Meta-analysis of RNA-seq expression data across species, tissues and studies
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13059_2015_Article_853.pdf
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2.26 MB
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Adobe PDF
Checksum (MD5)
f41d5f7a86689bdc86b095224aa63215
Author(s)
Sudmant, Peter
Alexis, Maria Sarah
Burge, Christopher B
Date Issued
December 2015
Journal
Genome Biology
Publisher
BioMed Central
Citation
Sudmant, Peter H., Maria S. Alexis, and Christopher B. Burge. "Meta-analysis of RNA-seq expression data across species, tissues and studies." Genome Biology. 2015 Dec 22;16(1):287.
Version
Final published version
Abstract
Background
Differences in gene expression drive phenotypic differences between species, yet major organs and tissues generally have conserved gene expression programs. Several comparative transcriptomic studies have observed greater similarity in gene expression between homologous tissues from different vertebrate species than between diverse tissues of the same species. However, a recent study by Lin and colleagues reached the opposite conclusion. These studies differed in the species and tissues analyzed, and in technical details of library preparation, sequencing, read mapping, normalization, gene sets, and clustering methods.
Results
To better understand gene expression evolution we reanalyzed data from four studies, including that of Lin, encompassing 6–13 tissues each from 11 vertebrate species using standardized mapping, normalization, and clustering methods. An analysis of independent data showed that the set of tissues chosen by Lin et al. were more similar to each other than those analyzed by previous studies. Comparing expression in five common tissues from the four studies, we observed that samples clustered exclusively by tissue rather than by species or study, supporting conservation of organ physiology in mammals. Furthermore, inter-study distances between homologous tissues were generally less than intra-study distances among different tissues, enabling informative meta-analyses. Notably, when comparing expression divergence of tissues over time to expression variation across 51 human GTEx tissues, we could accurately predict the clustering of expression for arbitrary pairs of tissues and species.
Conclusions
These results provide a framework for the design of future evolutionary studies of gene expression and demonstrate the utility of comparing RNA-seq data across studies.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biology
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s13059-015-0853-4
