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Exploring the functional landscape of gene expression: directed search of large microarray compendia
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , , , , , |
| In: | Bioinformatics, 23, 2007, 20, S. 2692-2699 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
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| Schlagwörter: |
| author_facet |
Hibbs, Matthew A. Hess, David C. Myers, Chad L. Huttenhower, Curtis Li, Kai Troyanskaya, Olga G. Hibbs, Matthew A. Hess, David C. Myers, Chad L. Huttenhower, Curtis Li, Kai Troyanskaya, Olga G. |
|---|---|
| author |
Hibbs, Matthew A. Hess, David C. Myers, Chad L. Huttenhower, Curtis Li, Kai Troyanskaya, Olga G. |
| spellingShingle |
Hibbs, Matthew A. Hess, David C. Myers, Chad L. Huttenhower, Curtis Li, Kai Troyanskaya, Olga G. Bioinformatics Exploring the functional landscape of gene expression: directed search of large microarray compendia Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
hibbs, matthew a. |
| spelling |
Hibbs, Matthew A. Hess, David C. Myers, Chad L. Huttenhower, Curtis Li, Kai Troyanskaya, Olga G. 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btm403 <jats:title>Abstract</jats:title><jats:p>Motivation: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium.</jats:p><jats:p>Results: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community.</jats:p><jats:p>Availability: Our query-driven search engine, called SPELL, is available at http://function.princeton.edu/SPELL</jats:p><jats:p>Contact: ogt@genomics.princeton.edu</jats:p><jats:p>Supplementary information: Several additional data files, figures and discussions are available at http://function.princeton.edu/SPELL/supplement</jats:p> Exploring the functional landscape of gene expression: directed search of large microarray compendia Bioinformatics |
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| title |
Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_unstemmed |
Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_full |
Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_fullStr |
Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_full_unstemmed |
Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_short |
Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_sort |
exploring the functional landscape of gene expression: directed search of large microarray compendia |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/btm403 |
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2007 |
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2692-2699 |
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<jats:title>Abstract</jats:title><jats:p>Motivation: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium.</jats:p><jats:p>Results: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community.</jats:p><jats:p>Availability: Our query-driven search engine, called SPELL, is available at http://function.princeton.edu/SPELL</jats:p><jats:p>Contact: ogt@genomics.princeton.edu</jats:p><jats:p>Supplementary information: Several additional data files, figures and discussions are available at http://function.princeton.edu/SPELL/supplement</jats:p> |
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| author | Hibbs, Matthew A., Hess, David C., Myers, Chad L., Huttenhower, Curtis, Li, Kai, Troyanskaya, Olga G. |
| author_facet | Hibbs, Matthew A., Hess, David C., Myers, Chad L., Huttenhower, Curtis, Li, Kai, Troyanskaya, Olga G., Hibbs, Matthew A., Hess, David C., Myers, Chad L., Huttenhower, Curtis, Li, Kai, Troyanskaya, Olga G. |
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| description | <jats:title>Abstract</jats:title><jats:p>Motivation: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium.</jats:p><jats:p>Results: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community.</jats:p><jats:p>Availability: Our query-driven search engine, called SPELL, is available at http://function.princeton.edu/SPELL</jats:p><jats:p>Contact: ogt@genomics.princeton.edu</jats:p><jats:p>Supplementary information: Several additional data files, figures and discussions are available at http://function.princeton.edu/SPELL/supplement</jats:p> |
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| spelling | Hibbs, Matthew A. Hess, David C. Myers, Chad L. Huttenhower, Curtis Li, Kai Troyanskaya, Olga G. 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btm403 <jats:title>Abstract</jats:title><jats:p>Motivation: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium.</jats:p><jats:p>Results: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community.</jats:p><jats:p>Availability: Our query-driven search engine, called SPELL, is available at http://function.princeton.edu/SPELL</jats:p><jats:p>Contact: ogt@genomics.princeton.edu</jats:p><jats:p>Supplementary information: Several additional data files, figures and discussions are available at http://function.princeton.edu/SPELL/supplement</jats:p> Exploring the functional landscape of gene expression: directed search of large microarray compendia Bioinformatics |
| spellingShingle | Hibbs, Matthew A., Hess, David C., Myers, Chad L., Huttenhower, Curtis, Li, Kai, Troyanskaya, Olga G., Bioinformatics, Exploring the functional landscape of gene expression: directed search of large microarray compendia, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_full | Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_fullStr | Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_full_unstemmed | Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_short | Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_sort | exploring the functional landscape of gene expression: directed search of large microarray compendia |
| title_unstemmed | Exploring the functional landscape of gene expression: directed search of large microarray compendia |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/btm403 |