Fast and scalable querying of eukaryotic linear motifs with gget elm

• DOI: 10.1093/bioinformatics/btae095
• Publication Date: 2024-02-20T20:25:07Z
• Authors: Laura Luebbert; Chi Hoang; Manjeet Kumar; Lior Pachter
• Keywords: Statistics and Probability; Computational Mathematics; Applications Note; Computational Theory and Mathematics; 000; Amino Acid Motifs; Proteins; Amino Acid Sequence; Databases, Protein; Molecular Biology; Biochemistry; Software; Computer Science Applications

Abstract Motivation Eukaryotic linear motifs (ELMs), or Short Linear Motifs, are protein interaction modules that play an essential role in cellular processes and signaling networks often involved diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become crucial resource for investigating biology recognizing candidate ELMs novel amino acid sequences. Users can search sequences UniProt Accessions on the web interface. However, as with many services, there limitations swift processing large-scale queries through interface API calls, and, therefore, integration into function analysis pipelines limited. Results To allow swift, analyses using database, we have extended gget suite Python command line tools new module, elm, which does not rely server efficiently finding user-submitted Accessions. elm increases accessibility to information stored allows scalable searches motif-mediated sites Availability implementation manual source code available at https://github.com/pachterlab/gget.