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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-12

AUTHORS

William L Trimble, Kevin P Keegan, Mark D’Souza, Andreas Wilke, Jared Wilkening, Jack Gilbert, Folker Meyer

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 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. More... »

PAGES

183

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2105-13-183

    DOI

    http://dx.doi.org/10.1186/1471-2105-13-183

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1043085431

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/22839106


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