FragGeneScan: predicting genes in short and error-prone reads

By Mina Rho, Haixu Tang, Yuzhen Ye. 2010

793 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

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Citations in the corpus

MGnify: the microbiome analysis resource in 2020 (2019, neutral): “20) (36).”
Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3 (2020, methodology): “Reads were then scanned for peptide fragments using “carnelian.py translate” wrapping FragGeneScan (Rho et al., 2010) version 1.31 with the “-n 3” option.”
Machine learning for metagenomics: methods and tools (2015, methodology): “FragGeneScan (Rho et al., 2010) is another HMM-based approach that integrates codon usage bias, start/stop codon patterns as well as an explicit modeling of sequencing errors; it seems to outperform other solutions and is currently used by the EBI Metagenomics pipeline.”
MyTaxa: an advanced taxonomic classifier for genomic and metagenomic sequences (2014, methodology): “For the homology-based algorithms, we ran a BLAT (23) search of the gene sequences predicted on the six query datasets by FragGeneScan (22) against the reference database, and the search results were used as input for the algorithms.”
WebMGA: a customizable web server for fast metagenomic sequence analysis (2011, methodology): “(cid:129) ORF calling include 3 tools for ORF prediction from DNA sequences: ORF-finder [27], Metagene [14] and FragGeneScan [15].”
Chapter 12: Human Microbiome Analysis (2012, background): “Conversely, de novo methods have been developed to directly bin [67,68,69] and call genes within [61,62] metagenomic sequences using DNA features alone (GC content, codon usage, etc.”
Metagenomic reconstruction of nitrogen cycling pathways in a CO2-enriched grassland ecosystem (2017, methodology): “Gene prediction was carried out by FragGeneScan (Rho et al., 2010), which predicts high quality gene fragments from short, error-prone reads and overcomes homopolymer errors.”
American Gut: an Open Platform for Citizen Science Microbiome Research (2018, positive): “file ”) (108) on quality-controlled reads to infer partial protein sequences and then ran HMMER (v3.”
Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies (2015, methodology): “CDSs are identified using a number of tools including MetaGeneMark, 84 Metagene, 85 Prodigal, 86 Orphelia, 87 and FragGeneScan, 88 all of which utilize ab initio gene prediction algorithms.”
MGnify: the microbiome sequence data analysis resource in 2023 (2022, methodology): “each contig (MGYC) using Prodigal (18) and FragGeneScan (19).”
PHASTER: a better, faster version of the PHAST phage search tool (2016, methodology): “In this mode, complete and partial genes are predicted using FragGeneScan (16), and the predicted prophages are arranged by contig in the presented results.”