Meta-analysis of RNA-seq expression data across species, tissues and studies

Abstract

BackgroundDifferences 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.ResultsTo 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.ConclusionsThese 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0853-4) contains supplementary material, which is available to authorized users.

Ancient Out-of-Africa Mitochondrial DNA Variants Associate with Distinct Mitochondrial Gene Expression Patterns

“…However, expression pattern clustering analysis grouped RNA-seq samples according to the study of origin, even when considering the same samples that were separately sequenced and analyzed independently by different groups (S1 Fig), thus arguing against co-analysis of RNA-seq data generated by different protocols. Hence, although Sudmant et al [27] recently showed that differences in gene expression patterns between tissues are greater than are differences between studies, our results reveal that while focusing on a single tissue, differences in gene expression patterns between studies exceeds differences among individuals. Therefore, to avoid such artifacts, we focused our analysis on the largest of the relevant studies, encompassing 462 publicly available RNA-seq samples from Caucasians and sub-Saharan Africans [26], all part of the 1000 Genomes Project [32].…”

“…Levels of gene expression can vary among individuals, tissues and species [27]. As such, we utilized RNA-seq experiments to assess differential mitochondrial gene expression patterns among individuals and ethnicities (Fig 1).…”

TISSUES 2.0: an integrative web resource on mammalian tissue expression

“…Instead, we opted to generate gold standard datasets for mouse, rat, and pig through orthology-based transfer of the human tissue annotations, under the assumption that a large portion of orthologous genes are similarly expressed in homologous tissues across mammals (3147). …”

RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes

“…Recently, a meta-analysis of RNA-Seq expression data across various species, tissues, and studies was performed 10 ; however, the interpretation of such data is difficult. Biologists are often at a loss because of the sheer number of datasets in public databases provided by numerous researchers.…”