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Nucleic Acids Res. 2017 Jun 20;45(11):e96. doi: 10.1093/nar/gkx137.

APRICOT: an integrated computational pipeline for the sequence-based identification and characterization of RNA-binding proteins.

Author information

1
Institute of Molecular Infection Biology, University of Würzburg, 97080 Würzburg, Germany.
2
Core Unit Systems Medicine, University of Würzburg, 97080 Würzburg, Germany.

Abstract

RNA-binding proteins (RBPs) have been established as core components of several post-transcriptional gene regulation mechanisms. Experimental techniques such as cross-linking and co-immunoprecipitation have enabled the identification of RBPs, RNA-binding domains (RBDs) and their regulatory roles in the eukaryotic species such as human and yeast in large-scale. In contrast, our knowledge of the number and potential diversity of RBPs in bacteria is poorer due to the technical challenges associated with the existing global screening approaches. We introduce APRICOT, a computational pipeline for the sequence-based identification and characterization of proteins using RBDs known from experimental studies. The pipeline identifies functional motifs in protein sequences using position-specific scoring matrices and Hidden Markov Models of the functional domains and statistically scores them based on a series of sequence-based features. Subsequently, APRICOT identifies putative RBPs and characterizes them by several biological properties. Here we demonstrate the application and adaptability of the pipeline on large-scale protein sets, including the bacterial proteome of Escherichia coli. APRICOT showed better performance on various datasets compared to other existing tools for the sequence-based prediction of RBPs by achieving an average sensitivity and specificity of 0.90 and 0.91 respectively. The command-line tool and its documentation are available at https://pypi.python.org/pypi/bio-apricot.

PMID:
28334975
PMCID:
PMC5499795
DOI:
10.1093/nar/gkx137
[Indexed for MEDLINE]
Free PMC Article

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