Overview #

Collaborative development of a suite of reproducible genomic analysis workflows designed to support rare disease research through automated processing of high-throughput sequencing data.

Problem #

Genomic analyses in rare diseases often require complex, multi-step processing pipelines involving numerous software dependencies, heterogeneous datasets and computationally intensive tasks. Ensuring reproducibility, portability and maintainability across projects and institutions represents a significant challenge.

Solution #

Contributed to the development, maintenance and validation of modular analysis workflows for genomic data processing. Integrated bioinformatic tools into reproducible execution frameworks, improved deployment through containerized environments and participated in collaborative workflow development and testing.

Stack #

Python · Nextflow · Docker · Apptainer · HPC · Linux · Git

Impact #

• Support genomic analyses across multiple rare disease research projects.
• Enabled reproducible execution of complex workflows across different computational environments.
• Contributed to collaborative software development used by researchers and clinicians.
• Facilitated scalable processing of large sequencing datasets in HPC infrastructures.

Repository #

GitHub: CBRA | GdTBioinfo-nf-mosaicism