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RNA-Seq Pipeline

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The RNA-seq pipeline publicly available in Via Foundry includes several key steps for processing RNA-seq data:

  1. Quality Control: FastQC is used to generate quality control (QC) outputs. Optional processes such as read quality filtering (trimmomatic), read quality trimming (trimmomatic), and adapter removal (cutadapt) are available.
  2. rRNA Filtering and Genome Alignment: Bowtie2, Bowtie, and STAR are utilized for counting or filtering out and estimating the abundance of both standard and predefined sets of genomic loci, such as rRNAs, miRNAs, tRNAs, piRNAs, snoRNAs, and ERCC.
  3. Gene and Isoform Expression Estimation: RSEM is employed to align RNA-seq reads to reference transcripts and estimate gene and isoform expression levels.
  4. Genome Alignment: HISAT2, STAR, Kallisto or Salmon are employed to align RNA-seq reads to the genome. Optional estimation of gene and isoform expression levels can be performed using featureCounts and Salmon.
  5. Quality Metrics and Reports: If the user opts to perform genomic alignments, the pipeline generates overall quality metrics, including coverage and the number of mapped reads to different genomic and transcriptomic regions. These reports rely on Picard's CollectRNASeqMetrics program (Broad Institute, n.d.) and the RSeQC program (Wang, Wang, and Li 2012).
  6. Visualization: Optional generation of Integrative Genomics Viewer (IGV) and Genome Browser Files (TDF and Bigwig) is available.
  7. Quantification Matrix and Analysis: The RNA-seq pipeline provides a quantification matrix that includes estimated counts and transcript per million (TPM) values for each gene and annotated isoform. These matrices serve as input for differential gene expression analysis and can be directly uploaded to an embedded instance of DEBrowser software for interactive exploration of the resulting data (Kucukural et al. 2019).

Presented here is the example report tab for the RNA-Seq Run. Each section within the report consists of its own set of files, allowing you to thoroughly investigate and visualize the data within each respective section.

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Below is a screenshot showcasing the interactive analysis of differential expression analysis using the Shiny app called DEbrowser.

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