Transcriptomic Analysis

Running RNA-seq analyses pipelines in KBase

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.

  1. Import Short Reads: The reads must be a set of single-end, paired-end, or interleaved paired-end reads in FASTA, FASTQ, or SRA format.

  2. Create a SampleSet: Run the Create RNA-seq Sample Set App to group together your reads into an RNA-seq sample set with associated experimental metadata to run RNA-seq Apps in batch mode wherever appropriate.

  3. QC SampleSet: Run FastQC to assess the read quality of the reads set from the previous step and if needed, run Trimmomatic, Cutadapt, or PRINSEQ to pre-process or filter the reads before starting RNA-seq analysis.

RNA-seq Pipeline

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.

  1. 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.

  2. 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).

  3. Differential Gene Expression: Generate gene- or transcript-level differential expression based on the quantification. Run Create Feature Set/Filtered Expression Matrix From Differential Expression after selecting appropriate q-value and fold change cutoffs as input parameters for the filtering of the differential gene expression.

Downstream Expression Analysis

  1. Filtering: You can create a filtered expression matrix and associated feature set based on fold-change or adjusted p-value. You can also filter an expression matrix based on LOR or ANOVA.

  2. 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.

  3. Functional Enrichment: Assess the functional enrichment in plant genomes for a set of features using associated GO terms.

  4. 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.

Narrative Tutorials

Video Tutorial

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