Whole transcriptome sequencing enables computational assembly of short RNA-seq reads into entire transcripts, either de novo or against a reference genome. The transcripts can then be identified, along with their location in the genome if one exists for the species. A comparison of the transcripts to known functional motifs can also be carried out.


  • List of assembled transcript sequences and a FASTA file
  • Transcript genome coordinates (if a genome is available)
  • Functions and annotated names for each transcript

The most common type of analysis carried out on RNA-seq data is gene expression analysis. Gene expression levels are computed for each annotated gene. Differential expression levels between sample groups are then inferred using robust statistical testing. Co-expressed genes can also be deduced using clustering approaches.


  • Gene expression levels for all genes/isoforms in all samples
  • Differentially expressed genes between sample groups
  • Time-dependent genes in a time-series experiment

RNA-seq data allows identification of novel splicing isoforms and quantification of expression levels for all transcript variants of a gene. While this requires a high expression level and a larger depth of sequencing, it is possible to differentiate between gene isoforms in expression analysis.


  • List of expressed transcript isoforms
  • List of previously unidentified splice variants
  • Quantified expression levels for all isoforms