What makes NextGenSeek special?

Why NextGenSeek?

Biological and genetic expertise

NextGenSeek is led by a postdoctoral scientist specialising in computational analysis of next-generation sequencing data, with deep domain expertise in molecular biology, mitochondrial RNA biology, epigenetics, and genome editing.

My work focuses on designing, implementing, and validating bioinformatics pipelines for complex NGS datasets, ensuring analyses are statistically robust, reproducible, and tailored to the biological question rather than constrained by off-the-shelf workflows.

I have applied these approaches across diverse research areas, including oncology, COVID-19 research, metagenomics, and functional genomics.

Computational expertise

Computational expertise includes:

  • Advanced use of R and Python for NGS data processing, statistical analysis, and visualisation

  • Bash scripting and workflow automation for scalable, reproducible pipelines

  • Development of custom analysis tools and workflows for complex or non-standard datasets

  • Working knowledge of machine learning concepts and methods relevant to genomics

  • Use of version control and best coding practices to ensure transparent, traceable analyses

Bridging biology and bioinformatics

The core strength of NextGenSeek lies in integrating computational bioinformatics with biological understanding. Extensive experience designing, implementing, and maintaining NGS analysis pipelines ensures that each stage — from raw data quality control to final statistical interpretation — is handled deliberately and transparently.

Computational decisions include:

  • Designing pipeline architecture appropriate for the data type and experimental design

  • Implementing quality control logic to identify technical artefacts, batch effects, and low-quality samples

  • Selecting normalisation and statistical strategies suited to the biological question

  • Ensuring analyses are reproducible, well-documented, and version-controlled

With 18 years of laboratory experience, including ten years working directly with NGS samples and library preparation, these computational choices are informed by a practical understanding of how sequencing data is generated and where technical bias is likely to arise.

This combined perspective enables:

  • Analysis workflows that reflect experimental reality

  • Robust, defensible computational results

  • Clear biological interpretation of complex datasets

Publication list