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What is RNA-seq?

RNA sequencing is a next generation sequencing technique employed to identify the presence and quantity of RNA in a biological sample at a particular moment. This technique is used to analyse the ever-changing transcriptome of a tissue type. Raw reads must be first preprocessed and quality checked – find tools below. Preprocessed reads are then used to quantify the gene expression. 

Prior to determining the differential gene expression, raw sequence reads need to be assigned to genomic features – assembling the transcriptome. Tools on how to do this can be found on the read alignment page. 


Preprocessing and QC tools

Gene expression quantification tools

Gene expression can be quantified by counting the number of reads that map to each locus in the  transcriptome assembly step. Contigs or reference transcript annotations can be used to quantift expression of a gene or exon. Other technologies, like qPCR or expression microarrays can be used instead of RNA-seq but cannot look at the whole transcriptome. 

The tools below determine read counts from aligned RNA-Seq data, but alignment-free counts can also be obtained with Sailfish or Kallisto.  The read counts can then be converted into appropriate metrics for  analyses. Parameters for this conversion are sequencing depth/coverage, total sample RNA output, gene length, and variance. 

Differential gene expression tools

Alternative splicing

RNA splicing refers to the process where pre-mRNA transcripts are  transformed into mRNA by the removal of introns. Which results in the  joining of exons. This process is important in eukaryotes for the regulation of proteins and their diversity.  Alternative splicing includes exon skipping, mutually exclusive exons, alternative donor or acceptor sites, intron retention (most common splicing mode in plants, fungi, and protozoa), alternative transcription start site (promoter), and alternative polyadenylation.RNA-seq can be employed to identify alternative splicing events. 

For short-read RNA-Seq, there are several methods for the detection of alternative splicing and they are classed into 1 of 3 categories: 

  • Count-based (also event-based, differential splicing): estimate exon retention. Examples are DEXSeq, MATS, and SeqGSEA. 
  • Isoform-based (also multi-read modules, differential isoform expression): estimate isoform abundance first, and then relative abundance between conditions. Examples are Cufflinks 2 and DiffSplice.
  • Intron excision based: calculate alternative splicing using split reads. Examples are MAJIQ  and Leafcutter.


Coexpression networks

A coexpression network represents similarly behaving  genes across tissues and conditions. These networks aid in hypotheses generation and aid in inferring functions to genes. RNA-seq data includes the whole transcriptome, which is advantageous over typical use of microarrays. It allows for the possibility to explore fulfilling representations of the gene regulatory networks.