Computational Modelling Group

AWK

AWK is a s a small, C-style programming language designed for text processing and typically used as a data extraction and reporting tool. It is a standard feature of most Unix-like operating systems. Many UNIX utilities generates rows and columns of information, AWK is an excellent tool for processing these rows and columns, and is easier to use AWK than most conventional programming languages.

For queries about this topic, contact Roshan Sood.

Projects

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.

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

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.

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.

Porcupine Basin Project

Louise Watremez

The Porcupine Basin is a narrow failed rift, offshore SW Ireland, featuring extreme crustal thinning. The M61/2 survey (May 2004, T. Reston and B. O'Reilly) allowed for the acquisition of seismic refraction data across and along the basin, along 5 transects. The processing of the data along these transects will give us information about the crustal structure across the basin, faulting due to the crustal extension, nature of the upper-mantle, etc. This project is funded by Petroleum Infrastructure Programme (PIP).

People

Andrew Collins
Professor, Medicine (FM)
Sarah Ennis
Professor, Medicine (FM)
Mahesan Niranjan
Professor, Electronics and Computer Science (FPAS)
Jon Strefford
Professor, Medicine (FM)
Jane Gibson
Lecturer, Biological Sciences (FNES)
Reuben Pengelly
Lecturer, Medicine (FM)
Louise Watremez
Research Fellow, Ocean & Earth Science (FNES)
Carolina Jaramillo Oquendo
Postgraduate Research Student, Medicine (FM)
Alejandra Vergara Lope
Postgraduate Research Student, Engineering Sciences (FEE)
Helen Parker
None, None
Andrea Silva
None, None
Roshan Sood
None, None