The genome-wide transcriptome profiling of cancerous and normal tissue samples can provide insights into the molecular mechanisms of cancer initiation and progression. RNA Sequencing (RNA-Seq) is a revolutionary tool that has been used extensively in cancer research.
However, no existing RNA-Seq database provides all of the following features:
(i) large-scale and comprehensive data archives and analyses, including coding-transcript profiling, long non-coding RNA (lncRNA) profiling and coexpression networks;
(ii) phenotype-oriented data organization and searching and
(iii) the visualization of expression profiles, differential expression and regulatory networks.
Now, researchers at the National Chung Hsing University, Taiwan have constructed the first public database that meets these criteria, the Cancer RNA-Seq Nexus. CRN has a user-friendly web interface designed to facilitate cancer research and personalized medicine. It is an open resource for intuitive data exploration, providing coding-transcript/lncRNA expression profiles to support researchers generating new hypotheses in cancer research and personalized medicine.
Framework for constructing the CRN database. Cancer RNA-Seq data sets were collected from NCBI GEO, SRA and TCGA, and then all samples were classified into the phenotype-specific subsets. For the GEO data sets, Bowtie2 and eXpress software were used to calculate isoform expressions using GENCODE v21 as a reference. For the TCGA data sets, the developers converted the expression values (tau values) of the TCGA Level 3 RNA-Seq version 2 data sets to TPM (transcripts per million). To identify phenotype-specific differentially expressed protein-coding transcripts and lncRNAs in each data set, they performed log2 scale t-tests with Benjamini–Hochberg adjustment between each pair of subsets with no overlapping samples and from the same data set. For each subset pair, they selected coding transcripts and lncRNAs with high expression variance, then calculated the correlations of expression profiles between selected coding transcripts and lncRNAs to construct an mRNA–lncRNA coexpression network.
Availability – http://syslab4.nchu.edu.tw/CRN