tolerance, we assembled a gene expres-sion profile by sequencing poly A-enriched RNA isolatedfrom the ventricle (heart) and telencephalon (brain) ofnormoxic and anoxic (n = 4 turtles/group, 24 h at 19ºC)adult western painted turtles (see Materials and Methods,Anoxic gene expression). FPKM (Fragments per kilobaseof exon model per million mapped fragments) valuesfrom 13,236 western painted turtle genes with humanorthologs were interrogated (from a starting pre-filteringpool of 22,174 gene orthologs) and analyzed withANOVA. Differential gene expression significantlyincreased in brain (19 genes) and heart (23 genes) (see00.0180.0350.0530.0700 100000 200000 300000 400000GC Heterogeneity at Different Spatial ScaleshumanchickenturtleXenopus frogAnolis lizardexpectedstandard deviation of GC proportionwindow size (bp)Figure 1 Standard deviation of GC content at different spatial scales. Genomes were partitioned into non-overlapping windows (5-, 20-, 80-, and 320-kb). As window size increases, variation in GC content naturally decreases. The western painted turtle exhibits a pattern consistent withhigh variation in nucleotide composition at smaller scales, rather than sustained isochoric variation at larger scales seen in mammals and birds.The expected pattern of decreasing standard variation assumes a compositionally homogeneous genome with a mean GC proportion of 0.41.Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 4 of 22Additional file 1, Tables S7, S8), mirroring previous workshowing up-regulated gene expression in response tohypoxia in other vertebrate tissues, including manycancers.The largest overall change in expression was inAPOLD1, an apolipoprotein encoding gene whose tran-script levels increased 128-fold in telencephalon and 19-fold in ventricle (see Additional file 1, Tables S7, S8;Additional file 2, Figure S10). APOLD1 expression mod-erately increases during hypoxia in human microvascularendothelial cell culture, although its exact functionremains unclear [25]. Other highly differentiallyexpressed genes (>10-fold; FOS, JUNB, ATF3, PTGS2,BTG1/2, and EGR1) encode proteins that, individuallyand in dimeric forms, have been implicated in the controlof cellular proliferation, cancers, and tumor suppression[26-29]. The 30-fold increase in a gene orthologous toSLC2A1 (see Additional file 1, Table S8, Additional file 2,Figure S11), which encodes the glucose transporterGLUT-1, is also exceptional since deficiencies in mem-brane glucose transport underlie diabetes in humans. Anunderstanding of the mechanism by which membraneGLUT-1 levels increase in the turtle would be a usefulcontribution to human diabetes research. Decreases ingene expression were fewer and found only in ventricle(see Additional file 1, Table S9; Additional file 2, FigureS12), but included decreases in CDO, which is importantin regulating intracellular cysteine as well as levels of theendogenous metabolic depressant hydrogen sulfide[30,31], and genes involved in mRNA splicing (SRSF5)[32] and tumor proliferation (MKNK2) [33].These analyses demonstrate the power of the westernpainted turtle as a model for the evolution of anoxia toler-ance by regulatory changes utilizing broadly conservedvertebrate genes, including many genes that lead to patho-genesis in humans. Clearly, further study of the processesthat link these regulatory changes to anoxia tolerance area next important step. Although this is yet to be tested, wealso note that the regulatory pathways that evolved in thewestern painted turtle could lead to the identification oftargets for therapeutic intervention in human diseasesinvolving hypoxic injury and possibly tumorigenesis.A novel microRNA associated with freeze tolerance inhatchling painted turtlesFreeze tolerance constitutes a second suite of physiologicaladaptations that are integral to winter survival for hatchl-ing painted turtles and other species that overwinter inFigure 2 A revised phylogeny of major amniote lineages and their rates of molecular evolution. (a) Bayesian phylogram depicting therelationships of the eight primary amniote lineages, and their rates of molecular evolution. The phylogeny demonstrates the sister grouprelationship of turtle and archosaurs (allligator plus birds). The numbers at nodes denote posterior probabilities (all are at the maximum of 1.0).(b) The histogram shows the relative rate of substitution inferred for each lineage under a relaxed clock. For analysis details, see Materials andMethods, Phylogeny and substitution rate).Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 5 of 22shallow terrestrial nests. Molecular adaptations thatunderlie natural freezing survival in C. p. bellii includestrong metabolic rate depression, use of anaerobic meta-bolism (see Extreme Anoxia Tolerance in the Painted Tur-tle), and selective up-regulation of genes involved in keycellular processes [34].Entrance into hypometabolism involves regulatorychanges in multiple metabolic processes coordinated byextracellular stimuli that are readily induced and reversedto allow smooth transitions to and from the frozen state.MicroRNA regulation of mRNA transcripts meets thesecriteria and is involved in other models of stress-inducedmetabolic rate depression [35]. Using the western paintedturtle genome, we retrieved the precursor sequence ofmiR-29b, a microRNA involved in DNA methylation andregulation of glucose transport [36,37] that is often asso-ciated with freeze and anoxia tolerance (see Materialsand Methods, Freeze tolerance). Based on this sequence,the secondary structure of western painted turtle pre-miR-29b was predicted to contain a single nucleotidemutation (nuc-43) resulting in a larger terminal stem-loop compared to the less freeze tolerant turtle Apalonespinifera and Homo sapiens. Although the functional sig-nificance of this mutation is unknown, microRNAs aregenerally extremely conserved across vertebrates, andnucleotide structures that restrain the terminal loopregion (as predicted for human and other turtles) candecrease the efficiency of Dicer processing of precursormicroRNA transcripts in the range of 50% (Figure 3A)[38]. In addition to loop flexibility, slight alterations toloop structure and nucleotide sequence can influenceinteractions between pre-microRNA and terminal loopbinding proteins, impacting processing efficiency. Consis-tent with the hypothesis that enhanced microRNA pro-cessing under low temperature stress facilitates freezingsurvival, quantitative RT-PCR (see Materials and Meth-ods, Freeze tolerance) revealed a mild but statistically sig-nificant 1.3-fold increase in processed mature miR-29blevels in liver of hatchling turtles in response to 24 hfreezing; expression was maintained and possiblyincreased during subsequent thawing (Figure 3B).Although these results require additional functional ana-lyses and are clearly preliminary, they point to future workon miR-29b as a potential candidate for freeze tolerancework on turtles with this physiological capacity. Withrefined genomic and comparative data across freeze toler-ant and intolerant turtles, future studies of turtle freezetolerance should help confirm or refute our interpretationthat mutations in miR-29b are an important componentof freeze tolerance in turtles.Tooth loss pseudogenizationTurtles lost the ability to form teeth approximately 150-200 million years ago, making them the oldest extantedentulous lineage of tetrapods (birds lost teeth approxi-mately 80-100 million years ago) [39]. Previous studies inbirds and edentulous mysticete (baleen) whales demon-strated that tooth loss is closely associated with the pseu-dogenization and subsequent degradation of the tooth-specific genes enamelin (ENAM), amelogenin (AMEL),ameloblastin (AMBN), dentin sialophosphoprotein(DSPP), and enamelysin (MMP20) [40,41]. We identifiedthe majority of turtle pseudo-exons in their chromoso-mally syntenic regions (see Materials and Methods, Toothloss) when compared to other amniotes (Figure 4), consis-tent with the very slow rate of genomic change seen inchelonians (see Figure 2). Turtle ENAM, AMEL, andMMP20 all contain premature stop codons (exons 5, 3,and 2, respectively) in addition to highly degeneratedsequences. AMBN, while somewhat more conserved, has apremature stop codon in exon 7. While DSPP exons 1 and2 are relatively conserved, all subsequent exons were uni-dentifiable. Sequence identity scores between pseudogeneexons identified in turtle and chicken were not signifi-cantly different from each other compared to their func-tional orthologs in crocodilians (see Materials andMethods, Tooth loss, Additional file 1, Tables S10, S11),even though turtles lost their teeth approximately 50-100million year earlier.This extremely conservative pattern of tooth-losspseudogenization across amniotes is consistent with asingle evolutionary origin (and regulatory network) ofteeth, and suggests that the deterioration of this pathwayevolved independently (that is, is homoplastic) in turtles,whales, and birds. This is also consistent with the fossilrecord, as early members of all three lineages are knownto be toothed. However, concordant with their overallslow rate of molecular evolution, the tooth-specificgenes in turtles have accumulated mutations at roughlyhalf the rate of accumulation found in birds.The genomic basis of longevity in turtlesOne of the defining features of turtles as a lineage is theirextreme longevity (many species live 100 years or more),and we used the western painted turtle genome to investi-gate this quintessential chelonian feature. Based on previouswork implicating the shelterin complex encoding genes inexceptional longevity in the naked mole rat [42], we evalu-ated (by BLAST searches of all available turtle sequencedata including unplaced scaffolds, see Materials and Meth-ods, Aging and longevity, Additional file 1, Table S12) thestatus of the shelterin complex in the western painted turtlegenome. Even with this comprehensive search, we wereunable to find orthologs for three of the five genes (POT1,TERF2IP, TEP1) in the western painted turtle. Given thatTEP1 is also absent in birds, this result strongly suggeststhat turtles (and their sister group, the archosaurs) do notshare this longevity mechanism with the naked mole rat.Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 6 of 22Figure 3 Western painted turtle miR-29b and response to freezing. (a) Nucleotide sequence and predicted secondary structure of pre-miR-29b transcripts from H. sapiens, A. spinifera, and C. p. bellii at 25 C. Nucleotide substitution which leads to differential terminal stem-loopformation that is unique to C. p. bellii is circled. (b) Relative expression levels of miR-29b as assessed by quantitative RT-PCR in liver samples ofhatchling western painted turtles under control (5°C acclimated), 24 h frozen (at -2.5°C), or 4 h thawed (at 5°C) conditions. Data are means ± s.e.m. (n = 5 different animals). Parallel analysis of 5S rRNA found no significant changes between control and experimental conditions for thisreference RNA. * Significantly different from the corresponding control (P <0.05).Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 7 of 22We also examined genes that have apparently beenlost in the western painted turtle (and were also absentin our searches of all other available turtle genomes) toinvestigate their relevance to aging based on theirorthology to known aging-linked genes in model organ-isms [43]. Specifically, lowered activity of ATP5O in the$0%1	(1$0$0(/'633003&K/3/ $0%1 (1$0/L]DUG 15* "&KLFNHQ )87/3/ " (1$0 15*&K=6FIOG 6FIOG$0%17XUWOH /3/ (1$0 15* )877XUWOH6FIOG$5+*$3$0(/0,' 06/6FIOG+XPDQ$0(/$5+*$3$0(/06/+&&60,'&K;&K+XPDQ '633 'PS 633,%6363$5&/18'7&K+XPDQ '&81G003 00370(0 003 003$0(/&K$5+*$3 0,'06/&KLFNHQ'PS&K&KLFNHQ '63363$5&/6FIOG6FIOG7XUWOH 18'7 'PS 633,%6363$5&/ '6336FIOG7XUWOH '&81G00370(0 003 003003&K70(0 003 003&KLFNHQ 00318'7 ,%63 633'&81GFigure 4 Conserved syntenic regions containing tooth-specific genes across toothed (human, anole) and edentulous (turtle, chicken)vertebrates. AMBN and ENAM are in a reptile-specific chromosomal region, precluding the use of human as a reference sequence for thesegenes. Dashed outlines indicate pseudogenization.Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 8 of 22nematode C. elegans increases longevity [44], whilePLCG2 is a crucial intracellular signaling modulator andseems to be negatively affected by aging [45]. Althoughconfirming the absence of genes is difficult with incom-pletely assembled genomes, the western painted turtlegenome is at least 93% complete, and their absence inother turtle genomes is compelling (see Additional file1, Table S12). Among these presumably missing genes,the lack of ATP5O (for which we found no hits in anyturtle) and PLCG2 (where we found evidence for a totalof six out of 30 exons across all turtles) may be impor-tant in the extraordinary longevity of turtles.Temperature-dependent sex determination/differentiation(TSD) genesSince the first realization that many, but not all, turtleshave TSD, turtles have become a model system for com-paring the gene networks controlling genotypic sexdetermination (GSD) and TSD. Phylogenetic reconstruc-tion indicates that the ancestral condition of sex deter-mination in turtles and crocodilians was thermosensitive(TSD), and that GSD has re-evolved in several turtlelineages [46]. Although it is now clear that TSD andGSD each encompass multiple mechanisms whosedivergence involves regulatory and structural evolutionaffecting the level of plasticity and canalization of verte-brate sexual development [47,48], it also remains thecase that transitions between TSD and GSD haveoccurred many times, and that TSD is the ancestral con-dition in turtles. Genomic analyses of TSD and GSDturtles (and crocodilians) can provide important clues tohelp decipher the changes in genetic architecture thatunderlie these evolutionary transitions. Comparativeanalysis of genomes and transcriptomes from TSD tur-tles (Chrysemys p. bellii, Chelydra serpentina, Trachemysscripta) and the GSD softshell turtle Apalone mutica (alldata produced by our group) from early through lateembryonic stages revealed that virtually all of the knownvertebrate genes involved in sexual differentiation arepresent in turtle genomes and active during sexualdevelopment (see Materials and Methods, Sex determi-nation/differentiation, Additional file 1, Table S13).We took a gene-tree reconstruction approach to exam-ine the phylogenies of the coding regions of five keygenes involved in the gonadogenesis regulatory networkwhose transcriptional responses have been studied in thewestern painted turtle (WT1, SF1, SOX9, DMRT1, andAROMATASE [48,49], Figure 5). Although the roles ofthese genes in the TSD/GSD transition remains incom-pletely understood, they are important in sexual differen-tiation in a variety of vertebrates including reptiles. Ourprimary goal was to ask whether these individual genetrees cluster taxa based on their phylogenetic relation-ships (as might be expected if independent TSD/GSDtransitions have evolved that do not mask phylogeny) oron their TSD/GSD phenotype. Consistent with their phy-logenetic relationships, our gene tree analyses generallyplaced the monophyletic set of turtle orthologs as the sis-ter group to archosaurs (compare the relationships ofturtles and crocodilians in Figure 2 with Figure 5),although in one case (WT1) TSD turtles and crocodilianswere sister groups (Figure 5). However, within-turtlerelationships of these five gene trees often resolve theGSD softshell Apalone spinifera as sister group to theremaining turtles, rather than in its generally establishedplacement as sister to the remaining cryptodires [50]. Itis well known that estimates of individual gene trees candiffer from species trees for purely statistical reasons, andthe inter-relationships of softshells to other turtles hasbeen notoriously difficult to determine with moleculardata [50-52].Overall, there is no compelling evidence of clusteringTSD and GSD turtles, or TSD and GSD vertebrates thatis contrary to their phylogenetic relationships, suggestingthat strong convergence at the molecular level has notoccurred in these markers. Interestingly, dn/ds analysisrevealed that the molecular evolution of these elements isdriven overwhelmingly by purifying selection, with onlyfew instances of neutral evolution between some closelyrelated species pairs such as Trachemys scripta (TSC)and Chrysemys p. bellii (CPI) for SF1, AROMATASE andWt1, TSC and Apalone spinifera (ASP) for SF1, ASP andCPI for SF1. Thus, these analyses indicate that the pri-mary patterns of gene tree evolution in these loci asso-ciated with sex determination are driven by theirorganismal (phylogenetic) history rather than TSD/GSDfunctionality.Immune system genomicsGiven the striking preponderance of expansions ofimmune function genes (see below), and their potentialimportance in the extended life spans of turtles, we char-acterized a large panel of immune-function genes in thewestern painted turtle genome. We aligned the C. pictabellii genome against a sequence database of approxi-mately 3,000 immune-function related genes developedfrom a diverse set of 14 vertebrates ranging from lampreyto mammals (see Materials and Methods, Immune system).Blast searches of the C. picta bellii genome against thisdatabase resulted in the identification of 110 genes, 100 ofwhich were confirmed with reciprocal alignments; 73 werealso identified in either cDNA or predicted gene sequences(see Additional file 1, Table S14). The cDNA represented asmall number of tissues/developmental stages, and 73/110(66%) confirmation of expression is very encouraging.The adaptive immune response of turtles is generallyslower and less robust than its mammalian counterpart,and does not consistently demonstrate evidence of aBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 9 of 22memory response [53,54]. However, we identified severalmajor components necessary for adaptive immunity andgeneration of immune memory including CD4, MHCII,and the immunoglobulin heavy chain locus (see Addi-tional file 1, Table S14). Our analysis also demonstratesthat the western painted turtle has a unique repertoire oftoll-like receptors (TLRs), comprised of those found inamphibians, fish, birds, and mammals. This includes aTLR15-like receptor that has previously only beendefined in birds, and is known to interact with bacterialpathogens including Salmonella [55] (see Additional file1, Table S15). Given the delayed adaptive response andpoor generation of immune memory, combined withtheir diverse set of TLRs, we predict that turtles shouldrely more heavily on the non-specific innate immuneresponse to effectively recognize and initiate appropriateresponses to pathogens. This initial response would befollowed by a more moderate adaptive response that,because of the low specificity due to lack of immunememory formation, may serve as a general mechanism tocombat remaining pathogens. Given the overall low spe-cificity of their innate and adaptive immune responses, itseems that turtles are able to adequately balance theirimmune compartments to eliminate pathogens, whilesimultaneously avoiding damage to self-tissues as a resultof an overactive immune response.Gene family expansionsGene family expansions point to candidate sets of genesof particular importance in chelonian survival and evolu-tion. After annotating the western painted turtle genome(see Materials and Methods, Identification of gene familyexpansion/contraction, Additional file 1, Table S16), weused phylogenetic reconstructions of the genomes ofthree mammals (human, mouse, platypus), two birds(chicken, zebrafinch), one lizard (green anole), and twofish (tetraodon, zebrafish) to identify one-to-one ortho-logs, as well as gene losses and gene family expansions inthe western painted turtle genome. We identified 3,222one-to-one orthologs across all nine species, 4,828 genesamong the seven amniote species, and 103 gene familiesincluding 957 gene predictions that show expansion inthe western painted turtle lineage. Among theseexpanded gene families, 15 of the 27 with four or moremembers, which jointly account for 623 of 957 gene pre-dictions, were annotated as being involved in immuneresponse (see Materials and Methods, Expansion of genefamilies involved in the immune response, Figure 6, Addi-tional file 1, Table S17); an additional large expansion(106 members, 101 confirmed by manual curation) wasevident among the beta-keratins (see Materials andMethods, Beta-keratin expansions) involved in the forma-tion of scales, claws, and scutes that encase the shell [56].Figure 5 Maximum likelihood estimates of the phylogenetic relationships among taxa for five genes involved in gonadogenesis.Branch lengths are proportional to the number of substitutions per site; numbers at nodes are bootstrap proportions based on 500pseudoreplicates. Colored branches denote the taxonomic group for each taxon. Tip font colors denote sex-determining mechanisms (red =TSD, gray = GSD). For all species, the full coding region was utilized except where only partial sequences were available, in which case the tip isdenoted as (P).Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 10 of 22Additional analyses using beta-keratin mRNAs extractedfrom the precursor cells of the shell of Pseudemys nelsoni[56] indicates that there have been independent lineage-specific expansions of the beta-keratins in birds and tur-tles associated with the formation of feathers and theshell (Li et al., unpublished results).Patterns of natural selectionGenomic scans for positive selection across turtles consti-tute a complementary strategy to identify genes underlyingchelonian adaptations. We examined a carefully screenedortholog set of 4,136 genes (see Materials and Methods,Ortholog sets) for eight vertebrate species (human, platy-pus, chicken, zebrafinch, anole, turtle, python, and alliga-tor) to detect signs of turtle lineage-specific positiveselection. Using branch-site likelihood-ratio tests [57] withreduced parameterization [58] (see Materials and Meth-ods, Positive selection), we identified 671 genes under posi-tive selection (false discovery rate <0.1) (Accessory DataFile 1). Among these genes were several categories ofinterest to notable physiological in turtles, several of whichwe highlight here.There were nine genes containing ankyrin repeat motifs(the most significant was ANKRD32, P = 1.1×10-17), whichare typically sites of protein-protein interactions. Further-more, some of these ankyrin-repeat-motif genes containedSOCS box (suppressor of cytokine signaling) domains aswell (ASB14, P = 1.2×10-2 and ASB18, P = 8.8×10-3)and are involved in protein turnover regulation [59]. Inaddition, a number of chemokine receptors, CCR4 (P =1.1×10-7), CCR5 (P = 7.2×10-5), and CCR10 (P =1.0×10-19), as well as CCRL1 (P = 4.2×10-9), showedevidence of positive selection in our analysis. These G-protein coupled receptors bind specific cytokines (chemo-kines), are involved in chemokine-mediated signaling, andare generally pro-inflammatory/immune responsive [60].We found evidence for significant positive selection inDMRT2 (P = 4.2×10-4). DMRTs have been found toassociate with sexual determination and development (seeearlier section on Temperature-Dependent Sex Determina-tion (TSD) genes, also reviewed in [61]).Related to oxidative phosphorylation and free-radicalscavenging, several positively selected genes were involveddirectly (for example, ATP5S, P = 3.7×10-6; ATP5H, P =3.1×10-4; COX15, P = 1.4×10-4; ATP5G3, P = 1.4×10-3;COX7A2, P = 6.0×10-3; ATP5B, P = 1.9×10-2; DAP3, P =8.3×10-6) or indirectly (for example, SOD1, P = 1.5×10-7;ACO2, P = 1.4×10-2) in this process. Adaptations withingenes in the process of ATP formation (specifically thosethat are subunits of ATP synthase) and anti-oxidantdefenses have been proposed as mechanisms of life-historyevolution in reptiles [62]. Several additional genes involvedin life history traits were also under positive selection,including those involved in fertility (FSHB, P = 7.4×10-6),0 2 4 6 8				 !"#$%&#'()(*+%,-./0%%0,%1,&%,/-((0%%0'#*/,0%00,'1 %0 !2%)(-)%00%00,,'"%0%00,2%(-3!-0,45Figure 6 Gene families showing expansion in the western painted turtle lineage. The number of genes within a family is provided in frontof each bar. Gene families associated with the immune response are shown in red.Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 11 of 22reproduction/immune functionality (prolactin receptor,P = 4.4×10-8), and aging (SIRT3, P = 2.1×10-3; CLK1, P =1.1×10-2). In general, these 671 positively selected genesare involved in diverse functions that span biological pro-cesses. Although a numerically large set, our careful filter-ing and criteria for ortholog consideration suggests theyare a robust set that is larger than would be expectedwhen compared to naked mole rat or human [42,63].We detected 171 GO functional categories showingenrichment for genes under positive selection (nominalP values < 0.05, Mann-Whitney U-test), however, nonewere statistically significant after multiple testing correc-tion (see Accessory Data File 1 for an overview of genesunder positive selection and GO category enrichments).ConclusionsThe western painted turtle, and chelonians generally,comprise a unique combination of extremely conserva-tive evolutionary history interspersed with some of themost unique physiological and behavioral adaptationsfound in amniotes. Our analyses of the western paintedturtle genome indicate that common vertebrate regula-tory pathways are often involved with these novel phe-notypes, and additional functional experiments can nowinvestigate the ways in which these pathways have beenmodified in turtles. Our extensive analyses of anoxia tol-erance provides particularly strong support for the inter-pretation that the western painted turtle utilizescommon vertebrate pathways to achieve its extraordin-ary physiological abilities; temperature-dependent sexdetermination and immune system functionality alsoappear to utilize common suites of vertebrate genes.Genomic analyses of longevity and particularly toothloss, both of which characterize all living chelonians,suggest that patterns of gene loss are also key elementsof turtle evolutionary novelties. The western paintedturtle genome, enabled by both comparative genomicsand functional experimentation, has provided and willcontinue to provide windows into the evolution of phy-siological novelties, perhaps including some with biome-dical and cryopreservation applications.One aspect of turtle evolution that is proceeding at arapid and accelerating pace is human-mediated extinc-tion. Although the lineages represented by living turtleshave survived countless challenges in the last 210 millionyears, current estimates are that at least 50% of the 330recognized species of living chelonians are threatenedwith extinction [64]. Turtles far outstrip amphibians,mammals, and birds in their proportion of at-risk species,and the survival likelihood of many species is bleak.Future comparative genomics work on turtles, includingcomparisons among species that vary in their longevity,anoxia and freeze tolerances, immunocompetency, and ahost of other key human challenges, requires healthypopulations of the remaining diversity of turtles. Thechallenge, for comparative biology and conservationalike, is to preserve the remaining diversity of living tur-tles as we continue to unravel their secrets for success.Materials and methodsSequencing and assemblyA single C. p. belli (western painted turtle) was sequencedat The Genome Center, Washington University School ofMedicine, St Louis, Missouri. The whole genome shotgunlibrary primary donor-derived reads (B. Shaffer lab, female,field number: RCT428, locality: WA Grant Co, small lake1.3 miles south of Potholes Reservoir, tissue accessionnumber: HBS 112648) and BAC end reads (BAC librarysource: VMRC CHY3: J. Froula, JGI (from C. Amemiyalab) female, strain: MVZ #238119, Locality: FrenchmanHills wasteway 9.0 mi S via Dodson road of junction withHwy I-90, Grant Co., Washington) were assembled usingRoche’s Newbler (version 2.6) with stringent parameters.Newbler uses all of the input single and paired end readdata (including the paired BAC end data) to create contigsand then, focusing on the paired end read data along withestimates of insert size, organizes those contigs into largerscaffolds. After removing contamination, the resultingassembly was labeled as 3.0.1. All scaffolds >500 bases(81,642 scaffolds with a total size of 2.59 Gb, N50 scaffoldsize of 3.01 Mb (N50 number is 248)) were retained forsubmission to the public databases.After the assembly was complete, 15X of paired endsequencing data were generated on the Illumina platformand used only for error correction in the reference assem-bly; the Illumina paired end data were not used to aid inscaffolding of existing contigs. For error correction, theIllumina data were aligned against the 3.0.1 assemblyusing bwa [65] and processed using samtools and bcftools[66]. Based on the paired end mapping data, all duplicatemapped reads were removed. One and two basepair indelswere introduced into the reference for all cases wherethere were ≥3 and ≤200 reads aligned (mapping quality≥40 and the indel was ≥10 bases from the end of the align-ment), and where all reads disagreed with the referenceand agreed with one another. There were a total of 27,296indels introduced into 24,712 contigs.The assembly data were aligned utilizing BLASTZ [67]to align and score non-repetitive turtle regions against thefollowing repeat-masked genomes: anole (anoCar2),human (hg19), chicken (galGal3), and opossum (mon-Dom5). Alignment chains differentiated between ortholo-gous and paralogous alignments [68] and only ‘reciprocalbest’ alignments were retained in the alignment set. Thealignments were post-filtered in the following ways: (1)only alignments that extended over at least 2,000 baseswhere the relative expansion/contraction was <10X wereretained; and (2) alignments were then smoothed byBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 12 of 22removing any single alignments that were <10 kb andoccurred as a single alignment in between a large block ofseparate alignments to the same chromosome. The relativescaffold ordering was then examined in the four pairwisealignments. If at least three of the different pairwise align-ments with the other species all suggested a given orderand orientation, that pairwise ordering was retained in alist of valid orders (and orientations). Then the consistentpairwise alignments were linked into groups. The AGPwas created using those lists of ordered and oriented scaf-folds. Because ordering by homology is not absolutely con-fident, the gaps between scaffolds were annotated as‘contig’ gaps including a ‘no’ in the final column indicatingthat there is no spanning clone closing the gap. There wasapproximately 1.2 Gb of sequence organized into 290ordered groups leaving 80,697 individual scaffolds totaling1.3 Gb. The N50 scaffold size rose to 5.2 Mb (N50 numberis 148).Assembly quality and coverage assessmentsAs indicated by comparisons of the submitted assemblywith a set of 64 finished western painted turtle BACs(BAC library source: VMRC CHY3; J. Froula JGI (fromC. Amemiya, Benaroya Research Institute, Seattle, WA)Female; Strain MVZ #238119; Locality: WA: Grant Co:small lake 1.3 miles south of potholes reservoir) totaling9.3 Mb of finished sequence, structural accuracy of theassembled sequence is sufficient for these analyses. Thesecompleted BAC sequences were not included in theassembly and thus provide an important dataset forassessing assembly accuracy and coverage. Some smallsupercontigs (most <5 kb) were not positioned withinlarger supercontigs (<1 event per 500 kb). While theseare not strictly errors, they do affect overall assembly sta-tistics. There are also small, undetected overlaps (most<1 kb) between consecutive contigs (approximately 1event per 30 kb), occasional local mis-ordering of smallcontigs (approximately 1 event per Mb), and small con-tigs incorrectly inserted within larger supercontigs (<1event per 275 kb). Overall, the rate of rearrangementswith respect to finished BACs was comparable to pre-vious next generation WGS assemblies. Nucleotide-levelaccuracy is high by several measures. Over 99% of theconsensus bases in the western painted turtle sequencehave quality scores [69] of at least Q40 corresponding toan error rate of ≤10-4. Comparison of the WGS sequenceto the 9.3 Mb of finished BACs from the sequenced indi-vidual is consistent with this estimate, giving a high qual-ity discrepancy rate of 3x10-3 substitutions and 2x10-4indels which is no more than expected given the hetero-zygosity rate. The rate of substitutions is due to the poly-morphism rate. By restricting analysis to high-qualitybases, the nucleotide-level accuracy of the WGS assemblyis sufficient for analyses presented here. As with thechimpanzee and other whole genome shotgun-basedassemblies, the most problematic regions are those con-taining segmental duplications (Chimpanzee Sequencingand Analysis Consortium, 2005).We estimate that western painted turtle genomesequence covers at least 93% of the full genome sequence.To obtain this estimate, we first evaluated the coverageusing the results of the alignments of the assembly againstthe 64 finished western painted turtle BACs. The overallcoverage of those BACs exceeded 93%. Second, we aligneda set of western painted turtle cDNAs generated by thisproject against the genome assembly using BLAT [70].The cDNA libraries were constructed from several tissuesources (see Additional file 1, Table S2) and weresequenced in our lab on the 454 Life Sciences instrumentusing methods previously reported [71]. The reads wereassembled using the Newbler software package providedby 454 Life Sciences. The coverage estimates per tissuerange from 93% to 98% when asking that at least 50% ofthe EST align to the genome or from 91% to 96% whenrequiring more than 90% of the EST aligns to the genome(see Additional file 1, Table S2).Finally, we estimated coverage by looking at the cover-age of a related genome using BLAT [70]. Over 96% of thedraft assembly of the 1.5 Gb Trachemys scripta genome(separated by approximately 10-15 My from the westernpainted turtle) aligned with the western painted turtlegenome.Repeat structureTE sequence divergence in three turtle genome assembliesreveal a distribution that contrasts with the high turnoverof younger L1s in the lizard (Anolis), the skewed accumu-lation of older TEs in the alligator, and near complete lackof SINEs and active CR1s in the small, homogenous gen-omes of birds (see Additional file 1, Figures S1-S5)[9,11,72]. The average G+C content of C. p. bellii mobileelements is the same as the genome-wide average of 43%and the range of values for TE content and G+C amongthe N50 scaffolds is more similar to those observed inchicken than in Anolis (see Additional file 2, Figure S6)[9], consistent with its closer phylogenetic relationships toarchosaurs.Identification and classification of repetitive elementsin the C. p. bellii assembly were carried out on the fulloriginal C. p. bellii assembly sequence (C. picta belliiv3.0.1) using the RepeatMasker version 3.3.0 [8], TandemRepeat Finder version 4.0.4 [73], and Phobos version3.3.12 [74] software packages. For all available genomeassemblies investigated RepeatMasker was run with theBLAST engine and repeat classification was carried outusing the Vertebrate library from version 20110920 ofthe RepBase database. We employed Phobos usingdefault parameters. Tandem Repeat Finder was run withBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 13 of 22the default alignment parameters except for a reducedMaxPeriod value of 200 instead of the default 500, andwith exclusion of HTML output. These parameter set-tings were directly comparable to summary statisticsavailable through TRDB for the most recent whole-genome assemblies of amniote species. Results fromRepeatMasker were analyzed using RMPipeline [75], a setof generalized programs for analyzing RepeatMasker out-put written using Perl. These programs can be used toprocess any RepeatMasker output files and are publiclyavailable and free to use under the GPLv3 license. Graphswere created using RMPipeline results, some additionalPerl scripts, and Microsoft Excel.IsochoresThe absolute GC content of the assembly (after removingscaffolds with >20% missing data) is 0.434. We examinedwhether the assembly exhibited any bias in GC content.We divided the assembly into four equally-sized bins ofincreasing scaffold size (after omitting scaffolds with>20% missing data). The absolute GC contents of eachbin were (range of scaffold lengths are indicated in bp):0.496 (501-591), 0.498 (591-735), 0.496 (735-1,039),0.433 (1,039-26,452,378). Because it appears there is abias for smaller scaffolds to have a larger GC proportion,we focused our analyses of genomic GC content to those>320 kb, a subset of the genome whose absolute GC is0.430, a value very close to the whole-genome absoluteGC. To generate the distributions of GC content, wedivided up the genomes of human, dog, frog, turkey, zeb-rafinch, chicken, and western painted turtle (scaffolds>320 kb) into 3-kb windows, using the GC content ofthese windows as measures (see Additional file 2, FigureS7). We also examined GC variation at different spatialscales, using non-overlapping windows of 5, 20, 80, and320 kb (Figure 1). As window size quadruples, standarddeviation should decrease by 50% for a completely homo-geneous genome [76]. To determine the relationshipbetween GC3 and flanking sequence, we used 10 kbupstream of the start codon and 10 kb downstream ofthe stop codon as the 20-kb flanking sequence. Onlythose flanking sequence with 80% complete data (allow-ing 20% combined missing data or clipped ends due toproximity of the gene to the ends of the scaffold) wereconsidered. To examine the relationship between genedensity and GC content, we divided up intergenicsequences into 10 equal-sized bins of increasing size andcalculated the GC content of each bin. For the westernpainted turtle, we found a weak but significant correla-tion between the GC content of protein-coding genesand their flanking sequence, indicating that genomicenvironment influences the nucleotide composition ofgenes (see Additional file 2, Figure S8).Multiple alignments and gene orthologsComparative genomic analyses (including studies ofphylogenetic relationships, selection, conserved ele-ments, and accelerated regions) are prone to artifactsderived from biases introduced by differences in geneprediction methods used in draft genome annotations ofindividual genomes included in the study, as well asgene prediction errors. In order to avoid having suchbiases dominate analyses, one can chose a well-anno-tated reference genome (in our case, human or chicken,whichever is more appropriate for a particular analysis),and annotations are remapped from the reference to thetarget genomes through multiple alignment. This step isfollowed by extensive checks to ensure the quality ofderived annotations in target genomes.A disadvantage of this approach is that novel elementsintroduced in non-reference genomes are not covered bythe analysis. In case of human-referenced orthologs, theanalysis only includes genes preserved throughout amnioteevolution (since mammals are the sister groups of theremaining amniotes), while in the case of chicken-refer-enced orthologs, we analyze genome elements preservedduring the evolutionary diversification of turtles and arch-osaurs (see Figure 2). Thus, reference derived orthologsets are best used in analyses requiring conservative high-confidence gene sets, and are not suitable for estimatingtarget genome characteristics, such as numbers of genes,exons, or novel elements.To construct a set of high-confidence orthologs, weused a methodology developed by Kosiol and colleagues[58]. First, we created a multiple alignment of human(hg19), platypus (ornAna1), chicken (galGal3), zebrafinch(ornAna1), anole (anoCar2), turtle, python, and alligator,using a standard UCSC genome browser pipeline [77]based on BLASTZ [67] and multiz [78]. We based ortho-log predictions on the human gene catalog of 21,360genes (including RefSeq, UCSC known genes, ENSEMBL,and VEGA genes), which were remapped to all of theabove species through these multiple alignments. Weobserved high variability for positions of translation startsites and stop codons, thus we also evaluated incompletegene models, where we removed 10% on each end of thegene. Altogether, our gene set contained more than378,000 alternative gene models.Series of filters were run to identify which of these genemodels can be considered high-confidence orthologs. Fora gene model to be considered clean in a particular gen-ome, we required that: (1) it was covered by a singlechain within the syntenic (for platypus and chicken) orreciprocal-best (for zebrafinch, anole, turtle, python, andalligator) net created using the UCSC genome browserpipeline; (2) there were no significant gaps in the genealignments; (3) there were no frameshifts uncorrectedBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 14 of 22within a short window of sequence; and (4) all elementsimportant for the gene structure (donor sites, acceptorsites, translations start sites, and stop codons) were pre-served. For each gene, we selected a single gene modelthat was clean in turtle, giving preference to the modelsthat were clean in the most species and were the longest.The gene was excluded if it did not have any gene modelsatisfying these conditions (see Additional file 1, TableS6, which shows the number of genes filtered out in eachstep.) This approach resulted in 4,786 high-confidenceorthologs, out of which 3,318 are incomplete (shiftedstart codon or stop codon). Out of these genes, 312 cov-ered two species (human and turtle), 622 covered threespecies, 757 covered four species, 896 covered five spe-cies, 1,048 covered six species, 842 covered seven species,and 309 covered all eight species. An additional 12 genesthat were incompletely covered in the reference genomewere detected in the last stages of comparison andremoved in postprocessing.Phylogeny and substitution rateWe estimated phylogeny using the set of 1,955 orthologsthat we identified in at least five of the eight genomes thatwe examined and that had the potential to be informativeabout the phylogenetic position of turtles. We partitionedthe dataset by codon position, using an independent GTRmodel for each position and allowing for gamma-distribu-ted rate variation among sites. We ran four independentanalyses for 10 million generations, sampling every 1,000generations in MrBayes v. 3.1.2 [79]. We then estimatedthe relative rate of substitution in a smaller dataset thatwas designed to minimize missing data. This dataset com-prised 309 orthologs that were identified in all eight spe-cies. We used a UCLN relaxed clock model implementedin BEAST v. 1.7.1 [80]. We partitioned the dataset bycodon position, using independent general time reversiblemodels of DNA substitution allowing gamma distributedrate variation for each position. We set the log normal dis-tribution describing among-branch substitution rate varia-tion to mean 1.0 and standard deviation of 0.33 andestimated relative substitution rates on the topologyshown in Figure 2. We carried out three replicate runs,ensuring convergence and adequate mixing by inspectingsamples from the MCMC in Tracer [81]. Each analysiswas run for 10 million generations and sampled every1,000 generations. Rates varied by a factor of approxi-mately 5, ranging from the lowest relative rate of 0.33 (inturtle) to a high of 1.67 (in python; see Additional file 1,Table S5).Anoxic gene expressionTo better understand the transcriptomic changes thatmight underlie the profound anoxia tolerance of thewestern painted turtle, differential gene expression wasinvestigated in telencephalon and ventricle from westernpainted turtles that were either normoxic or submergedin anoxic water 24 h at 19°C (n = 4 per group, 8 total;mean ± SD 238.6 ± 23 g, range, 198-274 g) using RNA-seq methodology. At the end of the submergence per-iod, the turtles, which appeared sedated due to profoundmetabolic depression, were removed from the chamberand quickly euthanized. The telencephalon was removedfrom the braincase, stripped of any adherent meninges,and flash-frozen in freeze-clamps previously cooled inliquid nitrogen. A 2 × 4 cm window was quickly cut inthe plastron with a bone saw, exposing the still-beatingheart, which was quickly removed, bisected, blotted ongauze to remove any blood, and quickly flash-frozen.Water was considered anoxic when oxygen concentra-tions were undetectable with a submerged oxygen elec-trode (YSI D200) while bubbling the water withnitrogen gas. Frozen tissue samples (22-109 mg) wereground to a fine powder under liquid nitrogen with amortar and pestle and transferred to a dry-ice cooledtest tube with a liquid nitrogen-cooled spatula. Onemilliliter of room-temperature Trizol® reagent (LifeTechnologies) per 50-100 mg tissue was added to thetube, which was immediately vortexed. All subsequentRNA isolation steps were performed according to theTrizol manufacturer’s instructions. The final RNA pelletwas resuspended in DEPC-treated water and treatedwith DNAse I (Life Technologies) according the manu-facturer’s instructions in order to remove any DNA con-tamination. RIN values for the samples were all >7.4(Agilent 2100 Bioanalyzer). cDNA library constructionand sequencing was carried out using previouslydescribed method [11,82].Paired-end 2x100 bp reads generated from poly(A)selected RNA-seq libraries from all 16 samples werealigned to the latest C. p. bellii assembled referencesequence, using TopHat 1.4.0 [83], which also splits readsto align them across known and novel splice junctions.For known splice junction loci, a GTF (Gene Transfer For-mat) file of OPTIC annotations was provided. To estimatetranscript and gene abundances, Cufflinks 1.3.0 [84] wasused. This generates normalized FPKMs (Fragments perkilobase of exon model per million mapped fragments) foreach annotated gene and transcript as defined in theOPTIC based annotations. The Cufflinks parameter -Gwas used to exclude novel isoforms, in order to excludelarge outliers (regions with extraordinarily high read-depths) that causes the Cufflinks normalization method tointroduce a loss of sensitivity. The per-gene FPKMs werelog2 transformed and compared across treatments and tis-sues by ANOVA assuming a normal/Gaussian distribution[85] with FPR multiple testing correction using JMPGenomics 5.1. Genes were excluded from the analysis ifthe median FPKM equaled zero for three out of the fourBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 15 of 22sampling groups. The results of genes showing greaterthan two-fold increases are shown in Additional file 1,Tables S7, S8; down-regulated genes are shown in Addi-tional file 1, Table S9; and RNA-seq read depths for themost highly up and down-regulated genes are shown inAdditional file 2, Figures S10-S12.Freeze toleranceThe Mfold (v.2.3) computer program was used to pre-dict RNA structure [86]. The program predicts second-ary structure based on the energy minimization methodand thermodynamic parameters. We initially searchedthe C. p. bellii assembly (v.3.0.1) for the sequence ofpremiR-29b using BLAST+ (v.2.2.18) [87]. We focus onthis micro-RNA because, in conjunction with ongoingexperiments (Storey, unpublished results), we found thatmiR-29b increases in expression levels for many modelsof metabolic rate depression (hibernating mammals,freeze tolerance, and anoxia tolerance). This is mostlikely due to its proposed role in regulating the PI3K/Akt signaling pathway, a pathway that is commonly dif-ferentially regulated in response to environmental stressand has been shown to control glucose metabolism andtransport, survival (apoptosis), translation processes andcell cycle arrest. This microRNA continually proves tobe a utilized regulatory response to severe environmen-tal stresses. Small RNAs, including miRNAs, were iso-lated using the mirVana miRNA isolation kit fromAmbion Inc. (P/N: 1560) according to the manufac-turer’s protocol. Samples (approximately 100 mg) werehomogenized 1:10 w:v in lysis/binding buffer, left on icefor 10 min and then a mixture of acid phenol:chloroformwas added in a 1:1 ratio. Samples were centrifuged for5 min at 10,000 ×g and the supernatant was collected.Small miRNAs (<200 nt) were isolated using the enrich-ment protocol provided with the kit involving two sequen-tial filtrations through glass-fiber filters at different ethanolconcentrations. RNA concentration was determined byabsorbance at 260 nm and the ratio of absorbance at 260/280 nm was used as an indicator of RNA purity.To determine the expression of mature miR-29b fromC. p. bellii, a modified v miRNA-specific reverse tran-scription and qRT-PCR procedure was performed. A5.0 μL aliquot of small RNA (0.2 ng/μL) was incubatedwith 1μL of 250 nM microRNA-specific stem-loop pri-mer (5’-CTCACAGTACGTTGGTAT CCTTGTGATGTTCGATGCCATATTGTACTGTGAGAACACTGA-3’).The reaction was heated at 95°C for 5 min to denaturethe RNA, and then incubated for 5 min at 60°C to annealthe stem loop primer. After cooling on ice for 1 min, theremaining reagents (4 μL of 5x first strand buffer, 2 μL of0.1 M DTT, 1 μL of dNTP mixture containing 25 mM ofeach nucleotide, and 1 μL of M-MLV reverse transcrip-tase) were added. The reaction proceeded for 30 min at16°C, followed by 30 min at 42°C, and 85°C for 5 min.Following reverse transcription, the RT product wasstored at -20°C. Real-time PCR was performed on aBioRad MyiQ2 Detection System (P/N: 170-9790,BioRad). The 25 μL qRT-PCR reaction included 5 μL RTproduct, 12.5 μL SsoFast EvaGreen Supermix (P/N: 172-5201, BioRad), 0.5 μL of 12.5 μM forward primer (5’-ACACTCCAGCTGGGTAGCACCATTTGAAATC-3’),0.5 μL of 12.5 μM reverse primer (5’-CTCACAG-TACGTTGGTATCCTTGTG-3’), and 6.5 μL nucleasefree water. Reactions were incubated in a 96-well plate at95°C for 3 min, followed by 40 cycles of 95°C for 15 sand 60°C for 1 min. A melting curve analysis was per-formed for each miRNA analyzed. All reactions were runin triplicate.Tooth lossWe initially searched the C. p. bellii scaffolds for individualexons of ENAM, AMEL, AMBN, DSPP, and MMP20 usingBLAST+ (v. 2.2.18) [87]. We used crocodilian sequencesfor AMEL - AF095568, AMBN - AY043290, and ENAM -GU344683.1, MMP20 - DQ885891.1 and humansequences for DSPP - NM_014208 and part of MMP20-NM_004771.3. We were able to identify from one to sev-eral conserved exons from the C. p. bellii pseudogenes,thus providing us with an anchor point for further analy-sis. Subsequently, we utilized UniDPlot, which is a tool forthe detection of poorly conserved DNA regions and waspreviously used to find pseudogenes in chicken [41,88].Finally, we utilized T-coffee to align homologous exonsand manual curation to identify GT-AG exon-intron junc-tions. Identity scores were calculated using LALIGN [89].Gene positions within chromosomally syntenic regionswere analyzed using lizard (A. carolinensis genome assem-bly 2.0), chicken (Gallus gallus genome assembly 4.0), andthe UCSC Human Genome Browser.Aging and longevityWe obtained the individual exon sequences for all fiveshelterin complex encoding genes and for the genes inHAGR, from NCBI. We then searched all C. p. belliisequence and RNA-seq data to identify orthologs of indivi-dual exons of these genes using BLAST+ (v. 2.2.18) [87].We used either chicken or anole (lizard) sequences as thequery sequence. While TEP1, TERF2IP, and ATP5O werecompletely absent from C. p. bellii genome, partial frag-mented forms of the other genes were found. To avoiddraft assembly artifacts, we confirmed our results by carry-ing out similar searches for these genes in all four turtlegenome available to us (See Additional file 1, Table S12).Sex determination/differentiation454 reads (generated by us) from the transcriptomes ofC. p. bellii, Chelydra serpentina, Apalone mutica, andBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 16 of 22Podocnemis expansa were combined and mapped to C.p. bellii assembly 3.0.1 using GMAP [90]. The resultingSAM file was then run through Cufflinks 1.3.0 [91] toobtain a GTF file containing a single list of putativegenes in the western painted turtle genome. This GTFfile was used in CuffLinks as the reference GTF for sub-sequent CuffLinks runs on each 454 dataset. cDNAsequences per tissue and species were extracted using Rand the bioconductor package ShortRead from thisreference GTF file. Each transcriptome 454 dataset wasmapped to the C. p. bellii assembly 3.0.1 using GMAP.DNA coding sequences from the genomes or transcrip-tomes of multiple vertebrates of 34 genes in the sex deter-mination/differentiation network of vertebrates or linkedto sex chromosomes in chicken (see Additional file 1,Table S13) were extracted and aligned using CLUSTALWin Geneious Pro [92] and artificial frameshifts and othererrors were manually corrected. Rates of molecular evolu-tion were evaluated by calculating dN, dS, and dN/dS pergene in MEGA5 [93]. Tests of neutrality, positive and pur-ifying selection were carried out in MEGA5 using thecodon-based Z-test, using the Nei-Gojobori method [94],where the variance of the difference was computed usingthe bootstrap method with 500 replicates. Optimal modelsof DNA evolution were inferred per gene and gene-speci-fic phylogenetic trees were built by maximum likelihoodwith MEGA5, and topologies contrasted among geneswith the species phylogenetic relationships.Immune systemA unique sequence database was generated from Ensembl[95] consisting of approximately 3,000 immune genesfrom human (Homo sapiens, GRCh37), mouse (Mus mus-culus, NCBIM37), rat (Rattus norvegicus, RGSC3.4),chicken (Gallus gallus, WASHUC2), Fugu (Takifugurubripes, FUGU4), Medaka (Oryzias latipes, MEDAKA1),Anole (Anolis carolinensis, AnoCar2.0), Stickleback (Gas-terosteus aculeatus, BROADS1), Turkey (Meleagris gallo-pavo, UMD2), Xenopus (Xenopus tropicalis, JGI_4.2),Tetraodon (Tetraodon nigroviridis, TETRAODON8), Zeb-rafinch (Taeniopygia guttata, taeGut3.2.4), Zebrafish(Danio rerio, Zv9), and Sea Lamprey (Petromyzon marinus,Pmarinus_7.0). Sequences, Ensembl Gene ID, and GeneName were obtained from Ensembl directly or using theBiomart mining utility [96] when available. Pairwise align-ments were obtained using in-house BLAST (BLASTN2.2.15) [97] comparing query immune gene sequences tothe C. p. bellii genome assembly and unassembled sequen-cing reads, gene predictions, and cDNA reads.Identification of gene family expansion/contractionTo identify gene family expansions and contractions, webuilt phylogenetic trees for all predicted genes in C. p.bellii with their orthologs in human, mouse, platypus,chicken, zebrafinch, green anole and using the pufferfishand zebrafish as outgroups.Orthology assignments and orthologous groups weredefined using the OPTIC pipeline [10,98]. Orthologyassignments are based upon the computation of pairwiseorthologs using PhyOP [99] using BLASTP searcheswith an E-value threshold of 10-5 and a minimum sizecut-off equal to 75% of the smaller sequence. The align-ments were weighted according to the normalized bitscore:sij = 1 - ((max[s’ij,s’ji])/min(s’ij,sji).Where s’ij is the bit score for a BLASTP alignmentbetween sequence i and j.A tree-based orthology method implemented withinPhyOP [99] was used to define clusters of orthologousgroups. For each cluster, genes were aligned using MUS-CLE [100], genes with multiple transcripts were col-lapsed into sequences of non-redundant exons andphylogenetic trees were estimated using TreeBeST [101].Rates of non-synonymous substitutions per non-synon-ymous sites (dN) and rates of synonymous substitutionsper synonymous sites (dS) and their ratio (dN/dS) wereestimated for each branch of the tree with PAML [102].Rates were not allowed to vary between sites. Toremove biases associated with poor alignments, trans-lated sequences were masked with SEG [103] and corre-sponding masked codons were removed; poorly alignedcolumns were also removed using Gblocks [104].A total of 20,234 orthologous groups were found, ofwhich 12,938 have at least one gene prediction from C. p.bellii and 1,176 groups contain at least two C. p. belliigene models. All orthology/paralogy predictions areavailable at [105]. We identified a total of 4,828 geneswith one-to-one orthologous relationship between allamniotes, and 3,222 between all species when pufferfishand zebrafish are included. A total of 604 predicted genemodels in C. p. bellii had no predicted orthologs; theseinclude rapidly-evolving genes as well as problematicgene models that survived our filters. We also identified568 groups with genes in human, mouse, platypus,chicken, zebrafinch, and green anole but that have nodetectable orthologs in the current version of the C. p.bellii genome assembly. These currently absent geneswill contain genes absent from the current assembly, aswell as rapidly-evolving genes.In order to reach a conservative estimate of the num-ber of genes within a family and to remove any residualbiases associated with the assembly process, we esti-mated the pairwise amino acid identity between everypair of members of a family and rejected duplicatedgenes that are more than 97% identical. A summary ofgene expansions is presented in Figure 6.Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 17 of 22Beta-keratin expansionsBeta keratins have previously been described to be animportant component of the corneous layers of the repti-lian epidermis forming the scales, claws, and beak. Inbirds, they are the major component of feathers [106]. Weidentified a total of 106 gene models (101 complete) inC. p. bellii that share significant sequence similarity withavian and green anole beta-keratins. Using beta-keratinmRNAs extracted from the precursor cells of the shell ofPseudemys nelsoni [56], and the phylogeny built withPhyML [107], we identified 41 and 60 putative non-shelland shell proteins in C. p. bellii, respectively.Expansion of gene families involved in the immuneresponseAmong the families with the largest expansions (≥4members), 15 are related to the innate or adaptiveimmune response.As part of the adaptive immune response, we identified365, 131, and 94 predicted gene models in C. p. bellii thatcluster with the immunoglobulin heavy chain, lambda, andkappa chain variable regions respectively in mouse. Thelarge number of genes from these two families is of primeimportance in the generation of antibody diversity throughV(D)J recombination. Both the immunoglobulin heavy andlight chain variable regions are known to be among themost dynamic gene regions in the human genome, andimmunoglobulin genes are known to show high allelic andcopy number variation [108,109]. Interestingly the imuno-globulin kappa chains have been lost in the bird genomes[110]. These authors predicted this loss to predatethe divergence between Passeriformes and Galliformes(100 Mya). In agreement with this, our analysis shows thatthe immunoglobulin kappa chains were present in thecommon ancestor of the birds and turtles approximately260 million years ago.We also identified expansions of several gene familiesthat form part of the innate immune system. These geneproducts are expressed on the surface of natural killer(NK) cells (NK cells’ C-type lectin-like and NK cells’immunoglobulin-like receptors) or are secreted by theseNK cells (for example, granzymes). NK receptors pre-viously shown to belong to the LCR in human, mouse andchicken are known to have undergone lineage-specificexpansion in each of these lineages [111-114]. Wesearched the C. p. bellii polypeptide predictions belongingto these two families for transmembrane domains [115]and found that only six of 27 putative NK cells’ C-type lec-tin-like and 14 of 35 putative NK cells’ immunoglobulin-like receptors possess transmembrane domains.Ortholog setsWe based our study of positive selection on the set ofcarefully screened orthologs for eight vertebrate species(human, platypus, chicken, zebrafinch, anole, turtle,python, and alligator; see Materials and Methods, Multi-ple Alignments and Gene Orthologs). From among 4,786high-confidence ortholog sets, each covering betweentwo and eight species, we selected 4,136 sets that cov-ered human, turtle, and at least one of the outgroupgenomes (chicken, alligator, zebrafinch).Positive selectionWe detected signs of positive selection using likelihoodratio tests [57] with reduced parameterization [58].P values were estimated assuming a null distributionthat is a 50:50 mixture of c2 distribution with onedegree of freedom, and a point mass at zero, leading toconservative P value estimates [116]. The branch leadingto the turtle was designated as a forward branch, withsome sites allowing dN/dS>1, while all other brancheswere background branches, disallowing positive selec-tion. The results were corrected for multiple testingusing Benjamini and Hochberg false discovery rate con-trol (FDR). Accessory Data File 1 shows the results oflikelihood-ratio tests for all genes with nominal P values<0.05 (890 genes), indicating genes with FDR <0.1 (671genes).We also examined GO functional categories forenrichment for genes under positive selection, usingMann-Whitney U-test with Holm’s correction for multi-ple testing [117]. No functional categories were statisti-cally significantly enriched for genes under positiveselection after multiple testing correction. AccessoryData File 1 shows 171 GO categories with nominalP values <0.05.Author informationThe Chrysemys picta bellii whole-genome shotgun projecthas been deposited in NCBI GenBank under the projectaccession AHGY00000000. The raw input data for Chrys-emys picta bellii (BioProject ID: 78657) was deposited tothe trace archive and the SRA under the project accessionSRP012057. The Apalone spinifera whole genome datacan be found at the NCBI SRA under the accession num-bers SRX217616-7. Specimen collection for the C. pictabellii was authorized by the Washington Department ofFish and Wildlife under scientific collecting permit 08-086(to RCT) and complied with IACUC standards at UCDavis (HBS protocol holder). Ethical (IACUC) approvalsfor all experiments involving living turtles were obtainedat the university where the experiment or field work wereconducted.Description of additional filesWe provide three Additional Files. Two Additional Filesare in Microsoft word (.docx). Additional file 1 containsTables S1-S17), and Additional file 2 contains FiguresBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 18 of 22S1-S12). We also provide Additional file 3 in support ofthe selection scan analysis in Microsoft Excel format. Init, the sheet labeled mwu_turtle_go details enrichmentof GO categories for positive selection on the westernpainted turtle lineage with nominal P values < 0.05(Mann-Whitney U-test). No categories were significantlyenriched for positive selection after application of multi-ple testing correction. The second sheet, labeled lrt_tur-tle, shows genes under positive selection on the westernpainted turtle lineage. All genes with nominal P values<0.05 (likelihood ratio branch-site test) are shown, andthe 671 genes that were statistically significant afterapplying multiple testing correction (FDR <0.1) are alsonoted.Additional materialAdditional file 1: Supplementary tables. Tables S1-S17 containadditional information in support of the painted turtle assembly (TablesS1-S2), transposable elements (Table S3), isochores (Table S4), phylogenyand evolutionary rates (Tables S5-S6), anoxia (Tables S7-S9), tooth loss(Tables S10-S11), longevity (Table S12), sex determination (Table S13),immune function (Tables S14-S15), and gene family expansions (TablesS16-S17).Additional file 2: Supplementary figures. Figures S1-S12 containadditional information in support of the painted turtle repeat analyses(Figures S1-S6), isochores (Figures S7-S9), and anoxia tolerance (FiguresS10-12).Additional file 3: Selection scan analysis. The sheet labeledmwu_turtle_go provides additional information on the enrichment ofGO categories for positive selection on the western painted turtle, whilethe sheet labeled lrt_turtle lists all genes under positive selection on thewestern painted turtle lineage. All genes with nominal P values <0.05(likelihood ratio branch-site test) are shown, and the 671 genes thatwere statistically significant after applying multiple testing correction(FDR <0.1) are also noted.Authors’ contributionsHBS, PM, AMS, RCT, and NV comprise the organizing committee of thewestern painted turtle genome sequencing project. Manuscript organizationand editing: HBS, PQS, RCT, WCW, CPP, WH, and PM. BAC libraryconstruction: CTA. Project management and data production: LF, KDD, CCF,MO, and TAG. Assembly and analysis: PM, NT, and LWH. RNA-seq and anoxiaanalysis: DEW, CK, and LTB. Freeze tolerance: KBS and KKB. Gene modelpredictions, orthology prediction, and analysis: CPP, WH, LK, and YL. Repeatelement and isochore analysis: AMS, CWB, and MKF. Comparativealignments: BJR. Phylogenetic analyses: RCT, TV, TAC, DDP, APJK, REG, JSt.J,and ELB. Gene analyses, sex chromosome genes: FJJ, SEM, AMB, TS, AS, NV,RL, DB, SR, DJ, SVE, BC, MC, OH, and LM. Gene analyses, aging: RH. Geneanalyses, immuno-response: RMB, GMB, LMZ, and RTP. Gene analyses,enamel: JA and JMR. Turtle accelerated regions: AKH and BGB. Principalinvestigators: ERM, WCW, and RKW. All authors read and approved the finalmanuscript.Competing interestsThe authors declare that they have no competing interests.AcknowledgementsThe sequencing of the western painted turtle genome was funded by theNational Human Genome Research Institute (NHGRI). Further researchsupport included grants to HBS (NSF DEB 0817042), NV (NSF IOS 0743284),NV & SVE (MCB 0815354), FJJ and AS (NSF DEB 0640932), KBS and LTB(NSERC), TV (FP7 IRG-224885, VEGA 1/1085/12), and SVE, CTA, and JRM (NSFIOS 0207870/0431717). Resources for exploring the sequence and annotationdata are available on browser displays available at UCSC [118], Ensembl[119], NCBI [120], and MRC [105]. We thank Andrew Severin (Iowa StateGenome Informatics Facility) for help with data analyses, Erik Larson (IllinoisState) for contributing Glen Borchert’s time to the project, The GenomeInstitute members and Michael Montague for manuscript review, LouiseWhitaker (UCLA) for bibliographic assistance, and Bronwen Aken (WellcomeTrust Sanger Institute) for displaying the genome annotation on ENSEMBL.During the development of this project we received input from Naoki Irie(RIKEN) and Guojie Zhang (BGI). We also thank the following members ofThe Genome Institute: Michael Montague for manuscript review, ChadTomlinson for assembling cDNAs, Bob Fulton and Aye Wollam for finishingBACs used in assembly quality assessment.Author details1Department of Ecology and Evolutionary Biology, University of California,Los Angeles, Los Angeles, CA 90095-1606, USA. 2La Kretz Center forCalifornia Conservation Science, Institute of the Environment andSustainability, University of California, Los Angeles, Los Angeles, CA 90095-1496, USA. 3The Genome Institute, Washington University School ofMedicine, Campus Box 8501, 4444 Forest Park Avenue, St Louis, MO 63108,USA. 4Department of Biology, Saint Louis University, St Louis, MO 63103,USA. 5College of Charleston Biology Department and Grice MarineLaboratory, Charleston, SC 29424, USA. 6Medical University of South CarolinaCollege of Graduate Studies and Center for Marine Biomedicine andEnvironmental Sciences, Charleston, SC 29412, USA. 7Department of Biology,University of Hawaii at Manoa, Honolulu, HI 96822, USA. 8Department ofEcology, Evolution, and Organismal Biology, Iowa State University, Ames, IA50011, USA. 9Faculty of Dentistry, Life Sciences Institute University of BritishColumbia, Vancouver BC, Canada. 10Benaroya Research Institute at VirginiaMason, Seattle, WA 98101 USA. 11Department of Biology and Institute ofBiochemistry, Carleton University, Ottawa, ON, Canada K1S 5B6, Canada.12School of Biological Sciences, Illinois State University, Normal, IL 61790,USA. 13Department of Biological Sciences, Life Sciences Building, Universityof South Alabama, Mobile, AL 36688-0002, USA. 14Research Computing,Harvard Medical School, Boston, MA 02115, USA. 15Department of Biology,University of Florida, Gainesville, FL 32611 USA. 16Gladstone Institute ofCardiovascular Disease, San Francisco, CA 94158, USA. 17CardiovascularResearch Institute and Department of Pediatrics, University of California, SanFrancisco, San Francisco, CA 94158, USA. 18Department of Cell and SystemsBiology, University of Toronto, Toronto, ON, Canada M5S 3G5, Canada.19Department of Cell Biology, Duke University Medical Center, Durham, NC27710, USA. 20Department of Biochemistry and Molecular Genetics,University of Colorado School of Medicine, Aurora, CO 80045, USA.21Department of Biology, University of Texas at Arlington, Arlington, TX76019, USA. 22Department of Organismic and Evolutionary Biology, HarvardUniversity, Cambridge, MA 02138, USA. 23Museum of Comparative Zoologyand Department of Organismic and Evolutionary Biology, Harvard University,Cambridge, MA 02138, USA. 24Baskin School of Engineering University ofCalifornia, Santa Cruz Santa Cruz, CA 95064, USA. 25MRC FunctionalGenomics Unit, Department of Physiology, Anatomy and Genetics, HenryWellcome Building of Gene Function, University of Oxford, Oxford, OX13PT,UK. 26Cancer Research Program, Rajiv Gandhi Centre for Biotechnology,Poojapura, Thycaud P.O, Thiruvananthapuram, Kerala 695014, India. 27FUDECI,Fundación para el Desarrollo de las Ciencias Físicas, Matemáticas y Naturales.Av, Universidad, Bolsa a San Francisco, Palacio de Las Academias, Caracas,Venezuela. 28Biology Department, Duke University, Durham, NC 27708, US.29Bioinformatics and Computational Biology Laboratory, Iowa StateUniversity, Ames, IA 50011, USA. 30Center for Biomolecular Science andEngineering, School of Engineering, University of California Santa Cruz(UCSC), Santa Cruz, CA 95064, USA. 31Faculty of Mathematics, Physics andInformatics, Comenius University, Mlynska Dolina, Bratislava 84248, Slovakia.Received: 18 October 2012 Revised: 15 March 2013Accepted: 28 March 2013 Published: 28 March 2013References1. Romer AS: Vertebrate paleontology. 3 edition. Chicago, IL: University ofChicago Press; 1967.Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 19 of 222. Li C, Wu X-C, Rieppel O, Wang L-T, Zhao L-J: An ancestral turtle from theLate Triassic of southwestern China. Nature 2008, 456:497-501.3. Gaffney ES, Jenkins FA: The cranial morphology of Kayentachelys, an EarlyJurassic cryptodire, and the early history of turtles. Acta Zoologica 2010,91:335-368.4. Ultsch GR, Jackson DC: Long-term submergence at 3-degrees-C of theturtle, Chrysemys picta bellii, in normoxic and severely hypoxic water. 1.Survival, gas-exchange and acid-base status. J Exp Biol 1982, 96:11-28.5. Johlin JM, Moreland FB: Studies of the blood picture of the turtle aftercomplete anoxia. J Biol Chem 1933, 103:107-114.6. Storey KB, Storey JM, Brooks SPJ, Churchill TA, Brooks RJ: Hatchling turtlessurvive freezing during winter hibernation. Proc Natl Acad Sci USA 1988,85:8350-8354.7. Keenan NL, Shaw KM: Coronary heart disease and stroke deaths - UnitedStates, 2006. Morb Mortal Weekly Rep 2011, 60:62-66.8. Smit AFA, Hubley R, Green P: RepeatMasker Open-3.0 1996.9. Alföldi J, Di Palma F, Grabherr M, Williams C, Kong L, Mauceli E, Russell P,Lowe CB, Glor RE, Jaffe JD, Ray DA, Boissinot S, Shedlock AM, Botka C,Castoe TA, Colbourne JK, Fujita MK, Moreno RG, ten Hallers BF, Haussler D,Heger A, Heiman D, Janes DE, Johnson J, de Jong PJ, Koriabine MY, Lara M,Novick PA, Organ CL, Peach SE, et al: The genome of the green anolelizard and a comparative analysis with birds and mammals. Nature 2011,477:587-591.10. Heger A, Ponting CP: Evolutionary rate analyses of orthologs andparalogs from 12 Drosophila genomes. Genome Res 2007, 17:1837-1849.11. Warren WC, Clayton DF, Ellegren H, Arnold AP, Hillier LW, Kunstner A, Searle S,White S, Vilella AJ, Fairley S, Heger A, Kong LS, Ponting CP, Jarvis ED, Mello CV,Minx P, Lovell P, Velho TAF, Ferris M, Balakrishnan CN, Sinha S, Blatti C,London SE, Li Y, Lin YC, George J, Sweedler J, Southey B, Gunaratne P,Watson M, et al: The genome of a songbird. Nature 2010, 464:757-762.12. Gilbert C, Hernandez SS, Flores-Benabib J, Smith EN, Feschotte C: Rampanthorizontal transfer of SPIN transposons in squamate reptiles. Mol BiolEvol 2012, 29:503-515.13. Shedlock AM, Botka CW, Zhao SY, Shetty J, Zhang TT, Liu JS, Deschavanne PJ,Edward SV: Phylogenomics of nonavian reptiles and the structure of theancestral amniote genorne. Proc Natl Acad Sci USA 2007, 104:2767-2772.14. Feschotte C: Opinion - Transposable elements and the evolution ofregulatory networks. Nat Rev Genet 2008, 9:397-405.15. Janes DE, Organ CL, Fujita MK, Shedlock AM, Edwards SV: GenomeEvolution in Reptilia, the Sister Group of Mammals. Annual Review ofGenomics and Human Genetics , Chakravarti A, Green E 2010, 11:239-264.16. Kazazian HH: Mobile elements: Drivers of genome evolution. Science 2004,303:1626-1632.17. Shedlock AM: Phylogenomic investigation of CR1 LINE diversity inreptiles. Syst Biol 2006, 55:902-911.18. Fujita MK, Edwards SV, Ponting CP: The Anolis lizard genome: an amniotegenome without isochores. Genome Biol Evol 2011, 3:974-984.19. Galtier N, Gouy M: Inferring pattern and process: Maximum-likelihoodimplementation of a nonhomogeneous model of DNA sequenceevolution for phylogenetic analysis. Mol Biol Evol 1998, 15:871-879.20. Crawford NG, Faircloth BC, McCormack JE, Brumfield RT, Winker K, Glenn TC:More than 1000 ultraconserved elements provide evidence that turtlesare the sister group of archosaurs. Biol Lett 2012, 8:783-786.21. Lyson TR, Sperling EA, Heimberg AM, Gauthier JA, King BL, Peterson KJ:MicroRNAs support a turtle plus lizard clade. Biol Lett 2012, 8:104-107.22. Chiari Y, Cahais V, Galtier N, Delsuc F: Phylogenomic analyses support theposition of turtles as the sister group of birds and crocodiles(Archosauria). BMC Biol 2012, 10:65.23. Bromham L: Molecular clocks in reptiles: Life history influences rate ofmolecular evolution. Mol Biol Evol 2002, 19:302-309.24. Sayres MAW, Venditti C, Pagel M, Makova KD: Do variations in substitutionrates and male mutation bias correlate with life-history traits? A study of32 mammalian genomes. Evolution 2011, 65:2800-2815.25. Regard JB, Scheek S, Borbiev T, Lanahan AA, Schneider A, Demetriades AM,Hiemisch H, Barnes CA, Verin AD, Worley PF: Verge: A novel vascular earlyresponse gene. J Neurosci 2004, 24:4092-4103.26. van Dam H, Castellazzi M: Distinct roles of Jun: Fos and Jun: ATF dimersin oncogenesis. Oncogene 2001, 20:2453-2464.27. Passegue E, Wagner EF: JunB suppresses cell proliferation bytranscriptional activation of p16(INK4a) expression. EMBO J 2000,19:2969-2979.28. Winkler GS: The mammalian anti-proliferative BTG/Tob protein family. JCell Physiol 2010, 222:66-72.29. Levin WJ, Press MF, Gaynor RB, Sukhatme VP, Boone TC, Reissmann PT,Figlin RA, Holmes EC, Souza LM, Slamon DJ: Expression patterns ofimmediate-early transcription factors in human nonsmall cell lung-cancer. Oncogene 1995, 11:1261-1269.30. Blackstone E, Morrison M, Roth MB: H2S induces a suspended animation-like state in mice. Science 2005, 308:518.31. Ueki I, Roman HB, Valli A, Fieselmann K, Lam J, Peters R, Hirschberger LL,Stipanuk MH: Knockout of the murine cysteine dioxygenase gene resultsin severe impairment in ability to synthesize taurine and an increasedcatabolism of cysteine to hydrogen sulfide. Am J Physiol Endocrinol Metab2011, 301:E668-E684.32. Long JC, Caceres JF: The SR protein family of splicing factors: masterregulators of gene expression. Biochem J 2009, 417:15-27.33. Wheater MJ, Johnson PW, Blaydes JP: The role of MNK proteins and eIF4Ephosphorylation in breast cancer cell proliferation and survival. CancerBiol Ther 2010, 10:728-735.34. Storey KB: Reptile freeze tolerance: Metabolism and gene expression.Cryobiology 2006, 52:1-16.35. Biggar KK, Storey KB: The emerging roles of microRNAs in the molecularresponses of metabolic rate depression. J Mol Cell Biol 2011, 3:167-175.36. Garzon R, Liu SJ, Fabbri M, Liu ZF, Heaphy CEA, Callegari E, Schwind S,Pang JX, Yu JH, Muthusamy N, Havelange V, Volinia S, Blum W, Rush LJ,Perrotti D, Andreeff M, Bloomfield CD, Byrd JC, Chan K, Wu LC, Croce CM,Marcucci G: MicroRNA-29b induces global DNA hypomethylation andtumor suppressor gene reexpression in acute myeloid leukemia bytargeting directly DNMT3A and 3B and indirectly DNMT1. Blood 2009,113:6411-6418.37. He AB, Zhu LB, Gupta N, Chang YS, Fang F: Overexpression of microribonucleic acid 29, highly up-regulated in diabetic rats, leads to insulinresistance in 3T3-L1 adipocytes. Mol Endocrinol 2007, 21:2785-2794.38. Zhang XX, Zeng Y: The terminal loop region controls microRNAprocessing by Drosha and Dicer. Nucleic Acids Res 2010, 38:7689-7697.39. Davit-Beal T, Tucker AS, Sire JY: Loss of teeth and enamel in tetrapods:fossil record, genetic data and morphological adaptations. J Anat 2009,214:477-501.40. Meredith RW, Gatesy J, Cheng J, Springer MS: Pseudogenization of thetooth gene enamelysin (MMP20) in the common ancestor of extantbaleen whales. Proc Biol Sci 2011, 278:993-1002.41. Sire JY, Delgado SC, Girondot M: Hen’s teeth with enamel cap: fromdream to impossibility. BMC Evol Biol 2008, 8:246.42. Kim EB, Fang XD, Fushan AA, Huang ZY, Lobanov AV, Han LJ, Marino SM,Sun XQ, Turanov AA, Yang PC, Yim SH, Zhao X, Kasaikina MV, Stoletzki N,Peng CF, Polak P, Xiong ZQ, Kiezun A, Zhu YB, Chen YX, Kryukov GV,Zhang Q, Peshkin L, Yang L, Bronson RT, Buffenstein R, Wang B, Han CL,Li QY, Chen L, et al: Genome sequencing reveals insights into physiologyand longevity of the naked mole rat. Nature 2011, 479:223-227.43. de Magalhaes JP, Costa J, Toussaint O: HAGR: the human ageing genomicresources. Nucleic Acids Res 2005, 33:D537-D543.44. Dillin A, Hsu AL, Arantes-Oliveira NA, Lehrer-Graiwer J, Hsin H, Fraser AG,Kamath RS, Ahringer J, Kenyon C: Rates of behavior and aging specifiedby mitochondrial function during development. Science 2002,298:2398-2401.45. Undie AS, Wang HY, Friedman E: Decreased phospholipase C-betaimmunoreactivity, phosphoinositide metabolism, and protein-kinase-Cactivation in senescent F344 rat-brain. Neurobiol Aging 1995, 16:19-28.46. Janzen FJ, Krenz JG: Phylogenetics: which was first, TSD of GSD?.Temperature-Dependent Sex Determination in Vertebrates. Valenzuela N, LanceVA edition. Smithsonian Books; 2004, 121-130.47. Bachtrog D, Kirkpatrick M, Mank JE, McDaniel SF, Pires JC, Rice WR,Valenzuela N: Are all sex chromosomes created equal?. Trends Genet 2011,27:350-357.48. Valenzuela N, Neuwald JL, Literman R: Transcriptional evolutionunderlying vertebrate sexual development. Dev Dyn 2013, 242:307-319.49. Valenzuela N: Multivariate expression analysis of the gene networkunderlying sexual development in turtle embryos with temperature-dependent and genotypic sex determination. Sex Dev 2010, 4:39-49.50. Barley AJ, Spinks PQ, Thomson RC, Shaffer HB: Fourteen nuclear genesprovide phylogenetic resolution for difficult nodes in the turtle tree oflife. Mol Phylogen Evol 2010, 55:1189-1194.Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 20 of 2251. Krenz JG, Naylor GJP, Shaffer HB, Janzen FJ: Molecular phylogenetics andevolution of turtles. Mol Phylogen Evol 2005, 37:178-191.52. Shaffer HB, Meylan P, McKnight ML: Tests of turtle phylogeny: Molecular,morphological, and paleontological approaches. Syst Biol 1997,46:235-268.53. Grey HM: Phylogeny of immune response-studies on some physicalchemical serologic characteristics of antibody produced in turtle. JImmunol 1963, 91:819-825.54. Zimmerman LM, Vogel LA, Bowden RM: Understanding the vertebrateimmune system: insights from the reptilian perspective. J Exp Biol 2010,213:661-671.55. Brownlie R, Allan B: Avian toll-like receptors. Cell Tissue Res 2011,343:121-130.56. Dalla Valle L, Nardi A, Toni M, Emera D, Alibardi L: Beta-keratins of turtleshell are glycine-proline-tyrosine rich proteins similar to those ofcrocodilians and birds. J Anat 2009, 214:284-300.57. Yang ZH, Nielsen R: Codon-substitution models for detecting molecularadaptation at individual sites along specific lineages. Mol Biol Evol 2002,19:908-917.58. Kosiol C, Vinar T, da Fonseca RR, Hubisz MJ, Bustamante CD, Nielsen R,Siepel A: Patterns of positive selection in six mammalian genomes. PLoSGenet 2008, 4:e1000144.59. Kohroki J, Nishiyama T, Nakamura T, Masuho Y: ASB proteins interact withCullin5 and Rbx2 to form E3 ubiquitin ligase complexes. FEBS Lett 2005,579:6796-6802.60. Murdoch C, Finn A: Chemokine receptors and their role in inflammationand infectious diseases. Blood 2000, 95:3032-3043.61. Kopp A: Dmrt genes in the development and evolution of sexualdimorphism. Trends Genet 2012, 28:175-184.62. Schwartz TS, Bronikowski AM: Molecular stress pathways and theevolution of reproduction and aging in reptiles. Molecular Mechanisms ofLife History Evolution. Flatt T, Heyland A edition. Oxford: Oxord UniversityPress; 2011, 193-209.63. Schneider A, Souvorov A, Sabath N, Landan G, Gonnet GH, Graur D:Estimates of positive Darwinian selection are inflated by errors insequencing, annotation, and alignment. Genome Biol Evol 2009, 1:114-118.64. Turtle Taxonomy Working Group: Turtles of the World, 2012 Update:annotated checklist of taxonomy, synonymy, distribution, andconservation Status. Chelonian Research Monographs, No 5. van Dijk PP,Iverson JB, Shaffer HB, Bour R, Rhodin AGJ edition. Lunenburg, MA:Chelonian Research Foundation; 2012.65. Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009, 25:1754-1760.66. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G,Abecasis G, Durbin R, Genome Project Data P: The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009, 25:2078-2079.67. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D,Miller W: Human-mouse alignments with BLASTZ. Genome Res 2003,13:103-107.68. Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D: Evolution’s cauldron:Duplication, deletion, and rearrangement in the mouse and humangenomes. Proc Natl Acad Sci USA 2003, 100:11484-11489.69. Ewing B, Hillier L, Wendl MC, Green P: Base-calling of automatedsequencer traces using phred. I. Accuracy assessment. Genome Res 1998,8:175-185.70. Kent WJ: BLAT - The BLAST-like alignment tool. Genome Res 2002,12:656-664.71. Shin H, Hirst M, Bainbridge MN, Magrini V, Mardis E, Moerman DG,Marra MA, Baillie DL, Jones SJM: Transcriptome analysis for Caenorhabditiselegans based on novel expressed sequence tags. BMC Biol 2008, 6:30.72. Hillier LW, Miller W, Birney E, Warren W, Hardison RC, Ponting CP, Bork P,Burt DW, Groenen MAM, Delany ME, Dodgson JB, Chinwalla AT, Cliften PF,Clifton SW, Delehaunty KD, Fronick C, Fulton RS, Graves TA, Kremitzki C,Layman D, Magrini V, McPherson JD, Miner TL, Minx P, Nash WE, Nhan MN,Nelson JO, Oddy LG, Pohl CS, Randall-Maher J, et al: Sequence andcomparative analysis of the chicken genome provide uniqueperspectives on vertebrate evolution. Nature 2004, 432:695-716.73. Benson G: Tandem repeats finder: a program to analyze DNA sequences.Nucleic Acids Res 1999, 27:573-580.74. Mayer C: Phobos 3.3.11. 2006.75. [https://github.com/hmsrc/RMPipeline].76. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K,Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A,Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K,Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M,Santos R, Sheridan A, Sougnez C, et al: Initial sequencing and analysis ofthe human genome. Nature 2001, 409:860-921.77. Dreszer TR, Karolchik D, Zweig AS, Hinrichs AS, Raney BJ, Kuhn RM,Meyer LR, Wong M, Sloan CA, Rosenbloom KR, Roe G, Rhead B, Pohl A,Malladi VS, Li CH, Learned K, Kirkup V, Hsu F, Harte RA, Guruvadoo L,Goldman M, Giardine BM, Fujita PA, Diekhans M, Cline MS, Clawson H,Barber GP, Haussler D, Kent WJ: The UCSC Genome Browser database:extensions and updates 2011. Nucleic Acids Res 2012, 40:D918-D923.78. Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AFA, Roskin KM,Baertsch R, Rosenbloom K, Clawson H, Green ED, Haussler D, Miller W:Aligning multiple genomic sequences with the threaded blocksetaligner. Genome Res 2004, 14:708-715.79. Ronquist F, Huelsenbeck JP: MrBayes 3: Bayesian phylogenetic inferenceunder mixed models. Bioinformatics 2003, 19:1572-1574.80. Drummond AJ, Rambaut A: BEAST: Bayesian evolutionary analysis bysampling trees. BMC Evol Biol 2007, 7:214.81. Rambaut A, Drummond AJ: Tracer v1.4. 2007 [http://beast.bio.ed.ac.uk/Tracer].82. Parkhomchuk D, Borodina T, Amstislavskiy V, Banaru M, Hallen L,Krobitsch S, Lehrach H, Soldatov A: Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res 2009, 37:e123.83. Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctionswith RNA-Seq. Bioinformatics 2009, 25:1105-1111.84. Roberts A, Pimentel H, Trapnell C, Pachter L: Identification of noveltranscripts in annotated genomes using RNA-Seq. Bioinformatics 2011,27:2325-2329.85. Lu C, King RD: An investigation into the population abundancedistribution of mRNAs, proteins, and metabolites in biological systems.Bioinformatics 2009, 25:2020-2027.86. Zuker M: Mfold web server for nucleic acid folding and hybridizationprediction. Nucleic Acids Res 2003, 31:3406-3415.87. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignmentsearch tool. J Mol Biol 1990, 215:403-410.88. Girondot M, Sire JY: UniDPlot: A software to detect weak similaritiesbetween two DNA sequences. J Bioinformatics Sequence Anal 2010,2:69-74.89. Huang XQ, Miller W: A time-efficient, linear-space local similarityalgorithm. Adv Appl Math 1991, 12:337-357.90. Wu TD, Watanabe CK: GMAP: a genomic mapping and alignmentprogram for mRNA and EST sequences. Bioinformatics 2005, 21:1859-1875.91. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ,Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification byRNA-Seq reveals unannotated transcripts and isoform switching duringcell differentiation. Nat Biotechnol 2010, 28:511-U174.92. Biomatters: Geneious Pro.[http://www.geneious.com/].93. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5:Molecular evolutionary genetics analysis using maximum likelihood,evolutionary distance, and maximum parsimony methods. Mol Biol Evol2011, 28:2731-2739.94. Nei M, Gojobori T: Simple methods for estimating the number ofsynonymous and nonsynonymous nucleotide substitutions. Mol Biol Evol1986, 3:418-426.95. Birney E, Andrews D, Caccamo M, Chen Y, Clarke L, Coates G, Cox T,Cunningham F, Curwen V, Cutts T, Down T, Durbin R, Fernandez-Suarez XM,Flicek P, Graf S, Hammond M, Herrero J, Howe K, Iyer V, Jekosch K, Kahari A,Kasprzyk A, Keefe D, Kokocinski F, Kulesha E, London D, Longden I,Melsopp C, Meidl P, Overduin B, et al: Ensembl 2006. Nucleic Acids Res2006, 34:D556-D561.96. Haider S, Ballester B, Smedley D, Zhang JJ, Rice P, Kasprzyk A: BioMartCentral Portal-unified access to biological data. Nucleic Acids Res 2009, 37:W23-W27.97. Altschul SF, Madden TL, Schaffer AA, Zhang JH, Zhang Z, Miller W,Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of proteindatabase search programs. Nucleic Acids Res 1997, 25:3389-3402.98. Heger A, Ponting CP: OPTIC: orthologous and paralogous transcripts inclades. Nucleic Acids Res 2008, 36:D267-D270.Bradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 21 of 2299. Goodstadt L, Ponting CP: Phylogenetic reconstruction of orthology,paralogy, and conserved synteny for dog and human. PLoS Comp Biol2006, 2:1134-1150.100. Edgar RC: MUSCLE: multiple sequence alignment with high accuracy andhigh throughput. Nucleic Acids Res 2004, 32:1792-1797.101. Vilella AJ, Severin J, Ureta-Vidal A, Heng L, Durbin R, Birney E:EnsemblCompara GeneTrees: Complete, duplication-aware phylogenetictrees in vertebrates. Genome Res 2009, 19:327-335.102. Yang ZH: PAML 4: Phylogenetic analysis by maximum likelihood. Mol BiolEvol 2007, 24:1586-1591.103. Wootton JC, Federhen S: Analysis of compositionally biased regions insequence databases. Computer Methods for Macromolecular SequenceAnalysis 1996, 266:554-571.104. Castresana J: Selection of conserved blocks from multiple alignments fortheir use in phylogenetic analysis. Mol Biol Evol 2000, 17:540-552.105. [http://genserv.anat.ox.ac.uk/clades/vertebrates_cpicta2/].106. Fraser RDB, Parry DAD: Molecular packing in the feather keratin filament.J Struct Biol 2008, 162:1-13.107. Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O: Newalgorithms and methods to estimate maximum-likelihood phylogenies:assessing the performance of PhyML 3.0. Syst Biol 2010, 59:307-321.108. Matsuda F, Ishii K, Bourvagnet P, Kuma K, Hayashida H, Miyata T, Honjo T:The complete nucleotide sequence of the human immunoglobulinheavy chain variable region locus. J Exp Med 1998, 188:2151-2162.109. Watson CT, Breden F: The immunoglobulin heavy chain locus: geneticvariation, missing data, and implications for human disease. GenesImmun .110. Das S, Mohamedy U, Hirano M, Nei M, Nikolaidis N: Analysis of theimmunoglobulin light chain genes in Zebra Finch: evolutionaryimplications. Mol Biol Evol 2010, 27:113-120.111. Barten R, Torkar M, Haude A, Trowsdale J, Wilson MJ: Divergent andconvergent evolution of NK-cell receptors. Trends Immunol 2001, 22:52-57.112. Laun K, Coggill P, Palmer S, Sims S, Ning ZM, Ragoussis J, Volpi E, Wilson N,Beck S, Ziegler A, Volz A: The leukocyte receptor complex in chicken ischaracterized by massive expansion and diversification ofimmunoglobulin-like loci. PLoS Genet 2006, 2:707-718.113. Nikolaidis N, Makalowska I, Chalkia D, Makalowski W, Klein J, Nei M: Originand evolution of the chicken leukocyte receptor complex. Proc Natl AcadSci USA 2005, 102:4057-4062.114. Viertlboeck BC, Gick CM, Schmitt R, Du Pasquier L, Gobel TW: Complexityof expressed CHIR genes. Dev Comp Immunol 2010, 34:866-873.115. Krogh A, Larsson B, von Heijne G, Sonnhammer ELL: Predictingtransmembrane protein topology with a hidden Markov model:Application to complete genomes. J Mol Biol 2001, 305:567-580.116. Zhang JZ, Nielsen R, Yang ZH: Evaluation of an improved branch-sitelikelihood method for detecting positive selection at the molecularlevel. Mol Biol Evol 2005, 22:2472-2479.117. Holm S: A simple sequentially rejective multiple test procedure. Scand JStat 1979, 6:65-70.118. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM,Haussler D: The human genome browser at UCSC. Genome Res 2002,12:996-1006.119. [http://pre.ensembl.org/index.html].120. [http://www.ncbi.nlm.nih.gov].doi:10.1186/gb-2013-14-3-r28Cite this article as: Bradley Shaffer et al.: The western painted turtlegenome, a model for the evolution of extreme physiologicaladaptations in a slowly evolving lineage. Genome Biology 2013 14:R28. Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitBradley Shaffer et al. Genome Biology 2013, 14:R28http://genomebiology.com/2013/14/3/R28Page 22 of 22


Citation Scheme:


Citations by CSL (citeproc-js)

Usage Statistics



Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            async >
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:


Related Items