KBase offers a powerful suite of expression analysis tools. Starting with short reads, you can use the tool suite to assemble, quantify long transcripts, and identify differentially expressed genes. You can also compare the expression data with the flux when studying metabolic models in KBase and identify pathways where expression and flux agree or conflict.
KBase requires a reference genome to guide the analysis of short reads.
The RNA-seq pipeline in KBase is modular and consists of three steps. You can pick any of the multiple Apps available for a given step depending on your preference or individual characteristics of the App.
Read Alignment:Align reads to map short reads to the reference genome. The output is a set of BAM alignments and Qualimap report. You can download the alignment output object generated by aligner Apps for further analysis.
Transcriptome Assembly and Quantification:Assemble aligned reads to generate full-length transcripts and quantify transcripts and genes as appropriate. You can view downloadable normalized full expression matrices in FPKM (fragments per kilobase of exon model per million mapped reads) and TPM (transcripts per million).
Clustering: Depending on preference, run the Hierarchical, K-Means or WGCNA clustering App to group features into clusters based on gene expression. You can also visualize the clusters as an interactive heatmap.
Integration into Metabolic Models: Assimilate the expression data from RNA-seq into the metabolic models to compare reaction fluxes with gene expression and thus identify pathways where expression and flux agree or conflict.