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.
@article{arxiv.2402.05953,
title = {idMotif: An Interactive Motif Identification in Protein Sequences},
author = {Ji Hwan Park and Vikash Prasad and Sydney Newsom and Fares Najar and Rakhi Rajan},
journal= {arXiv preprint arXiv:2402.05953},
year = {2024}
}