Session 1: Bioinformatics in Transcriptomics

The aim of these exercises is to learn how to proceed with a de novo transcriptome analysis of a real RNA-Seq experiment. The analysis begins with the raw RNA-Seq reads, and all the necessary analysis steps will be carried out to achieve these objectives:

  • To develop a reference transcriptome of the species under study. This involves assembling the RNA-Seq reads and the functional annotation of the resulting transcripts.

  • To understand the differences in gene expression between two experimental conditions through an RNA-Seq Differential Expression Analysis. This involves expression quantification, differential expression analysis, and functional enrichment analysis.

The analysis will be carried out in a de novo scenario, this means, there are not reference genome sequences that can be used as a reference.

Use case: Reanalyzing the A. galli transcriptomic response to an anthelmintic drug with OmicsBox.

The aim of this study was to investigate the response in gene expression before and after exposure to the Benzimidazoles drug flubendazole in adult female Ascaridia galli worms. The nematode Ascaridia galli (order Ascaridida) is an economically important intestinal parasite responsible for increased food consumption, reduced performance, and mortality in commercial poultry production. Parasite control relies on the repeated use of dewormers (anthelmintics). Benzimidazoles are currently the only anthelmintic registered against A. galli in the EU and there is an obvious risk that overuse of one drug class may lead to resistance. The worms were collected from a commercial laying hen farm before and on day three during a treatment period of 7 days with flubendazole.

Due to the large size of the data set, for some exercises, a subset extracted from the original data will be provided. This will avoid long computation times in the most demanding steps.

Additional information about this dataset in these links:


  1. Quality assessment and preprocessing of raw sequencing reads.

  2. RNA-Seq de novo Assembly.

  3. Functional Annotation.

  4. Quantify Expression at Transcript-level.

  5. Differential Expression Analysis.

  6. Functional Enrichment Analysis.