From Array Suite Wiki
- Raw Data QC of RNA-Seq Data - Before aligning your RNA-Seq data, it's important to get a sense of the quality of your data (as well as make decisions on adapter stripping, quality trimming, and more... The recommended module for Raw Data QC is the Raw Data QC Wizard (From NGS menu | Raw Data QC | Raw Data QC Wizard)
- Alignment of RNA-Seq Data - Alignment of your RNA-Seq to a reference is the most important step in the analysis of your data. If your data has not been aligned, follow this page for more details and recommendations on alignment. Choose Add Data | Add NGS Data | Add RNA-Seq Data | Map Reads to Genome (Illumina).
If your data has already been aligned by an aligner other than OSA (or you need to merge sets of separately aligned data so that you can do a single downstream analysis on the files), choose Add Data | Add NGS Data | Add RNA-Seq Data | Add Genome Mapped Reads.
- Aligned Data QC for RNA-Seq - After alignment or import, Omicsoft offers a variety of aligned data QC modules that can be run on your imported NgsData. This includes a comprehensive RNA-Seq QC metrics table, 5' to 3' bias detection, Paired Insert Size detection, and more. Follow this page for more information on these post-alignment QC modules.
- Coverage-based RNA-Seq modules - After alignment, the coverage based modules can be used for a variety of downstream activity. For RNA-Seq, Unannotated Peaks and Putative Exon detection are two of the modules of interest.
- Quantification of RNA-Seq - After alignment, the user can quantify the aligned BAM files. This includes calculation of RPKM (for visually comparing across samples or running some downstream analysis), or the calculation of count data (to be used by DESeq for detection of differential expression).
- Statistical Inference for RNA-Seq - After alignment, the user has options for detection of differentially expressed genes (using the DESeq module) as well as the detection of alternative splicing.
- Visualization of RNA-Seq Aligned Data - After alignment (and potentially qc, coverage-based modules, and statistical inference), visualization of the data becomes an integral part of analysis.