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FragGeneScan: predicting genes in short and error-prone reads

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Rho, Mina, Tang, Haixu, Ye, Yuzhen·2010·Nucleic Acids Research
DOI 10.1093/nar/gkq747
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Abstract

The advances of next-generation sequencing technology have facilitated metagenomics research that attempts to determine directly the whole collection of genetic material within an environmental sample (i.e. the metagenome). Identification of genes directly from short reads has become an important yet challenging problem in annotating metagenomes, since the assembly of metagenomes is often not available. Gene predictors developed for whole genomes (e.g. Glimmer) and recently developed for metagenomic sequences (e.g. MetaGene) show a significant decrease in performance as the sequencing error rates increase, or as reads get shorter. We have developed a novel gene prediction method FragGeneScan, which combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein-coding region in short reads. The performance of FragGeneScan was comparable to Glimmer and MetaGene for complete genomes. But for short reads, FragGeneScan consistently outperformed MetaGene (accuracy improved ∼62% for reads of 400 bases with 1% sequencing errors, and ∼18% for short reads of 100 bases that are error free). When applied to metagenomes, FragGeneScan recovered substantially more genes than MetaGene predicted (>90% of the genes identified by homology search), and many novel genes with no homologs in current protein sequence database.

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Metadata

Authors
Rho, M., Tang, H., & Ye, Y.
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2010-08-28
Volume
38
Issue
20
Pages
e191-e191
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkq747
OpenAlex
W2069689025
Cited by
840
References
5
FWCI
10.16
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by-nc/2.5 (vor, 2229 d)

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Rho, M., Tang, H., & Ye, Y. (2010). FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Research. https://doi.org/10.1093/nar/gkq747

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