idMotif: An Interactive Motif Identification in Protein Sequences
Ji Hwan Park Vikash Prasad Sydney Newsom Fares Najar Rakhi Rajan
This article introduces idMotif, a visual analytics framework designed to aid
domain experts in the identification of motifs within protein sequences.
Motifs, short sequences of amino acids, are critical for understanding the
distinct functions of proteins. Identifying these motifs is pivotal for
predicting diseases or infections. idMotif employs a deep learning-based method
for the categorization of protein sequences, enabling the discovery of
potential motif candidates within protein groups through local explanations of
deep learning model decisions. It offers multiple interactive views for the
analysis of protein clusters or groups and their sequences. A case study,
complemented by expert feedback, illustrates idMotif's utility in facilitating
the analysis and identification of protein sequences and motifs.