The challenges of delivering bioinformatics training in the analysis of high-throughput data

Abstract

High-throughput technologies are widely used in the field of functional genomics and used in an increasing number of applications. For many 'wet lab' scientists, the analysis of the large amount of data generated by such technologies is a major bottleneck that can only be overcome through very specialized training in advanced data analysis methodologies and the use of dedicated bioinformatics software tools. In this article, we wish to discuss the challenges related to delivering training in the analysis of high-throughput sequencing data and how we addressed these challenges in the hands-on training courses that we have developed at the European Bioinformatics Institute.

Keywords: bioinformatics training; high-throughput sequencing analysis; open-source software; practical courses; statistical methodologies.

Figure 1:

A typical RNA-seq data analysis workflow: the major steps involved in this pipeline are indicated, alongside some of the tools used to carry out individual steps. Quality assessment is first performed on the sequence reads before mapping them to a reference genome. The reads are then quantified into counts and normalized to minimize technical variability. Then statistical models for count data are applied to infer differential expression or differential exon usage.

Figure 2:

Number of applications (solid line) received since 2009 for HT data analysis courses at EMBL-EBI and number of participants to such courses (dashed line).