Sipros Ensemble Improves Database Searching and Filtering for Complex Metaproteomics

Sipros Ensemble Improves Database Searching and Filtering for Complex Metaproteomics

Xuan Guo, Zhou Li, Qiuming Yao, Ryan S. Mueller, Jimmy K. Eng, David L. Tabb, William Judson Hervey IV, Chongle Pan

Bioinformatics. 2017 Sep 22. [Link to Article]

MOTIVATION:
Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of true identifications from false identifications by database searching and filtering algorithms in metaproteomics.

RESULTS:
Sipros Ensemble was developed here for metaproteomics using an ensemble approach. Three diverse scoring functions from MyriMatch, Comet and the original Sipros were incorporated within a single database searching engine. Supervised classification with logistic regression was used to filter database searching results. Benchmarking with soil and marine microbial communities demonstrated a higher number of peptide and protein identifications by Sipros Ensemble than MyriMatch/Percolator, Comet/Percolator, MS-GF+/Percolator, Comet & MyriMatch/iProphet, and Comet & MyriMatch & MS-GF+/iProphet. Sipros Ensemble was computationally efficient and scalable on supercomputers.

AVAILABILITY AND IMPLEMENTATION:
Freely available under the GNU GPL license at http://sipros.omicsbio.org.

 

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