MultiSeq: unifying sequence and structure data for evolutionary analysis
Abstract
Background: Since the publication of the first draft of the human genome in 2000, bioinformatic data have been accumulating at an overwhelming pace. Currently, more than 3 million sequences and 35 thousand structures of proteins and nucleic acids are available in public databases. Finding correlations in and between these data to answer critical research questions is extremely challenging. This problem needs to be approached from several directions: information science to organize and search the data; information visualization to assist in recognizing correlations; mathematics to formulate statistical inferences; and biology to analyze chemical and physical properties in terms of sequence and structure changes. Results: Here we present MultiSeq, a unified bioinformatics analysis environment that allows one to organize, display, align and analyze both sequence and structure data for proteins and nucleic acids. While special emphasis is placed on analyzing the data within the framework of evolutionary biology, the environment is also flexible enough to accommodate other usage patterns. The evolutionary approach is supported by the use of predefined metadata, adherence to standard ontological mappings, and the ability for the user to adjust these classifications using an electronic notebook. MultiSeq contains a new algorithm to generate complete evolutionary profiles that represent themore »
- Authors:
-
- Univ. of Illinois at Urbana-Champaign, IL (United States). Center for Biophysics and Computational Biology
- Univ. of Illinois at Urbana-Champaign, IL (United States). Graduate School of Library and Information Sciences
- Univ. of Illinois at Urbana-Champaign, IL (United States). Center for Biophysics and Computational Biology; Univ. of Illinois at Urbana-Champaign, IL (United States). Dept. of Chemistry
- Publication Date:
- Research Org.:
- Univ. of Illinois at Urbana-Champaign, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
- OSTI Identifier:
- 1626326
- Grant/Contract Number:
- FG02-05ER64144
- Resource Type:
- Accepted Manuscript
- Journal Name:
- BMC Bioinformatics
- Additional Journal Information:
- Journal Volume: 7; Journal Issue: 1; Journal ID: ISSN 1471-2105
- Publisher:
- BioMed Central
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology
Citation Formats
Roberts, Elijah, Eargle, John, Wright, Dan, and Luthey-Schulten, Zaida. MultiSeq: unifying sequence and structure data for evolutionary analysis. United States: N. p., 2006.
Web. doi:10.1186/1471-2105-7-382.
Roberts, Elijah, Eargle, John, Wright, Dan, & Luthey-Schulten, Zaida. MultiSeq: unifying sequence and structure data for evolutionary analysis. United States. https://doi.org/10.1186/1471-2105-7-382
Roberts, Elijah, Eargle, John, Wright, Dan, and Luthey-Schulten, Zaida. Wed .
"MultiSeq: unifying sequence and structure data for evolutionary analysis". United States. https://doi.org/10.1186/1471-2105-7-382. https://www.osti.gov/servlets/purl/1626326.
@article{osti_1626326,
title = {MultiSeq: unifying sequence and structure data for evolutionary analysis},
author = {Roberts, Elijah and Eargle, John and Wright, Dan and Luthey-Schulten, Zaida},
abstractNote = {Background: Since the publication of the first draft of the human genome in 2000, bioinformatic data have been accumulating at an overwhelming pace. Currently, more than 3 million sequences and 35 thousand structures of proteins and nucleic acids are available in public databases. Finding correlations in and between these data to answer critical research questions is extremely challenging. This problem needs to be approached from several directions: information science to organize and search the data; information visualization to assist in recognizing correlations; mathematics to formulate statistical inferences; and biology to analyze chemical and physical properties in terms of sequence and structure changes. Results: Here we present MultiSeq, a unified bioinformatics analysis environment that allows one to organize, display, align and analyze both sequence and structure data for proteins and nucleic acids. While special emphasis is placed on analyzing the data within the framework of evolutionary biology, the environment is also flexible enough to accommodate other usage patterns. The evolutionary approach is supported by the use of predefined metadata, adherence to standard ontological mappings, and the ability for the user to adjust these classifications using an electronic notebook. MultiSeq contains a new algorithm to generate complete evolutionary profiles that represent the topology of the molecular phylogenetic tree of a homologous group of distantly related proteins. The method, based on the multidimensional QR factorization of multiple sequence and structure alignments, removes redundancy from the alignments and orders the protein sequences by increasing linear dependence, resulting in the identification of a minimal basis set of sequences that spans the evolutionary space of the homologous group of proteins. Conclusion: MultiSeq is a major extension of the Multiple Alignment tool that is provided as part of VMD, a structural visualization program for analyzing molecular dynamics simulations. Both are freely distributed by the NIH Resource for Macromolecular Modeling and Bioinformatics and MultiSeq is included with VMD starting with version 1.8.5. The MultiSeq website has details on how to download and use the software: http://www.scs.uiuc.edu/~schulten/multiseq/},
doi = {10.1186/1471-2105-7-382},
journal = {BMC Bioinformatics},
number = 1,
volume = 7,
place = {United States},
year = {Wed Aug 16 00:00:00 EDT 2006},
month = {Wed Aug 16 00:00:00 EDT 2006}
}
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