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Statistical issues associated with modeling of synonymous mutation data

Author

Listed:
  • Huzurbazar Snehalata

    (Statistical and Applied Mathematical Sciences Institute, 19 T.W. Alexander Drive, P.O. Box 14006, Research Triangle Park, NC 27709-4006, USA Department of Statistics, University of Wyoming, Dept. 3332, 1000 E. University Ave, Laramie, WY 82071, USA Department of Statistics, North Carolina State University, 5109 SAS Hall, 2311 Stinson Drive, Raleigh, NC 27695-8203, USA)

  • Singh Sarabdeep

    (National Center for Biotechnology Information, National Institutes of Health, Bldg. 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA)

  • Schlueter Jessica A.

    (Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd., Bioinformatics, Room 309, Charlotte, NC 28223, USA)

Abstract

The explosion of data in evolutionary bioinformatics has led to sometimes ad hoc, incomplete and even inaccurate data analyses. Taking dS data, namely, data on synonymous substitutions per synonymous sites, we go through a statistical analysis for modeling the time since duplications of genes. We explore the shortcomings of previous analyses, especially with a view towards their effect on inference for the gene duplication process. We present a statistical analysis which respects the assumptions of the models and the integrity of the data, and emphasize that exploratory data analysis, formulation of a data model, its estimation and finally, assessment of the model are important steps in a complete data analysis. Furthermore, for dS data, we develop Bayesian discrete-continuous mixture models and present analyses using two genomes.

Suggested Citation

  • Huzurbazar Snehalata & Singh Sarabdeep & Schlueter Jessica A., 2013. "Statistical issues associated with modeling of synonymous mutation data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(3), pages 361-374.
    • Handle: RePEc:bpj:sagmbi:v:12:y:2013:i:3:p:361-374:n:1005
    DOI: 10.1515/sagmb-2012-0033
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