pipeline-rnaseq

RNA-seq analysis processes raw FASTQ files through quality control, splice-aware alignment, and gene/transcript quantification to output expression matrices and QC reports. It integrates STAR, RSEM, Kallisto, and RSeQC to ensure reliable genomic results.

Splice-aware alignment for RNA-seq data is best handled using STAR to map sequencing reads to a reference genome. This pipeline automates the process while adhering to ENCODE standards and generating necessary QC metrics.

Yes, the RNA-seq pipeline explicitly supports stranded libraries and can operate on pre-aligned BAM files as well as raw FASTQ files. It ensures compatibility with ENCODE standards throughout the quantification process.

To generate bigWig signal tracks from RNA-seq data, the pipeline converts aligned read outputs using bedGraphToBigWig. This is part of the end-to-end workflow that also includes read trimming with Trim Galore and alignment via STAR.

Yes, you can use Kallisto and RSEM together for transcript and gene quantification within the same RNA-seq workflow. The pipeline integrates both tools to provide comprehensive expression measures from your sequencing data.

Quality control metrics for RNA-seq data are generated using FastQC, RSeQC, and MultiQC. These tools evaluate read quality, alignment saturation, and strand specificity, compiling the results into a comprehensive QC report.