Short-read reading-frame predictors are not created equal: sequence error causes loss of signal.

ABSTRACT:

Background

Gene prediction algorithms (or gene callers) are an essential tool for analyzing shotgun nucleic acid sequence data. Gene prediction is a ubiquitous step in sequence analysis pipelines; it reduces the volume of data by identifying the most likely reading frame for a fragment, permitting the out-of-frame translations to be ignored. In this study we evaluate five widely used ab initio gene-calling algorithms-FragGeneScan, MetaGeneAnnotator, MetaGeneMark, Orphelia, and Prodigal-for accuracy on short (75-1000 bp) fragments containing sequence error from previously published artificial data and "real" metagenomic datasets.

Results

While gene prediction tools have similar accuracies predicting genes on error-free fragments, in the presence of sequencing errors considera

SUBMITTER: Trimble WL 

PROVIDER: S-EPMC3526449 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature