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Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality
BACKGROUND: Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes t...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2005
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1334220/ https://www.ncbi.nlm.nih.gov/pubmed/16332255 http://dx.doi.org/10.1186/1471-2105-6-288 |
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author | Qi, Yan Ye, Ping Bader, Joel S |
author_facet | Qi, Yan Ye, Ping Bader, Joel S |
author_sort | Qi, Yan |
collection | PubMed |
description | BACKGROUND: Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets. RESULTS: We have developed Genetic Interaction Motif Finding (GIMF), an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast). Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees. CONCLUSION: GIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic lethality occurs between-pathway rather than within-pathway. |
format | Text |
id | pubmed-1334220 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2005 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-13342202006-01-30 Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality Qi, Yan Ye, Ping Bader, Joel S BMC Bioinformatics Methodology Article BACKGROUND: Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets. RESULTS: We have developed Genetic Interaction Motif Finding (GIMF), an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast). Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees. CONCLUSION: GIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic lethality occurs between-pathway rather than within-pathway. BioMed Central 2005-12-06 /pmc/articles/PMC1334220/ /pubmed/16332255 http://dx.doi.org/10.1186/1471-2105-6-288 Text en Copyright © 2005 Qi et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Qi, Yan Ye, Ping Bader, Joel S Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality |
title | Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality |
title_full | Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality |
title_fullStr | Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality |
title_full_unstemmed | Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality |
title_short | Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality |
title_sort | genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1334220/ https://www.ncbi.nlm.nih.gov/pubmed/16332255 http://dx.doi.org/10.1186/1471-2105-6-288 |
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