Using sound to understand protein sequence data : new sonification algorithms for protein sequences and multiple sequence alignments
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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 features was supported by both the survey and focus group findings. For protein multiple sequence alignments, there was limited evidence that the sonifications successfully conveyed information. Additional work is required to identify effective algorithms to render multiple sequence alignment sonification useful to researchers. Feedback from both our survey and focus groups suggests future directions for sonification of multiple alignments: animated visualisation indicating the column in the multiple alignment as the sonification progresses, user control of sequence navigation, and customisation of the sound parameters. Conclusions Sonification approaches undertaken in this work have shown some success in conveying information from protein sequence data. Feedback points out future directions to build on the sonification approaches outlined in this paper. The effectiveness assessment process implemented in this work proved useful, giving detailed feedback and key approaches for improvement based on end-user input. The uptake of similar user experience focussed effectiveness assessments could also help with other areas of bioinformatics, for example in visualisation.
Martin , E , Meagher , T R & Barker , D 2021 , ' Using sound to understand protein sequence data : new sonification algorithms for protein sequences and multiple sequence alignments ' , BMC Bioinformatics , vol. 22 , 456 . https://doi.org/10.1186/s12859-021-04362-7
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DescriptionFunding: This work was supported by the UKRI Biotechnology and Biological Sciences Research Council (BBSRC) grant number BB/M010996/1.
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