Article Progressive Cactus is a multiple-genome aligner for the thousand-genome era
https://doi.org/10.1038/s41586-020-2871-y Joel Armstrong1, Glenn Hickey1, Mark Diekhans1, Ian T. Fiddes1, Adam M. Novak1, Alden Deran1, Qi Fang2,3, Duo Xie2,4, Shaohong Feng2,5, Josefin Stiller3, Diane Genereux6, Received: 24 August 2019 Jeremy Johnson6, Voichita Dana Marinescu7, Jessica Alföldi6, Robert S. Harris8, Accepted: 27 July 2020 Kerstin Lindblad-Toh6,7, David Haussler1,9, Elinor Karlsson6,10,11, Erich D. Jarvis9,12, Guojie Zhang3,5,13,14 ✉ & Benedict Paten1 ✉ Published online: 11 November 2020
Open access New genome assemblies have been arriving at a rapidly increasing pace, thanks to Check for updates decreases in sequencing costs and improvements in third-generation sequencing technologies1–3. For example, the number of vertebrate genome assemblies currently in the NCBI (National Center for Biotechnology Information) database4 increased by more than 50% to 1,485 assemblies in the year from July 2018 to July 2019. In addition to this infux of assemblies from diferent species, new human de novo assemblies5 are being produced, which enable the analysis of not only small polymorphisms, but also complex, large-scale structural diferences between human individuals and haplotypes. This coming era and its unprecedented amount of data ofer the opportunity to uncover many insights into genome evolution but also present challenges in how to adapt current analysis methods to meet the increased scale. Cactus6, a reference-free multiple genome alignment program, has been shown to be highly accurate, but the existing implementation scales poorly with increasing numbers of genomes, and struggles in regions of highly duplicated sequences. Here we describe progressive extensions to Cactus to create Progressive Cactus, which enables the reference-free alignment of tens to thousands of large vertebrate genomes while maintaining high alignment quality. We describe results from an alignment of more than 600 amniote genomes, which is to our knowledge the largest multiple vertebrate genome alignment created so far.
Comparative genomics analyses, including species-tree inference7,8, restrictions; it can generate a reference-free multiple alignment that comparative annotation9,10, and selection detection11,12, require genome allows the detection of multiple-orthology relationships. alignments. Multi-species genome alignment involves creating a map- The version of Cactus available in 2012 performed very well in the ping from each region of each genome to a corresponding region in Alignathon14, an evaluation of genome aligners. However, the runtime of each other genome, taking into account the possibility of complex that initial iteration of Cactus scaled quadratically with the total number rearrangements and copy number changes13. Genome aligners are of bases in the alignment problem, making alignment of more than one of the most fundamental tools used in comparative genomics, about ten vertebrate genomes completely impractical. To address these but because the problem is difficult, different aligners frequently give difficulties, we present fundamental changes to the Cactus process different results14, and many intentionally limit the alignments they that incorporate a progressive alignment strategy15, which changes the produce to simplify the problem. Two of the most common limitations runtime of the alignment to scale linearly with the number of genomes. are ‘reference bias’, the result of constraining a multiple alignment to We show that the result, which we call Progressive Cactus, is an aligner only regions present in a single reference genome, and restricting the that retains state-of-the-art accuracy, and continues to lack reference alignment to be ‘single-copy’, which allows only a single alignment bias, but which is tractable to use on hundreds to thousands of large, in any column in any given genome, causing the alignment to miss vertebrate-sized input genomes. Progressive Cactus has been devel- multiple-orthology relationships created by lineage-specific duplica- oped over several years, and has already been successfully used as an tions. Cactus6 is a genome alignment program that has neither of these integral component of high-profile comparative genomics projects16–20.
1UC Santa Cruz Genomics Institute, UC Santa Cruz, Santa Cruz, CA, USA. 2BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, China. 3Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark. 4BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China. 5State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China. 6Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. 7Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden. 8Department of Biology, The Pennsylvania State University, University Park, PA, USA. 9Howard Hughes Medical Institute, Chevy Chase, MD, USA. 10Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA. 11Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA. 12Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA. 13Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China. 14China National GeneBank, BGI-Shenzhen, Shenzhen, China. ✉e-mail: guojie.zhang@bio. ku.dk; [email protected]
246 | Nature | Vol 587 | 12 November 2020 a Fig. 1 | The alignment process within Large guide tree Progressive Cactus. a, A large alignment problem is split into many smaller subproblems using an input guide tree. Each subproblem compares a set of ingroup genomes (the children of the internal node to be reconstructed) against each other as well as a sample of outgroup genomes Outgroup genome (non-descendants of the internal node in Reconstructed ancestor question). b, Flowchart represents the phases in which the overall alignment, as well as each subproblem alignment, proceeds through. The end result is a new genome assembly that Smaller subproblem Ingroup genome Ingroup genome represents the Progressive Cactus reconstruction of the ancestral genome, and an alignment b between this ancestral genome and its children. Additional After all subproblems have been completed, the Genome Input HAL repeat- sequences genomes alignment parent–child alignments are combined to create masking the full reference-free alignment in the HAL27 format.
Ingroups, outgroups
CAF BAR Ancestral Local Filter to Extend from genome alignment anchors anchors inference
Individual subproblems
later combined to produce the full alignment. Two sets of genomes are Progressive Cactus considered: the children of the internal node (which we call the ‘ingroup The new Progressive Cactus pipeline is freely available and open source. genomes’), and a set of non-descendants of that node (the ‘outgroup The only inputs needed are a guide tree and a FASTA file for each genome genomes’). The ingroup genomes form the core alignment relationship assembly. being established at this node. The outgroup genomes serve to answer The key innovation of Progressive Cactus is to adapt the classic ‘pro- the question of what sequence from the ingroups is also present in the gressive’ strategy (used in collinear multiple alignment for decades) to a ancestor (whether an indel among the ingroups is likely to be a deletion whole-genome alignment setting. Progressive aligners use a ‘guide tree’ rather than an insertion), and in how many copies (whether a duplication to recursively break a multiple alignment problem into many smaller predates or postdates the speciation event the node represents). The sub-alignments, each of which is solved independently; the resulting outgroups also provide information for guiding the ancestral assembly sub-alignments are themselves aligned together according to the tree by providing order-and-orientation information, as well as base-level structure to create the final alignment. Progressive alignment has been information when generating ancestral sequences. These genome sets successfully applied to whole-genome alignment before—for example, are used as the input to the main subproblem workflow (Fig. 1b). by progressiveMauve21 and TBA/MULTIZ22. Cactus now follows a similar Each individual subproblem follows a similar procedure to the strategy, with the key innovation being that Progressive Cactus imple- original Cactus process. The subproblem procedure begins with a ments a progressive-alignment strategy for whole-genome alignment set of pairwise local alignments generated via the sensitive pairwise using reconstructed ancestral assemblies as the method for combining local-alignment program LASTZ24. These pairwise alignments are sub-alignments. This strategy (analogous to the MAVID23 strategy of then filtered and combined into a cactus graph representing an initial using ancestral reconstruction in collinear multiple alignment) not multiple alignment using the previously described CAF algorithm6— only results in a much faster alignment runtime but also produces although we note important changes to the filtering in Methods and ancestral reconstructions. Extended Data Fig. 1—to attempt to recover the homologies that date to Figure 1a shows the overall organization of the Progressive Cactus the most recent common ancestor of the ingroup genomes. The initial process. The guide tree, which need not be fully resolved (binary), is used alignment is refined using the previously described BAR algorithm6 to recursively split a large alignment problem (comparing every genome to create a more complete alignment. The ancestral assembly is then to every other genome) into many small subproblems, each of which created by ordering the blocks in this final alignment and establishing compares only a small number (usually 2–5) of genomes against one a most-likely base call for each column in each block. The resulting another. The purpose of each subproblem is to reconstruct an ancestral ancestral sequence is then fed into later subproblems (unless the root assembly at each internal node in the guide tree, as well as to generate of the guide tree has been reached, which ends the alignment process). alignments between that internal node’s children and its ancestral recon- As a practical matter, Progressive Cactus uses the Toil25 workflow struction. The ancestral assemblies are then used as input genomes in framework to organize and distribute its computational tasks. Although subproblems further up the tree, and the parent–child alignments are genome alignment is a computationally intensive task, using Toil, we
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Fig. 2 | Comparing alignments of varying numbers of simulated genomes not further decomposed, but the linear scaling of the progressive mode means using Progressive Cactus. a, The progressive mode of Progressive Cactus is it is much faster with large numbers of genomes than the quadratic scaling shown, versus the mode without progressive decomposition that is similar to required without progressive alignment. b, The precision, recall and F1 score that previously described6 (‘star’). The average total runtime of the two (harmonic mean of precision and recall) of aligned pairs for each alignment alignment methods across three runs is shown. Data are mean and s.d. The compared with pairs from the true alignment produced by the simulation. runtime is identical when aligning two genomes as the alignment problem is can break up the problem into small pieces that can work in hetero- species), owing to its linear runtime scaling, as opposed to the quad- geneous compute environments, playing to the advantages of both ratic scaling of the star-tree (Fig. 2a). The progressive strategy is also cheap CPU-rich machines and more expensive memory-rich machines. more accurate than the star strategy (Fig. 2b) and maintains accuracy Because it runs on Toil and supports container execution via Docker as the number of species (and therefore nodes in the tree) increases. and Singularity26, Progressive Cactus can be run on many different environments: single machines (for small alignments), conventional clusters, and commercial clouds. Effect of the guide tree Given the rate of arrival of new assembly versions and newly sequenced Because Progressive Cactus uses an input guide tree to decompose genomes, adding new information to an alignment without recomput- the alignment problem, the guide tree can potentially impact the ing it from scratch is valuable, especially for large alignments in which resulting alignment. This could be problematic when the exact spe- recomputing the entire alignment is often cost-prohibitive. Progressive cies tree relating the input set of genomes is unknown or controversial. Cactus, combined with special functionality in the HAL toolkit27, there- However, we reduce any effect of the guide tree by including a great fore supports the addition and removal of genomes from the alignment deal of outgroup information, including multiple outgroups when by taking advantage of the tree structure of the progressive alignments possible. To quantify the effect of the guide tree on a large alignment it produces (Methods, Extended Data Fig. 2, Extended Data Table 1). with an uncertain species tree, we created four alignments of a set of 48 avian species (Supplementary Table 3), which we subset down to a single chromosome (chromosome 1). The avian species tree is still Evaluation on simulated data being actively debated28,29 and there are different plausible hypotheses, The Alignathon simulated datasets14 have been aligned with many making birds an excellent test case with no single clearly correct guide competing genome aligners and have a known truth set, providing tree. We aligned these birds using four different guide trees: two trees a way to compare Progressive Cactus against other genome aligners. that represent two different hypotheses about the avian species tree28,29, Progressive Cactus produces alignments with higher accuracy for both one consensus tree between the former two trees, and one tree that 29 simulated primate (F1 score of 0.989) and mammal (F1 score of 0.795) was randomly permuted from one of the previously published trees clades than any aligner that participated in the Alignathon (Supple- (Methods, Supplementary Fig. 1). The four alignments were highly mentary Tables 1, 2), including the original version of Cactus. similar, with an average of 98.5% of aligned pairs identical between To evaluate the improvements in quality and runtime of the align- any two different alignments (Extended Data Table 2). ments produced using the new progressive alignment strategy, we We further examined whether these small differences in the guide simulated the evolution of twenty 30-megabase genomes using Evolver tree affect some species more than others. For any pair of these 48 spe- 28,29 (https://www.drive5.com/evolver) along a tree of catarrhines. We ran cies, the F1 score for aligned pairs between the previously published two alignment strategies—one using a fully resolved binary guide tree alignments was at least 0.955 (Supplementary Fig. 2). As an example, (which takes full advantage of the new progressive mode), and one using the phylogenetic relationship between the species Cuculus canorus, a fully unresolved star guide tree (which is similar to the originally pub- Chlamydotis macqueenii and Tauraco erythrolophus is different in the lished version of Cactus)—across variously sized subsets of genomes guide tree based on Prum et al.28 than that based on Jarvis et al.29 (Sup- roughly evenly spaced throughout the catarrhine tree. The alignments plementary Fig. 3). The F1 score for aligned pairs within this clade between using the progressive strategy finished more quickly, with the speed the two alignments was 0.972, lower but comparable to the score for a improvement growing larger with the increasing number of species similar clade that had an identical phylogenetic relationship in both trees, (for example, a 15% reduction in runtime for 10 species and 48% for 20 0.982 (for Merops nubicus, Picoides pubescens and Buceros rhinoceros).
248 | Nature | Vol 587 | 12 November 2020 Fig. 3 | Analysing the 600-way amniote alignment. a, The species tree relating the 600 a genomes. Branches are coloured by clades as in b and c. b, Percentage coverage on human within the eutherian mammals, grouped by clade from highest to lowest coverage. c, As in b, but for coverage on chicken within the avian alignment. d, Percentage of various regions within the human genome mappable to each ancestral genome reconstructed along the path leading from human to the root. The positions of selected ancestors are labelled by dotted lines to indicate useful taxonomic reference A
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Effect of assembly quality on alignment (usually using much older shorter-read technologies) (Supplemen- Our progressive approach means that the alignment between two tary Table 4). The remaining four assemblies were held constant to genomes distant in the guide tree is informed by the reconstructions facilitate a comparison between the two alignments. Despite alignment of the ancestral genomes along the path, which is in turn formed using differences between the long-read and short-read assemblies (Sup- data from other genomes in the tree. To evaluate the practical effect plementary Table 5), the alignment between these four assemblies was of differing quality of input assemblies, we created two alignments similar in both datasets (for example, 0.855 Jaccard similarity between of 11 boreoeutherian mammal species, 7 of which represented either induced pairwise human–dog alignments) (Supplementary Fig. 4), high-quality assemblies in one alignment (using modern assemblers and a level of similarity higher than seen between alignment strategies, often long-read data) or lower quality assemblies in the other alignment indicating that the progressive alignment strategy can tolerate poor
Nature | Vol 587 | 12 November 2020 | 249 Article Table 1 | Aggregate statistics for the 600-way alignment Reference Chicken Zebra nch 100 Alignment No. of Total Instance- Core-hours Common genomes bases hours ancestor size ) Zoonomia 242 669 billion 68,166 1.9 million 1.73 Gb B10K 363 400 billion 5,302 0.2 million 1.13 Gb 80 Combined 605 1.07 trillion 73,692 2.1 million 181 Mb
The increase in computational work for the mammal alignment compared with the bird alignment is largely caused by the increase in the pairwise alignment phase runtime because it scales quadratically with the size of the genomes being aligned. 60
assemblies. Reinforcing this, comparing the induced pairwise align- erence in Cactus alignment (% ments of human–dog to direct pairwise alignments computed using the established chains and nets pipeline30, we find the same level of Jaccard similarity for both the high- and low-quality assembly align- 40 rage on ref ments (Supplementary Fig. 5). Of the aligned pairs in the induced ve pairwise Progressive Cactus alignments, 82% were found in the chains Co and nets alignment, and, vice versa, 78% of pairs in the chains and nets alignment were found in each Progressive Cactus alignment. Concord- ant results were found comparing human–mouse pairwise alignments 40 60 80 100 (Supplementary Fig. 5). Coverage on reference in MULTIZ alignment (%) Fig. 4 | Comparing Cactus and MULTIZ alignment coverage. A comparison of coverage in the Progressive Cactus avian alignment compared to a chicken- 600-way amniote alignment referenced MULTIZ22 alignment of the same genomes. Coverage of both To demonstrate Progressive Cactus, we present results from an align- alignments on chicken and zebra finch is shown to illustrate the effects of ment of 605 amniote genomes, relating in a reference-free manner to reference bias on the completeness of the MULTIZ alignment. The diagonal more than 1 trillion bases of DNA across hundreds of millions of years of dotted line indicates a slope of 1 (that is, if the coverage of MULTIZ and genome evolution (an estimated 35.4 neutral substitutions per site). The Progressive Cactus were equal). amniote-wide alignment combines two smaller alignments: one created for the initial release of the Zoonomia project31, which includes 242 placen- in avians, and 0.39 and 0.40, respectively, for eutherians), although the tal mammals representing most eutherian mammal families, and one for relative rates remained lower for insertions (1.2% of the substitution the Bird 10,000 genomes (B10K) project32, which includes 363 avians, also rate for both clades) and for deletions (2.4% and 1.2% of the substitu- representing most bird families. The overall topology is shown in Fig. 3a. tion rate for avians and eutherians, respectively). Notably, we observe To our knowledge, this represents the largest whole-genome alignment similar rates of deletions between eutherian and avian lineages, but evi- created so far. Table 1 contains aggregate statistics on this alignment. dence of a slightly increased rate of insertions in avians (Extended Data Coverage within the 600-way alignment closely tracks phylogenetic Fig. 3a). The ancestral assemblies also represent even difficult-to-align distance and genome size; for example, a median coverage on human of regions such as transposable elements. We ran RepeatMasker35 on sev- 2.3 gigabases (Gb) from Euarchonta mammalian species, versus 1.2 Gb and eral human ancestors, focusing on the recently-emerged L1PA6 family 1.0 Gb from more distant Laurasiatheria and Glires mammalian species, of L1 retrotransposons. When ascending the primate tree, approaching respectively (Fig. 3b, c). The ancestral reconstructions within the 600-way the origin of modern L1PA6 elements above the human–rhesus ances- alignment are highly complete, especially for functional sequence: 86% of tor, L1PA6 elements appear increasingly similar to their consensus human coding bases are represented in our reconstruction of the ancestor sequence (Extended Data Fig. 3b, Supplementary Fig. 6). of all placental mammals, whereas 95% of chicken coding bases are repre- Despite its scale, sub-alignments of the 600-way are similar to smaller sented in our reconstruction of the common ancestor of avians (Fig. 3d, e). alignments of the same species. Within the 7.1 billion aligned base pairs Owing to the long branch length (approximately 0.7 substitutions-per-site involving human, mouse, rat or dog within the 600-way, 76.49% were divergence between the two clades), the amniote (human–chicken) ances- present in an alignment with less than a tenth the number of species tral assembly has a much lower proportion of reconstructed sequence (Supplementary Fig. 7)—this similarity is in line with that observed than its immediate children, the avian and eutherian mammal ancestors, between different alignments of these same species14. As expected, the for example, retaining 16.3% of chicken intron bases versus 84.4% in the alignments more strongly agree in functional regions, such as coding avian ancestor, and 7.2% of human intron bases versus 56.5% in the euthe- exons, than for the genome as a whole (Supplementary Fig. 8). The rian ancestor. However, coding bases are still well retained (86.8% from size and fraction of functional elements reconstructed in ancestors chicken and 58.7% from human). The ancestral assemblies consistently shared between the 600-way and smaller alignments of mammals and, contain a relatively higher proportion of sequence for avians than for separately, avians were also highly similar (Supplementary Figs. 9, 10). mammals even across similar phylogenetic distances, consistent with a To evaluate the relative accuracy of the progressive alignment pro- more conservative mode of genome evolution in avians that is influenced cess back to the amniote ancestor, human protein-coding transcripts by lower repeat counts and denser gene content33. and genes were mapped to the chicken genome using translated BLAT36, The ancestral reconstructions provide a history of substitution, indel translated BLAST37, LASTZ24 and the 600-way alignment. Of 84,001 and rearrangement events. Although this history is by its nature only a transcripts, BLAT mapped 70%, BLAST mapped 80%, LASTZ mapped hypothetical reconstruction of the true history of genome evolution 67%, and Progressive Cactus mapped 74%. Both Progressive Cactus and along the tree, it is accurate enough to be useful. To demonstrate the LASTZ had much lower levels of multi-mapping (2–3% of transcripts) utility of the indel history, we examined rates of small (less than or equal than either translated method (16–51%) (Supplementary Tables 6–8). to 20 base pairs (bp)) insertion and deletion events in the 600-way Comparison of Cactus and LASTZ coding sequence mappings to the alignment. As expected from previous studies16,34, the rate of small union of the translated alignments, both in terms of individual gene indels in any given branch was correlated with the rate of nucleotide counts and coding and mRNA bases, showed that Cactus has a margin- substitution (an R2 value of 0.69 for insertions and 0.80 for deletions ally higher fraction of shared elements with the translated alignments
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Nature | Vol 587 | 12 November 2020 | 251 Article Methods Evaluation of the effect of the guide tree Data reporting The guide-tree analysis was performed on a set of 48 bird genomes pre- Sample selection was made according to the needs of the Zoonomia viously published29. To reduce the amount of alignment work required, and B10K projects. The experiments were not randomized, and there we subset these genomes down to the size of only a single chromosome, was no blinding. chicken chromosome 1 (by removing any contig or scaffold that had less than 20% of its sequence alignable to chicken chromosome 1). We used Evaluation on simulated data Progressive Cactus commit 36304707 for all alignments in this analysis. Twenty primate genomes were simulated using Evolver, managed using The Prum and Jarvis topologies were adapted from Prum et al.28 the evolverSimControl (https://github.com/dentearl/evolverSimCon- and Jarvis et al.29, respectively. The ‘permuted’ topology was gen- trol, commit b3236deb) pipeline. The root genome used was derived erated starting from the Jarvis topology, via three randomly cho- from 30 megabases selected from the hg19 genome, and is available sen subtree-prune-regraft operations followed by three random at http://courtyard.gi.ucsc.edu/~jcarmstr/datastore/progressiveCac- nearest-neighbour-interchange operations. Each of these three topolo- tusEvolverSim.tar.gz along with the Evolver configuration files that gies had branch-length estimates performed using phyloFit from the were used. The species tree used for the simulation was obtained from PHAST package (https://github.com/CshlSiepelLab/phast, commit a catarrhine subtree of the 100-way alignment tree available on the 52e8de9) based on fourfold-degenerate sites of BUSCO orthologues. UCSC browser. Finally, the ‘consensus’ tree was produced as a strict consensus of the The tree used was, in Newick format: Jarvis and Prum trees (collapsing all groupings that were not the same in (((((((Human:0.00655,Chimp:0.00684)anc0e:0.00122,Bonobo: both trees) using the ape::consensus method from the APE R package44. 0.00784)anc1e:0.003,Gorilla:0.008964)anc2e:0.009693,Orangu The branch-lengths for this tree were generated from the fitted branch tan:0.01894)anc3e:0.003471,Gibbon:0.02227)anc4e:0.01204,(((((Rh lengths for the two input trees, using the consensus.edges function of esus:0.004991,Crab_eating_macaque:0.005991)anc5e:0.001,Sooty_ the phytools R package45. The four final trees that were used in the four mangabey:0.001)anc6e:0.005,Baboon:0.003042)anc7e:0.01061, Progressive Cactus alignments are shown in Supplementary Fig. 1, and (Green_monkey:0.027,Drill:0.03)anc8e:0.002)anc9e:0.003,((Proboscis_ available in supplementary data in Newick format. monkey:0.0007,Angolan_colobus:0.0008)anc10e:0.005,(Golden_ We further focused on the alignments with guide trees based on snub-nosed_monkey:0.0007,Black_snub_nosed_monkey:0.0008) Jarvis29 and Prum28 (Supplementary Fig. 3) to establish what alignment anc11e:0.004)anc12e:0.009)anc13e:0.02)anc14e:0.02183,(((Marm differences resulted from different phylogenetic hypotheses. Supple- oset:0.03,Squirrel_monkey:0.01035)anc15e:0.01065,White-faced_ mentary Fig. 2 shows a refinement of the overall alignment-to-alignment sapajou:0.009)anc16e:0.01,Nancy_Mas_night_monkey:0.01)anc17e: F1 scores shown in Extended Data Table 2, showing the F1 scores for each 0.01)anc18e; species pair between the Jarvis- and Prum-based alignments. Each pair
The alignments were generated using Progressive Cactus commit of species has an F1 score between Jarvis- and Prum-based alignments 51eb980b. The input files (the simulated genomes, input files and Pro- of at least 0.955. gressive Cactus configuration file) are available at http://courtyard. gi.ucsc.edu/~jcarmstr/datastore/progressiveCactus.EvolverSim.Cac- Effect of assembly quality on alignment quality tusInput.EvenlySpread.tar.gz. A non-default configuration (included in We aligned two sets of 11 boreoeutherian genomes: one in which 7 of the dataset) was used to change the alignment filtering in both runs to the species were represented by relatively low-quality assemblies, and better support the high degree of polytomy in the star-tree runs. Four another in which the same 7 species were represented by higher-quality sets of 2, 6, 10 and 20 genomes were used, each of which were run three assemblies; the assemblies used are listed in Supplementary Table 4. times to generate runtime estimates. The sets are as follows: 2 species: The remaining four genomes had the same assemblies in both align- rhesus and marmoset; 6 species: rhesus, marmoset, gorilla, drill and ments to facilitate comparison (human, hg38; mouse, mm10; rat, rn6; black snub-nosed monkey, white-faced sapajou; 10 species: species and dog, canFam3). We used Progressive Cactus commit 36304707 for from 6-species alignment and human, sooty mangabey, proboscis all alignments in this analysis. Monkey and Nancy Ma’s night monkey; 20 species: all species. The runtime statistics were gathered using the toil stats command Generation of the 600-way alignment (the overall clock time was used, which represents central processing The Zoonomia alignment was composed of two sets of mammalian unit (CPU) time spent across all jobs). To generate the recall and preci- genomes: newly assembled DISCOVAR assemblies and GenBank assem- sion statistics, multiple alignment format files (MAFs) were exported blies. The DISCOVAR genomes were masked with RepeatMasker commit for each run (using hal2maf from the HAL27 package (https://github. 2d947604, using Repbase46 version 20170127 as the repeat library and com/ComparativeGenomicsToolkit/hal, commit 68db41d) with the CrossMatch as the alignment engine. The pipeline used is available at --onlyOrthologs option using the rhesus genome as a reference) and https://github.com/joelarmstrong/repeatMaskerPipeline, commit compared with the Evolver MAF using mafComparator (https://github. a6ad966. The guide-tree topology was taken from the TimeTree data- com/dentearl/mafTools, commit 82077ac3). base47 (using release current in October 2018), and the branch lengths were estimated using the least-squares-fit mode of PHYLIP (http:// Comparison using Alignathon data evolution.genetics.washington.edu/phylip/getme-new1.html, version For comparison against other genome alignment methods, we aligned 3.695)48. The distance matrix used was largely based on distances from data (both the simulated ‘primates’ and ‘mammals’ datasets) used in the 4d site trees from the UCSC browser49. To add those species not the Alignathon using Progressive Cactus. For comparison, we down- present in the UCSC tree, approximate distances estimated by Mash loaded all the original Alignathon entries in MAF format. We used the (https://github.com/marbl/Mash, commit 541971b)49 to the closest original Alignathon analysis workflow (https://github.com/dentearl/ UCSC species were added to the distance between the two closest UCSC mwgAlignAnalysis, commit df98753) to reanalyse the MAFs, with the species. We used the hal package to process the HAL file (https://github. output of the newest Progressive Cactus version added, to generate com/ComparativeGenomicsToolkit/hal, commit 68db41d). the precision/recall statistics (which we extracted from the compari- The final guide tree is embedded in the HAL file, and available using son against the most recent common ancestor (MRCA) truth set). The the halStats --tree command. The 363 assemblies in the B10K align- simulated-mammal results are shown in Supplementary Table 1, and ment comprised four sets: 236 newly sequenced species for the ‘family’ the simulated-primates results are shown in Supplementary Table 2. phase of the project, assembled using SOAPdenovo2 and AllpathsLG, 42 assemblies already sequenced from the ‘order’ phase of the project, We then filter to get near-best in genome. Alignment to chicken 36 assemblies taken from GenBank, and 49 assemblies contributed by uses near best filter of -localNearBest = 0.010 while to human it uses other research groups. For the avian guide-tree, we used a tree that the -globalNearBest = 0.010: B10K consortium derived as preliminary data from ultraconserved faPolyASizes
Extended Data Fig. 2 | Methods of adding a genome to a Progressive Cactus would need to be computed for the new genome to be properly merged into the alignment. The top row shows the different ways of adding a new genome existing alignment. Green circles represent a new genome, and red circles given its phylogenetic position, and the bottom row shows what subproblems represent newly reconstructed genomes. Deletions Insertions 0.3
0.2 Av ia n
0.1
Clade 0.0 Avian 0.3 Eutherian Euther 0.2 ia n
Branch length (substitutions/site) 0.1
0.0
0.0000.0010.0020.0030.0040.0050.0000.0010.0020.0030.0040.005 Rate of micro−indels per site
Extended Data Fig. 3 | Analysing insertions, deletions and L1PA6 repeats in category. b, Violin plot showing the increasing similarity to consensus of L1PA6 the 600-way alignment. a, Rates of micro-insertions and -deletions elements within reconstructed ancestral genomes along the path to the (micro-indels) along each branch within the 600-way alignment, compared to emergence of modern L1PA6 elements (in the human-rhesus ancestor). conventional substitutions/site branch length. The data from avian and Horizontal lines indicate the median values. eutherian branches are separated. Lines show a best-fit linear model for each Article Extended Data Table 1 | Adding a new genome to an alignment of simulated genomes
Precision, recall and F1 score statistics are all of the aligned pairs that contain a base of the added genome. An alignment in which the genome was included initially is shown for comparison. Extended Data Table 2 | Alignment similarity between four alignments of the same 48 avian genomes with different guide trees
The similarity between each pair of alignments is represented by the F1 score of aligned-pair relationships in the two alignments.
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