ArticleNucleic acids research2017
APRICOT: an integrated computational pipeline for the sequence-based identification and characterization of RNA-binding proteins.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 27 citations in OpenAlex.
- The type 6 secretion system immunity protein RhsFI has been repurposed for small RNA regulation in pathogenic Escherichia coli.Nucleic acids research · 2025Article
- RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes.Computational and structural biotechnology journal · 2024Article
- High-throughput quantitation of protein-RNA UV-crosslinking efficiencies as a predictive tool for high-confidence identification of RNA-binding proteins.RNA (New York, N.Y.) · 2024Article
- RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins.Briefings in bioinformatics · 2022Article
- Computational tools to study RNA-protein complexes.Frontiers in molecular biosciences · 2022Review
- Global discovery of bacterial RNA-binding proteins by RNase-sensitive gradient profiles reports a new FinO domain protein.RNA (New York, N.Y.) · 2020Article
- Article
- Deciphering the functional diversity of DNA-binding transcription factors in Bacteria and Archaea organisms.PloS one · 2020Article
- TriPepSVM: de novo prediction of RNA-binding proteins based on short amino acid motifs.Nucleic acids research · 2019Article
- Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.Scientific reports · 2018Article
- RNA-binding proteins in bacteria.Nature reviews. Microbiology · 2018Review
- A systematic analysis of the RNA-targeting potential of secreted bacterial effector proteins.Scientific reports · 2017Article
- Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers.Avicenna journal of medical biotechnologyArticle
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4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
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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