ArticleBMC bioinformatics2017
fLPS: Fast discovery of compositional biases for the protein universe.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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44 citing papers in PubMed, 94 citations in OpenAlex.
- Compositionally Biased Regions Within Structured Protein Domains.Biomolecules · 2026Article
- CB-Search: a method for searching for similar protein motifs with biased compositions.BMC bioinformatics · 2026Article
- A benchmarking framework for comparative evaluation of low-complexity region detection tools in the human proteome.Scientific reports · 2026Article
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- Germ granule localization of nematode Argonaute WAGO-4 ensures fidelity in small RNA loading.The EMBO journal · 2025Article
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- Identification of Low-Complexity Domains by Compositional Signatures Reveals Class-Specific Frequencies and Functions Across the Domains of Life.PLoS computational biology · 2024Article
- Optimizing strategy for the discovery of compositionally-biased or low-complexity regions in proteins.Scientific reports · 2024Article
- Intrinsically Disordered Compositional Bias in Proteins: Sequence Traits, Region Clustering, and Generation of Hypothetical Functional Associations.Bioinformatics and biology insights · 2024Article
- Bioinformatics tools for the sequence complexity estimates.Biophysical reviews · 2023Review
- Feature architecture aware phylogenetic profiling indicates a functional diversification of type IVa pili in the nosocomial pathogen Acinetobacter baumannii.PLoS genetics · 2023Article
- A joint proteomic and genomic investigation provides insights into the mechanism of calcification in coccolithophores.Nature communications · 2023Article
- FAS: assessing the similarity between proteins using multi-layered feature architectures.Bioinformatics (Oxford, England) · 2023Article
- Compensatory relationship between low-complexity regions and gene paralogy in the evolution of prokaryotes.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Insights from analyses of low complexity regions with canonical methods for protein sequence comparison.Briefings in bioinformatics · 2022Article
- Article
- Integrative analysis and prediction of human R-loop binding proteins.G3 (Bethesda, Md.) · 2022Article
- A Census of Human Methionine-Rich Prion-like Domain-Containing Proteins.Antioxidants (Basel, Switzerland) · 2022Article
- BIAPSS: A Comprehensive Physicochemical Analyzer of Proteins Undergoing Liquid-Liquid Phase Separation.International journal of molecular sciences · 2022Article
- Low Complexity Regions in Mammalian Proteins are Associated with Low Protein Abundance and High Transcript Abundance.Molecular biology and evolution · 2022Article
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProteins often contain regions that are compositionally biased (CB), i.e., they are made from a small subset of amino-acid residue types. These CB regions can be functionally important, e.g., the prion-forming and prion-like regions that are rich in asparagine and glutamine residues.
resultsHere I report a new program fLPS that can rapidly annotate CB regions. It discovers both single-residue and multiple-residue biases. It works through a process of probability minimization. First, contigs are constructed for each amino-acid type out of sequence windows with a low degree of bias; second, these contigs are searched exhaustively for low-probability subsequences (LPSs); third, such LPSs are iteratively assessed for merger into possible multiple-residue biases. At each of these stages, efficiency measures are taken to avoid or delay probability calculations unless/until they are necessary. On a current desktop workstation, the fLPS algorithm can annotate the biased regions of the yeast proteome (>5700 sequences) in <1 s, and of the whole current TrEMBL database (>65 million sequences) in as little as ~1 h, which is >2 times faster than the commonly used program SEG, using default parameters. fLPS discovers both shorter CB regions (of the sort that are often termed 'low-complexity sequence'), and milder biases that may only be detectable over long tracts of sequence.
conclusionsfLPS can readily handle very large protein data sets, such as might come from metagenomics projects. It is useful in searching for proteins with similar CB regions, and for making functional inferences about CB regions for a protein of interest. The fLPS package is available from: http://biology.mcgill.ca/faculty/harrison/flps.html , or https://github.com/pmharrison/flps , or is a supplement to this article.
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