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

View ORCID ProfileMalvika Sharan, View ORCID ProfileKonrad U. Förstner, View ORCID ProfileAna Eulalio, View ORCID ProfileJörg Vogel
doi: https://doi.org/10.1101/055178
Malvika Sharan
1Institute for Molecular Infection Biology, University of Würzburg, Würzburg, Germany
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Konrad U. Förstner
2Core Unit Systems Medicine, University of Würzburg, Würzburg, Germany
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Ana Eulalio
1Institute for Molecular Infection Biology, University of Würzburg, Würzburg, Germany
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Jörg Vogel
1Institute for Molecular Infection Biology, University of Würzburg, Würzburg, Germany
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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

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 24, 2016.
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APRICOT: an integrated computational pipeline for the sequence-based identification and characterization of RNA-binding proteins
Malvika Sharan, Konrad U. Förstner, Ana Eulalio, Jörg Vogel
bioRxiv 055178; doi: https://doi.org/10.1101/055178
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APRICOT: an integrated computational pipeline for the sequence-based identification and characterization of RNA-binding proteins
Malvika Sharan, Konrad U. Förstner, Ana Eulalio, Jörg Vogel
bioRxiv 055178; doi: https://doi.org/10.1101/055178

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