An intelligent data-centric approach toward identification of conserved motifs in protein sequences

Kathryn Dempsey, Benjamin Currall, Richard J. Hallworth, Hesham Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The continued integration of the computational and biological sciences has revolutionized genomic and proteomic studies. However, efficient collaboration between these fields requires the creation of shared standards. A common problem arises when biological input does not properly fit the expectations of the algorithm, which can result in misinterpretation of the output. This potential confounding of input/output is a drawback especially when regarding motif finding software. Here we propose a method for improving output by selecting input based upon evolutionary distance, domain architecture, and known function. This method improved detection of both known and unknown motifs in two separate case studies. By standardizing input considerations, both biologists and bioinformaticians can better interpret and design the evolving sophistication of bioinformatic software.

Original languageEnglish
Title of host publication2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
Pages398-401
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 - Niagara Falls, NY, United States
Duration: Aug 2 2010Aug 4 2010

Other

Other2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
CountryUnited States
CityNiagara Falls, NY
Period8/2/108/4/10

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All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Information Management

Cite this

Dempsey, K., Currall, B., Hallworth, R. J., & Ali, H. (2010). An intelligent data-centric approach toward identification of conserved motifs in protein sequences. In 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 (pp. 398-401) https://doi.org/10.1145/1854776.1854839