Fast and scalable querying of eukaryotic linear motifs with gget elm
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
DOI:
10.1093/bioinformatics/btae095
Publication Date:
2024-02-20T20:25:07Z
AUTHORS (4)
ABSTRACT
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.
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