A comparative analysis of tissue gene expression data from high-throughput studies

Abstract

High-throughput technologies were employed over the past decade to study the expression profiles of cells and tissues. There are large collections of accumulated data from public databases and numerous research articles were published on these data. In the current study, we performed meta-analysis on the gene expression data from human liver and kidney tissues produced from five different technologies: EST, SAGE, MPSS, microarray, and RNA-Seq. We found RNA-Seq was the most sensitive in the number of genes it detected while SAGE and MPSS were the least sensitive. For the genes detected by all the platforms, there were generally good correlations to the measured expression levels of corresponding genes. We further compared detected genes to liver/kidney proteomics data from the Human Protein Atlas, and found 960 of the 8764 genes only detected by RNA-Seq were validated by proteomics results. In conclusion, RNA-Seq is a more sensitive and consistent technology compared to the other four high-throughput platforms, though their results are in general agreement. Average coverage was determined to be the preferred measurement to represent gene expression levels by RNA-Seq data and will be used in future works. high-throughput sequencing, tissue transcriptome, comparative analysis Citation:Ping J, Wang Y J, Yu Y, et al. A comparative analysis of tissue gene expression data from high-throughput studies.

Global transcriptome analysis profiles metabolic pathways in traditional herb Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao

“…The limited data hamper the research of active ingredients biosynthetic and metabolic mechanisms in A. mongolicus . In recent years, RNA-seq is quickly developed as a massively parallel sequencing method to generate transcriptome profiles at large scale and relatively low cost[ 20 , 21 ]. Since RNA-seq is not restricted to detecting transcripts of known genes, it is particularly useful for non-model organisms with no available genomic data[ 22 ].…”

A survey of lineage‐specific genes in Triticeae reveals de novo gene evolution from genomic raw material

“…Our approach differed form that in a recent study (Ma et al, 2020) which identified 3812 candidate TSGs using expressed sequence tags (ESTs) but did not further verify for their protein‐coding potential. ESTs do not cover the transcriptome as well as RNA‐seq data (Lowe et al, 2017; Ping et al, 2012), especially for lineage‐specific genes, which are often expressed at low levels or only under specific conditions. Furthermore, in contrast to Ma et al (2020), we considered it crucial to include TE‐derived TSGs, because studies in primates and rice have shown that over 50% of lineage‐specific genes have homology to TEs (Jin et al, 2019; Toll‐Riera et al, 2009).…”

“…Our approach differed form that in a recent study (Ma et al, 2020) which identified 3812 candidate TSGs using expressed sequence tags (ESTs) but did not further verify for their protein-coding potential. ESTs do not cover the transcriptome as well as RNA-seq data (Lowe et al, 2017;Ping et al, 2012), especially for lineage-specific genes, which are often expressed at low levels or only under specific conditions. Furthermore, in contrast to Ma et al…”

A survey of lineage-specific genes in Triticeae reveals de novo gene evolution from genomic raw material

“…Our approach differed form that in a recent study (Ma et al .,2020) which identified 3,812 candidate TSGs using expressed sequence tags (ESTs), but did not further verify for their protein-coding potential. ESTs do not cover the transcriptome as well as RNA-seq data (Ping et al ., 2012; Lowe et al ., 2017), especially for lineage-specific genes, which are often expressed at low levels or only under specific conditions. Furthermore, in contrast to Ma et al .…”

“…for their protein-coding potential. ESTs do not cover the transcriptome as well as RNA-seq data (Ping et al, 2012;Lowe et al, 2017), especially for lineage-specific genes, which are often expressed at low levels or only under specific conditions. Furthermore, in contrast to Ma et al (2020), we considered it crucial to include TE-derived TSGs, because studies in primates and rice have shown that over 50% of lineage-specific genes have homology to TEs (Toll-Riera et al, 2009;Jin et al, 2019).…”