FragGeneScan: Predicting Genes in Short and Error-Prone Reads

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.

Survival strategy of comammox bacteria in a wastewater nutrient removal system with sludge fermentation liquid as additional carbon source

“…DNA libraries were sequenced on the Illumina Novaseq 6000 platform (PE150). Raw reads were quality controlled by Readfq v8 (http://github.com/cjfields/readfq), and genes were predicted by a hidden Markov model (HMM) predictor tool, FragGeneScan (Yuzhen 2013). HMMs of the taxonomic marker gene gyrB and the key nitrification gene amoA were downloaded from FunGene to identify the function-specific taxonomic profiles and relative abundances of nitrifiers by MetAnnotate (Petrenko et al 2015, Spasov et al 2020.…”