Article

FragGeneScan: Predicting genes in short and error-prone reads

School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.
Nucleic Acids Research (Impact Factor: 9.11). 11/2010; 38(20):e191. DOI: 10.1093/nar/gkq747
Source: PubMed

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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Available from: Yuzhen Ye
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    • "For DNA assembly the following kmer lengths were used: 51, 55 with Velvet and 31 with Meta-Ray. Genes were predicted from the obtained contigs using FragGeneScan with suggested options for contigs (Rho, Tang & Ye, 2010) generating 429,162 cDNA genes and 3,176,262 DNA genes, respectively (Table S1). The predicted gene sequences obtained with the different assemblers and kmer lengths were clustered at 99% similarity using UCLUST (Edgar, 2010). "
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    • "The filtered sequences were assembled using Velvet/ Metavelvet (Namiki et al., 2012; Zerbino and Birney, 2008), and then contigs were generated. The contigs were then converted to putative protein sequences using FragGeneScan (Rho et al., 2010). For protein annotation and analysis, the assembled sequences were uploaded on the Metagenomics Analysis Server (MG-RAST) (Meyer et al., 2008), and subjected to similarity search using the SEED database, keeping 10 −5 as the maximum E-value (Overbeek et al., 2005). "
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    • " for both the microbialite and sediment contigs . Nevertheless , only 0 . 64 and 1 . 74% of the raw reads from the sediment and microbialite metagenomes , respectively , assembled into contigs , indicating that both environments had complex microbial communities . FragGeneScan was used to predict and translate contig open reading frames ( ORFs ) ( Rho et al . , 2010 ) and ProPas ( Wu and Zhu , 2012 ) was used to calculate predicted protein isoelectric points ( pI ) ."
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