Abstract | ||
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While one might expect the phenotypes of progeny to be an additive combination of the parents, Mendelian analysis reveals that this is not always the case. Deviations from additive expectation are observable even at the level of gene expression, and identifying such instances is a prerequisite to the understanding of gene regulation and networks. Many biological studies employ mRNA-seq to identify instances where the overall and allelic expression in hybrids deviates from expectation. We describe a pipeline, POPE (Pipeline of Parentally-biased Expression), that is capable of detecting these instances, building off of a linear model of gene expression in terms of regulatory sequence strength and concentration of synergistic transcriptional regulators. We illustrate the performance of POPE on an existing mRNA-seq data set. POPE is implemented entirely in shell, python, and R, and it is designed for unix-based platforms. The code can be found at <ExternalRef><RefSource><Emphasis FontCategory=\"NonProportional\"http://www.cs.ucdavis.</Emphasis> </RefSource> <RefTarget Address=\"http://www.cs.ucdavis.edu/~filkov/POPE/\" TargetType=\"URL\"/</ExternalRef> <ExternalRef><RefSource><Emphasis FontCategory=\"NonProportional\"edu/~filkov/POPE/</Emphasis> </RefSource> <RefTarget Address=\"http://www.cs.ucdavis.edu/~filkov/POPE/\" TargetType=\"URL\"/</ExternalRef> . |
Year | DOI | Venue |
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2012 | 10.1007/978-3-642-30191-9_17 | ISBRA |
Keywords | Field | DocType |
parentally-biased expression,existing mrna-seq data,gene expression,mendelian analysis,allelic expression,additive combination,additive expectation,hybrids deviate,gene regulation,biological study | Allele,Mendelian inheritance,Computer science,Linear model,Unix,Theoretical computer science,Regulation of gene expression,Artificial intelligence,Bioinformatics,Python (programming language),Machine learning,Regulatory sequence | Conference |
Citations | PageRank | References |
2 | 0.43 | 10 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Victor Missirian | 1 | 10 | 0.77 |
Isabelle Henry | 2 | 2 | 0.43 |
Luca Comai | 3 | 10 | 0.77 |
Vladimir Filkov | 4 | 1503 | 75.32 |