The Bioinformatic team at COI aim to help researchers use data generated from Next-Generation Sequencing (NGS) technologies to understand the complexities of disease, including responses to infection and cancer.
Using machine learning, mathematical modelling, statistical inference and bioinformatics algorithms our team addresses a broad spectrum of questions in biomedicine:
- Understanding the pathways responsible for immune responses
- Investigating potential therapeutic targets and predictive biomarkers for improving precision medicine
- Exploring the contribution of genetic variation on the susceptibility to disease
- Understanding the epigenetic regulation of T cell activity
- Exploring immune-tissue-specific methylation patterns,
- TCR repertoire investigation and interrogation of specificity
- Tracing and elucidating informative spatial transcriptomic signatures and features related to immune and tumour cell interactions in cancer
- Applying mathematical modelling to immune cell dynamics for quantifying differences between various microenvironment scenarios and conditions
We offer help and advice to all COI-affiliated researchers, regardless of biology, question or omic technology. We have expertise combining both biology and bioinformatics/statistics (Elie) and mathematical modelling and statistical inference (Maria), with expertise in:
- Bulk RNAseq
- DNA methylation
- TCR repertoire analysis
- scRNAseq and multiome datasets (SmartSeq and 10X Genomics)
- Spatial transcriptomics/proteomics (Nanostring)
- CRISPR screens
- Multi-omic integration
Note: The skills highlighted above are not exhaustive and the team welcomes the chance to expand and enhance their skills to support researchers. If you would like to discuss your own data/required skills please do get in touch.
We can also provide experimental support, as the team has extensive experience with molecular biology and library preparation for many different library types, to complement the bioinformatic analysis.
We are happy to provide training and support for any researchers wanting to undertake their own bioinformatics analysis. We aim to provide a short quiz to evaluate individuals' proficiency in using R/python for basic bioinformatic analyses.
Please do get in touch if we can support you in undertaking your own bioinformatic analyses.
High performance computing Clusters
We currently have access to two High Performance Computing Clusters (HPC) for running all analyses and data storage, and are open to collaborations and happy to help with setting up storage and access depending on your needs.