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dc.contributor.authorMartin, Edward
dc.contributor.authorMeagher, Thomas R.
dc.contributor.authorBarker, Daniel
dc.date.accessioned2021-09-24T08:30:07Z
dc.date.available2021-09-24T08:30:07Z
dc.date.issued2021-09-23
dc.identifier.citationMartin , 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/s12859-021-04362-7en
dc.identifier.issn1471-2105
dc.identifier.otherPURE: 275884580
dc.identifier.otherPURE UUID: 9e63831a-0586-4800-9c85-0696c41c3984
dc.identifier.otherScopus: 85115340181
dc.identifier.urihttp://hdl.handle.net/10023/24019
dc.descriptionFunding: This work was supported by the UKRI Biotechnology and Biological Sciences Research Council (BBSRC) grant number BB/M010996/1.en
dc.description.abstractBackground 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.
dc.format.extent17
dc.language.isoeng
dc.relation.ispartofBMC Bioinformaticsen
dc.rightsCopyright © The Author(s), 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en
dc.subjectSonificationen
dc.subjectSequence analysisen
dc.subjectProtein sequenceen
dc.subjectMultiple sequence alignmenten
dc.subjectRaspberry Pien
dc.subjectSonic Pien
dc.subjectAlgorithmsen
dc.subjectQualitative researchen
dc.subjectVisualisationen
dc.subjectBioinformaticsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.subjectDASen
dc.subject.lccQA75en
dc.subject.lccQH301en
dc.titleUsing sound to understand protein sequence data : new sonification algorithms for protein sequences and multiple sequence alignmentsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.contributor.institutionUniversity of St Andrews.Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews.St Andrews Sustainability Instituteen
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.identifier.doihttps://doi.org/10.1186/s12859-021-04362-7
dc.description.statusPeer revieweden
dc.identifier.urlhttps://doi.org/10.7488/ds/3023en
dc.identifier.urlhttps://sonifyed.com/bmc-bioinformatics-2021en


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