NGS Bioinformatics and Reproducible Research: From Sequencing Data to Scientific Insights
Strengthening computational genomics skills
As part of the VISION project, a four-day training programme on Next-Generation Sequencing (NGS) Bioinformatics and Reproducible Research provided participants with practical experience in computational genomics and the analysis of sequencing data.
The training focused on the bioinformatics workflows used to process and interpret NGS data, combining analytical techniques with reproducible research practices. The sessions were delivered by Rania Ouazahrou, Etienne Kornobis, and Marc Monot from Biomics at Institut Pasteur, with coordination by Imène Najjar.
Exploring key NGS bioinformatics workflows
Throughout the four days, participants followed a practical, step-by-step approach to the analysis of sequencing data, exploring several essential applications in genomics and transcriptomics.
The programme covered:
RNA-seq analysis: Exploring approaches to analysing RNA sequencing data to investigate gene expression and biological processes.
Variant calling: Learning computational approaches for identifying genetic variants from sequencing data.
Genome assembly: Understanding how sequencing reads can be assembled to reconstruct genomic sequences.
Reproducible bioinformatics pipelines: Exploring how to structure and document computational workflows to improve consistency, traceability, and reproducibility.
By working through these different applications, participants gained a broader understanding of how raw sequencing data can be transformed into interpretable results that support scientific investigation.
Integrating reproducibility into genomic research
As NGS technologies generate increasingly large and complex datasets, reproducibility has become a fundamental component of reliable bioinformatics analysis.
Well-structured workflows, documented analytical steps, and consistent computational procedures help researchers verify results, facilitate collaboration, and reuse analytical approaches across projects. Integrating these practices into genomics research supports greater transparency and strengthens confidence in scientific findings.
The training therefore addressed both the technical aspects of NGS data analysis and the methodological principles needed to ensure that computational results can be reliably reproduced.