Fast and scalable querying of eukaryotic linear motifs with gget elm

Abstract

Motivation: Eukaryotic linear motifs (ELMs), or Short Linear Motifs (SLiMs), are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for cataloging motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt IDs on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited. Results: To allow swift, large-scale motif analyses on protein sequences using ELMs curated on the ELM database, we have developed a Python and command line tool, gget elm , which relies on local computations for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt identifiers. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences. Availability and implementation: The manual and source code are available at https://github.com/pachterlab/gget .