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Activity Number: 434
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #320381 View Presentation
Title: A Statistical Method for Cross-Species Analysis of RNA-Seq Data
Author(s): Yered Pita-Juarez* and Rafael A. Irizarry and Michael I. Love
Companies: Harvard and Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
Keywords: RNA-seq ; comparative genomics ; mouse ; human ; gene expression
Abstract:

The mouse is the most commonly used model organism for human biology and disease because of the widely held notion that many biological processes in the mouse are highly conserved in humans. The accurate characterization of expression levels and RNA transcripts across species is crucial for understanding evolutionarily conserved transcriptional responses. When comparing RNA-seq expression data across human and mouse tissues the differences in sequence features between the human and mouse orthologs bias the differences in gene expression patterns. We present a novel method to adjust the ortholog models that takes into account these differences in order to reduce the bias of cross-species transcriptome comparisons. As the availability of RNA-seq data increases, there is a need for methods that build annotations for cross-species RNA-seq analysis. A deeper understanding of the differences and similarities in the transcriptional landscape between mice and humans is fundamental not only to studies employing the mouse as a model organism in biomedical research, but to comparative genomics.


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