Discovery of high-confidence human protein-coding genes and exons by whole-genome PhyloCSF helps elucidate 118 GWAS loci.
Journal
Genome research
ISSN: 1549-5469
Titre abrégé: Genome Res
Pays: United States
ID NLM: 9518021
Informations de publication
Date de publication:
12 2019
12 2019
Historique:
received:
15
11
2018
accepted:
09
09
2019
pubmed:
21
9
2019
medline:
16
4
2020
entrez:
21
9
2019
Statut:
ppublish
Résumé
The most widely appreciated role of DNA is to encode protein, yet the exact portion of the human genome that is translated remains to be ascertained. We previously developed PhyloCSF, a widely used tool to identify evolutionary signatures of protein-coding regions using multispecies genome alignments. Here, we present the first whole-genome PhyloCSF prediction tracks for human, mouse, chicken, fly, worm, and mosquito. We develop a workflow that uses machine learning to predict novel conserved protein-coding regions and efficiently guide their manual curation. We analyze more than 1000 high-scoring human PhyloCSF regions and confidently add 144 conserved protein-coding genes to the GENCODE gene set, as well as additional coding regions within 236 previously annotated protein-coding genes, and 169 pseudogenes, most of them disabled after primates diverged. The majority of these represent new discoveries, including 70 previously undetected protein-coding genes. The novel coding genes are additionally supported by single-nucleotide variant evidence indicative of continued purifying selection in the human lineage, coding-exon splicing evidence from new GENCODE transcripts using next-generation transcriptomic data sets, and mass spectrometry evidence of translation for several new genes. Our discoveries required simultaneous comparative annotation of other vertebrate genomes, which we show is essential to remove spurious ORFs and to distinguish coding from pseudogene regions. Our new coding regions help elucidate disease-associated regions by revealing that 118 GWAS variants previously thought to be noncoding are in fact protein altering. Altogether, our PhyloCSF data sets and algorithms will help researchers seeking to interpret these genomes, while our new annotations present exciting loci for further experimental characterization.
Identifiants
pubmed: 31537640
pii: gr.246462.118
doi: 10.1101/gr.246462.118
pmc: PMC6886504
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2073-2087Subventions
Organisme : Wellcome Trust
ID : WT108749/Z/15/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 208349/Z/17/Z
Pays : United Kingdom
Organisme : NHGRI NIH HHS
ID : R01 HG004037
Pays : United States
Organisme : NHGRI NIH HHS
ID : U41 HG007234
Pays : United States
Organisme : NHGRI NIH HHS
ID : U24 HG003345
Pays : United States
Organisme : Wellcome Trust
Pays : United Kingdom
Informations de copyright
© 2019 Mudge et al.; Published by Cold Spring Harbor Laboratory Press.
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