Computational Modelling Group

RStudio

For queries about this topic, contact Marcin Knut.

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Projects

Application of RNA-Seq for gene fusion identification in blood cancers

William Tapper (Investigator), Marcin Knut

Gene fusions are often the cause of different blood cancers. As such, accurate identification of them provides information on the underlying cause of a cancer, ensuring appropriate choice of treatment. However, due to shortcomings of the currently applied methods for gene fusion identification, some of them escape undetected. We are employing RNA-Seq, a cutting-edge method for sequencing RNA, the messenger of genetic information, to investigate gene fusions.

BRECcIA - Building REsearch Capacity for sustainable water and food security In sub-saharan Africa

The BRECcIA project is aimed at developing research and researchers to understand water and food security challenges in sub-Saharan Africa

Characterisation of the Genomic Landscape in Splenic Marginal Zone Lymphoma

Sarah Ennis, Jane Gibson, Jon Strefford (Investigators), Carolina Jaramillo Oquendo, Helen Parker

This project aims to expand the catalogue of mutated genes in splenic marginal zone lymphoma (SMZL) and construct a detailed characterisation of the genetic landscape of this disease. Using next generation sequencing, we aim to identify somatic mutations in over 100 samples, and enrich clinical data with this information to improve patient treatment and prognosis.

Development and application of powerful methods for identifying selective sweeps

Andrew Collins, Reuben Pengelly, Timothy Sluckin, Sarah Ennis (Investigators), Clare Horscroft

This project is about detecting regions of the genome which have experienced selective pressure. To achieve this, mathematical models will be developed and applied to human genomic data sets, as well as to those of other species.

E ffects of Sample Contamination on Alternate Allele Frequency

Jane Gibson (Investigator), Roshan Sood

Accurate calling of genetic variants is reliant on the purity of samples, contamination will reduce the accuracy of results. Currently there are few programs able to identify contamination in samples, potentially misinforming a researcher or clinician. To better understand the changes caused by sample contamination in
silico simulations were performed where a known percentage of DNA sequence reads from a contaminating
fi le were added. Understanding the changes will assist the development of a new method and program to
detect sample contamination.

Mathematical tools for analysis of genome function, linkage disequilibrium structure and disease gene prediction

Mahesan Niranjan, Andrew Collins, Reuben Pengelly (Investigators)

This iPhD project uses a Gaussian Bayesian Networks framework through Machine learning methods to predict which genes are involved in the development of different diseases.

Mathematical tools for analysis of genome function, linkage disequilibrium structure and disease gene prediction

Mahesan Niranjan, Andrew Collins, Reuben Pengelly (Investigators)

This PhD project uses a Monte Carlo molecular simulation processes approach to predict which genes are involved in the development of different diseases.

Mathematical tools for analysis of genome function, linkage disequilibrium structure and disease gene prediction

Andrew Collins, Mahesan Niranjan, Reuben Pengelly (Investigators), Alejandra Vergara Lope

This iPhD project uses a Gaussian Bayesian Networks approaches framework through machine learning approach to predict which genes are involved in the development of different diseases.

SAVE: Solent Achieving Value through Efficiency

Patrick James, Ben Anderson (Investigators), Luke Blunden

Analysis of 15 minute electricity consumption and 10 second instantaneous power data from 4,000+ households in the Solent region collected over 3 years of a randomised control trial study.

Studying microevolution in clinical isolates of Neisseria lactamica

Robert Read (Investigator), Jay Laver, Anish Pandey

We intranasally infected and successfully colonised six volunteers with Neisseria lactamica, a commensal species genetically similar to Neisseria meningitidis. A bioinformatics approach was then used to understand the microevolution of this bacterium and its adaptations to the nasopharynx.

People

Andrew Collins
Professor, Medicine (FM)
Sarah Ennis
Professor, Medicine (FM)
Mahesan Niranjan
Professor, Electronics and Computer Science (FPAS)
Robert Read
Professor, Medicine (FM)
Timothy Sluckin
Professor, Mathematics (FSHS)
Jon Strefford
Professor, Medicine (FM)
Patrick James
Senior Lecturer, Civil Engineering & the Environment (FEE)
Jane Gibson
Lecturer, Biological Sciences (FNES)
Reuben Pengelly
Lecturer, Medicine (FM)
Ben Anderson
Senior Research Fellow, Civil Engineering & the Environment (FEE)
Jay Laver
Senior Research Fellow, Medicine (FM)
Alistair Bailey
Research Fellow, Medicine (FM)
Luke Blunden
Research Fellow, Civil Engineering & the Environment (FEE)
Jason Hilton
Research Fellow, Social Sciences (FSHS)
Daniel Cernin
Postgraduate Research Student, Mathematics (FSHS)
Joseph Egan
Postgraduate Research Student, Mathematics (FSHS)
Graham Elliott
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Carolina Jaramillo Oquendo
Postgraduate Research Student, Medicine (FM)
Marcin Knut
Postgraduate Research Student, Medicine (FM)
Anish Pandey
Postgraduate Research Student, Medicine (FM)
Daniel Powell
Postgraduate Research Student, Engineering Sciences (FEE)
Alejandra Vergara Lope
Postgraduate Research Student, Engineering Sciences (FEE)
Clare Horscroft
None, None
Helen Parker
None, None
Roshan Sood
None, None
William Tapper
None, None