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