Evidence map›Paper›PMID 28859620›Full record

ArticleBMC genomics2017

Orthonome - a new pipeline for predicting high quality orthologue gene sets applicable to complete and draft genomes.

Rahul V Rane, John G Oakeshott, Thu Nguyen, Ary A Hoffmann, Siu F Lee

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Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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10citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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10 citing papers in PubMed.

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  9. De-Extinction.Genes · 2018
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Rahul V RaneBio21 Institute, School of Biosciences, The University of Melbourne, Melbourne, Victoria, Australia. rahul.rane@csiro.au.ORCID http://orcid.org/0000-0003-4616-6244
John G OakeshottCSIRO, Canberra, Australian Capital Territory, Australia.
Thu NguyenBio21 Institute, School of Biosciences, The University of Melbourne, Melbourne, Victoria, Australia.
Ary A HoffmannBio21 Institute, School of Biosciences, The University of Melbourne, Melbourne, Victoria, Australia.
Siu F LeeCSIRO, Canberra, Australian Capital Territory, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDistinguishing orthologous and paralogous relationships between genes across multiple species is essential for comparative genomic analyses. Various computational approaches have been developed to resolve these evolutionary relationships, but strong trade-offs between precision and recall of orthologue prediction remains an ongoing challenge.

resultsHere we present Orthonome, an orthologue prediction pipeline, designed to reduce the trade-off between orthologue capture rates (recall) and accuracy of multi-species orthologue prediction. The pipeline compares sequence domains and then forms sequence-similar clusters before using phylogenetic comparisons to identify inparalogues. It then corrects sequence similarity metrics for fragment and gene length bias using a novel scoring metric capturing relationships between full length as well as fragmented genes. The remaining genes are then brought together for the identification of orthologues within a phylogenetic framework. The orthologue predictions are further calibrated along with inparalogues and gene births, using synteny, to identify novel orthologous relationships. We use 12 high quality Drosophila genomes to show that, compared to other orthologue prediction pipelines, Orthonome provides orthogroups with minimal error but high recall. Furthermore, Orthonome is resilient to suboptimal assembly/annotation quality, with the inclusion of draft genomes from eight additional Drosophila species still providing >6500 1:1 orthologues across all twenty species while retaining a better combination of accuracy and recall than other pipelines. Orthonome is implemented as a searchable database and query tool along with multiple-sequence alignment browsers for all sets of orthologues. The underlying documentation and database are accessible at http://www.orthonome.com .

conclusionWe demonstrate that Orthonome provides a superior combination of orthologue capture rates and accuracy on complete and draft drosophilid genomes when tested alongside previously published pipelines. The study also highlights a greater degree of evolutionary conservation across drosophilid species than earlier thought.

Indexed as

Sequence Homology, Nucleic AcidAnimalsDrosophila melanogasterEvolution, MolecularGenomicsGene birthGene duplicationInparalogueOrthologue

Identifiers

PMID28859620
PMCPMC5580312

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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.