%0 Journal Article
%T Short-read reading-frame predictors are not created equal: sequence error causes loss of signal
%A Trimble William L
%A Keegan Kevin P
%A D¡¯Souza Mark
%A Wilke Andreas
%J BMC Bioinformatics
%D 2012
%I BioMed Central
%R 10.1186/1471-2105-13-183
%X 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¡ªFragGeneScan, MetaGeneAnnotator, MetaGeneMark, Orphelia, and Prodigal¡ªfor accuracy on short (75¨C1000 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 considerable differences between tools become evident. For error-containing short reads, FragGeneScan finds more prokaryotic coding regions than does MetaGeneAnnotator, MetaGeneMark, Orphelia, or Prodigal. This improved detection of genes in error-containing fragments, however, comes at the cost of much lower (50%) specificity and overprediction of genes in noncoding regions. Conclusions Ab initio gene callers offer a significant reduction in the computational burden of annotating individual nucleic acid reads and are used in many metagenomic annotation systems. For predicting reading frames on raw reads, we find the hidden Markov model approach in FragGeneScan is more sensitive than other gene prediction tools, while Prodigal, MGA, and MGM are better suited for higher-quality sequences such as assembled contigs.
%K Gene prediction
%K Sequence errors
%K Short reads
%K Reading frames
%K Gene callers
%K Ab-initio gene prediction
%U http://www.biomedcentral.com/1471-2105/13/183