Estimating the strength of expression conservation from high throughput RNA-seq data
Abstract Motivation Evolution of gene across species is usually subject to the stabilizing selection to maintain the optimal expression level. While it is generally accepted that the resulting expression conservation may vary considerably among genes, statistically reliable estimation remains challe...
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Published in | Bioinformatics Vol. 35; no. 23; pp. 5030 - 5038 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
01.12.2019
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Online Access | Get full text |
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Summary: | Abstract
Motivation
Evolution of gene across species is usually subject to the stabilizing selection to maintain the optimal expression level. While it is generally accepted that the resulting expression conservation may vary considerably among genes, statistically reliable estimation remains challenging, due to few species included in current comparative RNA-seq data with high number of unknown parameters.
Results
In this paper, we develop a gamma distribution model to describe how the strength of expression conservation (denoted by W) varies among genes. Given the high throughput RNA-seq datasets from multiple species, we then formulate an empirical Bayesian procedure to estimate W for each gene. Our case studies showed that those W-estimates are useful to study the evolutionary pattern of expression conservation.
Availability and implementation
Our method has been implemented in the R-package software, TreeExp, which is publically available at Github develop site https://github.com/hr1912/TreeExp. It involves three functions: estParaGamma, estParaQ and estParaWBayesian. The manual for software TreeExp is available at https://github.com/hr1912/TreeExp/tree/master/vignettes. For any question, one may contact Dr Hang Ruan (Hang.Ruan@uth.tmc.edu). |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1367-4803 1367-4811 1460-2059 1367-4811 |
DOI: | 10.1093/bioinformatics/btz405 |