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

Please always quote using this URN: urn:nbn:de:bvb:20-opus-157963
  • 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. WeRNA-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.show moreshow less

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Metadaten
Author: Malvika Sharan, Konrad U. Förstner, Ana Eulalio, Jörg Vogel
URN:urn:nbn:de:bvb:20-opus-157963
Document Type:Journal article
Faculties:Medizinische Fakultät / Institut für Molekulare Infektionsbiologie
Language:English
Parent Title (English):Nucleic Acids Research
Year of Completion:2017
Volume:45
Issue:11
Pagenumber:e96
Source:Nucleic Acids Research, 2017, Vol. 45, No. 11, e96. DOI: 10.1093/nar/gkx137
DOI:https://doi.org/10.1093/nar/gkx137
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Tag:RNA-binding proteins; characterization; identification
Release Date:2018/02/26
Collections:Open-Access-Publikationsfonds / Förderzeitraum 2017
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International