What makes NextGenSeek special?
Why NextGenSeek?
Biological and computational expertise
NextGenSeek is led by a PhD-trained molecular and computational biologist with extensive experience in molecular biology, bioinformatics and the analysis of complex biological data.
My research background includes mitochondrial RNA biology, epigenetics, and genome editing, alongside computational work across transcriptomics, genomics, proteomics and other high-dimensional biological datasets.
I develop and apply analysis approaches around the experiment and the biological question, rather than relying on a standard pipeline for every dataset. This can range from established bioinformatics and statistical methods to multiomics integration and machine learning.
I have applied computational approaches across diverse areas of research, including oncology, COVID-19 research, metagenomics, mitochondrial disease and functional genomics.
Computational expertise
Computational expertise includes:
Advanced use of R and Python for biological data analysis, statistics and visualisation
Development of reproducible bioinformatics pipelines and custom analysis workflows
Integration of transcriptomic and proteomic data
Pathway, regulatory-network and functional analysis
Supervised and unsupervised machine learning
Model interpretation and explainable AI
Bash scripting and workflow automation
Version control and reproducible computational research
Alongside my research experience, I have undertaken Level 7 training in AI and Data Science, extending my computational work into machine learning, predictive modelling and explainable AI for scientific data.
Bridging experimental biology and computational analysis
One of the main strengths of NextGenSeek is the combination of computational expertise with first-hand understanding of how biological data are generated.
With around two decades of laboratory experience, including extensive experience with NGS experiments and library preparation, I understand the experimental decisions and technical factors behind the numbers in a dataset.
That matters when deciding:
whether an unexpected result is biological or technical
which samples and comparisons should be included
how technical variation and batch effects should be handled
which statistical approach is appropriate
whether different molecular datasets support the same conclusion
when a standard analysis is sufficient — and when a custom approach is needed
The same principle applies to machine learning and AI. Being able to build or run a model is not the same as knowing whether it is scientifically appropriate, properly validated, or telling you something useful about the biology.
NextGenSeek brings these perspectives together: experimental biology, bioinformatics, multiomics and data science, with the analysis determined by the research question rather than by the tool.
