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Cited by 209Genomics and Phylogenetic StudiesChromosomal and Genetic VariationsBioinformatics and Genomic Networks

Integrating gene annotation with orthology inference at scale

Open access
Kirilenko, Bogdan M., Munegowda, Chetan, Osipova, Ekaterina, Jebb, David, Sharma, Virag, Blumer, Moritz, Morales, Ariadna E., Ahmed, Alexis-Walid +109 more·2023·Science
DOI 10.1126/science.abn3107PMC PMC10193443

Published version of a preprint

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Abstract

Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era.

AI snapshot

Written from the paper’s full text.

TL;DR

TOGA integrates structural gene annotation and orthology inference by using genome alignments and machine learning to project reference genes to query genomes. Applied to 488 placental mammal and 501 bird assemblies, it improves conserved-gene annotation, detects gene losses, joins fragmented genes, and provides a genome-quality measure.

Key findings

  • TOGA detected orthologs at scale: with human GENCODE 38 (19,464 genes) as reference, median 19,192 orthologs in six Hominoidea and 18,049 in the remaining 482 placental mammal assemblies; with mouse GENCODE M25 (22,257 genes), median 20,918 in 20 Muridae and 18,115 in the remaining 466; with chicken (18,039 genes), median 14,058 across 501 bird assemblies.
  • Compared with Ensembl Compara, TOGA detected 97.6%, 98.9%, and 96.5% of human orthologs for rat, cow, and elephant, and found 1,532, 1,711, and 2,174 additional orthologs; gene-loss specificity was 99.80–99.89% on 11,161 conserved genes (21 mouse, 22 rat, 12 cow, 21 dog misclassified).
  • TOGA improved BUSCO completeness of conserved genes: with human reference for 97% of Ensembl and 91.5% of NCBI species, average +4.1% (~377 genes) and +0.7% (~64 genes); with mouse reference for 98.5% Ensembl and 64% NCBI species. In fragmented sperm whale, joined genes reached 97% of human CDS length vs 59% for the largest fragment; Steller’s sea cow improved from 28% to 70%.
  • TOGA’s ancestral-gene completeness correlated with BUSCO genome completeness (Pearson r = 0.73, P = 10−81) but had larger dynamic range; e.g., *R. ferrumequinum* vs *R. sinicus* BUSCO 96.4% vs 96.3% but TOGA 94.4% vs 88.2%, and gaur vs cow inactivating mutations 14.2% vs 4.3% despite BUSCO 95.8% vs 95.5%.

Methods

  • TOGA takes a reference gene annotation and pairwise whole-genome alignment chains; classifies orthologous chains with XGBoost using features from coding, intronic, intergenic, flanking alignments and synteny; aligns exons with CESAR 2.0; classifies transcripts as intact, partially intact, missing, uncertain loss, or lost; infers orthology type and resolves weak many:many relationships with a graph approach.
  • Data: human GENCODE 38, mouse GENCODE M25, and chicken RefSeq references; applied to 488 placental mammal assemblies (427 species) and 501 bird assemblies (476 species); benchmarks against Ensembl Compara, NCBI annotations, BUSCO odb10, and 11,161 conserved genes; generalized linear models for ortholog counts.
  • Analyses included BUSCO protein/genome mode, EVidenceModeler annotation tests in six bats, gene-joining evaluation in sperm whale/pygmy sperm whale, and an ancestral placental mammal gene set of 18,430 genes.

Limitations

  • Stated: TOGA requires “alignable” genomes, generally ~0.6 substitutions per neutral site; for marsupials/monotremes human is not a powerful reference; homology-based TOGA cannot annotate orthologs of genes absent from the reference, and lineage-specific exons/genes require transcriptomics or ab initio predictions; non-mammalian use may need retraining, new features, and adjusted alignment/CESAR parameters.
  • Stated/evident: reference annotation quality and transcript filtering affect results; the classifier is trained on human-mouse orthologs and uses thresholds (e.g., orthology score ≥0.5, exon %identity ≥45%, %BLOSUM ≥20%) that may not generalize.
  • Evident: gene-loss specificity was benchmarked on 11,161 conserved genes after excluding short-intron genes, and assembly gaps/base errors can cause missing or inactivated classifications; performance depends on alignment sensitivity and assembly quality.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1126/science.abn3107

Cites

68 of 68 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

51 of 58 cited sources checked · 28 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Gene annotation quality has a large influence on the accuracy of orthology inference.

    “First, gene annotation quality has a large influence on the accuracy of orthology inference (15).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [15] Trachana K, Orthology prediction methods: a quality assessment using curated protein families. Bioessays 33, 769– 780 (2011). 21853451 PMC3193375 source
  • Findingcitation

    Half of the false negatives may be related to faster X chromosome evolution.

    “Manual investigation of misclassifications showed that false positives mostly represent partial or full gene duplications (actual co-orthologous loci) and that half of the false negatives may be related to faster X chromosome evolution (17) (fig. S5–S6).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [17] Meisel RP, Connallon T, The faster-X effect: integrating theory and data. Trends in Genetics 29, 537– 544 (2013). 23790324 PMC3755111 source
  • Interpretationcitation

    As a homology-based method, TOGA benefits from the quality and comprehensiveness of the human and mouse input annotation.

    “As a homology-based method, TOGA benefits from the quality and comprehensiveness of the human and mouse input annotation (21, 22).”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [21] O’Leary NA, Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44, D733– 745 (2016). 26553804 PMC4702849 source
    • cites [22] Frankish A, Gencode 2021. Nucleic Acids Res 49, D916– D923 (2020). PMC7778937 33270111 source
  • Findingcitation

    For an assembly of the extinct Steller's sea cow with a scaffold N50 value of 1.4 kb, TOGA improved the relative coding sequence length from 28% to 70%.

    “Even for an assembly of the extinct Steller’s sea cow with a scaffold N50 value of just 1.4 kb (30), TOGA improved the relative coding sequence length from 28% to 70%.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [30] Sharko FS, Steller’s sea cow genome suggests this species began going extinct before the arrival of Paleolithic humans. Nature communications 12, 2215 (2021). PMC8044168 33850161 source
  • Premisecitation

    Mutations in a single exon of an otherwise-conserved gene are not sufficient to infer gene loss.

    “The latter requirement is motivated by previous observations that mutations in a single exon of an otherwise-conserved gene are not sufficient to infer gene loss (5).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [5] Sharma V, A genomics approach reveals insights into the importance of gene losses for mammalian adaptations. Nature communications 9, 1215 (2018). PMC5865188 29572503 source
  • Classified as probably not a substantial claim. 31
  • May combine two claims. 45
  • The quote only partly states this claim. 1

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Premisecitation

    Distinguishing orthologs and paralogs is a fundamental problem in evolutionary and molecular biology and a prerequisite for many genomic analyses.

    “Distinguishing orthologs and paralogs is a fundamental problem in evolutionary and molecular biology (1) and a prerequisite for many genomic analyses, including reconstructing phylogenetic trees, predicting gene function, investigating molecular and genome evolution, and discovering differences in genes that underlie phenotypes of the sequenced species (2–6).”
    Quote in the paperNot a substantial claim5 of 6 sources checked
    • cites [1] Gabaldon T, Koonin EV, Functional and evolutionary implications of gene orthology. Nature reviews. Genetics 14, 360– 366 (2013). PMC5877793 23552219 source
    • cites [2] Kapli P, Yang Z, Telford MJ, Phylogenetic tree building in the genomic age. Nature reviews. Genetics 21, 428– 444 (2020). 32424311 source
    • cites [3] Altenhoff AM, Studer RA, Robinson-Rechavi M, Dessimoz C, Resolving the ortholog conjecture: orthologs tend to be weakly, but significantly, more similar in function than paralogs. PLoS computational biology 8, e1002514 (2012). 22615551 PMC3355068 source
    • cites [4] Huerta-Cepas J, Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper. Molecular biology and evolution 34, 2115– 2122 (2017). 28460117 PMC5850834 source

      The Introduction (page unavailable) calls identifying orthologs a long-standing evolutionary problem and explains its importance for predicting gene function. The Conclusions (page unavailable) also describe orthology-based functional annotation of genomes. The paper does not establish the broader claim that distinguishing orthologs and paralogs is a prerequisite for the other listed analyses, such as reconstructing phylogenetic trees or identifying phenotype-related gene differences. (LLM, conf=0.83)

      Experimental — accuracy not yet measured
    • cites [5] Sharma V, A genomics approach reveals insights into the importance of gene losses for mammalian adaptations. Nature communications 9, 1215 (2018). PMC5865188 29572503 source
    • cites [6] Jebb D, Six reference-quality genomes reveal evolution of bat adaptations. Nature 583, 578– 584 (2020). 32699395 PMC8075899 source

      The citation is attached to the first clause, which calls distinguishing orthologs from paralogs a fundamental problem. In Methods > Phylogenomics (page unavailable), the paper uses genes classified as 1:1 orthologues to infer a species tree, showing the relevance of orthology to evolutionary analysis. It does not discuss distinguishing orthologs from paralogs or characterize that distinction as a fundamental problem. (LLM, conf=0.76)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Premisecitation

    Graph-based methods cluster genes into pairs or groups of orthologs based on pairwise sequence similarity such as reciprocal best alignment hits.

    “Graph-based methods cluster genes into pairs or groups of orthologs based on pairwise sequence similarity such as (reciprocal) best alignment hits (8–12).”
    Quote in the paperSubstantial claim5 of 5 sources checked
    • cites [8] Li L, Stoeckert CJ Jr., Roos DS, OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13, 2178– 2189 (2003). 12952885 PMC403725 source
    • cites [9] Train CM, Glover NM, Gonnet GH, Altenhoff AM, Dessimoz C, Orthologous Matrix (OMA) algorithm 2.0: more robust to asymmetric evolutionary rates and more scalable hierarchical orthologous group inference. Bioinformatics 33, i75– i82 (2017). 28881964 PMC5870696 source
    • cites [10] Zdobnov EM, OrthoDB v9.1: cataloging evolutionary and functional annotations for animal, fungal, plant, archaeal, bacterial and viral orthologs. Nucleic Acids Res 45, D744– D749 (2017). 27899580 PMC5210582 source

      The Algorithm and Software section (page unavailable) says OrthoDB clusters genes into ortholog groups after Smith–Waterman assessments of gene homology. It does not specify graph-based clustering or reciprocal best alignment hits; searches for those terms found no supporting passage in our copy. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
    • cites [11] Jensen LJ, eggNOG: automated construction and annotation of orthologous groups of genes. Nucleic Acids Res 36, D250– 254 (2008). 17942413 PMC2238944 source
    • cites [12] Emms DM, Kelly S, OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol 20, 238 (2019). 31727128 PMC6857279 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Current methods for orthology inference are either based on graph or gene tree approaches or a combination of both.

    “Current methods for orthology inference are either based on graph or gene tree approaches or a combination of both (7).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [7] Altenhoff AM, Glover NM, Dessimoz C, Inferring Orthology and Paralogy. Methods in molecular biology 1910, 149– 175 (2019). 31278664 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Gene tree-based methods determine whether the evolutionary lineages of two genes coalesce in a speciation or a duplication node.

    “Gene tree-based methods determine whether the evolutionary lineages of two genes coalesce in a speciation or a duplication node (12–14).”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [12] Emms DM, Kelly S, OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol 20, 238 (2019). 31727128 PMC6857279 source
    • cites [13] Huerta-Cepas J, Capella-Gutierrez S, Pryszcz LP, Marcet-Houben M, Gabaldon T, PhylomeDB v4: zooming into the plurality of evolutionary histories of a genome. Nucleic Acids Res 42, D897– 902 (2014). 24275491 PMC3964985 source
    • cites [14] Vilella AJ, EnsemblCompara GeneTrees: Complete, duplication-aware phylogenetic trees in vertebrates. Genome Res 19, 327– 335 (2009). 19029536 PMC2652215 source

  • May combine two claims.

    Findingown data

    With mouse GENCODE M25 as reference, TOGA resulted in a higher BUSCO completeness for 98.5% of Ensembl species and 64% of NCBI species.

    “Second, we used TOGA with the mouse GENCODE M25 annotation (22) as the reference. This resulted in a higher BUSCO completeness for 98.5% (Ensembl) and 64% (NCBI) of the species (Fig. 3A,B, Tables S6–S7).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [22] Frankish A, Gencode 2021. Nucleic Acids Res 49, D916– D923 (2020). PMC7778937 33270111 source

  • May combine two claims.

    Findingown data

    TOGA annotations have a higher BUSCO completeness score for 97% of Ensembl species and 91.5% of NCBI species, increasing annotation completeness of conserved genes by an average of 4.1% or ~377 genes (Ensembl) and 0.7% or ~64 genes (NCBI).

    “TOGA annotations have a higher completeness score for 97% (Ensembl) and 91.5% (NCBI) of the species (Fig. 3A,B, Tables S6–S7), increasing annotation completeness of conserved genes by an average of 4.1% or ~377 genes (Ensembl) and 0.7% or ~64 genes (NCBI) (fig. S30).”
    Quote in the paperSubstantial claim3 of 4 sources checked
    • cites [20] Thibaud-Nissen F, P8008 The NCBI Eukaryotic Genome Annotation Pipeline. Journal of Animal Science 94, 184– 184 (2016). source
    • cites [21] O’Leary NA, Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44, D733– 745 (2016). 26553804 PMC4702849 source

      The Introduction and Vertebrates sections describe RefSeq annotation and curation, but do not report a comparison with TOGA annotations. Searches for TOGA, BUSCO, Ensembl, and the stated percentages and gene counts found no passage addressing the claimed completeness results. (LLM, conf=0.78)

      Experimental — accuracy not yet measured
    • cites [22] Frankish A, Gencode 2021. Nucleic Acids Res 49, D916– D923 (2020). PMC7778937 33270111 source

      The Gene Annotation Updates and Conclusion sections discuss improvements to GENCODE’s human and mouse annotations, not a comparison of TOGA annotations with Ensembl and NCBI across species. Searches for TOGA, BUSCO, and the claimed percentages and gene counts found no passage reporting those results in our copy. (LLM, conf=0.76)

      Experimental — accuracy not yet measured
    • cites [23] Manni M, Berkeley MR, Seppey M, Simao FA, Zdobnov EM, BUSCO Update: Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes. Molecular biology and evolution 38, 4647– 4654 (2021). 34320186 PMC8476166 source

      The Upgrade of Data Sets and Benchmark of Predictions sections describe BUSCO scores as estimates of genome and gene-set completeness, supporting the use of BUSCO as the assessment method. Our copy does not report a TOGA comparison with Ensembl or NCBI annotations, or the claimed percentages and gene counts. No page numbers are available in our copy. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    TOGA detected 97.6%, 98.9% and 96.5% of the orthologs provided by Ensembl for human-rat, human-cow, and human-elephant comparisons, respectively.

    “Using orthologs between human and three representative mammals (rat, cow, elephant), TOGA detected 97.6%, 98.9% and 96.5% of the orthologs provided by Ensembl (Fig. 2A, Table S5), showing a good agreement.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [14] Vilella AJ, EnsemblCompara GeneTrees: Complete, duplication-aware phylogenetic trees in vertebrates. Genome Res 19, 327– 335 (2009). 19029536 PMC2652215 source

      The 'Overlap of ortholog sets' section describes EnsemblCompara ortholog predictions, supporting the identification of Ensembl as an ortholog source. It does not report a TOGA comparison or the 97.6%, 98.9%, and 96.5% detection rates; those results are not supported by this paper. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    Orthologous chains tend to cover several genes located in a conserved order, resulting in higher synteny values, which can help distinguish orthologs from paralogs.

    “Orthologous chains tend to cover several genes located in a conserved order, resulting in higher synteny values, which can help to distinguish orthologs from paralogs (14, 41–43).”
    Quote in the paperSubstantial claim4 of 4 sources checked
    • cites [14] Vilella AJ, EnsemblCompara GeneTrees: Complete, duplication-aware phylogenetic trees in vertebrates. Genome Res 19, 327– 335 (2009). 19029536 PMC2652215 source

      The Gene synteny metric section (page unavailable) says that ordered, flanking orthologous genes indicate conserved gene order, and the Comparison using the synteny metric section uses that measure to assess prediction specificity. The paper does not describe orthologous chains or show that they have higher synteny values than paralogous chains; it also says synteny was not used directly to build its trees. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites [41] Lehmann J, Stadler PF, Prohaska SJ, SynBlast: assisting the analysis of conserved synteny information. BMC Bioinformatics 9, 351 (2008). 18721485 PMC2543028 source

      The Background defines conserved synteny as maintenance of gene content and order, and the Results (Algorithms and Implementation; page unavailable) say gene-order scores help distinguish putative orthologous from paralogous regions. The paper does not establish that orthologous chains generally span several genes or have higher synteny values than paralogous chains. (LLM, conf=0.78)

      Experimental — accuracy not yet measured
    • cites [42] Jun J, Mandoiu II, Nelson CE, Identification of mammalian orthologs using local synteny. BMC Genomics 10, 630 (2009). 20030836 PMC2807883 source

      The Results (‘Using Local Synteny to Infer Orthology,’ page unavailable) define synteny by matches among neighboring genes, and the Conclusions say local synteny can distinguish true orthologs from recent retrogenes. But the Methods (‘Local Synteny,’ page unavailable) explicitly say those matches need not be co-linear, so the paper does not support conserved gene order as a requirement or describe the claimed orthologous chains. (LLM, conf=0.87)

      Experimental — accuracy not yet measured
    • cites [43] Jahangiri-Tazehkand S, Wong L, Eslahchi C, OrthoGNC: A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation. Genomics Proteomics Bioinformatics 15, 361– 370 (2017). 29133277 PMC5828658 source

      The Introduction says conserved gene neighborhoods can help distinguish orthologs from out-paralogs, and the Method describes checking multiple neighboring genes, with one routine considering gene order. Our copy does not state that orthologous chains tend to span several genes or that they produce higher synteny values. The indexed passages provide no page numbers. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    CESAR has high accuracy in correctly aligning shifted splice sites, is able to detect precise intron deletions that merge two neighboring exons, and generates alignments of intact exons whenever possible.

    “CESAR has a high accuracy in correctly aligning shifted splice sites, is able to detect precise intron deletions that merge two neighboring exons, and generates alignments of intact exons (defined as exon alignments with consensus splice sites and an intact reading frame) whenever possible (18, 19).”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [18] Sharma V, Schwede P, Hiller M, CESAR 2.0 substantially improves speed and accuracy of comparative gene annotation. Bioinformatics 33, 3985– 3987 (2017). 28961744 source

      The Introduction reports that CESAR aligned shifted splice sites with 91% accuracy and sought alignments with intact reading frames and consensus splice sites. But it says the original CESAR failed to annotate exons joined by complete intron deletions; the Results attribute precise annotation of those joined exons to CESAR 2.0's new gene mode, not to CESAR generally. (LLM, conf=0.89)

      Experimental — accuracy not yet measured
    • cites [19] Sharma V, Elghafari A, Hiller M, Coding exon-structure aware realigner (CESAR) utilizes genome alignments for accurate comparative gene annotation. Nucleic Acids Res 44, e103 (2016). 27016733 PMC4914097

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    TOGA enables phylogenomic analyses as well as screens for selection patterns and gene losses that are linked to relevant phenotypes.

    “First, by detecting inactivated genes and providing orthologous sequences for codon alignments, TOGA enables phylogenomic analyses as well as screens for selection patterns and gene losses that are linked to relevant phenotypes (6, 35–38).”
    Quote in the paperSubstantial claim5 of 5 sources checked
    • cites [6] Jebb D, Six reference-quality genomes reveal evolution of bat adaptations. Nature 583, 578– 584 (2020). 32699395 PMC8075899 source

      The Methods > Phylogenomics section says TOGA classified 12,931 genes as orthologues for sequence alignments and phylogenomic analysis; Methods > Gene selection, loss and gain describes a selection screen of those genes. The paper also reports gene-loss screens linked to bat traits, but attributes the loss-screening procedure to a previously developed approach, not explicitly to TOGA. Our copy therefore does not establish the TOGA-enabled gene-loss part of the claim. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites [35] Damas J, Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proceedings of the National Academy of Sciences of the United States of America 117, 22311– 22322 (2020). 32826334 PMC7486773 source

      The Materials and Methods section, “Identifying Sites Undergoing Positive Selection” (page unavailable), describes codon alignments generated with pal2nal and selection analyses performed with CodeML. Searches for TOGA, inactivated genes, and gene losses found no passage attributing these capabilities to TOGA; the claim therefore cannot be verified from our copy. (LLM, conf=0.76)

      Experimental — accuracy not yet measured
    • cites [36] Roscito JG, Convergent and lineage-specific genomic differences in limb regulatory elements in limbless reptile lineages. Cell Rep 38, 110280 (2022). 35045302 source

      The Gene identification section says TOGA identifies orthologous gene loci, and Screen for gene losses says it detects gene-inactivating mutations in a search for limb-related gene losses. These passages support the orthology and phenotype-linked gene-loss parts of the claim, but do not establish that TOGA provides codon alignments or enables phylogenomic analyses and screens for selection patterns. Page numbers are unavailable in our copy. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
    • cites [37] Blumer M, Gene losses in the common vampire bat illuminate molecular adaptations to blood feeding. Sci Adv 8, eabm6494 (2022). PMC8956264 35333583 source
    • cites [38] Indrischek H, Vision-related convergent gene losses reveal SERPINE3’s unknown role in the eye. eLife 11, (2022). PMC9355568 35727138 source

      The Materials and methods sections on investigating SERPINE3 losses and selection analysis say TOGA classifies potentially lost genes and supplies codon sequences used to test selection. The phenotype-linked gene-loss screen used a previously generated dataset and the Forward Genomics approach, rather than TOGA. Thus the paper supports TOGA’s role in gene-loss detection and selection analysis, but not the attributed role in that screen. (LLM, conf=0.83)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    CESAR 2.0 is a Hidden Markov model-based method that takes the coding exons of the reference species as input and considers reading frame and splice site information when generating exon alignments in the query sequence.

    “CESAR 2.0 is a Hidden Markov model-based method that takes the coding exons of the reference species as input and considers reading frame and splice site information when generating exon alignments in the query sequence.”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [18] Sharma V, Schwede P, Hiller M, CESAR 2.0 substantially improves speed and accuracy of comparative gene annotation. Bioinformatics 33, 3985– 3987 (2017). 28961744 source
    • cites [19] Sharma V, Elghafari A, Hiller M, Coding exon-structure aware realigner (CESAR) utilizes genome alignments for accurate comparative gene annotation. Nucleic Acids Res 44, e103 (2016). 27016733 PMC4914097

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    Assembly gaps result in missing parts of the CDS and individual exons can get lost in otherwise clearly conserved genes.

    “Transcript classification is not a straightforward problem, as assembly gaps result in missing parts of the CDS and individual exons can get lost in otherwise clearly conserved genes, as shown in previous work (5).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [5] Sharma V, A genomics approach reveals insights into the importance of gene losses for mammalian adaptations. Nature communications 9, 1215 (2018). PMC5865188 29572503 source

  • May combine two claims.

    Findingown data

    For the 488 placental mammal and 501 bird assemblies, the paper provides comparative gene annotations, ortholog sets, lists of inactivated genes and multiple codon alignments generated with MACSE v2.

    “For the 488 placental mammal and 501 bird assemblies, we provide comparative gene annotations, ortholog sets, lists of inactivated genes and multiple codon alignments generated with MACSE v2 (33) for download at http://genome.senckenberg.de/download/TOGA/.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [33] Ranwez V, Douzery EJP, Cambon C, Chantret N, Delsuc F, MACSE v2: Toolkit for the Alignment of Coding Sequences Accounting for Frameshifts and Stop Codons. Molecular biology and evolution 35, 2582– 2584 (2018). 30165589 PMC6188553 source

  • Classified as probably not a substantial claim.

    Premisecitation

    The basal split of placental mammals is not yet resolved.

    “Given that the basal split of placental mammals is not yet resolved (59)”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [59] Foley NM, Springer MS, Teeling EC, Mammal madness: is the mammal tree of life not yet resolved? Philosophical transactions of the Royal Society of London. Series B, Biological sciences 371, 20150140 (2016). PMC4920340 27325836 source

  • Classified as probably not a substantial claim.

    Interpretationcitation

    The human GENCODE V38 (Ensembl 104) gene annotation implies that each gene is intact in Boreoeutheria.

    “We used the human GENCODE V38 (Ensembl 104) gene annotation (22), which implies that each gene is intact in Boreoeutheria”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [22] Frankish A, Gencode 2021. Nucleic Acids Res 49, D916– D923 (2020). PMC7778937 33270111 source

      The Introduction identifies GENCODE as a provider of human gene annotations, and Data Access says its releases are available through Ensembl. But Data Access names GENCODE 35 as the current human release; our copy does not document V38 (Ensembl 104) or say that the annotation implies every gene is intact in Boreoeutheria. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingnone given

    TOGA improves ortholog detection and annotation of conserved genes compared to state-of-the-art methods.

    “TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared to state-of-the-art methods, and handles even highly-fragmented assemblies.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Findingnone given

    TOGA automatically provides a superior measure of mammalian genome quality.

    “Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The evolutionary distance between human and other placental mammals and between chicken and other birds is ≤0.55 substitutions per neutral site.

    “For example, the evolutionary distance between human and other placental mammals and between chicken and other birds is ≤0.55 substitutions per neutral site (fig. S1, Tables S1–S2), explaining why orthologous introns and intergenic regions partially align within these clades (Fig. 1A,E and fig. S2).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    TOGA predicted additional orthologs not listed in Ensembl: 1,532 for rat, 1,711 for cow, and 2,174 for elephant.

    “TOGA predicted for the three species 1,532 (rat), 1,711 (cow) and 2,174 (elephant) additional orthologs that are not listed in Ensembl (Fig. 2A).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Manual inspection showed that misclassified cases include highly-diverged genes, genes that evolved drastic changes in exon-intron structure or protein length, and a lost gene compensated by a processed pseudogene copy.

    “Manual inspection showed that misclassified cases include highly-diverged genes, genes that evolved drastic changes in exon-intron structure or protein length, and a lost gene that is compensated by a processed pseudogene copy, which highlights cases of less certain gene conservation (fig. S20–S23).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Benchmarking the specificity of gene loss detection on 11,161 conserved genes found only 21, 22, 12 and 21 genes misclassified as inactivated in mouse, rat, cow and dog, respectively, indicating a specificity of 99.80-99.89%.

    “We benchmarked the specificity of this approach on 11,161 conserved genes. Only 21, 22, 12 and 21 of these genes are misclassified as inactivated in mouse, rat, cow and dog, respectively, indicating a very high specificity of 99.80–99.89% (Table S4).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The additional orthologs exhibit alignment identity (mean 70%), alignment coverage (mean 83%) and orthology confidence (mean 94%) values more similar to orthologs detected by both methods (means 82%, 94%, 99%), supporting that these genes are undetected orthologs.

    “These genes exhibit alignment identity (mean 70%), alignment coverage (mean 83%) and orthology confidence (mean 94%) values that are more similar to orthologs detected by both methods (means 82%, 94%, 99%) (Fig. 2D), supporting that these genes are undetected orthologs.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    For orthologs detected only by Ensembl, TOGA did identify an orthologous locus in >93% of cases, but detected either reading frame inactivating mutations or that more than half of the coding region overlaps assembly gaps.

    “For the orthologs detected only by Ensembl, TOGA did identify an orthologous locus in >93% of the cases, but detected either reading frame inactivating mutations, indicating a lost gene, or that more than half of the coding region overlaps assembly gaps in the query (classified as a missing gene) (Fig. 2C and fig. S27–S28).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Further adding TOGA annotations with generated additional references (cow, horse and cat) increases the total number of annotated genes and detects additional lineage-restricted genes.

    “Third, further adding TOGA annotations with generated additional references (cow, horse and cat) increases the total number of annotated genes and detects additional lineage-restricted genes (fig. S32–S34).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Synteny is the least important feature, likely reflecting training data sets deliberately enriched with translocated orthologs.

    “In contrast, synteny is the least important feature, likely reflecting our training data sets that we deliberately enriched with translocated orthologs (fig. S7).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    BUSCO's values saturate at ~97% for highly complete assemblies, while TOGA's completeness values exhibit a larger dynamic range.

    “However, BUSCO’s values saturate at ~97% for highly complete assemblies, while TOGA’s completeness values exhibit a larger dynamic range (Fig. 6A,B), providing a better resolution to distinguish highly contiguous from less contiguous assemblies.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The spotted hyena NCBI GCA_008692635.1 assembly has less missing sequence, but a noticeably higher base error rate compared to the DNAzoo assembly.

    “Assemblies of the same species can suffer from different issues, illustrated by the spotted hyena, where the NCBI GCA_008692635.1 assembly has less missing sequence, but a noticeably higher base error rate compared to the DNAzoo assembly (Fig. 6E).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Fitting generalized linear models shows that the number of annotated orthologs is positively correlated with assembly quality metrics (contig and scaffold N50) and negatively correlated with evolutionary distance and divergence time to human.

    “Fitting generalized linear models shows that the number of annotated orthologs is positively correlated with assembly quality metrics (contig and scaffold N50) and negatively correlated with the evolutionary distance (substitutions per neutral site) and divergence time (millions of years) to human (fig. S38, Table S12).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The local CDS coverage feature did not increase classification performance further and hampered detection of partial lineage-specific duplications of multi-exon genes.

    “We did not use this feature when training the multi-exon model, as it did not increase classification performance further and hampered the detection of partial lineage-specific duplications of multi-exon genes.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the highly-fragmented sperm whale assembly, the mean coding sequence length after joining fragmented genes is 97% of the length of the orthologous human gene, a substantial improvement over the single largest orthologous fragment present in the assembly (mean 59%).

    “Demonstrating the effectiveness of TOGA’s gene joining procedure, in the highly-fragmented sperm whale assembly the mean coding sequence length after joining fragmented genes is 97% of the length of the orthologous human gene. This is a substantial improvement over the single largest orthologous fragment present in the assembly (mean 59%) (Fig. 4C, Table S10).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The thresholds %nucleotide identity ≥45% and %BLOSUM ≥20% correspond to a sensitivity of 0.98 and a precision of 0.99.

    “These thresholds correspond to a sensitivity of 0.98 and a precision of 0.99.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    TOGA detects a higher percentage of genes exhibiting inactivating mutations in the Bos gaurus (gaur, 14.2%) compared to the Bos taurus (cow, 4.3%) assembly, indicating that the B. gaurus assembly has an elevated base error rate, whereas both assemblies are indistinguishable in terms of BUSCO completeness (95.8 vs. 95.5%).

    “For example, TOGA detects a higher percentage of genes exhibiting inactivating mutations in the Bos gaurus (gaur, 14.2%) compared to the Bos taurus (cow, 4.3%) assembly, indicating that the B. gaurus assembly has an elevated base error rate, whereas both assemblies are indistinguishable in terms of BUSCO completeness (95.8 vs. 95.5%) (Fig. 6D).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The UCSC browser mirror provides TOGA track functionality for 488 placental mammal, 21 non-placental mammal and 501 bird assemblies.

    “Our UCSC browser mirror at https://genome.senckenberg.de/ provides the TOGA track functionality for 488 placental mammal, 21 non-placental mammal and 501 bird assemblies.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    TOGA observed an equally high identity for orthologous genes joined from two, three or even more fragments, indicating a high accuracy of the gene joining procedure.

    “However, we observed an equally high identity for orthologous genes joined from two, three or even more fragments (Fig. 4B), indicating a high accuracy.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Two closely related bats, a high-quality Rhinolophus ferrumequinum and a less-contiguous R. sinicus assembly, have similar BUSCO (96.4% vs. 96.3% complete genes) but different TOGA completeness values (94.4% vs. 88.2%).

    “This is exemplified by two closely related bats: a high-quality Rhinolophus ferrumequinum and a less-contiguous R. sinicus assembly have similar BUSCO (96.4% vs. 96.3% complete genes) but different TOGA completeness values (94.4% vs. 88.2%) (Fig. 6C).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Applying TOGA with human as the reference to 18 marsupial and two monotreme mammals still annotates on average 13,397 and 10,238 orthologs, primarily because gene order is conserved.

    “Interestingly, applying TOGA with human as the reference to 18 marsupial and two monotreme mammals, where neutrally evolving regions are diverged because of the larger evolutionary distance (~0.8 and ~1 substitution per neutral site between human and marsupials/monotremes), still annotates on average 13,397 and 10,238 orthologs (Fig. 5A,B), primarily because gene order is conserved (fig. S43).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Among seals, 56% of the genes in the Antarctic fur seal have inactivating mutations and 31% of the genes in the Weddell seal have missing exonic sequence.

    “Finally, illustrating extreme cases among seals, 56% of the genes in the Antarctic fur seal have inactivating mutations and 31% of the genes in the Weddell seal have missing exonic sequence (Fig. 6F).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The dog canFam5 assembly exhibits an elevated base error rate compared to dog canFam4 or dingo, whereas all three assemblies have similar BUSCO scores.

    “Similarly, the dog canFam5 assembly exhibits an elevated base error rate compared to dog canFam4 or dingo, whereas all three assemblies have similar BUSCO scores (Fig. 6E).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given

    The final input annotations that TOGA used with human as the reference comprised 39,664 transcripts of 19,464 genes; for mouse, 33,460 transcripts of 22,257 genes; and for chicken, 38,252 transcripts of 18,039 genes.

    “The final input annotations that TOGA used with human as the reference comprised 39,664 transcripts of 19,464 genes. For mouse, input annotations comprised 33,460 transcripts of 22,257 genes, and for chicken 38,252 transcripts of 18,039 genes.”
    Quote in the paperNot a substantial claim

  • The quote only partly states this claim.

    Findingown data

    The best-fitting model, determined through model selection according to AIC, was a negative binomial GLM that included all four predictors: divergence time to human, evolutionary distance to human, log contig N50, and log scaffold N50.

    “The best-fitting model, determined through model selection according to the Akaike Information Criterion (AIC; Table S12), was a negative binomial GLM that included all four predictors.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The variance-function-based R2 value of the best-fitting model was 11.2%.

    “The variance-function-based R2 value (58), which we calculated using the R package rsq (https://CRAN.R-project.org/package=rsq, version 2.2), was 11.2%.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the best-fitting model, the most influential variable was contig N50, whereas the least influential was scaffold N50.

    “By varying one predictor at a time and keeping the remaining predictors fixed at their mean values (fig. S38), we found that the most influential variable was contig N50, whereas the least influential was scaffold N50.”
    • Also stated in: § Generalized linear models · plain
      “Examining the distribution of AIC values across candidate GLMs (Table S12) led to the same conclusion.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    The human reference proteome downloaded from UniProt provides a BUSCO completeness of 99.5%.

    “which provides a BUSCO completeness of 99.5%.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Performing the same GLM analysis after excluding Hominoidea (Apes) led to qualitatively identical results and only slightly different model coefficients, P-values, and R2 values.

    “Performing the same analysis after excluding Hominoidea (Apes) led to qualitatively identical results and only slightly different model coefficients, P-values, and R2 values”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Replacing in-frame TGA stop codons in the reference sequence by NNN enables CESAR to align such TGA stop codons to sense or to stop codons.

    “This replacement enables CESAR to align such TGA stop codons to sense or to stop codons.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    Paralogous projection can arise if the real orthologous gene is entirely missing in the assembly or if TOGA misclassifies the orthologous gene because of excessive divergence of intronic/intergenic regions.

    “This can arise if the real orthologous gene is entirely missing in the assembly (thus only a paralog aligns) or if TOGA misclassifies the orthologous gene because of excessive divergence of intronic/intergenic regions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    TOGA's classification of ancestral genes provides a useful assembly quality benchmark.

    “Additionally, TOGA’s classification of ancestral genes provides a useful assembly quality benchmark.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Large frame-preserving deletions result in substantially shorter but likely functional proteins.

    “These large frame-preserving deletions result in substantially shorter but likely functional proteins (though it is not known whether the function is truly conserved).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Including problematic transcripts such as fusion transcripts or potential NMD targets in the input set can lead to wrong gene classifications or orthology types.

    “however, including problematic transcripts such as fusion transcripts or potential NMD targets can lead to wrong gene classifications or orthology types.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    If the locus represented by the paralogous chain does not receive any annotation via an orthologous chain, then this locus likely encodes a gene.

    “If the locus represented by the paralogous chain does not receive any annotation via an orthologous chain, then TOGA also annotates a gene at this locus (shown in fig. S6), as this locus likely encodes a gene.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationnone given

    TOGA can provide an initial annotation of conserved genes for newly-sequenced genomes or be integrated together with available transcriptomics data and ab initio gene predictions to comprehensively annotate conserved and lineage-specific genes.

    “Second, TOGA can provide an initial annotation of conserved genes for newly-sequenced genomes or be integrated together with available transcriptomics data and ab initio gene predictions to comprehensively annotate conserved and lineage-specific genes.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    Exons not spanned by an orthologous chain are often due to assembly fragmentation and incompleteness.

    “Exons not spanned by an orthologous chain are also classified as missing (M), as such cases are often due to assembly fragmentation and incompleteness.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    The models were separately tested on real orthologous genes and artificially rearranged orthologous genes to evaluate performance in detecting translocated or inverted orthologous genes.

    “To evaluate the performance of the models in detecting translocated or inverted orthologous genes, we separately tested them on real orthologous genes (typically high synteny values) and artificially rearranged orthologous genes (typically low synteny values of log10(1)) (Fig. 1F, Table S3).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisenone given

    A gene in the reference can have several transcripts (isoforms) and a given gene can have several inferred orthologous loci in the query.

    “Importantly, a gene in the reference can have several transcripts (isoforms) and a given gene can have several inferred orthologous loci in the query.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Counting missing sequence as aligning codons results in a consistent classification of transcripts that have the same inactivating mutations and only differ in the amount of missing sequence.

    “since this procedure results in a consistent classification of transcripts that have the same inactivating mutations and only differ in the amount of missing sequence.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Missing codons in the unknown query sequence correspond to sense codons.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    TOGA only annotates the CDS.

    “This step reduces redundancy as TOGA only annotates the CDS.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    TOGA implements two features that help to avoid codon alignment errors: masking all gene-inactivating mutations such as frameshifts, and an exon-by-exon procedure that aligns and joins individual orthologous exons.

    “TOGA implements two features that help to avoid codon alignment errors. First, TOGA masks all gene-inactivating mutations such as frameshifts, which otherwise can result in misalignments (fig. S40). Second, whereas existing methods align entire orthologous coding sequences, TOGA is aware of orthology at the exon level. This enables an “exon-by-exon” procedure that generates alignments by aligning”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Selection in UTR exons is variable.

    “Because selection in UTR exons is variable, alignments overlapping UTR exons are ignored for feature computation.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    Assigning two transcripts to the same gene when their coding exons overlap by at least one base on the same strand allows TOGA to correctly annotate and distinguish nested genes on the same strand and overlapping genes located in antisense orientation.

    “This allows TOGA to correctly annotate and distinguish nested genes on the same strand and overlapping genes located in antisense orientation (fig. S24B and C).”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The single- and multi-exon models were tested on independent test sets obtained for three representative placental mammals that include both a close sister species to mouse (rat) and more distant outgroups (dog, armadillo).

    “We tested the single- and multi-exon model on independent test sets obtained for three representative placental mammals that include both a close sister species to mouse (rat) and more distant outgroups (dog, armadillo).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisenone given

    An orthologous chain provides not only the orthologous query locus, but its aligning blocks also provide information about the location of individual exons.

    “To distinguish between classes P, M, and D, TOGA utilizes that an orthologous chain provides not only the orthologous query locus, but the aligning blocks of the chain also provide information about the location of individual exons.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisenone given

    The Viterbi algorithm used in CESAR's HMM may output alignments of exons that do not exist in the query locus, either because the exon is truly deleted or diverged to an extent that no meaningful alignment is possible or because the exon overlaps an assembly gap.

    “This step is necessary as the Viterbi algorithm used in CESAR’s HMM may also output alignments of exons that do not exist in the query locus, either because the exon is truly deleted or diverged to an extent that no meaningful alignment is possible (class D) or because the exon overlaps an assembly gap in the query genome (class M).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    TOGA can detect four orthologous loci in the query, but if only one of these loci encodes a functional gene, this results in a 1:1 orthology relationship.

    “Similarly, as exemplified in Fig. 1H, TOGA can detect four orthologous loci in the query, but if only one of these loci encodes a functional gene, this results in a 1:1 orthology relationship.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    For more distant clades, human is not a powerful reference and a marsupial and a monotreme mammal should be used as the reference instead.

    “Nevertheless, for these more distant clades, human is not a powerful reference and a marsupial and a monotreme mammal should be used as the reference instead.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    For orthology inference, individual predicted transcripts need to be consolidated into predicted genes.

    “For orthology inference, individual predicted transcripts need to be consolidated into predicted genes.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Most of the features quantify how well intronic and intergenic regions, which largely evolve neutrally, align in comparison to coding exons, which largely evolve under purifying selection.

    “Most of these features quantify how well intronic and intergenic regions, which largely evolve neutrally, align in comparison to coding exons, which largely evolve under purifying selection.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Knowledge about U12 introns in the reference may be incomplete or not always available.

    “Since knowledge about U12 introns in the reference may be incomplete or not always available, TOGA considers every intron in the reference without canonical GT/GC-AG splice sites as a putative U12 intron.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The NCBI RefSeq identifier and the assembly name provided by Ensembl were used to assure that all comparisons between TOGA and NCBI or Ensembl were done for the same genome assembly.

    “We used the NCBI RefSeq identifier and the assembly name provided by Ensembl to assure that all comparisons between TOGA and NCBI or Ensembl were done for the same genome assembly.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisenone given

    TOGA but not Ensembl distinguishes between 1:many and many:1 orthology types.

    “Since TOGA but not Ensembl distinguishes between 1:many (more than 1 ortholog in the query species) and many:1 (one ortholog in the query, but more than one in the reference), we updated those Ensembl 1:many types as many:1, for which the orthology group had >1 gene annotated in reference and exactly one gene annotated in the query.”
    Quote in the paperSubstantial claim
69 of 69

  • The paper relies on this without support.

    Generating high-quality annotations is time-consuming and typically requires comprehensive transcriptomics data, leading to a growing gap between genome sequencing and annotation, including orthology inference.

    “Second, generating high-quality annotations is time-consuming and typically requires comprehensive transcriptomics (gene expression) data, leading to a growing gap between genome sequencing and annotation, including orthology inference.”

  • The paper relies on this without support.

    Alignment chains capture both orthologous gene loci as well as loci containing paralogs or processed pseudogenes.

    “These alignment chains capture both orthologous gene loci as well as loci containing paralogs or processed pseudogenes (Fig. 1A).”

  • The paper relies on this without support.

    Genes split between different scaffolds are often missed or annotated as fragments, hampering downstream analyses.

    “Genes split between different scaffolds are often missed or annotated as fragments, hampering downstream analyses.”

  • The paper relies on this without support.

    Sequences of orthologous but not paralogous genes from closely related species are expected to be highly similar.

    “To evaluate the accuracy of this procedure, we leveraged that sequences of orthologous but not paralogous genes from closely related species are expected to be highly similar.”

  • The paper relies on this without support.

    BUSCO’s fragmented or missing gene classification indicates how much of the gene was detected, but does not distinguish between assembly incompleteness that results in missing gene sequence and assembly base errors that destroy the reading frame.

    “BUSCO’s fragmented or missing gene classification indicates how much of the gene was detected, but does not distinguish between the two major underlying reasons: assembly incompleteness that results in missing gene sequence vs. assembly base errors that destroy the reading frame.”

  • The authors state this as an assumption.

    Missing codons in the unknown query sequence correspond to sense codons.

    “To distinguish between “uncertain loss” and “lost” transcripts, we count missing sequence as aligning codons, making the conservative assumption that missing codons correspond to sense codons in the unknown query sequence (fig. S13), since this procedure results in a consistent classification of transcripts that have the same inactivating mutations and only differ in the amount of missing sequence”

  • The paper relies on this without support.

    Artificially rearranged chain-gene pairs used as positive training data are representative of real orthologous genes that underwent genomic rearrangements.

    “Since we aimed at also accurately detecting orthologous genes that underwent genomic rearrangements, we enriched the positive training dataset with artificially rearranged chain-gene pairs, generated by trimming long syntenic chains to new single gene-covering chains.”

  • The paper relies on this without support.

    Human hg38 and mouse mm10 assemblies contain alternative haplotypes and structural variants.

    “The assemblies of human (hg38) and mouse (mm10) also contain alternative haplotypes and structural variants (e.g. chr22_KI270876v1_alt).”

  • The paper relies on this without support.

    Even for highly fragmented genome assemblies, low-scoring chains are extremely unlikely to represent orthologous parts of genes.

    “Even for highly fragmented genome assemblies, low-scoring chains are extremely unlikely to represent orthologous parts of genes.”

  • The paper relies on this without support.

    Evolutionary distance to human and divergence time to human are strongly positively correlated.

    “To account for the strong positive correlation between evolutionary distance and divergence time, we specified both variables not as separate but as interacting predictors in models that included both.”

  • The paper relies on this without support.

    Micro-introns shorter than 20 bp are often used to mask frameshifting mutations.

    “Fifth, we excluded isoforms that have introns shorter than 20 bp, as such micro-introns are often used to mask frameshifting mutations.”
11 of 11

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Conserved-gene benchmark is called both sensitivity and specificity testing

    Minor

    The methods label the conserved-gene benchmark a sensitivity evaluation, but the results call it a specificity benchmark. Treating losses among genes presumed conserved as false positives evaluates specificity, not sensitivity.

  • 509 mammalian alignments are described as placental

    Minor

    The methods call all 509 mammalian alignment chains placental, whereas the paper elsewhere distinguishes 488 placental from 21 non-placental mammal assemblies.

  • Loss precedence conflicts with the all-transcripts-loss rule

    Minor

    Putting "lost" ahead of "missing" would classify a locus with one lost and one missing transcript as lost. The stated rule says a locus is lost if and only if all its annotated transcripts are lost.

3 of 3

Cites

Flagged first, then by citations

1 of the 69 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

  1. T. Chen C. Guestrin paper presented at the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining San Francisco CA 13–17 August 2016.
  2. R. S. Harris Thesis The Pennsylvania State University (2007).

Cited by

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Authors
Kirilenko, B. M., Munegowda, C., Osipova, E., Jebb, D., Sharma, V., Blumer, M., Morales, A. E., Ahmed, A.-W., Kontopoulos, D.-G., Hilgers, L., Lindblad-Toh, K., Karlsson, E. K., Zoonomia Consortium‡, Hiller, M., Andrews, G., Armstrong, J. C., Bianchi, M., Birren, B. W., Bredemeyer, K. R., Breit, A. M., Christmas, M. J., Clawson, H., Damas, J., Di Palma, F., Diekhans, M., Dong, M. X., Eizirik, E., Fan, K., Fanter, C., Foley, N. M., Forsberg-Nilsson, K., Garcia, C. J., Gatesy, J., Gazal, S., Genereux, D. P., Goodman, L., Grimshaw, J., Halsey, M. K., Harris, A. J., Hickey, G., Hiller, M., Hindle, A. G., Hubley, R. M., Hughes, G. M., Johnson, J., Juan, D., Kaplow, I. M., Karlsson, E. K., Keough, K. C., Kirilenko, B., Koepfli, K.-P., Korstian, J. M., Kowalczyk, A., Kozyrev, S. V., Lawler, A. J., Lawless, C., Lehmann, T., Levesque, D. L., Lewin, H. A., Li, X., Lind, A., Lindblad-Toh, K., Mackay-Smith, A., Marinescu, V. D., Marques-Bonet, T., Mason, V. C., Meadows, J. R. S., Meyer, W. K., Moore, J. E., Moreira, L. R., Moreno-Santillan, D. D., Morrill, K. M., Muntané, G., Murphy, W. J., Navarro, A., Nweeia, M., Ortmann, S., Osmanski, A., Paten, B., Paulat, N. S., Pfenning, A. R., Phan, B. N., Pollard, K. S., Pratt, H. E., Ray, D. A., Reilly, S. K., Rosen, J. R., Ruf, I., Ryan, L., Ryder, O. A., Sabeti, P. C., Schäffer, D. E., Serres, A., Shapiro, B., Smit, A. F. A., Springer, M., Srinivasan, C., Steiner, C., Storer, J. M., Sullivan, K. A. M., Sullivan, P. F., Sundström, E., Supple, M. A., Swofford, R., Talbot, J.-E., Teeling, E., Turner-Maier, J., Valenzuela, A., Wagner, F., Wallerman, O., Wang, C., Wang, J., Weng, Z., Wilder, A. P., Wirthlin, M. E., Xue, J. R., & Zhang, X.
Affiliations
  • Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany.
  • Goethe University Frankfurt (DE)
  • Max Planck Institute for the Physics of Complex Systems (DE)
  • Broad Institute (US)
ORCID
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2023-04-28
Volume
380
Issue
6643
Pages
eabn3107-eabn3107
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.abn3107
PMC
PMC10193443
OpenAlex
W4367173676
Semantic Scholar
f705bccddc093db6418ab960b89c42a9b0efa95f
Cited by
209
References
69
Citations by year
2026: 68 · 2025: 68 · 2024: 51 · 2023: 21 · 2022: 1
FWCI
18.46
Type
journal article
Language
en
Open access
Licence
cc-by-nd
Locations
  • Science (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
Funders
  • Deutsche Forschungsgemeinschaft (HI1423/5-1, HI1423/4-1)
  • Hessisches Ministerium für Wissenschaft und Kunst
  • Max-Planck-Gesellschaft
  • Max-Planck-Institut für Physik Komplexer Systeme
  • National Institutes of Health
  • National Human Genome Research Institute (R01HG010485, R01HG008742, U24HG007234)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Chromosomal and Genetic Variations
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
orthology inference, genome annotation, TOGA, genome alignment, conserved genes, placental mammals, birds, comparative genomics, genome assembly quality
MeSH terms
Eutheria, Animals, Birds, Female, Genome, Genomics, Mice, Molecular Sequence Annotation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-11-02.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-11-02.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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