Evidence map›Paper›PMID 25158799›Full record

ArticleMolecular biology and evolution2014

Orthology inference in nonmodel organisms using transcriptomes and low-coverage genomes: improving accuracy and matrix occupancy for phylogenomics.

Ya Yang, Stephen A Smith

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 papers.

0numbers the graph read from it
0cells of the map it votes in
164citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

164 citing papers in PubMed.

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  19. Global phylogeny and taxonomy of Artemisia.Nature communications · 2025
    Article
  20. Review

104 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ya YangDepartment of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor yangya@umich.edu eebsmith@umich.edu.
Stephen A SmithDepartment of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor yangya@umich.edu eebsmith@umich.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Orthology inference is central to phylogenomic analyses. Phylogenomic data sets commonly include transcriptomes and low-coverage genomes that are incomplete and contain errors and isoforms. These properties can severely violate the underlying assumptions of orthology inference with existing heuristics. We present a procedure that uses phylogenies for both homology and orthology assignment. The procedure first uses similarity scores to infer putative homologs that are then aligned, constructed into phylogenies, and pruned of spurious branches caused by deep paralogs, misassembly, frameshifts, or recombination. These final homologs are then used to identify orthologs. We explore four alternative tree-based orthology inference approaches, of which two are new. These accommodate gene and genome duplications as well as gene tree discordance. We demonstrate these methods in three published data sets including the grape family, Hymenoptera, and millipedes with divergence times ranging from approximately 100 to over 400 Ma. The procedure significantly increased the completeness and accuracy of the inferred homologs and orthologs. We also found that data sets that are more recently diverged and/or include more high-coverage genomes had more complete sets of orthologs. To explicitly evaluate sources of conflicting phylogenetic signals, we applied serial jackknife analyses of gene regions keeping each locus intact. The methods described here can scale to over 100 taxa. They have been implemented in python with independent scripts for each step, making it easy to modify or incorporate them into existing pipelines. All scripts are available from https://bitbucket.org/yangya/phylogenomic_dataset_construction.

Indexed as

GenomePhylogenySoftwareTranscriptomeAnimalsArthropodsBase SequenceEvolution, MolecularHymenopteraMolecular Sequence DataSequence AlignmentSequence Analysis, DNASequence Homology, Nucleic AcidVitaceaeDiplopodaphylotranscriptomicsRNA-seqVitaceae

Identifiers

PMID25158799
PMCPMC4209138

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.