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

Trimble WL, Keegan KP, D'Souza M, Wilke A, Wilkening J, Gilbert J, Meyer F - BMC Bioinformatics (2012)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computation Institute, University of Chicago, Chicago, IL 60637, USA. trimble@anl.gov

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.

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Reading frame accuracy as function of fragment length for fragments at varying insertion/deletion error rates. (A) Error-free fragments. (B) Fragments with 0.2% insertion/deletion errors. (C) Fragments with 0.5% insertion/deletion errors. (D) Fragments with 2.8% insertion/deletion errors. For error-free fragments, longer fragments result in more accurate predictions.
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Figure 1: Reading frame accuracy as function of fragment length for fragments at varying insertion/deletion error rates. (A) Error-free fragments. (B) Fragments with 0.2% insertion/deletion errors. (C) Fragments with 0.5% insertion/deletion errors. (D) Fragments with 2.8% insertion/deletion errors. For error-free fragments, longer fragments result in more accurate predictions.

Mentions: The overall accuracy of the five gene callers was determined on simulated shotgun sequences from fourteen prokaryotes as a function of fragment lengths between 75 and 1000 bp at four rates of artificially introduced insertion/deletion error (0%, 0.2%, 0.5%, and 2.8%). These error rates were selected for comparison with previous studies [19]. The overall accuracy is plotted against fragment length in Figure 1.


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

Trimble WL, Keegan KP, D'Souza M, Wilke A, Wilkening J, Gilbert J, Meyer F - BMC Bioinformatics (2012)

Reading frame accuracy as function of fragment length for fragments at varying insertion/deletion error rates. (A) Error-free fragments. (B) Fragments with 0.2% insertion/deletion errors. (C) Fragments with 0.5% insertion/deletion errors. (D) Fragments with 2.8% insertion/deletion errors. For error-free fragments, longer fragments result in more accurate predictions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3526449&req=5

Figure 1: Reading frame accuracy as function of fragment length for fragments at varying insertion/deletion error rates. (A) Error-free fragments. (B) Fragments with 0.2% insertion/deletion errors. (C) Fragments with 0.5% insertion/deletion errors. (D) Fragments with 2.8% insertion/deletion errors. For error-free fragments, longer fragments result in more accurate predictions.
Mentions: The overall accuracy of the five gene callers was determined on simulated shotgun sequences from fourteen prokaryotes as a function of fragment lengths between 75 and 1000 bp at four rates of artificially introduced insertion/deletion error (0%, 0.2%, 0.5%, and 2.8%). These error rates were selected for comparison with previous studies [19]. The overall accuracy is plotted against fragment length in Figure 1.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computation Institute, University of Chicago, Chicago, IL 60637, USA. trimble@anl.gov

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.

Show MeSH