ArticleBMC genomics2015
Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Computational optimization of angiotensin-converting enzyme 2 for SARS-CoV-2 Spike molecular recognition.Computational and structural biotechnology journal · 2021Article
- Structural analysis of SARS-CoV-2 genome and predictions of the human interactome.Nucleic acids research · 2020Article
- RNA-binding and prion domains: the Yin and Yang of phase separation.Nucleic acids research · 2020Article
- CROSSalive: a web server for predicting the in vivo structure of RNA molecules.Bioinformatics (Oxford, England) · 2020Article
- Review
- RNA structure drives interaction with proteins.Nature communications · 2019Article
- An Integrative Study of Protein-RNA Condensates Identifies Scaffolding RNAs and Reveals Players in Fragile X-Associated Tremor/Ataxia Syndrome.Cell reports · 2018Article
- Advances in the characterization of RNA-binding proteins.Wiley interdisciplinary reviews. RNA · 2016Review
- Non-random distribution of homo-repeats: links with biological functions and human diseases.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundComparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities.
resultsWe illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis.
conclusionsIn an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal . Each of the pages contains links to the corresponding documentation and tutorial.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.