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Squamate reptiles challenge paradigms of genomic repeat element evolution set by birds and mammals


Broad paradigms of vertebrate genomic repeat element evolution have been largely shaped by analyses of mammalian and avian genomes. Here, based on analyses of genomes sequenced from over 60 squamate reptiles (lizards and snakes), we show that patterns of genomic repeat landscape evolution in squamates challenge such paradigms. Despite low variance in genome size, squamate genomes exhibit surprisingly high variation among species in abundance (ca. 25–73% of the genome) and composition of identifiable repeat elements. We also demonstrate that snake genomes have experienced microsatellite seeding by transposable elements at a scale unparalleled among eukaryotes, leading to some snake genomes containing the highest microsatellite content of any known eukaryote. Our analyses of transposable element evolution across squamates also suggest that lineage-specific variation in mechanisms of transposable element activity and silencing, rather than variation in species-specific demography, may play a dominant role in driving variation in repeat element landscapes across squamate phylogeny.


Transposable elements (TEs) and other repetitive sequences represent a major fraction of vertebrate genomes—in most mammals, repeat elements comprise 28–58% of the genome1,2, and may comprise more than two-thirds of the human genome3. Several decades of genome research has led to the prevailing view that genome size and genome repeat content are tightly linked, such that shifts in genomic repeat content are expected to result in proportional shifts in vertebrate genome sizes4,5,6. Recently, this correlation has come into question in favor of alternative hypotheses, such as the “accordion” model of co-variation between genomic DNA gained by repeat element expansion and genomic DNA lost through deletion7. It has also been demonstrated that the relationship between genome size and repeat content may vary between vertebrate lineages4,5,8, with some lineages adhering more or less to a particular model or pattern4,6,7,9, underscoring the value of comparative analyses across diverse lineages.

Within vertebrates, our understanding of genome and repeat element evolution is largely biased towards mammals and archosaurian reptiles (mainly birds). The emerging pattern from studies of these groups is that large differences in the repeat element landscape exist among major amniote vertebrate lineages, yet fairly little variation in repeat content and diversity are observed within major amniote groups. For example, estimates based on de novo annotation of TEs in mammal and bird species suggest 1.7-fold and 2.2-fold variation in TE content across species for each group, respectively1,7. Although squamate reptiles (lizards and snakes) represent a major portion of the amniote tree with over 10,000 species spanning more than 200 million years of evolution10, variation in genomic repeat content across squamate reptiles has remained poorly studied. From the few studies to date, genome size appears to be highly conserved in squamate reptiles11, yet the little that we know about repeat element variation suggests that squamate reptile genomes vary greatly in repeat element content12,13.

Motivated to assess whether squamate reptile genomic repeat element landscapes adhere to patterns observed in birds and mammals, we analyzed genomic repeat landscapes across 66 squamate species using low-coverage random whole-genome shotgun sample sequencing data12,13 and draft genome assemblies. We find that squamate reptile genomes indeed challenge the paradigm that genome size and repeat content are tightly linked, and the view that major differences in repeat element content occur only between lineages of amniotes. In addition to contributions from TEs, snake genome repeat content variation is further increased by the largest known instance of microsatellite seeding by long interspersed nuclear elements (LINEs) observed in any living organism. We also find evidence that multiple independent horizontal transfer events and highly idiosyncratic patterns of TE proliferation across squamates have further contributed to extreme variation in genome repeat content in this lineage. We further tested a demographic explanation for variation in repeat content, whereby fluctuations in the effective population size (Ne) of species impact the efficacy of selection against repetitive element insertion14. We find no evidence that Ne explains the distribution and variation in characteristics of the repeat landscape in squamate reptiles, which indicates instead that variation in molecular mechanisms of TE proliferation, silencing, removal, and truncation may underlie the extreme repeat variation observed across squamates. Collectively, our findings challenge existing views related to repeat element and genome size co-evolution, and provide new evidence for unappreciated variation in genomic repeat content within and among major amniote lineages.


Comparison of sampled and assembled genome data

Our analyses of genomic repeat content were based on the assemblies of 12 squamate genomes (including 1 new and 11 published assemblies), and low-coverage, unassembled genomic shotgun read datasets obtained from 54 squamate species (Supplementary Data 1; Castoe et al.13). Previous studies have shown that genomic repeat content estimated from unassembled shotgun genomic datasets are similar to estimates derived from assembled genomes12,13. We confirmed this by comparing repeat annotations from assembled and unassembled genome data from the same species (Supplementary Fig. 1), and also confirmed that repeat estimates derived from unassembled genomic shotgun datasets are effectively independent of the amount of sequence data obtained (Supplementary Fig. 1).

Genome size and repeat content in major amniote groups

Squamate reptile genomes challenge the commonly accepted paradigm that genome size and repeat content are tightly linked4,5,6, and also challenge the prevailing view that large variation in repeat content tends to be characteristic of major clades, rather than highly dynamic within clades1 (Fig. 1). For example, mammalian genome sizes tend to be more highly variable (2.2–6.0 Gbp11; Supplementary Data 2) in comparison with squamate and bird genomes, yet genomic TE estimates demonstrate only moderate levels of clade-specific variation (33.4–56.3%, mean = 44.5%; Fig. 1a, Supplementary Data 3, and Supplementary Note 1). In contrast, birds have smaller genomes and higher conservation of genome sizes (1.0–2.1 Gbp11; Supplementary Data 2), with relatively low levels of TE content (4.6–10.4%, mean = 7.8%, with the only notable exception being the downy woodpecker with an extremely high genomic TE content of 22.5%, which we excluded as an outlier from analyses here; Fig. 1b, Supplementary Data 3, and Supplementary Note 1).

Fig. 1
figure 1

Genomic transposable element (TE) abundance and genome size variation in mammals, birds, and squamate reptiles. Branches on the time-calibrated consensus phylogeny are colored according to the estimated rate of genomic TE evolution. Violin plots show distributions of flow cytometry-based genome size estimates for major groups of a mammals, b birds, and c squamate reptiles, and the associated heat maps reflect the total genomic TE content (%) for each taxon. For squamate reptiles, additional heat maps show percent genomic repeat element content, percent genomic GC content, and percentages of major components contributing to the overall repeat element landscape

With highly conserved genome sizes (1.3–2.8 Gbp) yet extensive variation in genomic content of readily detectable TEs (23.7–56.3%, mean = 41.8%; Fig. 1c), we find that squamate reptiles do not adhere to either of these trends. The relatively high degree of variation in genomic repeat content across remarkably short evolutionary time scales in squamates presents the greatest contrast with birds and mammals. Unlike the clade-specific pattern observed in mammals, the genomic repeat content variation of squamate reptiles exhibits a high degree of variation even between species within the same genus (e.g., within the genera Ophisaurus (44.8–48.9%), Coniophanes (59.4–73%), and Crotalus (35.3–47.3%); Fig. 1c, Supplementary Figs. 2 and 3, and Supplementary Data 4). Across the 66 squamate species sampled, total genomic repeat element content varied from 24.4% to 73.0% (3-fold variation; Fig. 1c). Collectively, our analyses highlight the remarkable finding that the comparatively small genomes of squamates, similar to those of birds, can contain large and highly variable amounts of repeat elements, exceeding the range reported for mammals.

Genomic TE composition across squamate reptiles

The content and evolutionary dynamics of TEs in squamate genomes are unique in many ways when compared to that of mammals and birds, yet squamate genomes also share several key features with both lineages. All three groups have TE landscapes largely dominated by non-long-terminal repeat (non-LTR) retrotransposons. However, unlike mammalian genomes in which L1 LINEs and associated short interspersed nuclear elements (SINEs) are the most dominant and active elements3,15, squamate genomes tend to contain three similarly abundant and active LINE families (CR1, BovB, and L2 LINEs; Fig. 1, Supplementary Fig. 2, and Supplementary Data 4). While CR1 LINEs are ubiquitous across amniote genomes, CR1s are particularly abundant and recently active in squamate genomes (5.1%, compared to ~3.5% in birds and <1% in mammals1), as they tend to be in other non-avian reptiles (i.e., ~10% in crocodilians16). In addition to non-LTR elements, DNA elements are also highly variable and particularly abundant in multiple divergent squamate lineages (Fig. 1). For example, Tc1-Mariner elements have experienced a 2.4-fold expansion in colubroid snakes compared to lizards (mean genomic abundance = 4.23% in colubroid snakes and 1.7% in lizards; Fig. 1, Supplementary Fig. 2, and Supplementary Data 4). The most striking contrast between squamate vs. bird and mammal genomes is that squamate genomes contain an unusually broad diversity of types, subtypes, and families of TEs that appear simultaneously active12,16,17,18,19 (see also below and Supplementary Fig. 3), whereas genomes of mammals and birds tend to have a very small number of active elements (e.g., L1 LINEs and Alu SINEs in mammals, and endogenous retroviruses (ERVs) in birds6,15,20,21).

Guanine-cytosine (GC) content is known to play an important role in genome and repeat element evolution22,23,24,25,26. We found evidence of significant relationships between GC content and total TE content, as well as GC and total microsatellite (or simple sequence repeat; SSR) content, in lizards and colubroid snakes (Supplementary Fig. 4). In contrast, we found no correlation between genomic GC content and any aspect of the genomic repeat element landscape in non-colubroid snake genomes (Supplementary Fig. 4). Consistent with previous studies13, our analyses highlight the surprisingly variable nature of GC content across squamate genomes, which tends to be higher in lizards than in snakes, yet highest in the colubroid snake Coniophanes fissidens (GC = 47.8%; Fig. 1c). These findings are also broadly consistent with previously reported shifts in GC isochore structure in squamate genomes17,26, including the absence of isochore structure in lizard species, and intermediate structure in snakes that appears to represent isochore reacquisition after isochore loss in a squamate ancestor13.

Unparalleled microsatellite abundance in squamate genomes

Our analyses revealed that some squamate genomes contain astonishingly high levels of SSRs, and that genomic SSR content in some snake species is the highest of any previously studied vertebrate (e.g., 14% according to RepeatMasker estimates in Coniophanes fissidens, Supplementary Data 4 and 5, and Supplementary Fig. 5). While previous studies have suggested that the highest variation in SSR content tends to exist among major vertebrate lineages27, with fish, squamate reptiles, and mammalian genomes having similarly high genomic content12,13,17,28, our results provide new evidence that the highest variation known in genomic SSR content exists within lineages—squamates and snakes, specifically. We found up to 10.9-fold variation in the genomic density of SSR loci (262–2845 loci/Mbp) and 16.6-fold variation in SSR-occupied bases per Mbp (4.08–67.94 Kbp/Mbp) among squamates overall, with non-colubroid snakes tending to have the lowest genomic SSR abundance, and colubroid snakes having the highest (Supplementary Data 5, Fig. 2, and Supplementary Figs. 5 and 6). This extreme variation in the genomic SSR content of squamate reptiles exceeds the previous high benchmark set by fish genomes (8.2-fold loci/Mbp and 18.0-fold bp/Mbp variation), and dwarfs that of mammals (5.8-fold loci/Mbp and 5.4 bp/Mbp) and bird genomes (1.8-fold loci/Mbp and 2.8 bp/Mbp)12,13,17,28.

Fig. 2
figure 2

Microsatellite seeding by transposable elements (TEs) in squamate reptiles. a Branches on the time-calibrated consensus phylogeny are colored according to estimated rates of genomic CR1-L3 LINE evolution. Heat maps show the total genomic content (%) of LINE retrotransposon types involved in microsatellite seeding. Associated bar plots represent the total (left), 5mer (middle), and AATAG (right) microsatellite bp/Mbp density frequencies for each genome sampled. Red lines to the right of the bar plots highlight pronounced seeding of 5mer and AATAG microsatellites in colubroid snakes. b The ratio between TE mapping at the 5′ tail of AATAG microsatellite loci (AATAG-adjacent) and TE content averaged over five independent, randomly simulated genomic backgrounds for each class of TEs (SINEs; CR1-L3, Rex, CR1-L2 and BovB LINEs; LTRs; and DNA transposons). Ratios are plotted on a log scale to highlight enriched elements flanking AATAG loci (ratio >1) in contrast to elements more abundant in the genomic background (ratio <1). c Histogram shows joint and conditional probabilities of associations between AATAG loci and CR1-L3 and Rex. Genomic joint probabilities are shown in orange and light blue for CR1-L3 and Rex, respectively. AATAG-adjacent conditional probabilities are shown in red and dark blue for CR1-L3 and Rex, respectively

Largest instance of microsatellite seeding among vertebrates

A peculiar feature of SSR evolutionary dynamics in squamate genomes is the significant shifts in 4mer and 5mer abundances across the squamate tree, including extreme expansion of specific 4mer and 5mer SSRs motifs in colubroid snake genomes (Kruskal–Wallis test p value <0.001, Supplementary Fig. 6 and Supplementary Data 6). Two specific SSR sequence motifs, ATAG and AATAG, account for most of the microsatellite expansion in colubroid snakes, representing a 7.4-fold increase in ATAG (bp/Mbp) and an 87.7-fold increase in AATAG (bp/Mbp) compared to the averages of other squamate genomes (Supplementary Figs. 7 and 8). The extremely high genomic representation of these two similar SSR sequence motifs in snake genomes suggests a motif-specific mechanism has driven their expansion. Previous studies12,13 have suggested that LINE retrotransposons that contain microsatellites on their 3′ end in snakes might lead to SSR genomic expansion through a process called “microsatellite seeding.”

To test the hypothesis that microsatellite seeding is responsible for the expansion of particular SSR sequence motifs, we surveyed the regions adjacent to the two most highly expanded SSR motifs (AATAG and ATAG) in eight complete reptile genome assemblies. Consistent with the expectations of microsatellite seeding, we found strong statistical support that CR1-L3 LINEs tend to be immediately adjacent to AATAG loci in colubroid genomes (Fisher’s exact test p value <2.2e−16), as well as strong statistical enrichment of AATAG loci at the 3′ end tail of Rex LINEs (p value <2.2e−16) in all squamate genomes sampled, suggesting that both CR1/CR1-L3 and Rex LINEs contribute to microsatellite seeding in squamate genomes (Fig. 2b and Supplementary Data 7). In contrast to elements adjacent to AATAG repeats, we found no evidence of enrichment in adjacency for any particular TE for the second most expanded SSR motif (ATAG) compared to randomly sampled genomic regions; this suggests that the expansion of this motif is not directly driven by microsatellite seeding, although its similarity to AATAG suggests that it might be indirectly related. To further identify the specific LINE element that is responsible for microsatellite seeding of AATAG SSR loci, we calculated the conditional probability of TE-SSR co-occurrence in a genome-wide context compared to the AATAG-adjacent context. Conditional probabilities of AATAG loci and CR1-like LINEs genomic co-occurrence are noticeably different only for CR1-L3 LINEs between colubroid snakes and other squamates (Fig. 2c), and are only barely detectable for Rex LINEs. Additionally, CR1 LINEs are a major contributor to the genomic TE landscape of squamates (particularly colubroid snakes), whereas Rex elements represent a very small fraction. Taken together, our data indicate that microsatellite seeding may be a common ancestral feature of multiple families of squamate LINEs, yet the high activity and expansion of CR1-L3 LINEs has driven associated AATAG loci to extremely high frequencies in colubroid snakes, leading to an astounding 74.73-fold genomic AATAG loci/Mbp increase in this lineage, and the highest levels of genomic SSR content among vertebrates. The ramifications of such extreme levels of homologous SSRs in colubroid snakes, in terms of genome function and evolution, remains uninvestigated. A potential role in mediating increased ectopic recombination leading to gene duplication has been suggested by previous studies that have identified an enrichment of these repeats surrounding tandemly duplicated venom genes in snakes12,29,30. Collectively, these findings imply the exciting possibility that LINE-SSR hybrid elements may have played key roles in the evolution of prominent phenotypes in snakes (i.e., venom evolution).

Multiple independent TE horizontal transfer events

Evidence for the horizontal transfer of BovB LINEs has been identified by previous studies12,31,32,33,34, and our analysis of squamate genomes provides new insight into the complexities of BovB horizontal transfer. Our phylogenetic reconstruction of BovB LINEs, including samples from our squamate genomes and other sequences from GenBank35, highlights multiple horizontal transfer events, and supports ectoparasite-mediated transfers of BovB LINEs into and out of squamate reptile genomes (Fig. 3 and Supplementary Fig. 9a, Supplementary Data 9). We found BovB LINE sequences from squamate species clustering with other groups of metazoans in all branches of our phylogenetic tree, consistent with multiple horizontal transfer events of BovB from lizards to mammals and to other squamates, and from snakes to mammals and other squamates. Previous studies found support for virus-mediated transfer of TEs36, and suggested ectoparasites as potential transmission vectors34,37,38,39,40. Our analyses support the horizontal transfer of BovB from one reptile tick species (Amblyomma limbatum) to colubroid snakes (Supplementary Fig. 9a), and provide the first ever evidence for ectoparasite-mediated transfer from squamate genomes in the case of the reptile tick Bothriocroton hydrosauri. Samples containing BovB elements sequenced from this tick species are deeply nested among lizard-derived BovB sequences, yet are unique in containing a large internal deletion (1691 nt) relative to all other lizard-derived BovB sequences in this clade. Collectively, our analyses of BovB LINE evolution showcase a dynamic history of horizontal transfer that encompasses essentially all forms of the process of transfer into and out of squamate genomes, implicating the role of ectoparasites in both directions of the transfer process.

Fig. 3
figure 3

Evidence for ectoparasite-mediated horizontal transfer of BovB LINEs in squamate reptile genomes. A summarized Bayesian phylogenetic tree of full-length BovB LINE sequences for 87 metazoan species, including two reptile ticks. Branches have been collapsed and colored to represent major clades. Posterior probabilities are shown only at nodes that had posterior support <0.99

Testing explanations of variation in genomic TE abundance

Multiple studies have suggested that purifying selection acting against TE insertions may manifest in correlations between Ne and features of the genomic TE landscape. This prevailing demographic explanation for variation in repeat content has been invoked to describe patterns of genome complexity and evolution across the tree of life, and predicts that lineages with higher Ne should undergo more effective purifying selection and thus lower genomic accumulation of mutationally hazardous DNA41,42. Indeed, previous population (within-species) and phylogenetic (among-species) studies have provided rationale and empirical evidence that TE insertion rates, fixation rates, and abundance may be correlated with Ne14,42,43,44,45. Relative insert length has also been linked to population size at the population level by an ectopic recombination model in which element length is correlated with the strength of selection14,18,43,46,47,48.

Using our phylogenetic-scale dataset, we tested if features of TE landscapes (i.e., genomic abundance, estimated age of activity, and degree of truncation for BovB and CR1-L3 LINEs) showed evidence of a correlation with estimates of Ne consistent with a demographic model of TE landscape evolution. We first tested for a relationship between Ne and TE landscape characteristics using the median values of Ne estimates derived from pairwise sequentially Markovian coalescent (PSMC) analyses49 for eight published squamate genomes (Fig. 4b–d and Supplementary Fig. 10). With this dataset, we found no evidence supporting a correlation between Ne and CR1-L3 and BovB length or genomic repeat element abundance (Fig. 4c, d and Supplementary Fig. 10c–e). Notably, we found that species with similar Ne estimates (Fig. 4b) showed different levels of truncation and of TE genomic abundance, and that even within a species TE truncation and abundance were poorly correlated (Fig. 4a, c, d and Supplementary Figs. 10 and 11). Second, to further test for correlations between Ne and element abundance or truncation using an approach that is independent of inferences of generation time and mutation rates, and independent of potential biases associated with coalescence-based estimates of Ne (i.e., population substructure, migration, selection)49,50,51,52,53,54,55, we used adult body mass as a proxy for Ne for all species included in our study (as in ref. 56; Supplementary Data 8)57. This approach has the added benefit of leveraging the much larger sample size of our entire dataset (compared to our PSMC analyses using eight complete genomes). Similar to our PSMC-based analyses, we compared body mass to CR1-L3 and BovB genomic abundance, their degree of truncation, and total genomic repeat element and TE abundances. Consistent with our PSMC-based analyses, we failed to find a correlation between body mass and truncation (Fig. 4e and Supplementary Fig. 12b) that would support a demographic model of TE landscape evolution; the only correlative trend that we did find was a correlative trend that is opposite of that predicted by the demographic model between Ne and genomic repeat element abundance instead (i.e., higher Ne was positively correlated with TE abundance; Supplementary Fig. 12d). Finally, to test more generally for evidence that selection acts on TE length at the phylogenetic scale, we tested for a link between TE truncation and TE age18,48,58 using median pairwise divergence of TE copies from their subfamily consensus, π, as a proxy for age for CR1-L3 and BovB families, and found no correlation (Fig. 4f, Supplementary Fig. 13, and detailed in Supplementary Figs. 1416). While we acknowledge the complexity of testing links between two highly dynamic evolutionary processes (e.g., Ne and TE abundance), and the limitations of methods used to make inferences about these processes (i.e., Ne estimation), all of our analyses fail to provide support for Ne as a strong determinant of variation in the composition and characteristics of the repeat element landscape at the phylogenetic level across squamate reptiles. Although our analyses cannot fully reject a demographic hypothesis that a relationship between Ne and TE characteristics exists (i.e., we can only fail to reject a lack of relationship), the apparently poor explanatory power of the demographic hypothesis in predicting squamate TE activity and abundance suggests that perhaps other factors, such as variation in molecular mechanisms of TE proliferation, silencing, and removal, may better explain the majority of variation in TE abundance at the phylogenetic level in squamates.

Fig. 4
figure 4

Relationships between truncation, effective population size, body mass, and divergence estimates for CR1-L3 and BovB LINE retrotransposons among squamates. a Branches on the time-calibrated consensus phylogenies are colored according to the calculated 3′:5′ read depth coverage ratio for CR1-L3 (left) and for BovB (right) LINEs. Heat maps show the genomic content of CR1-L3 LINEs, total repeats, and BovB LINE retrotransposons represented as percentages of the total genome. For each major clade, violin plots show the density distributions of divergence estimates (pairwise π) for all CR1-L3 and BovB elements compared to the species-specific consensus sequence. b Variation in effective population size (Ne) over time for five snake species scaled by generation time and mutation rate (“g” and “u” on the x-axis). c Relationship between Ne and truncation of CR1-L3 (top) and of BovB (bottom) LINEs for eight squamate species. d Relationship between total genomic abundance of CR1-L3 (top) and BovB (bottom) LINEs and Ne. e Relationship between adult body mass and degree of truncation across 66 squamate species for CR1-L3 (top) and BovB (bottom) LINEs. f Relationship between age (median pairwise π) and truncation for CR1-L3 (top) and BovB (bottom). Summary statistics from phylogenetically independent contrasts (PIC) are shown as insets for each plot in cf. Statistical analyses were performed after log transformation of truncation values in plots e and f


This broad glimpse into the diversity of repeat structure and composition of squamate reptile genomes suggests that this lineage possesses particularly distinct and often extreme repeat landscape characteristics compared to other amniotes. Our results provide evidence for surprisingly high variation in the content and composition of genomic repeat elements across squamate lineages, including 3-fold variation in the identifiable genomic repeat element content. We also discovered that some snake genomes have experienced microsatellite expansion at unprecedented scales through the process of microsatellite seeding by specific LINEs, leading to genomic microsatellite abundances that are the highest of any known vertebrate genome. Despite such extreme variation in genomic repeat element content, genome size across squamates is remarkably conserved (~0.2-fold variation), challenging the prevailing view that genomic repeat abundance and genome size tend to tightly co-evolve4. These findings provide some of the strongest evidence for a dynamic equilibrium or an “accordion” model, in which genomic DNA gain through TE expansion may be approximately balanced by genomic DNA loss through deletion7,58,59. Overall, these results highlight extreme shifts in the structure of squamate reptile genomes, and further beg the question of whether particular aspects of squamate genome function and evolution are also more unique and variable compared to other vertebrates. These findings argue that squamates may represent a particularly powerful model system for testing hypotheses about genome structure, function, and evolution, and their interactions.

Many previous studies focused on population-level dynamics of TE evolution have shown that differences in Ne and the efficacy of purifying selection acting against TE proliferation have played a major role in structuring the repeat landscape of many eukaryote genomes9,18,46,47,48,58,60,61,62,63. Even in squamate species (e.g., Anolis lizards), variation in Nes has been linked to TE insertion length and fixation probability18,62,63. Our phylogenetic-scale analyses across squamate species, however, recovered no clear evidence linking genomic repeat abundance or activity with Ne estimates in squamates. Although coalescent-based estimates of Ne can be biased by a number of model violations (i.e., population substructure, selection), we also failed to find a significant relationship between genomic repeat characteristics and body mass—a known correlate of Ne. Population size is, however, likely to have influenced other aspects of genome evolution, such as fixation of deletions, that could contribute to the maintenance of nearly constant genome size in squamates.

Our results together with those from previous studies suggest that different evolutionary forces may dominate different evolutionary scales, and that while demographic processes (and purifying selection) may dominate population-level trends in TE evolution, phylogenetic-scale patterns in TE landscapes may be more strongly determined by other processes. Evidence for extreme variation in transcriptional levels of TE-derived transcripts across squamates12, together with evidence from this study of lineage-specific swings in repeat element proliferation, suggest that molecular mechanisms related to TE regulation may be particularly relevant at the phylogenetic scale in squamates. Squamates may, therefore, represent a valuable system for studying the impacts of variation in molecular mechanisms of TE control, such PIWI-interacting RNA dynamics and efficacy, epigenetic silencing of TEs, lineage-specific TE activity, DNA repair mechanisms, and post-insertion 5′ removal of TEs. Further studies are needed to address the question of whether variation in molecular mechanisms of TE silencing and activity, as well as DNA repair, explain variation in squamate genomic TE content, and would provide fascinating insight into the factors that shape genomic repeat landscape variation.


Taxon sampling and library preparation

DNA extraction of 52 squamate samples (total = 45 species) was performed using a phenol–chloroform–isoamyl alcohol (PCI) extraction protocol. Random shotgun genome libraries were prepared by fragmenting DNA samples to an average length of 300–600 bp using a M220 Covaris Ultrasonicator. The NEBNext Illumina DNA Library Prep Kit (New England Biolabs) was used following the manufacturer’s protocol to perform fragment-end repair, poly-A tailing, adapter ligation, and library amplification. After library preparation, fragments were size-selected using a BluePippin (Sage Science) for a length of 350–450 bp. Pooled multiplexed libraries were sequenced on an Illumina MiSeq with 300 bp paired-end reads. Paired reads were merged based on sequence overlap and were adapter and quality trimmed using CLC genomics workbench 9.0.1 64. Roche 454 shotgun sequencing data of nine snake species from previous studies12,13 and draft genome assemblies of 12 additional squamate species (Supplementary Data 1) were also included. Our final sampling included a total of 66 different squamate species. For each species, mitochondrial reads were filtered out in CLC genomics workbench 9.0.1 using the complete mitochondrial genome of the most closely related species available on GenBank35. Reads that mapped to the reference were used to assemble species-specific mitochondrial genomes. Reads that did not map to the reference (i.e., nuclear reads) were used for downstream repeat element annotation and analyses.

SSR identification and analysis

We used Pal_finder v.0.02.0365 (Palfinder hereafter) to identify microsatellites. Default Parfinder parameters were used to identify perfect dinucleotide (2mer), trinucleotide (3mer), and tetranucleotide (4mer) that were tandemly repeated for a total length of at least 12 bp. Perfect pentanucleotide (5mer) and hexanucleotide (6mer) tandemly repeated motifs were annotated only if longer than 15 bp. Loci/Mbp and bp/Mbp frequencies were calculated for all microsatellite motifs, length classes (2–6mers), and total content, and summarized per genome and major taxonomic group. Tests for multiple evolutionary rates of microsatellite abundance across lineages, ancestral state reconstruction of genomic microsatellite frequencies, and quantification of microsatellite landscape differentiation among species were performed using the R packages Phytools v.0.4–6066 and APE v.3.367. For the multiple evolutionary rate analysis of microsatellite (and TE) abundance, we conducted censored rate tests using Phytools with 1000 simulations (to compute p values) on 100 randomly sampled posterior trees using the restricted maximum likelihood technique to obtain unbiased estimates of the evolutionary rate parameter (σ)28. We used the time-calibrated phylogeny and the pic function in R (provided by the APE package) to compute phylogenetic independent contrasts for tests of clade-specific differences in genomic microsatellite content. We performed the nonparametric Kruskal–Wallis H test in R after the data rejected normality (Shapiro–Wilks test; p values <0.05 before and after log transformation) and homogeneity of variances (Bartlett’s test; p values <0.05 before and after log transformation). Between lineages variation was tested using a post hoc Dunn test for multiple comparisons using the Benjamini–Hochberg correction method in R (Supplementary Data 6).

TE identification and analysis

Squamate genomic repeat elements were annotated according to homology-based and de novo identification approaches. Because repeat element annotation can be highly dependent on the repeat library used, we built large multi-species (clade-specific) repeat libraries that we used to annotate repeats for all members of a clade. To build these clade-specific libraries, we first performed de novo repeat element annotation on each species (except where already published) using RepeatModeler v.1.0.968, followed by further repeat classification in CENSOR69. Second, we built clade-specific de novo repeat element libraries, one for all lizard species (33 species de novo reference library) and one for all snake species (de novo TE libraries for 21 species were combined, and merged with the reference library generated by Castoe et al.13). Each clade-specific library was then filtered to avoid redundancy of highly similar elements. We tested whether using a single squamate-specific library for all species would change the inferred relative TE content and overall amount of repeat identified; we found no detectable difference between the results of the two masking protocols (Supplementary Fig. 17), and therefore decided to use the two clade-specific libraries in order to reduce masking time by reducing the overall library size. Additional classification of unknown (unclassified) elements was achieved by comparing these unclassified elements to all elements that were classified using BLAST70. Additionally, we generated squamate-specific BovB and CR1-L3 LINEs reference sequence libraries for all 66 species included (additional information regarding library generation are provided in the following paragraph).

Repeat element analyses were performed in RepeatMasker v.4.0.671 with default parameter settings. To maximize element identification, we used a custom bash script to specify the order of the four libraries used as references for the masking process: (i) BovB-L3 LINEs library, (ii) Tetrapoda RepBase library (version 20.11, 07 August 201572), (iii) classified elements from the clade-specific library for either snakes or lizards, and (iv) unknown elements from the clade-specific library. We used the BovB-L3 LINEs library first to control for limited sampling and low-quality reference sequences of squamate reptile BovB and L3 LINEs in the tetrapoda library. RepeatMasker output files were post-processed using a custom-modified implementation of the ProcessRepeat script included in the RepeatMasker package. Specifically, we modified the output to include additional summary information in the .tab output file for TE subfamilies that are important and/or frequent in squamate reptiles (e.g., CR1-L3, L2, and Rex). Also, because the provided ProcessRepeat script still reflects old and outdated classification schemes of TEs (e.g., Penelope elements are inappropriately classified as LINEs), we made other modifications to the ProcessRepeat script to correct for such errors according to the classification reported by Chalopin et al6.

Comparing sampled and assembled genomes

We tested whether genomic repeat content estimated from unassembled shotgun genomic datasets were similar to estimates derived from fully assembled genomes. We compared RepeatMasker estimates of total TE genomic abundance between assembled genomes and unassembled shotgun genomic datasets for the same species (Python molurus, Boa constrictor, Thamnophis sirtalis, and Deinagkistrodon acutus) or for two closely related species belonging to the same genus (Gekko gecko vs. Gekko japonicus and Ophisaurus attenuatus vs. Ophisaurus gracilis). We also tested for potential biases due to unequal genomic sampling in the shotgun datasets. We extracted at random subsamples of 3, 5, 8, 10, 30, 50, 100, and 250 Mbp from unassembled genomic shotgun datasets of four species (Python molurus, Gekko gecko, Ophisaurus attenuatus, and Pantherophis emoryi), and compared RepeatMasker estimates of total TE genomic abundance for each. Read extraction was performed using the script from the QIIME pipeline73. Finally, we compared RepeatMasker estimates of total TE genomic abundance in relation to the amount of sequence data obtained for all Illumina and 454 genomic shotgun datasets to test for biases related to sequencing technology, and for biases related to the amount of sequence data collected per individual, vs. estimates of total TE genomic abundance.

CR1 and BovB LINEs phylogenetic and evolutionary analyses

Species-specific consensus sequences for both CR1-L3 and BovB LINE retrotransposons were generated in CLC genomic workbench 9.0.1 using default parameters, a linear gap cost, and the global alignment setting. Nuclear reads for each species were mapped to the consensus sequence of the LINE consensus sequence from the most closely related species available, which was used as initial reference (e.g., both CR1-L3 and BovB reference sequences for the Burmese python were generated by Castoe et al.13 and used as reference for building the consensus for the Mexican burrowing python). The first consensus generated was then used as a new reference for further rounds of re-mapping of nuclear reads until no additional mapping reads were recovered. Consensus sequences were determined by simple majority rule consensus, removing regions with coverage <10x after the second mapping iteration, and <20x in the final mapping. Consensus sequences were aligned in ClustalW74 with a gap open penalty of 50, and alignments were manually adjusted prior to downstream analyses (Supplementary Data 10). To the CR1 consensus sequences generated from our 66 squamate species, we added CR1-L3 and CR1-L2 vertebrate consensus sequences available in RepBase, for a total of 155 sequences (Supplementary Data 10). Squamate BovB consensus sequences we generated from our 66 squamates were combined with other metazoan consensus sequences available in RepBase, for a total of 87 sequences (Supplementary Data 9). Bayesian phylogenetic tree reconstruction analyses of squamate CR1 and BovB LINEs were performed in BEAST275. Two independent analyses were run for 200 million generations each, following the Yule model of speciation and a relaxed log-normal clock model; MCMC chains were sampled every 1000 generations. The program Tracer v.1.676 was used to confirm that the MCMC chains had reached convergence. We conservatively discarded the first 25% of collected MCMC generations as burn-in, based on evidence that the likelihood and parameter values reached stationarity after approximately 15% of the sampling process.

CR1 and BovB LINEs coverage and age analyses

For each species, the species-specific CR1-L3 and BovB consensus sequence was used as a reference to estimate read coverage using the BWA mem alignment tool77, and the BEDTools2 (version 2.26.0) coverage tool78. Coverage counts were normalized by the total number of reads aligned to the full-length reference sequence. Read coverage was estimated for: (i) each 10 bp sliding window, (ii) for the first and second half of the reference sequence, and (iii) for each third of the reference.

We used pairwise sequence divergence from the consensus (pairwise π) as a proxy to infer age and relative element level of activity through time. Pairwise distances values for each element and species were estimated following a custom pipeline starting from BWA alignments. An R79 custom script built on the pegas80 and stringr packages was used to calculate pairwise π estimates using multi-fasta pairwise alignments of reads to the reference. Because we expected multiple TE subfamilies to exist, sequence divergence was estimated by excluding sites that define different CR1 and BovB subfamilies. For each species, we calculated the relative nucleotide frequency for each position in the multiple sequence alignment, and then calculated the mode of the frequency distribution (bins of 0.01) of the most frequent nucleotide at each position. Sites for which the most frequent nucleotide was in a bin more than three bins away from the mode were discarded as defining a separate subfamily.

Time-calibrated phylogeny of 66 squamate reptiles

We estimated a time-calibrated phylogeny for the 66 squamate species in our study and an additional eight outgroup vertebrates. We downloaded and parsed 12 mitochondrial-encoded protein-coding genes for each species with a mitochondrial genome sequence available on GenBank. The same genes were parsed from our de novo assembled mitochondrial genomes after genes were annotated for these using MITOS81. We aligned the 12 protein coding genes encoded on the mitochondrial heavy strand using MUSCLE v.3.8.2182 and concatenated the sequences into an alignment that we used for divergence dating (10,479 bp). Prior to divergence dating, we estimated the best-fit partitioning scheme and associated models of nucleotide substitution using Bayesian information criterion and the heuristic search algorithm provided in PartitionFinder v.1.1.183. We provided a starting partitioning scheme that defined 36 partitions (splitting codon positions for each of the 12 genes), and PartitionFinder identified the best-fit partitioning scheme comprising a single partition for each codon position (three total) and a GTR+I+G model for each partition. We estimated divergence times using BEAST v.2.3.484 with a calibrated Yule model of speciation and a log-normal relaxed clock model. We constrained the topology to that provided from previous studies of the squamate phylogeny and diversification85,86; we also constrained divergence times of a total of seven nodes using fossil calibrations also provided in previous studies. Calibration points and associated prior distributions are given in Supplementary Table 1. Two independent MCMC runs were conducted for 100 million generations each, with MCMC chain sampling every 10,000 generations. We assessed convergence to the posterior based on likelihood and parameter stationarity (effective sample size >200 for all parameters) using the program Tracer. We discarded the first 10% of generations as burn-in, based on the likelihood and parameter values exhibiting stationarity before 10% of sampling was completed.

AATAG microsatellite seeding by TE analyses

We performed adjacency analyses of AATAG and ATAG SSR loci on high-quality assembled genomes for seven snake species, and used the green anole lizard as an outgroup. To increase specificity, genomes were first masked only for simple repeats. We extracted coordinates of annotated AATAG and ATAG SSR loci from the .out RepeatMasker output files, and used these coordinates to extract target regions 400 bp upstream and downstream of each microsatellite locus. We then performed a second run of RepeatMasker to mask only TEs located in the extracted target regions that flank AATAG and ATAG loci. Following this strategy, we were able to annotate TEs located in close proximity to SSR loci, and to differentiate TEs that harbor microsatellite-like regions in their reference sequences. The composition of TEs physically associated with SSR loci regions was then compared to the average of five independent randomly generated genomic backgrounds matching in sample size the corresponding microsatellite landscape. For each species, genomic background reads were generated by using the random tool in the BEDTools2 v.2.26.0 package, in which we specified the number of sequences to be extracted and that their length was to match the SSR-adjacent genomic subsample. The generation of random bed files was performed independently five times per species, the TE composition was averaged across these five genomic backgrounds, and then compared to SSR loci adjacent regions. Fisher’s one-tailed exact tests were performed to evaluate the enrichment of TE families in SSR loci regions (at α = 0.01). Finally, to identify the specific element types involved in microsatellite seeding, we estimated genomic and SSR-adjacent conditional probabilities of TE-SSR co-occurrences. We estimated the conditional probability of sampling an AATAG SSR with an adjacent CR1 LINE present within 400 bp, and compared this to the estimated joint probability of sampling an AATAG SSR locus and a CR1 LINE using the genome-wide frequencies. We also calculated the conditional and joint probabilities for Rex LINEs, and compared those to the conditional and joint probabilities of CR1 LINEs, respectively.

Effective population size (N e) estimation

Whole genomic Illumina paired-end reads for eight squamate reptiles species were first preprocessed for quality using Trimmomatic87. Clean paired and unpaired reads were aligned to their respective reference genome assemblies using BWA v.0.7.12, and single nucleotide polymorphisms were called with SAMtools (v.0.1.18) mpileup88. We applied the PSMC49 using a generation time of 3 years across all eight species (which represents the average of generation time approximations available from the literature; Supplementary Table 2) after verifying that the application of a single generation time yielded results consistent with estimates of average Ne produced by the application of generation times within the range reported in the literature. Multiple studies have provided evidence of relatively similar mutation rates across lineages of squamates13,89. Therefore, in our PSMC analyses we used the generalized squamate mutation rate reported in Green et al.89 of 2.4 × 109 /year/site (as estimated from 4-fold degenerate sites between anole and python). To test the robustness of inferred population size estimates, we conducted 100 bootstrap replicate analyses by splitting the scaffolds into smaller segments and randomly sampling the segments with replacement. Default outputs of the script were used to graphically summarize Ne changes over time estimations per each bootstrapped sample (Supplementary Fig. 10b).

Coalescent approaches for estimating Ne and changes in Ne over time (like PSMC) have several intrinsic limitations. Importantly, they rely on explicit assumptions of a single population coalescent model (without subdivision, gene flow, or selection) to estimate the time since the most recent common ancestor of alleles at each locus, as well as an assumed generation time and substitution rate. Population structure has been identified as one major factor that can bias PSMC-based estimates of Ne50,52,90,91. For example, the inferred trend in Ne variation of a structured population can portrait either a bottleneck or an expansion in population size whether the alleles were sampled from the same subpopulation or from different subpopulations, respectively51. Episodes of natural selection can also bias estimates of Ne obtained using PSMC, as selection can manipulate the rate of coalescence at specific loci that are directly or indirectly linked to targets of selection54,55. Given the nature of our data, we are not able to assess the presence and extent of population substructure or selection, and therefore cannot exclude that our PSMC estimates are immune to such biases. Additionally, PSMC has low power at recovering rapid changes in Ne, which may be incorrectly estimated to have occurred over a longer period of time, and cannot recover recent nor very ancient changes in Ne (e.g., younger than ~10 kyBP and older than ~3 myBP for humans)49,51. Thus, we suggest caution when interpreting our PSMC estimates of Ne and Ne changes through time. However, we found low variance across bootstrapped Ne estimates once the most recent and most ancient time points were removed, and patterns of expansion and contraction of Ne are consistent with alternations of glacial and interglacial periods during the middle Miocene climate transition, the Pliocene and the Pleistocene92. In an attempt to reduce potential biases associated with PSMC estimates of recent and ancient changes in Ne, median Ne values were calculated after removing the first and the last time points from each sample. We replicated each analysis (see below) after applying different filtering schemes to the standard PSMC outputs (e.g., removal of 10 and 25% of time point data, and inclusion of only time points between 20 kyBP and 10 myBP). Since all tests provided the same conclusions, we report only analyses performed using median Ne values that were calculated according to the original filtering scheme. Additionally, we replicated all of our analyses using adult body mass as a proxy for Ne56 to avoid potential biases associated with our coalescence-based methods of Ne estimation (i.e., Fig. 4e). For each of the 66 squamate species, we obtained adult body mass measurements from the literature57 which were used to further test for a demographic explanation for variation in repeat content alongside coalescent-based estimates of Ne.

Testing demographic explanations of repeat content variation

We performed linear regression analyses to test for correlations between Ne and LINE truncation, Ne and genomic abundance of BovB and CR1-L3 LINEs, truncation and genomic abundance of repeats, and between truncation and estimates of ages of repeat element activity. We used the pic function in APE and the time-calibrated phylogeny to compute phylogenetic independent contrasts to be used for all linear regressions. These analyses were conducted for both the coalescent-based estimates of Ne and adult body mass as a proxy for Ne. Since truncation values violated assumptions of normality and homogeneity of variance (Shapiro–Wilks test; p values <0.05 and Bartlett’s test; p values <0.05), we performed statistical analyses on log-transformed values (Shapiro–Wilks test; p values >0.05 and Bartlett’s test; p values >0.05).

Data availability

New raw, unassembled shotgun sequencing data and new assembled genome data have been deposited at NCBI under the following accessions:PRJNA413172 and PRJNA413201. The authors declare that all data and scripts used in this study are available via public databases or available from the corresponding author upon request.


  1. 1.

    Smit, A. F. A., Hubley R. & Green P. RepeatMasker Genomic Datasets. Accessed August 2017.

  2. 2.

    Platt, R. N., Vandewege, M. W. & Ray, D. A. Mammalian transposable elements and their impacts on genome evolution. Chromosome Res. 26, 25–43 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. 3.

    de Koning, A. P. J., Gu, W. J., Castoe, T. A., Batzer, M. A. & Pollock, D. D. Repetitive elements may comprise over two-thirds of the human genome. PLoS Genet. 7, doi: 10.1371/journal.pgen.1002384 (2011).

  4. 4.

    Elliott, T. A. & Gregory, T. R. What’s in a genome? The C-value enigma and the evolution of eukaryotic genome content. Philos. Trans. R. Soc. Lond. Ser. B 370, 20140331 (2015).

    Article  CAS  Google Scholar 

  5. 5.

    Canapa, A., Barucca, M., Biscotti, M. A. & Forconi, M. & Olmo, E. Transposons, genome size, and evolutionary insights in animals. Cytogenet. Genome Res. 147, 217–239 (2016).

    Article  Google Scholar 

  6. 6.

    Chalopin, D., Naville, M., Plard, F., Galiana, D. & Volff, J. N. Comparative analysis of transposable elements highlights mobilome diversity and evolution in vertebrates. Genome Biol. Evol. 7, 567–580 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. 7.

    Kapusta, A., Suh, A. & Feschotte, C. Dynamics of genome size evolution in birds and mammals. Proc. Natl. Acad. Sci. USA 114, E1460–E1469 (2017).

    Article  PubMed  CAS  Google Scholar 

  8. 8.

    Agren, J. A. & Wright, S. I. Co-evolution between transposable elements and their hosts: a major factor in genome size evolution? Chromosome Res. 19, 777–786 (2011).

    Article  PubMed  CAS  Google Scholar 

  9. 9.

    Blass, E., Bell, M. & Boissinot, S. Accumulation and rapid decay of non-LTR retrotransposons in the genome of the three-spine stickleback. Genome Biol. Evol. 4, 687–702 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. 10.

    Murphy, W. J., Pringle, T. H., Crider, T. A., Springer, M. S. & Miller, W. Using genomic data to unravel the root of the placental mammal phylogeny. Genome Res. 17, 413–421 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. 11.

    Gregory T. R. Animal genome size database. Accessed August 2017.

  12. 12.

    Castoe, T. A. et al. Discovery of highly divergent repeat landscapes in snake genomes using high-throughput sequencing. Genome Biol. Evol. 3, 641–653 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. 13.

    Castoe, T. A. et al. The Burmese python genome reveals the molecular basis for extreme adaptation in snakes. Proc. Natl. Acad. Sci. USA 110, 20645–20650 (2013).

    ADS  Article  PubMed  CAS  Google Scholar 

  14. 14.

    Lynch, M. & Conery, J. S. The origins of genome complexity. Science 302, 1401–1404 (2003).

    ADS  Article  PubMed  CAS  Google Scholar 

  15. 15.

    Huang, C. R. L., Burns, K. H., & Boeke, J. D. Active transposition in genomes. Annu. Rev. Genet. 46, 651–675 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. 16.

    Suh, A. et al. Multiple lineages of ancient CR1 retroposons shaped the early genome evolution of amniotes. Genome Biol. Evol. 7, 205–217 (2015).

    Article  CAS  Google Scholar 

  17. 17.

    Alfoldi, J. et al. The genome of the green anole lizard and a comparative analysis with birds and mammals. Nature 477, 587–591 (2011).

    ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. 18.

    Tollis, M. & Boissinot, S. Lizards and LINEs: selection and demography affect the fate of L1 retrotransposons in the genome of the green anole (Anolis carolinensis). Genome Biol. Evol. 5, 1754–1768 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. 19.

    Yin, W. et al. Evolutionary trajectories of snake genes and genomes revealed by comparative analyses of five-pacer viper. Nat. Commun. 7, 13107 (2016).

    ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. 20.

    Brouha, B. et al. Hot L1s account for the bulk of retrotransposition in the human population. Proc. Natl. Acad. Sci. USA 100, 5280–5285 (2003).

    ADS  Article  PubMed  CAS  Google Scholar 

  21. 21.

    Zhang, G. J. et al. Comparative genomics reveals insights into avian genome evolution and adaptation. Science 346, 1311–1320 (2014).

    ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. 22.

    Boissinot, S., Entezam, A. & Furano, A. V. Selection against deleterious LINE-1-containing loci in the human lineage. Mol. Biol. Evol. 18, 926–935 (2001).

    Article  PubMed  CAS  Google Scholar 

  23. 23.

    Hellen, E. H. & Brookfield, J. F. Alu elements in primates are preferentially lost from areas of high GC content. PeerJ 1, e78 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. 24.

    Fryxell, K. J. & Moon, W. J. CpG mutation rates in the human genome are highly dependent on local GC content. Mol. Biol. Evol. 22, 650–658 (2005).

    Article  PubMed  CAS  Google Scholar 

  25. 25.

    Rizzon, C., Marais, G., Gouy, M. & Biemont, C. Recombination rate and the distribution of transposable elements in the Drosophila melanogaster genome. Genome Res. 12, 400–407 (2002).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. 26.

    Georges, A. et al. High-coverage sequencing and annotated assembly of the genome of the Australian dragon lizard Pogona vitticeps. Gigascience 4, 45 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. 27.

    Neff, B. D. & Gross, M. R. Microsatellite evolution in vertebrates: inference from AC dinucleotide repeats. Evolution 55, 1717–1733 (2001).

    Article  PubMed  CAS  Google Scholar 

  28. 28.

    Adams, R. H. et al. Microsatellite landscape evolutionary dynamics across 450 million years of vertebrate genome evolution. Genome 59, 295–310 (2016).

    Article  PubMed  CAS  Google Scholar 

  29. 29.

    Dowell, N. L. et al. The deep origin and recent loss of venom toxin genes in rattlesnakes. Curr. Biol. 26, 2434–2445 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. 30.

    Ikeda, N. et al. Unique structural characteristics and evolution of a cluster of venom phospholipase A2 isozyme genes of Protobothrops flavoviridis snake. Gene 461, 15–25 (2010).

    Article  PubMed  CAS  Google Scholar 

  31. 31.

    Kordis, D. & Gubensek, F. Bov-B long interspersed repeated DNA (LINE) sequences are present in Vipera ammodytes phospholipase A2 genes and in genomes of Viperidae snakes. Eur. J. Biochem. 246, 772–779 (1997).

    Article  PubMed  CAS  Google Scholar 

  32. 32.

    Kordis, D. & Gubensek, F. The Bov-B lines found in Vipera ammodytes toxic PLA2 genes are widespread in snake genomes. Toxicon 36, 1585–1590 (1998).

    Article  PubMed  CAS  Google Scholar 

  33. 33.

    Kordis, D. & Gubensek, F. Unusual horizontal transfer of a long interspersed nuclear element between distant vertebrate classes. Proc. Natl. Acad. Sci. USA 95, 10704–10709 (1998).

    ADS  Article  PubMed  CAS  Google Scholar 

  34. 34.

    Walsh, A. M., Kortschak, R. D., Gardner, M. G., Bertozzi, T. & Adelson, D. L. Widespread horizontal transfer of retrotransposons. Proc. Natl. Acad. Sci. USA 110, 1012–1016 (2013).

    ADS  Article  PubMed  CAS  Google Scholar 

  35. 35.

    Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 44, D67–D72 (2016).

    Article  PubMed  CAS  Google Scholar 

  36. 36.

    Piskurek, O. & Okada, N. Poxviruses as possible vectors for horizontal transfer of retroposons from reptiles to mammals. Proc. Natl. Acad. Sci. USA 104, 12046–12051 (2007).

    ADS  Article  PubMed  CAS  Google Scholar 

  37. 37.

    Novick, P., Smith, J., Ray, D. & Boissinot, S. Independent and parallel lateral transfer of DNA transposons in tetrapod genomes. Gene 449, 85–94 (2010).

    Article  PubMed  CAS  Google Scholar 

  38. 38.

    Silva, J. C., Loreto, E. L. & Clark, J. B. Factors that affect the horizontal transfer of transposable elements. Curr. Issues Mol. Biol. 6, 57–71 (2004).

    PubMed  CAS  Google Scholar 

  39. 39.

    Gilbert, C., Schaack, S., Pace, J. K. II, Brindley, P. J. & Feschotte, C. A role for host–parasite interactions in the horizontal transfer of transposons across phyla. Nature 464, 1347–1350 (2010).

    ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. 40.

    Gilbert, C. et al. Endogenous hepadnaviruses, bornaviruses and circoviruses in snakes. Proc. R. Soc. Ser. B 281, 20141122 (2014).

  41. 41.

    Charlesworth, B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 10, 195–205 (2009).

    Article  PubMed  CAS  Google Scholar 

  42. 42.

    Lynch, M. & Walsh, B. The Origins of Genome Architecture (Sinauer Associates, Sunderland, MA, 2007).

  43. 43.

    Petrov, D. A., Aminetzach, Y. T., Davis, J. C., Bensasson, D. & Hirsh, A. E. Size matters: non-LTR retrotransposable elements and ectopic recombination in Drosophila. Mol. Biol. Evol. 20, 880–892 (2003).

    Article  PubMed  CAS  Google Scholar 

  44. 44.

    Le Rouzic, A., Boutin, T. S. & Capy, P. Long-term evolution of transposable elements. Proc. Natl. Acad. Sci. USA 104, 19375–19380 (2007).

    ADS  Article  PubMed  Google Scholar 

  45. 45.

    Blumenstiel, J. P., Chen, X., He, M. M. & Bergman, C. M. An age-of-allele test of neutrality for transposable element insertions. Genetics 196, 523–538 (2014).

    Article  PubMed  CAS  Google Scholar 

  46. 46.

    Song, M. Z. & Boissinot, S. Selection against LINE-1 retrotransposons results principally from their ability to mediate ectopic recombination. Gene 390, 206–213 (2007).

    Article  PubMed  CAS  Google Scholar 

  47. 47.

    Petrov, D. A., Fiston-Lavier, A. S., Lipatov, M., Lenkov, K. & Gonzalez, J. Population genomics of transposable elements in Drosophila melanogaster. Mol. Biol. Evol. 28, 1633–1644 (2011).

    Article  PubMed  CAS  Google Scholar 

  48. 48.

    Barron, M. G., Fiston-Lavier, A. S., Petrov, D. A. & Gonzalez, J. Population genomics of transposable elements in Drosophila. Annu. Rev. Genet. 48, 561–581 (2014).

    Article  PubMed  CAS  Google Scholar 

  49. 49.

    Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–U484 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. 50.

    Nielsen, R. & Beaumont, M. A. Statistical inferences in phylogeography. Mol. Ecol. 18, 1034–1047 (2009).

    Article  PubMed  CAS  Google Scholar 

  51. 51.

    Mazet, O., Rodriguez, W., Grusea, S., Boitard, S. & Chikhi, L. On the importance of being structured: instantaneous coalescence rates and human evolution—lessons for ancestral population size inference? Heredity 116, 362–371 (2016).

    Article  PubMed  CAS  Google Scholar 

  52. 52.

    Nadachowska-Brzyska, K., Burri, R., Smeds, L. & Ellegren, H. PSMC analysis of effective population sizes in molecular ecology and its application to black-and-white Ficedula flycatchers. Mol. Ecol. 25, 1058–1072 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Orozco-TerWengel, P. The devil is in the details: the effect of population structure on demographic inference. Heredity 116, 349–350 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. 54.

    Schrider, D. R., Shanku, A. G. & Kern, A. D. Effects of linked selective sweeps on demographic inference and model selection. Genetics 204, 1207–1223 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Adams, R. H., Schield, D. R., Card, D. C. & Castoe T. A. Assessing the impacts of positive selection on coalescent-based species tree estimation and species delimitation. Syst. Biol. (2018).

  56. 56.

    Figuet, E. et al. Life history traits, protein evolution, and the nearly neutral theory in amniotes. Mol. Biol. Evol. 33, 1517–1527 (2016).

    Article  PubMed  CAS  Google Scholar 

  57. 57.

    Feldman, A., Sabath, N., Pyron, R. A., Mayrose, I. & Meiri, S. Body sizes and diversification rates of lizards, snakes, amphisbaenians and the tuatara. Glob. Ecol. Biogeogr. 25, 187–197 (2016).

    Article  Google Scholar 

  58. 58.

    Neafsey, D. E., Blumenstiel, J. P. & Hartl, D. L. Different regulatory mechanisms underlie similar transposable element profiles in pufferfish and fruitflies. Mol. Biol. Evol. 21, 2310–2318 (2004).

    Article  PubMed  CAS  Google Scholar 

  59. 59.

    Petrov, D. A. Mutational equilibrium model of genome size evolution. Theor. Popul. Biol. 61, 531–544 (2002).

    Article  PubMed  Google Scholar 

  60. 60.

    Charlesworth, B., Sniegowski, P. & Stephan, W. The evolutionary dynamics of repetitive DNA in Eukaryotes. Nature 371, 215–220 (1994).

    ADS  Article  PubMed  CAS  Google Scholar 

  61. 61.

    Le Rouzic, A., Payen, T. & Hua-Van, A. Reconstructing the evolutionary history of transposable elements. Genome Biol. Evol. 5, 77–86 (2013).

    Article  PubMed  CAS  Google Scholar 

  62. 62.

    Ruggiero, R. P., Bourgeois, Y. & Boissinot, S. LINE insertion polymorphisms are abundant but at low frequencies across populations of Anolis carolinensis. Front. Genet. 8, 44 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. 63.

    Xue, A. T., Ruggiero, R. P., Hickerson, M. J. & Boissinot, S. Differential effect of selection against line retrotransposons among vertebrates inferred from whole-genome data and demographic modeling. Genome Biol. Evol. 10, 1265–1281 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    CLC Genomics Workbench 9.0.1.

  65. 65.

    Castoe, T. A. et al. Rapid identification of thousands of copperhead snake (Agkistrodon contortrix) microsatellite loci from modest amounts of 454 shotgun genome sequence. Mol. Ecol. Resour. 10, 341–347 (2010).

    Article  PubMed  CAS  Google Scholar 

  66. 66.

    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Article  Google Scholar 

  67. 67.

    Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics 20, 289–290 (2004).

  68. 68.

    Smit, A. F. A. & Hubley, R. RepeatModeler Open-1.0.9. (2008–2017).

  69. 69.

    Kohany, O., Gentles, A. J., Hankus, L. & Jurka, J. Annotation, submission and screening of repetitive elements in Repbase: RepbaseSubmitter and Censor. BMC Bioinform. 7, 474 (2006).

    Article  CAS  Google Scholar 

  70. 70.

    Johnson, M. et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 36, W5–W9 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. 71.

    Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker Open-4.0.2. http://www.repeatmasker.or (2013–2015).

  72. 72.

    Bao, W., Kojima, K. K. & Kohany, O. Repbase update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6, 11 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. 74.

    Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).

    Article  PubMed  CAS  Google Scholar 

  75. 75.

    Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. 76.

    Rambaut, A. & Drummond, A. J. Tracer v.1. 4. (2007).

  77. 77.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. 78.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. 79.

    Team R. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2013).

  80. 80.

    Paradis, E. pegas: an R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419–420 (2010).

    Article  PubMed  CAS  Google Scholar 

  81. 81.

    Bernt, M. et al. MITOS: improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).

    Article  PubMed  Google Scholar 

  82. 82.

    Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. 83.

    Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. Partitionfinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29, 1695–1701 (2012).

    Article  PubMed  CAS  Google Scholar 

  84. 84.

    Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. 85.

    Benton, M. J. & Donoghue, P. C. J. Paleontological evidence to date the tree of life. Mol. Biol. Evol. 24, 26–53 (2007).

    Article  PubMed  CAS  Google Scholar 

  86. 86.

    Pyron, R. A., Burbrink, F. T. & Wiens, J. J. A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evol. Biol. 13, 93 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. 87.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. 88.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. 89.

    Green, R. E. et al. Three crocodilian genomes reveal ancestral patterns of evolution among archosaurs. Science 346, 1254449 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. 90.

    Mazet, O., Rodriguez, W. & Chikhi, L. Demographic inference using genetic data from a single individual: separating population size variation from population structure. Theor. Popul. Biol. 104, 46–58 (2015).

    Article  PubMed  MATH  Google Scholar 

  91. 91.

    Boitard, S., Rodriguez, W., Jay, F., Mona, S. & Austerlitz, F. Inferring population size history from large samples of genome-wide molecular data—an approximate Bayesian computation approach. PLoS Genet. 12, e1005877 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. 92.

    Zachos, J., Pagani, M., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).

    ADS  Article  PubMed  CAS  Google Scholar 

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Support for this work was provided from startup funds from the University of Texas at Arlington to TAC. We acknowledge the Texas Advanced Computing Center (TACC) for providing access to computational resources.

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G.I.M.P. and T.A.C. designed and performed experiments, analyzed results, and wrote the manuscript; R.H.A., D.C.C., A.B.C., and D.R.S. collected data and analyzed results; J.R.-V. collected data; B.W.P., R.P.R., M.W.V., and J.A.S. analyzed results; all authors contributed to editing the final manuscript.

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Correspondence to Todd A. Castoe.

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Pasquesi, G.I.M., Adams, R.H., Card, D.C. et al. Squamate reptiles challenge paradigms of genomic repeat element evolution set by birds and mammals. Nat Commun 9, 2774 (2018).

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