From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data

Abstract

RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. RNAMiner—a multi-level bioinformatics protocol and pipeline—has been developed for such datasets. It includes five steps: Mapping RNA-Seq reads to a reference genome, calculating gene expression values, identifying differentially expressed genes, predicting gene functions, and constructing gene regulatory networks. To demonstrate its utility, we applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks. The RNAMiner web service is available at http://calla.rnet.missouri.edu/rnaminer/index.html.

Sequence of the supernumerary B chromosome of maize provides insight into its drive mechanism and evolution

“…The threshold of maximum number of distinct alignments was set to 10. MULTICOM-MAP ( 71 ) was used to remove the reads mapped to a unique location on the maize W22 or B73 sequences with at most two mismatches. The remaining reads were aligned to the B chromosome assembly using Bowtie2.…”

Effects of aged garlic extract and FruArg on gene expression and signaling pathways in lipopolysaccharide-activated microglial cells

“…The trimmed and filtered reads were mapped to mouse genome (Mus_musculus.GRCm38) from Ensemble using Tophat2 59 . Mapped reads were quantified as raw counts by the Subreads package function “featureCounts”.…”

Heme oxygenase promotes B‐Raf‐dependent melanosphere formation

“…Since both CoPP treatment and B‐Raf V600E expression promote melanosphere formation in Hs936T cells, our interest was predominantly focused on where the transcriptome signatures for these two conditions overlapped. Two R Bioconductor packages, DESeq and EdgeR (Li et al., 2015), were used to construct a consensus list of differentially expressed transcripts (Figure 9). This list contained transcripts that were up‐ or downregulated by a minimum of twofold following either stable expression of B‐Raf V600E (14,411 transcripts) or treatment with CoPP (1,246 transcripts) (Figure 9a).…”

“…Then, 2.5 μg of RNA was diluted to 100 ng/μl in RNAse‐free water and submitted to the DNA Core at the University of Missouri for RNA‐seq analysis using an Illumina HiSeq 2000 with a 1 x 50 run type, returning approximately 34 million reads per sample. Pairwise comparisons were done using both DESeq and EdgeR Bioconductor packages as described previously (Li et al., 2015). Those genes found to be differentially expressed by a minimum of a twofold increase by both packages were used for DAVID functional annotation clustering (http://david.abcc.ncifcrf.gov).…”