RNA sequencing is a high-throughput sequencing technology that provides a genome-wide assessment of the RNA content of an organism, tissue or cell. mRNA-seq can be used to measure gene expression levels, assist in new gene and transcript discovery, and identify novel splice variants. Our mRNA-Seq analysis uses open-source tools, including a de novo splice aligner, either TopHat or STAR, in conjunction with Cufflinks to elucidate the transcriptome and analyze differential expression. The analysis results are provided as visual representations, including interactive tabular and heat map views linked to an integrated genome browser.

WAVES™ analysis overview for differential expression using RNA-seq

Maverix Analytic Platform analysis overview for differential expression using RNA-seq

Quality Assessment

Sequencing data is analyzed for quality and contamination. Raw reads are trimmed to remove adapters, then trimmed and filtered based on quality scores. The scores used for trimming and filtering are specific to the sequencing platform. The preprocessed reads are then assessed for quality and plots are generated of per base quality before and after trimming.

Read Alignment

mRNAseq reads are mapped to the genome/transcriptome and statistics are generated, including number of aligned reads, and number of alignments over exons, introns, and intergenic regions. BAM files with chromosomal location of mapped reads and the alignment quality are made available for visualization in the integrated UCSC Genome Browser.

Abundance Determination

Sequence alignments are used to measure the abundance of all transcripts in the transcriptome. The FPKM values of the transcripts are provided as downloadable files and also converted into BED tracks to visualize in the genome browser. An annotation report is generated cataloguing the number of reads overlapping genome features, including exons, introns, and intergenic regions.

Differential Expression

Differences in gene expression between treatment and control samples are calculated, including expression levels in a sample, the difference in expression between two samples, and the statistical significance of that difference. Visual representation of the analysis results are provided, including interactive tabular and a heat map views linked to the integrated genome browser.