Yi-Kuo Yu1, John A Capra2, Aleksandar Stojmirović1, David Landsman1, Stephen F Altschul1. 1. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, Center for Human Genetics Research and Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA. 2. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, Center for Human Genetics Research and Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, Center for Human Genetics Research and Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA.
Abstract
MOTIVATION: DNA and protein patterns are usefully represented by sequence logos. However, the methods for logo generation in common use lack a proper statistical basis, and are non-optimal for recognizing functionally relevant alignment columns. RESULTS: We redefine the information at a logo position as a per-observation multiple alignment log-odds score. Such scores are positive or negative, depending on whether a column's observations are better explained as arising from relatedness or chance. Within this framework, we propose distinct normalized maximum likelihood and Bayesian measures of column information. We illustrate these measures on High Mobility Group B (HMGB) box proteins and a dataset of enzyme alignments. Particularly in the context of protein alignments, our measures improve the discrimination of biologically relevant positions. AVAILABILITY AND IMPLEMENTATION: Our new measures are implemented in an open-source Web-based logo generation program, which is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/logoddslogo/index.html. A stand-alone version of the program is also available from this site. CONTACT: altschul@ncbi.nlm.nih.gov SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.
MOTIVATION: DNA and protein patterns are usefully represented by sequence logos. However, the methods for logo generation in common use lack a proper statistical basis, and are non-optimal for recognizing functionally relevant alignment columns. RESULTS: We redefine the information at a logo position as a per-observation multiple alignment log-odds score. Such scores are positive or negative, depending on whether a column's observations are better explained as arising from relatedness or chance. Within this framework, we propose distinct normalized maximum likelihood and Bayesian measures of column information. We illustrate these measures on High Mobility Group B (HMGB) box proteins and a dataset of enzyme alignments. Particularly in the context of protein alignments, our measures improve the discrimination of biologically relevant positions. AVAILABILITY AND IMPLEMENTATION: Our new measures are implemented in an open-source Web-based logo generation program, which is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/logoddslogo/index.html. A stand-alone version of the program is also available from this site. CONTACT: altschul@ncbi.nlm.nih.gov SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.
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