Improved recovery and annotation of genes in metagenomes through the prediction of fungal introns.
artificial intelligence
eukaryote
fungi
gene prediction
intron
metagenomics
Journal
Molecular ecology resources
ISSN: 1755-0998
Titre abrégé: Mol Ecol Resour
Pays: England
ID NLM: 101465604
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
revised:
27
06
2023
received:
21
11
2022
accepted:
31
07
2023
pubmed:
10
8
2023
medline:
10
8
2023
entrez:
10
8
2023
Statut:
ppublish
Résumé
Metagenomics provides a tool to assess the functional potential of environmental and host-associated microbiomes based on the analysis of environmental DNA: assembly, gene prediction and annotation. While gene prediction is straightforward for most bacterial and archaeal taxa, it has limited applicability in the majority of eukaryotic organisms, including fungi that contain introns in gene coding sequences. As a consequence, eukaryotic genes are underrepresented in metagenomics datasets and our understanding of the contribution of fungi and other eukaryotes to microbiome functioning is limited. Here, we developed a machine intelligence-based algorithm that predicts fungal introns in environmental DNA with reasonable precision and used it to improve the annotation of environmental metagenomes. Intron removal increased the number of predicted genes by up to 9.1% and improved the annotation of several others. The proportion of newly predicted genes increased with the share of eukaryotic genes in the metagenome and-within fungal taxa-increased with the number of introns per gene. Our approach provides a tool named SVMmycointron for improved metagenome annotation, especially of microbiomes with a high proportion of eukaryotes. The scripts described in the paper are made publicly available and can be readily utilized by microbiome researchers analysing metagenomics data.
Identifiants
pubmed: 37561110
doi: 10.1111/1755-0998.13852
doi:
Banques de données
RefSeq
['PRJNA603240', 'SRX099567', 'SRX1686623', 'SRX1990991', 'SRX2488989', 'SRX2575203', 'SRX2720157', 'SRX3197864', 'SRX691280', 'SRX1557139', 'SRX1944669', 'SRX2316877', 'SRX2538108', 'SRX2648762', 'SRX2939063', 'SRX665338', 'SRX732059']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1800-1811Subventions
Organisme : Grantová Agentura České Republiky
ID : 21-17749S
Organisme : Ministry of Education, Youth and Sports of the Czech Republic
ID : e-INFRA CZ LM2018140
Informations de copyright
© 2023 John Wiley & Sons Ltd.
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