Using sound to understand protein sequence data: new sonification algorithms for protein sequences and multiple sequence alignments.

ABSTRACT:

Background

The use of sound to represent sequence data-sonification-has great potential as an alternative and complement to visual representation, exploiting features of human psychoacoustic intuitions to convey nuance more effectively. We have created five parameter-mapping sonification algorithms that aim to improve knowledge discovery from protein sequences and small protein multiple sequence alignments. For two of these algorithms, we investigated their effectiveness at conveying information. To do this we focussed on subjective assessments of user experience. This entailed a focus group session and survey research by questionnaire of individuals engaged in bioinformatics research.

Results

For single protein sequences, the success of our sonifications for conveying featu

SUBMITTER: Martin EJ 

PROVIDER: S-EPMC8459479 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature