Dense Sampling of Bird Diversity Increases Power of Comparative Genomics
UC Santa Cruz UC Santa Cruz Previously Published Works
Title Dense sampling of bird diversity increases power of comparative genomics.
Permalink https://escholarship.org/uc/item/6bz492wj
Journal Nature, 587(7833)
ISSN 0028-0836
Authors Feng, Shaohong Stiller, Josefin Deng, Yuan et al.
Publication Date 2020-11-11
DOI 10.1038/s41586-020-2873-9
Peer reviewed
eScholarship.org Powered by the California Digital Library University of California Article Dense sampling of bird diversity increases power of comparative genomics
https://doi.org/10.1038/s41586-020-2873-9 A list of authors and affiliations appears at the end of the paper.
Received: 9 August 2019 Whole-genome sequencing projects are increasingly populating the tree of life and Accepted: 27 July 2020 characterizing biodiversity1–4. Sparse taxon sampling has previously been proposed Published online: 11 November 2020 to confound phylogenetic inference5, and captures only a fraction of the genomic
Open access diversity. Here we report a substantial step towards the dense representation of avian phylogenetic and molecular diversity, by analysing 363 genomes from 92.4% of bird Check for updates families—including 267 newly sequenced genomes produced for phase II of the Bird 10,000 Genomes (B10K) Project. We use this comparative genome dataset in combination with a pipeline that leverages a reference-free whole-genome alignment to identify orthologous regions in greater numbers than has previously been possible and to recognize genomic novelties in particular bird lineages. The densely sampled alignment provides a single-base-pair map of selection, has more than doubled the fraction of bases that are confdently predicted to be under conservation and reveals extensive patterns of weak selection in predominantly non-coding DNA. Our results demonstrate that increasing the diversity of genomes used in comparative studies can reveal more shared and lineage-specifc variation, and improve the investigation of genomic characteristics. We anticipate that this genomic resource will ofer new perspectives on evolutionary processes in cross-species comparative analyses and assist in eforts to conserve species.
Comparative genomics is rapidly growing, fuelled by the advancement (Pedionomus torquatus) and the Bali myna (Leucopsar rothschildi, which of sequencing technologies. Many large-scale initiatives have been has fewer than 50 adults remaining in the wild13). proposed with a core mission of producing genomes for hundreds of Two hundred and sixty-seven of the 363 species represented in our species, representing the phylogenetic diversity of particular taxa6–8. genome data are newly released, comprising 18.4 trillion base pairs Although the generation of genomes is now more routine, an immediate (bp) of raw data and 284 billion bp of assemblies. The assemblies are challenge is how to efficiently compare large numbers of genomes in comparable in quality to previously published bird genomes9,10, but an evolutionary context. A critical first step is the accurate detection of vary in contiguity (average scaffold N50 = 1.42 megabases (Mb), contig orthologous sequences. In this study, we release a large-scale dataset of N50 = 42.57 kilobases (kb); see interactive supplementary figure 1, bird genomes, which we use to establish a framework for comparative hosted at https://genome-b10k.herokuapp.com/main). The sequencing analysis. We provide insight on how scaling-up genome sampling assists coverage ranged from 35× (blue-throated roller (Eurystomus gularis) in our understanding of avian genomic diversity and in the detection and yellowhead (Mohoua ochrocephala)) to 368× (song sparrow (Melo- of signals of natural selection down to individual bases. spiza melodia)) and genomic completeness was high (average 95.8%). The B10K Project began with the Avian Phylogenomics Consortium We annotated all 363 genomes using a homology-based method with a (phase I), which analysed 48 genomes from representatives of most uniform gene set that included gene models from chicken, zebra finch bird orders9,10. Here we report the genome sequencing outcomes from and human, and published transcriptomes (Supplementary Tables 6– phase II of the project: these outcomes include a total of 363 species in 8), to predict an average of 15,464 protein-coding genes for each species 92.4% (218 out of 23611,12) of avian families (Supplementary Tables 1–5). (Supplementary Table 1). We also assembled mitochondrial genomes Species were selected to span the overall diversity and to subdivide long for 336 species, with 216 species fully circularized and 228 species branches, when possible (Fig. 1, Supplementary Data). Our sampling with a complete mitochondrial annotation (Supplementary Table 1). covers bird species from every continent (Extended Data Fig. 1) and Bird genomes at the ordinal level were previously found to contain a more than triples the previous taxonomic coverage of avian genome low proportion of transposable elements, except for the downy wood- sequencing; to our knowledge, 155 bird families are represented here pecker (Picoides pubescens) in Piciformes10. Consistent with these for the first time. We chose short-read sequencing as our main strategy findings, 96.1% of birds at the family level had a transposable element for generating data, which enabled us to use older samples (the oldest of content lower than 15%—but we found additional outliers (Extended which was collected in 1982) and access rare museum specimens—such Data Fig. 2a, Supplementary Table 1). In particular, long interspersed as one of the few vouchered tissues of the Henderson crake (Zapornia nuclear elements were prevalent in all nine sequenced species in Pici- atra), which occurs on a single island. We incorporated 68 species of formes, which suggests an ancestral expansion in this lineage (24% on concern on the International Union for Conservation of Nature (IUCN) average, Welch two-sample t-test, P = 9.98 × 10−5) (Extended Data Fig. 2b, Red List of Threatened Species (Supplementary Table 1); these include d). The common scimitarbill (Rhinopomastus cyanomelas) and com- 12 endangered and 2 critically endangered species—the plains-wanderer mon hoopoe (Upupa epops) in Bucerotiformes also had exceptionally
252 | Nature | Vol 587 | 12 November 2020 Uria lomvia
Arenaria interpres
Fregetta grallaria
Aquila chrysaetos
Ramphastos sulfuratus Alopecoenas beccarii
Agapornis roseicollis Piaya cayana
Calypte anna
*
Odontophorus gujanensis
Onychorhynchus coronatus
Apteryx owenii
Orthonyx spaldingii
Dyaphorophyia castanea ** Agelaius phoeniceus
Chaetops frenatus Tichodroma muraria Leptocoma aspasia Hemignathus wilsoni
Fig. 1 | Newly sequenced genomes densely cover the bird tree of life. The per sequenced family (purple branches). The grey arc marks the diverse 10,135 bird species11,12 are shown on a draft phylogeny that synthesizes Passeriformes radiation, with 6,063 species, of which 173 species have genome taxonomic and phylogenetic information36 (Supplementary Data). In total, assemblies now. Chicken (*) and zebra finch (**) are marked for orientation. 363 species, covering 92.4% of all families, now have at least 1 genome assembly Paintings illustrate examples of sequenced species.
high transposable element content (23% and 18%, respectively) owing Project phase I. Nonetheless, 498 genes remained absent across all to recent expansions of long interspersed nuclear elements, whereas 363 bird species, which adds to evidence that these genes were truly two hornbill species in the same order exhibited the typical low propor- lost in the common ancestor. tions (Extended Data Fig. 2e). We also investigated a number of genes that were previously associ- Previous studies have suggested that hundreds of genes were lost ated with phenotypes and physiological pathways. For example, we in the ancestor of birds10,14. Gene-loss inference is complicated by found that rhodopsin (encoded by RH1) and the medium-wavelength incomplete assemblies and can be unreliable with only a few species15. sensitive opsin (encoded by RH2) were present in all 363 birds, but We found that 142 genes previously considered to be absent in Aves10 were incomplete or pseudogenized in 5 and 11 species, respectively were detected in at least one of the newly sequenced bird genomes (Supplementary Table 10). The other three conopsin genes showed a (Supplementary Table 9), which implies that these genes were either more varied pattern of presence and absence. OPN1sw2 and OPN1lw lost multiple times or missed in the assemblies of the 48 birds of B10K existed either as partial sequences or were completely absent in 310
Nature | Vol 587 | 12 November 2020 | 253 Article
a Chr. 1 b Lineage-speci c sequence (%) GH GH 024 Chicken 2 P Chr. 27 AT AP Coliiformes GH_L GH_L Zebra nch Chr. 27 Trogoniformes MOG Columbiformes GH_L GH_L Atlantic canary HG009 Pterocliformes 1 251.1 B Otidiformes GH_S Chr. 1 GH_S HM Psittaciformes Zebra nch RDM1 K HG009 SCN4A CD79 RC37A3 Podicipediformes GH_S 281.1 PLE LR Atlantic canary GH_S Rheiformes c Eurypygiformes e Corvidae Musophagiformes ik Paridae Cuculiformes Scotocercidae Cariamiformes
KLHL1 Passeriformes KLF5 PIBF1 RRP44 BORA MZT1 DACH1 DNAJC15-l Sylviidae Falconiformes MRCA of Passeriformes Emberizidae Anseriformes Apterygiformes Passeriformes Tachurididae Tinamiformes Eurylaimidae Sphenisciformes Acanthisittidae Galliformes Psittaciformes Coraciiformes Accipitriformes Charadriiformes Strigiformes Pelecaniformes Piciformes Charadriiformes Gruiformes Caprimulgiformes Casuariiformes Bucerotiformes Columbiformes Procellariiformes Passerea Anseriformes Accipitriformes Columbea Galliformes Caprimulgiformes Palaeognathae Tinamiformes Pelecaniformes Galloanseres
Fig. 2 | Improved orthologue distinction and detection of lineage-specific than one sequenced representative, lineage-specific sequences are those sequences. a, Incorporating synteny in the orthologue assignment pipeline present in the reconstructed ancestral genome but absent in other lineages. resolves complex cases of orthology. The growth hormone gene (GH) has one Colours denote higher-level taxonomic groupings9. c, A novel gene in copy in chicken and two copies in Passeriformes (exemplified by zebra finch Passeriformes. Phylogeny based on the B10K Project phase I9 plotted with and Atlantic canary). On the basis of the conserved synteny of the GH_L in synteny of a putative lineage-specific gene (DNAJC15L) and its surrounding Passeriformes with GH of chicken, the pipeline recognized GH_L as the genes. DNAJC15L is found in 131 out of 173 sequenced Passeriformes and their ancestral copy—despite high similarity to the other copy. b, The whole-genome reconstructed ancestral genome, but is not found in non-Passeriformes. alignment allows detecting lineage-specific sequences. For orders with more MRCA, most-recent common ancestor.
and 308 species, respectively, and OPN1sw1 was functional in more Gene duplications are an important mechanism that shapes genome than half of the 363 birds—especially in Passeriformes (perching birds) evolution, because duplicated copies often evolve under different (Extended Data Fig. 3, Supplementary Table 10). selective pressures and evolutionary rates20. We developed an ortho- Passeriformes also had a notably higher GC content than other birds logue assignment pipeline that incorporates information about the in coding regions (Welch two-sample t-test, P = 7.59 × 10−43) (Extended genomic context of the gene copies (synteny) with the Cactus alignment Data Fig. 4a) but not in non-coding regions (Welch two-sample t-test, to permit distinguishing between the ancestral copies, those inherited P = 0.06). Differences in GC content can result in biased use of particular from a more recent common ancestor and duplicated novel copies synonymous codons over others, which can affect gene expression (Extended Data Fig. 5a, Supplementary Tables 11, 12). An example is the and translation efficiency16. Consistent with this hypothesis, relative growth hormone (GH) gene that was previously found to be duplicated synonymous codon use values for 59 synonymous codons (excluding in 24 Passeriformes (to produce GH_L and GH_S)21. We confirmed that non-degenerate codons, Met, Trp and three stop codons) showed sub- this gene duplication occurred exclusively in Passeriformes (found in stantial differences between Passeriformes and other birds, especially 161 out of 173 species; its absence in 12 species is caused by incomplete in the preference of codons ending in G or C (Extended Data Fig. 4b, c, e). assemblies), resulting in a one-to-many relationship with the single Passeriformes significantly deviated from random use of synonymous copy in other birds (Extended Data Fig. 6). The synteny with surround- codons with a smaller average effective number of codons compared ing genes identified the passeriform GH_L as the ancestral copy, and to other birds (paired-sample t-test, P < 2.2 × 10−16) (Extended Data GH_S as a newly derived copy located in a different genomic context Fig. 4f). These results indicate that the GC content may have affected (Fig. 2a). Moreover, when the pipeline was applied to both datasets the gene evolution of the speciose Passeriformes. (of 48 and 363 bird species), the higher taxon sampling allowed the To gain further evolutionary insight from the genomes, we con- detection of 439 additional orthologues with conserved synteny to structed a whole-genome alignment of the 363 genomes using a chicken—many of which were lineage-specific gene copies. These addi- progressive version of the reference-free aligner Cactus17,18. Cactus tional orthologues, improved by the denser representation of species produced a substantially more-complete alignment than the com- and the Cactus alignment, will drive downstream comparative analyses. monly used reference-based method MULTIZ19, particularly when the Using the Cactus alignment, we reconstructed an ancestral aligned species were phylogenetically distant from the chicken refer- genome for each evolutionary node to characterize both shared and ence17. In comparison to a previous alignment of the 48 bird genomes lineage-specific genomic diversity. Being able to identify sequences using chicken and zebra finch as references10, our reference-free unique to particular lineages, and not only those shared with a refer- approach and extended sampling unlocked a far greater proportion ence genome, is a major advantage of a reference-free alignment22. We of orthologous sequences: 981 Mb across the whole genome (a 149% found that lineage-specific sequences constitute 0.2% to 5.5% of the increase), 24 Mb of orthologous coding sequence (an 84.4% increase) reconstructed genome of the most-recent common ancestor of each and 141 Mb of orthologous introns (a 631% increase) that derived order (Fig. 2b, Supplementary Table 13). Among these, we identified from the common avian ancestors between chicken and any other 154 Passeriformes-specific genes (Supplementary Table 14). The gene bird species. present in the largest number of passerines (131 out of 173 species) is
254 | Nature | Vol 587 | 12 November 2020 ab c
10.0 Neutral rate 0.6 7.5 53-way 0.40 5.0 0.4 77-way
2.5 53-way 363-way 0 0.2 0.30
10.0 (FDR < 0.05)
7.5 conserved single bp 0 Not signi cant 5.0 Proportion of signi cantly Coding UTR lncRNA Whole Introns Ancestral exons exons exons genome repeats 0.20 Signi cantly 2.5 77-way conserved (FDR < 0.05): 0 d 4 10.0 13.21% 363-way 2 FDR < 0.05 Proportion of genome (% ) 53-way
with a true-positive call 0.10 7.5 0 5.0 Expected fraction of the genome –2 3.76% 77-way 2.5 4 363-way
2.11% 53-way −log( q value) 2 FDR < 0.05
0 0 363-way 0 0.05 0.10 0.15 0.20 0 0.25 0.50 0.75 1.00 0 Expected fraction of the genome Rate of column AAACCTGC TTGGAAT with a false-positive call (as a proportion of neutral rate) Position on chromosome 2 (MAF::NFE2)
Fig. 3 | Denser phylogenomic sequencing increases the power to detect non-significantly conserved columns. A rate of zero contains a relatively high selective constraints. Results are shown from 3 alignments for 53 birds, proportion of recent insertions present in only a few species; there is limited 77 vertebrates, and 363 birds. a, Proportion of alignment columns labelled as statistical power to classify such insertions. c, Proportion of various functional conserved. The cumulative portion of the genome with a conserved call is regions of the chicken genome that contain single-bp conserved elements in shown, starting from the column with the smallest P value and proceeding to the large alignment compared to alignments with fewer species. UTR, the columns with the highest P values. The dotted lines show the path after untranslated region. d, An example of a MAF::NFE2 motif overlaid on one of its hitting the false-discovery rate (FDR) P value cutoff of 0.05, below which calls predicted binding sites demonstrates the high resolution of our conserved site are significant (marked by arrows). b, Histograms of the rate of alignment predictions and the increased power to predict conservation in the larger columns evolving slower relative to the neutral rate (labelled 1.00). Coloured alignment. areas indicate significantly conserved columns, and light grey areas indicate
a paralogue of the heat shock protein gene DNAJC15, which has many genome. We scaled our model of the genome-wide mutation rate to copies in bird genomes and is thought to be associated with the bio- match the neutral rate observed in microchromosomes, macrochro- genesis of mitochondria23 and fertilization24. We identified a novel mosomes and sex chromosomes, because each chromosome type Passeriformes-specific copy (which we named DNAJC15-like (DNA- shows different evolutionary rates in birds29,30. This resulted in one JC15L)) at a newly derived genomic region between the MZT1 and DACH1 model for each chromosome type, which together were then used to genes (based on the chicken coordinates), which was reconstructed as evaluate the degree of departure from the neutral rate and to estimate a duplication in the most-recent common ancestor of Passeriformes the conservation score for each site. With the 363-way data, we found (Fig. 2c, Extended Data Fig. 7c). The DNAJC15L gene model showed that the neutral rate within sex chromosomes is 16% faster than in mac- exon fusions compared to its parental gene, which suggests that a ret- rochromosomes, and that the neutral rate within macrochromosomes rotransposition mechanism was the probable origin of this duplication is 9% faster than in microchromosomes. (Extended Data Fig. 7d). We compared these results against conservation scores derived Moreover, we identified lineage-specific losses of genes such as cor- from two smaller alignments: a MULTIZ 77-way alignment including nulin (CRNN), which encodes a prominent structural protein of the birds and other vertebrate outgroups31, and a 53-way alignment con- oesophageal and oral epithelium in humans and chicken25. This gene is taining only birds of the 77-way alignment. A previous comparison disrupted by mutations or is entirely absent in Accipitriformes (eagles of 48 bird genomes found that at least 7.5% of the chicken genome and related birds of prey), Phalacrocoracidae (cormorants) and Passeri was conserved, with significantly lower substitution rates than the (songbirds, a group of Passeriformes) (Extended Data Fig. 8a). The background10. This ratio was reached at 10-bp resolution by integrat- latter use rapid changes in the diameter of the upper oesophagus to ing across multiple adjacent bases, trading off a lower resolution for tune their vocal tract to the fundamental frequency of their song26. The a necessary increase in statistical power. This is because the statistical absence of CRNN might correspond to changes in visco-elastic proper- power to detect conserved elements is roughly proportional to the total ties of the oesophageal epithelium, and the loss of this gene may have branch length between the aligned species32. Our reference-free align- contributed to the evolution of the diverse pure-tone vocalizations of ment of 363 bird species resulted in a predicted total branch length of songbirds (Extended Data Fig. 8b). 16.5 expected substitutions per site, compared to 9.9 within the 77-way We next explored avian conserved sequences, genomic regions that and 4.3 within the 53-way alignments. We transformed the conserva- evolve at a substantially slower substitution rate than expected under tion scores into calls of significantly conserved single-base-pairs at an neutral evolution. Conserved sequences are often indicators of puri- expected false-discovery rate33 of 5%. The 363-way alignment provided fying selection27 and are therefore useful for investigating function ample increases in the number of bases detectable as conserved relative within the genome28. To identify and measure conserved regions, we to alignments that contain fewer taxa (13.2% of the chicken genome in created conservation scores for each base pair of the 363-species Cactus the 363-way alignment versus 3.8% in the 77-way and 2.1% in the 53-way) alignment projected onto the chicken genome. The dense sampling (Fig. 3a, Supplementary Table 15). Such an improvement cannot be increased our ability to detect purifying selection enormously, and explained by the alignment method, as a Cactus 48-way alignment allowed us to produce what is—to our knowledge—the first base-by-base of birds showed very similar results to the 53-way MULTIZ alignment conservation annotation that covers a substantial portion of a bird (Extended Data Fig. 10d). In the Z chromosome (which has a generally
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256 | Nature | Vol 587 | 12 November 2020 D. Ketterson85, Andrew Eric Fidler86, Guillermo Friis87, Ángela M. Parody-Merino88, Phil F. Biology, Harvard University, Cambridge, MA, USA. 58Museum of Comparative Zoology, Battley88, Murray P. Cox89, Nicholas Costa Barroso Lima62,90, Francisco Prosdocimi91, Thomas Harvard University, Cambridge, MA, USA. 59Informatics Group, Harvard University, 92 93,94 95,96 95 Lee Parchman , Barney A. Schlinger , Bette A. Loiselle , John G. Blake , Haw Chuan Cambridge, MA, USA. 60Sylvan Heights Bird Park, Scotland Neck, NC, USA. 61GenPhySE, INRA, 75,97 98 99 100 101,102 Lim , Lainy B. Day , Matthew J. Fuxjager , Maude W. Baldwin , Michael J. Braun , INPT, INP-ENVT, Université de Toulouse, Castanet-Tolosan, France. 62Laboratório Nacional de 103 50 104 105 Morgan Wirthlin , Rebecca B. Dikow , T. Brandt Ryder , Glauco Camenisch , Lukas F. 63 105 106 107 53,107,108 Computação Científica, Petrópolis, Brazil. Department of Ecology and Evolutionary Biology, Keller , Jeffrey M. DaCosta , Mark E. Hauber , Matthew I. M. Louder , Christopher 64 109 110 111 112 112 University of California Santa Cruz, Santa Cruz, CA, USA. Howard Hughes Medical Institute, C. Witt , Jimmy A. McGuire , Joann Mudge , Libby C. Megna , Matthew D. Carling , 65 Biao Wang113, Scott A. Taylor114, Glaucia Del-Rio9, Alexandre Aleixo115, Ana Tereza Ribeiro University of California Santa Cruz, Santa Cruz, CA, USA. Institut de Recerca de la 66 Vasconcelos62, Claudio V. Mello116, Jason T. Weir27,28,117, David Haussler5, Qiye Li1,3, Huanming Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain. Departament de Biologia Yang3,118, Jian Wang3, Fumin Lei24,119, Carsten Rahbek19,120,121,122, M. Thomas P. Gilbert11,123, Gary Evolutiva, Ecologia i Ciències Ambientals (BEECA), Universitat de Barcelona, Barcelona, R. Graves19,101, Erich D. Jarvis17,124,125, Benedict Paten5 ✉ & Guojie Zhang1,2,4,119 ✉ Spain. 67Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain. 68Department of Molecular, Cellular and Biomedical Sciences, University of 1China National GeneBank, BGI-Shenzhen, Shenzhen, China. 2State Key Laboratory of Genetic New Hampshire, Durham, NH, USA. 69Department of Biology, Queen’s University, Kingston, Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Ontario, Canada. 70Department of Bioinformatics and Genetics, Swedish Museum of Natural Kunming, China. 3BGI-Shenzhen, Shenzhen, China. 4Villum Centre for Biodiversity Genomics, History, Stockholm, Sweden. 71Centre for Palaeogenetics, Stockholm, Sweden. 72Milner Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Centre for Evolution, University of Bath, Bath, UK. 73Instituto de Ecologia, UNAM, Mexico City, Copenhagen, Denmark. 5UC Santa Cruz Genomics Institute, UC Santa Cruz, Santa Cruz, CA, Mexico. 74State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, USA. 6Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Guangzhou, China. 75Center for Conservation Genomics, Smithsonian Conservation Biology Denmark. 7BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, Institute, Smithsonian Institution, Washington, DC, USA. 76New York Genome Center, New China. 8Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA. York, NY, USA. 77Department of Anatomy, University of Otago, Dunedin, New Zealand. 78AG 9Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA. 10Centre of Neurosensory Sciences, Institut für Biologie und Umweltwissenschaften, University of Excellence for Omics-Driven Computational Biodiscovery (COMBio), Faculty of Applied Oldenburg, Oldenburg, Germany. 79Biology Department, Texas A&M University, College Sciences, AIMST University, Kedah, Malaysia. 11Section for Evolutionary Genomics, The GLOBE Station, TX, USA. 80MPRG Behavioural Genomics, Max Planck Institute for Evolutionary Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Biology, Plön, Germany. 81Institute of Clinical Molecular Biology, Christian-Albrechts- Denmark. 12MOE Laboratory of Biosystems Homeostasis and Protection, Life Sciences University of Kiel, Kiel, Germany. 82Department of Wildlife Diseases, Leibniz Institute for Zoo Institute, Zhejiang University, Hangzhou, China. 13Department of Neuroscience and and Wildlife Research, Berlin, Germany. 83Environmental Resilience Institute, Indiana Developmental Biology, University of Vienna, Vienna, Austria. 14Center for Reproductive University, Bloomington, IN, USA. 84National Museum of Natural Sciences, Spanish National Medicine, The 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Research Council (CSIC), Madrid, Spain. 85Department of Biology, Indiana University, China. 15Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia. Bloomington, IN, USA. 86Institute of Marine Science, University of Auckland, Auckland, New 16Novogene, Durham, NC, USA. 17Duke University Medical Center, Durham, NC, USA. Zealand. 87Center for Genomics and Systems Biology, Department of Biology, New York 18Department of Dermatology, Medical University of Vienna, Vienna, Austria. 19Center for University – Abu Dhabi, Abu Dhabi, UAE. 88Wildlife and Ecology Group, Massey University, Macroecology, Evolution, and Climate, GLOBE Institute, University of Copenhagen, Palmerston North, New Zealand. 89School of Fundamental Sciences, Massey University, Copenhagen, Denmark. 20Southern Cross University, Coffs Harbour, New South Wales, Palmerston North, New Zealand. 90Departamento de Bioquímica e Biologia Molecular, Centro Australia. 21Centre for Zoo and Wild Animal Health, Copenhagen Zoo, Frederiksberg, de Ciências, Universidade Federal do Ceará, Fortaleza, Brazil. 91Laboratório de Genômica e Denmark. 22Center of Natural History, Universität Hamburg, Hamburg, Germany. 23Department Biodiversidade, Instituto de Bioquímica Médica Leopoldo de Meis, Rio de Janeiro, Brazil. of Zoology, University of Otago, Dunedin, New Zealand. 24Key Laboratory of Zoological 92Department of Biology, University of Nevada Reno, Reno, NV, USA. 93Department of Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, Integrative Biology and Physiology, UCLA, Los Angeles, CA, USA. 94Smithsonian Tropical China. 25Environmental Futures Research Institute, Griffith University, Nathan, Queensland, Research Institute, Panama City, Panama. 95Department of Wildlife Ecology and Conservation, Australia. 26Department of Biology, University of Maryland, College Park, MD, USA. University of Florida, Gainesville, FL, USA. 96Center for Latin American Studies, University of 27Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada. Florida, Gainesville, FL, USA. 97Department of Biology, George Mason University, Fairfax, VA, 28Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, USA. 98Department of Biology and Neuroscience Minor, University of Mississippi, University, Canada. 29Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA. 30Biodiversity and MS, USA. 99Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, Conservation Science, Department of Biodiversity Conservation and Attractions, Perth, USA. 100Max Planck Institute for Ornithology, Seewiesen, Germany. 101Department of Western Australia, Australia. 31Ornithology Section, Zoology Department, National Museums Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution, of Kenya, Nairobi, Kenya. 32Ornithology and Mammalogy, California Academy of Sciences, Washington, DC, USA. 102Behavior, Ecology, Evolution and Systematics Program, University of San Francisco, CA, USA. 33San Diego Zoo Institute for Conservation Research, Escondido, CA, Maryland, College Park, MD, USA. 103Computational Biology Department, Carnegie Mellon USA. 34Evolution, Behavior, and Ecology, Division of Biology, University of California San University, Pittsburgh, PA, USA. 104Migratory Bird Center, Smithsonian National Zoological Diego, La Jolla, CA, USA. 35Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum Park and Conservation Biology Institute, Washington, DC, USA. 105Department of Evolutionary National d’Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, Biology and Environmental Studies, University of Zurich, Zurich, Switzerland. 106Biology France. 36Trace and Environmental DNA (TrEnD) Laboratory, School of Molecular and Life Department, Boston College, Chestnut Hill, MA, USA. 107Department of Evolution, Ecology, Sciences, Curtin University, Western Australia, Perth, Australia. 37UQ Genomics, University of and Behavior, School of Integrative Biology, University of Illinois at Urbana-Champaign, Queensland, Brisbane, Queensland, Australia. 38Department of Ornithology, American Urbana, IL, USA. 108International Research Center for Neurointelligence, University of Tokyo, Museum of Natural History, New York, NY, USA. 39Integrative Research Center, Field Museum Tokyo, Japan. 109Museum of Southwestern Biology, Department of Biology, University of New of Natural History, Chicago, IL, USA. 40FitzPatrick Institute of African Ornithology, University of Mexico, Albuquerque, NM, USA. 110Museum of Vertebrate Zoology, Department of Integrative Cape Town, Cape Town, South Africa. 41Department of Biology, University of Florida, Biology, University of California, Berkeley, Berkeley, CA, USA. 111National Center for Genome Gainesville, FL, USA. 42Department of Biology, New Mexico State University, Las Cruces, NM, Resources, Santa Fe, NM, USA. 112Department of Zoology and Physiology, University of USA. 43Department of Electrical and Computer Engineering, University of California San Wyoming, Laramie, WY, USA. 113School of BioSciences, The University of Melbourne, Diego, La Jolla, CA, USA. 44Department of Ecology and Genetics – Evolutionary Biology, Melbourne, Victoria, Australia. 114Department of Ecology and Evolutionary Biology, University Evolutionary Biology Centre (EBC), Science for Life Laboratory, Uppsala University, Uppsala, of Colorado Boulder, Boulder, CO, USA. 115Finnish Museum of Natural History, University of Sweden. 45Department of Organismal Biology – Systematic Biology, Evolutionary Biology Helsinki, Helsinki, Finland. 116Department of Behavioral Neuroscience, Oregon Health and Centre (EBC), Science for Life Laboratory, Uppsala University, Uppsala, Sweden. 46School of Science University, Portland, OR, USA. 117Department of Biological Sciences, University of Biological Sciences, University of East Anglia, Norwich, UK. 47Department of Biology, Lund Toronto Scarborough, Toronto, Ontario, Canada. 118James D. Watson Institute of Genome University, Lund, Sweden. 48Institute of Ecology and Evolution, University of Oregon, Eugene, Sciences, Hangzhou, China. 119Center for Excellence in Animal Evolution and Genetics, OR, USA. 49Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT, Chinese Academy of Sciences, Kunming, China. 120Danish Institute for Advanced Study, USA. 50Data Science Lab, Office of the Chief Information Officer, Smithsonian Institution, University of Southern Denmark, Odense, Denmark. 121Institute of Ecology, Peking University, Washington, DC, USA. 51Focus Area for Human Metabolomics, North-West University, Beijing, China. 122Department of Life Sciences, Imperial College London, Ascot, UK. Potchefstroom, South Africa. 52Department of Animal Sciences, University of Pretoria, 123University Museum, Norwegian University of Science and Technology, Trondheim, Norway. Pretoria, South Africa. 53Department of Biology, East Carolina University, Greenville, NC, USA. 124The Rockefeller University, New York, NY, USA. 125Howard Hughes Medical Institute, Chevy 54Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA. 55Avian Chase, MD, USA. 126These authors contributed equally: Shaohong Feng, Josefin Stiller, Yuan Ecology Program, Archbold Biological Station, Venus, FL, USA. 56Department of Biology, Deng, Joel Armstrong. 127Deceased: Ian G. Jamieson. ✉e-mail: [email protected]; guojie. University of Rochester, Rochester, NY, USA. 57Department of Organismic and Evolutionary [email protected]
Nature | Vol 587 | 12 November 2020 | 257 Article Methods green anole (Anolis carolinensis) (v.AnoCar2.0) and green sea turtle (Chelonia mydas) (v.CheMyd1.0). These outgroups were not included No statistical methods were used to predetermine sample size. The in the alignment, but used only to provide outgroup information for experiments were not randomized and investigators were not blinded subproblems near the root (by using the --root option to select only to allocation during experiments and outcome assessment. the bird subtree).
Sample selection, DNA extraction, sequencing and assembly Orthologue identification A total of 363 species from 218 families were included. The 363 genomes Definitions for the terms regarding homology and orthology that are came from 4 data sources, and included 267 newly sequenced genomes used throughout the Article are based on previous publications49,50 and and 96 publicly available genomes (Extended Data Fig. 1a, Supplemen- the resources of Ensembl (https://asia.ensembl.org/info/genome/com- tary Table 1). A total of 236 genomes were sequenced specifically for para/homology_types.html). Two genes are considered homologues if this project, drawing on samples from 17 scientific collections. The 3 they share a common origin; that is, if they are derived from a common largest contributors were the National Museum of Natural History of ancestor. A homologous group is a cluster of genes that evolved from one the Smithsonian Institution (140 species), Louisiana State University ancestor. Orthologues are homologous genes that result from a specia- Museum of Natural Science (31 species) and Southern Cross University tion event. Paralogues are homologous genes that result from a duplica- (23 species). According to the tissue type, we used different commercial tion event. A one-to-one orthologue is an orthologue of which only one extraction kits following the manufacturers’ guidelines. B10K Project copy is found in each species. A one-to-many orthologue occurs when genomes were sequenced at BGI using the short-read sequencing strat- one gene in one species is orthologous to multiple genes in another spe- egy and assembled with SOAPdenovo v.2.0437 and Allpaths-LG v.5248838 cies. Many-to-many orthologues represent situations in which multiple (if applicable). Supplementary Table 1 provides summaries of assem- orthologues are found in both species. In one-to-many and many-to-many bly quality and BUSCO completeness assessment39. We conducted orthologues, the gene copy that is located in a specific genomic con- de novo assembly of the mitochondrial genomes using NOVOPlasty text by synteny is identified as the ancestral copy. In one-to-many and v.2.7.240 and annotated them with MitoZ v.2.341. Species identity was many-to-many orthologues, the gene copy that is out of the genomic confirmed with mitochondrial and nuclear barcodes (Supplementary context (no synteny) is considered as the duplicated gene copy. Tables 4–6). Detailed descriptions of these procedures are available in We identified orthologues using a synteny-based orthologue assign- the Supplementary Information. ment approach that built on the whole-genome alignment generated by Cactus and synteny evidence (Extended Data Fig. 5a). All potential Annotation of repeat and protein-coding genes homologous groups were captured from the Cactus alignment without Tandem repeats were identified by Repeats Finder v.4.07b42 and trans- specifying a reference genome. To gain insight into the relationships posable elements were annotated using both homology-based (Repeat- among different copies of one-to-many and many-to-many orthologue Masker v.open-4.0.743) and de novo (RepeatModeler v.open-1-0-844) groups, we further applied the synteny evidence to distinguish the approaches. The ancestral state of total transposable elements was ancestral and novel copy, using the following steps. reconstructed with maximum likelihood using the fastAnc function in the R package phytools v.0.7-2045. Data preparation. To obtain the putative homologous regions across Annotation of protein-coding genes across the 363 bird genomes all 363 species, we extracted the aligned protein-coding regions from was conducted with a homology-based method using a primary refer- HAL-format files of the Cactus pipeline, on the basis of the coordinate ence gene set containing 20,194 avian genes (Supplementary Table 7). information of all the genes in each species. These annotations were then supplemented by non-redundant anno- tations from the 20,169 human gene set and the 5,257 transcriptomes Homologous group construction. The intersection of the putative set (Supplementary Information, Supplementary Table 8). Analyses of homologous regions from the data preparation step and the coordinate genetic and functional diversity of previously reported genes10,21, and information of the coding regions of protein-coding genes of each spe- of the cornulin gene in songbirds are described in the Supplementary cies from GeneWise predictions constituted the candidate homologous Information. relationships between all possible pairs of species. All of these pairwise relationships were used to construct the homologous groups across Cactus whole-genome alignment all 363 bird species. To achieve this objective, we clustered all genes We ran Cactus (at commit f88f23d) on the Amazon Web Services (AWS) with the relevant pairwise relationship into a homologous group by cloud, using the AWSJobStore of Toil to store intermediate files. We setting the single-linkage clustering with minimum edge weight as generated a phylogenetic hypothesis to use as a guide tree for Cac- zero (Supplementary Table 11). tus by extracting ultraconserved element regions46 from each of the 363 bird assemblies following a standard protocol47 (https://phyluce. Detection of ancestral and derived copies. The synteny evidence readthedocs.io/en/latest/tutorial-three.html) and performed maxi- makes positional information valuable in distinguishing the ancestral mum likelihood inference on the concatenated dataset using ExaML and novel copy in one-to-many and many-to-many orthologues. For v.3.0.948 on an high-performance computing system, assuming a gen- example, we could use the gene synteny between chicken and other eral time-reversible model of rate substitution, γ-distributed rates species to identify the ancestral copy in the pairwise orthologues of among sites and five tree searches (Supplementary Information). We chicken genes in any other species, which is the copy with the consistent used an auto-scaling cluster, which varied in size during the course synteny as in the chicken (Supplementary Table 12). This step refines the of the alignment but used a combination of c3.8xlarge (high-CPU) relationships using the synteny evidence to distinguish the ancestral and r3.8xlarge (high-memory) worker nodes. A MAF-format file was and novel copies in one-to-many and many-to-many orthologues. The derived from this alignment using a parallelized version of the com- ancestral copy of a one-to-many orthologue is sometimes referred to mand hal2maf --onlyOrthologs --refGenome Gallus_gallus. as the strict orthologue or positional orthologue51,52. Chicken and zebra finch were marked as preferred outgroups (mean- ing that they would be chosen as outgroups if they were candidates), Effect of adding species on orthologues with conserved synteny to ensure that a high-quality assembly was almost always available with chicken. To test whether adding species helps to identify more as an outgroup. Three genomes were used as outgroups to the avian orthologues with conserved synteny with chicken, we also applied this tree: common alligator (Alligator mississippiensis) (v.ASM28112v4), method to the 48 birds analysed in phase I of the project. neutral-model-estimator). We extracted the ancestral genome from the Intron dataset construction alignment representing the MRCA of all birds, and ran RepeatMasker43 Introns of the 15,671 orthologues among 363 species with conserved to find avian repeats present in that genome (using the species library synteny with chicken were extracted from the Cactus alignment ‘aves’ from RepBase v.2017012755). A random sample of 100,000 bases (Extended Data Fig. 5b). Detailed descriptions are available in the Sup- within these repeats was used to extract a MAF, which was used as input plementary Information. to the phyloFit program from the PHAST v.1.556 package to create the neutral model (using a general reversible model of nucleotide substi- Codon preference tution). The PHAST framework allows only at most a single entry per To examine the variation in codon usage, we conducted a correspond- genome per column, whereas the output MAFs may contain alignments ence analysis on the relative synonymous codon usage (RSCU) values53 to multiple copies. To resolve this, maf_stream (https://github.com/ at the species level and used the mean values of the effective number of joelarmstrong/maf_stream) was used to combine multiple entries per codons (Nc)54 as an gene-level index to assess the differences between genome into a single entry (using maf_stream dup_merge consensus). the Passeriformes and other species. Detailed descriptions are available Sex-determining chromosomes are known to evolve at a different in the Supplementary Information. rate than autosomes (the fast-X and fast-Z hypothesis)10,29,57. Further- more, micro- and macro-chromosomes in birds have been shown to Lineage-specific sequences on the basis of whole-genome evolve at different rates as well10,30. To remove any potential differ- alignments ences in neutral nucleotide substitution rates among these chromo- We built a pipeline to identify lineage-specific sequences from Cactus somes as a factor, we generated a second set of neutral models that alignments. We define lineage-specific sequences as sequences that represent the neutral rate on these three chromosome sets (we call occur only in the target lineage, do not align to the non-target lineages this set the ‘three-rate model’). These models were generated by map- and that can be located in the reconstructed genome of the MRCA of ping the ancestral repeat sample from the root ancestral genome to the target lineage. Cactus reconstructs the ancestor sequences at each the chicken genome, then separating the resulting bases into bins on node according to the guide tree. By comparing the target lineage the basis of whether they are in macro-, micro- and sex-chromosomes genome and its MRCA genome to their parent nodes on nodes deeper in chicken. For our purposes, we defined micro-chromosomes as any into the tree, we could identify newly emerged sequences of each MRCA chicken autosomal chromosomes other than chromosomes 1–8. and terminal branches as unaligned regions. Lineage-specific dupli- Then, we used the Cactus alignments and the chicken karyotypes to cation with high similarity is not in the scope of this pipeline. Such infer the chromosomal assignment for other species. The training lineage-specific duplication events need to be clarified by introducing was referenced on chicken, so we note that—owing to rare fusion or synteny information, and our orthologue assignment pipeline has a fission events—it is possible that some chicken micro-chromosomes good ability to distinguish these events (for example, the specific copy may have become macro-chromosomes in other species or vice versa. of GH in Passeriformes, as shown in Fig. 2a). We then scaled the ancestral-repeats model separately for each of the To obtain the total length of the lineage-specific sequences for all 37 three bins using phyloFit --init-model
Extended Data Fig. 1 | Sampling and processing of the 363 genomes. are light blue. b, Map63 of geographical origin of the 281 bird samples for a, Sources of the 363 genomes. Each genome is a square; colour indicates the which geographical coordinates are available. c, Summary of the species data source. Newly published genomes from the B10K Project phase II are red; confirmation of 236 B10K Project newly sequenced species. The downward unpublished genomes contributed by external labs are yellow; published arrows are excluded genomes. d, Summary of mitochondrial genome assembly genomes from phase I are orange; genomes contributed by the community that and annotation for 336 species. The downward arrows are excluded have since been published are dark blue; and other genomes available on NCBI mitochondrial genomes. Extended Data Fig. 2 | Distribution of transposable elements. a, Percentage sequenced species. d, S.d. of the per cent LINE content for orders with at least of the genome that is a transposable element (TE). Box plots are shown for three sequenced species. e, Ancestral state reconstruction of total groups with at least three sequenced species. b, Per cent base pairs of the transposable elements. The branch colour from blue to red indicates an genome that are long interspersed nuclear elements (LINEs), grouped by increase in transposable elements. Two orders with noticeable patterns— orders. Box plots are shown for groups with at least three sequenced species. Piciformes and Bucerotiformes—are labelled on the tree. A zoomable figure c, S.d. of the transposable element content for orders with at least three with labels for all terminals is available at www.doi.org/10.17632/fnpwzj37gw. Article
Extended Data Fig. 3 | Patterns of the presence and absence of 5 visual five annotated states of opsin sequences. A zoomable figure with labels for all opsins in 363 bird species. This figure shows patterns for the visual opsins terminals is available at www.doi.org/10.17632/fnpwzj37gw. encoded by RH1, RH2, OPN1sw1, OPN1sw2 and OPN1lw. Colours correspond to Extended Data Fig. 4 | GC content and codon use. a, Principal component over-represented codons (>1.6, orange box plots). e, Pearson correlation analysis (PCA) of GC content in the coding regions of orthologues with between GC content of the third codon position and the primary axis in conserved synteny with chicken for 340 bird species, including b, colour-coded to distinguish Passeriformes and non-Passeriformes. The 164 Passeriformes species. b, Correspondence analysis of RSCU for all 363 strong correlation (R2 = 0.9, P = 4.1 × 10−184) indicates that the frequencies of birds. The primary and secondary axes account for 78.18% and 14.82% of the codons ending in G or C is the main driver of the codon bias in Passeriformes. total variation, respectively. c, The distribution of codons on the same two axes f, Comparison of the mean Nc values between the Passeriformes and other as shown in b, with each codon coloured according to its ending nucleotide. species for orthologues with conserved synteny with chicken (Supplementary This showed that the axis-1 score of a species is primarily determined by Table 12). Each dot represents the mean Nc value of an orthologue in the differences in frequencies of codons ending in G, C, A or T. d, RSCU analysis of Passeriformes and other species, respectively. Orthologues with at least 59 codons across avian genomes (n = 363 biologically independent species for 20 individuals in both the Passeriformes and the non-Passeriformes were each box plot). The horizontal lines indicate thresholds of under-represented included in this analysis. codons (<0.6, blue box plots), average representation (1.0, white box plots) and Article
Extended Data Fig. 5 | Overview of the pipelines for identifying genomic Cactus whole-genome alignments. The credible intron fragments in chicken regions. a, Assignment of orthologous protein-coding regions. All pairwise were picked out after filtering out regions mapped by RNA sequences, and relationships between homologous regions obtained from the Cactus chicken-specific or repetitive regions. Orthologous relationships of intron alignment (4 species shown here in different colours) were used to construct fragments were detected on the basis of the aligned Cactus hits and the the homologous groups across all 363 birds. Using chicken as the reference, we orthologues with conserved synteny with chicken. The non-intron regions of further generated a table containing homologues with conserved synteny to each bird in the alignments were masked as gaps. chicken. b, Annotation of conserved orthologous intron regions on the basis of Extended Data Fig. 6 | Gene tree for copies of the growth hormone gene GH. Passeriformes, corresponding to the GH_S gene copy (blue) and the GH_L gene The tree was generated by maximum likelihood phylogenetic analysis64 of copy (orange). Twelve species with only one copy are indicated by green stars. A avian GH gene copies. Only nodes with >80 bootstrap are annotated as dots; zoomable figure with labels for all terminals and the tree file is available at the larger the dot, the higher the bootstrap. All Passeriformes sequences are www.doi.org/10.17632/fnpwzj37gw. clustered in a single clade and there are two sister gene clades within Article
Extended Data Fig. 7 | See next page for caption. Extended Data Fig. 7 | Identification of lineage-specific sequences. a, An (filled shapes), one copy (empty shapes) and no gene (blank). Passeriformes are example of a 36-bp insertion (red) identified by Cactus in the southern highlighted in red. A zoomable figure with labels for all terminals is available at cassowary (Casuarius casuarius) compared to the Okarito brown kiwi (Apteryx www.doi.org/10.17632/fnpwzj37gw. d, Exon fusion patterns of the DNAJC15- rowi) (both in Palaeognathae) with mapped sequence reads shown as lines. b, like gene (DNAJC15L) in three Passeriformes, compared to exon structure of the Proportion of lineage-specific sequence for each order correlated with the ancestral DNAJC15. For L. aspasia, gene models for the ancestral and novel copy distance from parent node to MRCA node (branch length). c, Presence and are shown. The structure of the ancestral copy is highly conserved across all absence of the DNAJC15-like gene (DNAJC15L), and its surrounding genes, in all bird species with five introns. The Passeriformes-specific copy has no intron or 363 birds. Upstream: KLHL1 and DACH1; downstream: MZT1, BORA, RRP44, newly derived minor intron and includes a poly-(A) at the 5′ end, which implies PIBF1 and KLF5. The state is shown for each bird in three ways: multiple copies that this new gene was derived from retroduplication of DNAJC15. Article
Extended Data Fig. 8 | The evolution of songbirds was associated with the gene not found (blank). Genes were identified by Exonerate65 using loss of the cornulin gene. a, Presence and absence of the cornulin gene phylogenetically diverse EDDM, CRNN and S100A11 sequences as queries. A (CRNN) and its surrounding genes (EDDM and S100A11) in all 363 birds. zoomable figure with labels for all terminals is available at www.doi. Branches are coloured as oscine Passeriformes (blue), non-oscine org/10.17632/fnpwzj37gw. b, Hypothesis on the evolutionary loss of cornulin Passeriformes (green) and non-Passeriformes (black). The states of genes are and the appearance of a fine-tuned extensibility of the oesophagus as a vocal shown in three ways: functional gene (filled box), pseudogene (empty box) and tract filter in songbirds. Extended Data Fig. 9 | Acceleration and conservation scores. Results are Proportion of chicken functional regions covered by significantly accelerated shown from 3 alignments for 53 birds, 77 vertebrates, and 363 birds. a, or conserved sites. This panel is similar to Fig. 3c, but includes accelerated Acceleration (left) and conservation (right) within alignment columns on columns. chicken. This panel is similar to Fig. 3a, but includes accelerated columns. b, Article
Extended Data Fig. 10 | Distribution of acceleration and conservation realignment with MAFFT for a random sample of significantly conserved sites. scores. a, Distribution of conservation and acceleration scores within d, Comparison of the distribution of PhyloP scores across alignments. Scores different functional region types across alignments. Lines mark quartiles of indicate log-scaled probabilities of conservation (positive values) or the density estimates. b, Larger histogram of chicken column rates. This panel acceleration (negative values) for each base in the genome. a and d show results is similar to Fig. 3b, but includes accelerated columns ending at a rate of 10× the from three alignments for 53 birds, 77 vertebrates and 363 birds. neutral rate. c, Difference in PhyloP scores (compared to original scores) after
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