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  • RNA-seq Pipeline
  • Downstream Expression Analysis
  • Narrative Tutorials
  • Video Tutorial

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  1. Running Common Workflows

Transcriptomic Analysis

Running RNA-seq analyses pipelines in KBase

PreviousFAQ: Metagenomics & Community AnalysisNextFAQ: RNA-seq Analysis

Last updated 2 years ago

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KBase offers a powerful suite of . 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.

Prerequisites

KBase requires a reference genome to guide the analysis of short reads.

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

  2. : Run the 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. : Run to assess the read quality of the reads set from the previous step and if needed, run , , or 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: 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: 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 based on the quantification. Run 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 based on fold-change or adjusted p-value. You can also based on LOR or ANOVA.

  2. Clustering: Depending on preference, run the , or clustering App to group features into clusters based on gene expression. You can also visualize the clusters as an interactive heatmap.

  3. 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 and thus identify pathways where expression and flux agree or conflict.

Narrative Tutorials

Video Tutorial

– bacterium-based example of an RNAseq workflow using a HISAT2/StringTie/DESeq2 pipeline

– plant-based example of an RNAseq workflow using a HISAT2/StringTie/DESeq2 pipeline

– plant-based example of using KBase to integrate full genome RNA-seq analysis and a metabolic model to generate a reaction matrix

Kumari et al. (2021) A KBase case study on genome-wide transcriptomics and plant primary metabolism in response to drought stress in Sorghum. Current Plant Biology 28.

E. coli RNA-seq Analysis Tutorial
Arabidopsis RNA-seq Analysis Tutorial
Case Study: Genome-wide Transcriptomics and Plant Primary Metabolism in response to Drought Stress in Sorghum
https://doi.org/10.1016/j.cpb.2021.100229
expression analysis tools
Import Genome
Import Short Reads
Create a SampleSet
Create RNA-seq Sample Set
FastQC
Trimmomatic
Cutadapt
PRINSEQ
Create Feature Set/Filtered Expression Matrix From Differential Expression
create a filtered expression matrix and associated feature set
filter an expression matrix
Hierarchical
K-Means
WGCNA
Assess the functional enrichment
compare reaction fluxes with gene expression
Functional Genomics
QC SampleSet
Align reads
Assemble aligned reads
differential expression