RNA-Seq is a next-generation sequencing (NGS) application that analyzes the transcriptome, or every transcript being expressed at a given point in time. Gene expression analysis allow the comparison between two conditions, such as healthy and diseased states. RNA-Seq can also be used quantitatively to determine absolute quantity of each transcript.

General Workflow

A typical RNA-Seq experimental workflow involves the isolation of RNA from samples of interest, generation of sequencing libraries, and use of a high-throughput sequencer to produce hundreds of millions of short single or paired-end reads. There are several variations of the experimental protocol:

mRNA-Seq vs. Whole Transcriptome

Researchers are usually interested in the expression of mRNA, but up to 95% of cellular RNA is rRNA. To avoid wasting reads on rRNA, you can use a selection or a depletion approach.


For mRNA-Seq, polyA+ RNAs are selected using oligo-dT beads. This excludes rRNAs, most smRNAs, some mRNAs that are not polyadenylated (e.g. histone mRNAs), and nascent mRNAs that have not been fully processed and polyadenylated.


Alternatively, for whole transcriptome sequencing, rRNAs can be removed by hybridization to rRNA-specific LNA probes with kits like Ribo-minus/Ribo-zero. This is less efficient, but keeps other RNAs, including unprocessed RNAs in the mixture. some non-mRNA and mRNAs that have not been fully processed (e.g., with introns) appear in the results.

Strand-Specific (aka dUTP) vs. Non-Strand-Specific

To distinguish sense and antisense expression, the dUTP method is used to determine the strand from which the signal comes in RNA-Seq. With this method, the reads will map to the strand opposite to the RNA. Another strand-specific method is based on Smarter approach from Takara. In this case, reads are mapped to the same strand as the RNA.

Data Analysis

SciDAP is a no-code bioinformatics platform that enables biologists to analyze NGS-based data without a bioinformatician. It has built-in RNA-Seq pipelines based on open-source workflows, such as DESeq for differential gene expression and pipelines optimized for the dUTP method.

Starting with FASTQ files, analysis of RNA-Seq data begins with raw data quality control (QC) and read trimming followed by alignment of reads against a reference genome or transcriptome with RNA-STAR. Specific algorithms are applied for downstream analysis such as expression estimation, transcript isoform discovery, differential expression with DESeq or DESeq2 , GSEA analysis and other applications. Analysis ends with summarization and visualization of results.

Steps in the SciDAP RNA-Seq workflow


Start with FASTQ files

Using local files or public data accessions
input user reads in FASTQ format or using public accession numbers

Read Mapping

Map reads to reference genome, and create tracks for viewing data on IGV browser
map reads to reference genome

Quality Control

Plot read and mapping statistics
adapter trimming, read clipping, and base quality and mapping assessments

Quantitate Gene Expression

count reads aligned to reference genome annotations

Differenetial Expression Analysis

Run DESeq/DESeq2 for differential expression analysis and create plots such as PCAs, Volcanos, and heatmaps
differential expression analysis usig DESeq

Gene Set Enrichment Analysis

Run GSEA to identify pathways perturbed in your experiment
gene set enrichment pathway analysis

With SciDAP, you can immediately compare expression between two or more conditions and generate publication quality images in just a few hours.