ORCID Profile
0000-0002-6667-4943
Current Organisations
Health Data Research UK
,
The Alan Turing Institute
,
University of Oxford
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Publisher: Oxford University Press (OUP)
Date: 20-11-2008
DOI: 10.1093/BIOINFORMATICS/BTN607
Abstract: Motivation: Conventional phylogenetic analysis for characterizing the relatedness between taxa typically assumes that a single relationship exists between species at every site along the genome. This assumption fails to take into account recombination which is a fundamental process for generating ersity and can lead to spurious results. Recombination induces a localized phylogenetic structure which may vary along the genome. Here, we generalize a hidden Markov model (HMM) to infer changes in phylogeny along multiple sequence alignments while accounting for rate heterogeneity the hidden states refer to the unobserved phylogenic topology underlying the relatedness at a genomic location. The dimensionality of the number of hidden states (topologies) and their structure are random (not known a priori) and are s led using Markov chain Monte Carlo algorithms. The HMM structure allows us to analytically integrate out over all possible changepoints in topologies as well as all the unknown branch lengths. Results: We demonstrate our approach on simulated data and also to the genome of a suspected HIV recombinant strain as well as to an investigation of recombination in the sequences of 15 laboratory mouse strains sequenced by Perlegen Sciences. Our findings indicate that our method allows us to distinguish between rate heterogeneity and variation in phylogeny caused by recombination without being restricted to 4-taxa data. Availability: The method has been implemented in JAVA and is available, along with data studied here, from www.stats.ox.ac.uk/~webb. Contact: cholmes@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: EMBO
Date: 2011
DOI: 10.1038/MSB.2011.57
Publisher: Oxford University Press (OUP)
Date: 16-06-2010
DOI: 10.1093/BIOINFORMATICS/BTQ327
Abstract: Motivation: Quantifying differences in linkage disequilibrium (LD) between sub-groups can highlight genetic regions or sites under selection and/or associated with disease, and may have utility in trans-ethnic mapping studies. Results: We present a novel pseudo Bayes factor (PBF) approach that assess differences in covariance of genotype frequencies from single nucleotide polymorphism (SNP) data from a genome-wide study. The magnitude of the PBF reflects the strength of evidence for a difference, while accounting for the s le size and number of SNPs, without the requirement for permutation testing to establish statistical significance. Application of the PBF to HapMap and Gambian malaria SNP data reveals regional LD differences, some known to be under selection. Availability and implementation: The PBF approach has been implemented in the BALD (Bayesian analysis of LD differences) C++ software, and is available from homepages.lshtm.ac.uk/tgclark/downloads Contact: taane.clark@lshtm.ac.uk
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 11-2013
DOI: 10.1038/AJG.2013.292
Abstract: Microsatellite instability (MSI) is an established marker of good prognosis in colorectal cancer (CRC). Chromosomal instability (CIN) is strongly negatively associated with MSI and has been shown to be a marker of poor prognosis in a small number of studies. However, a substantial group of "double-negative" (MSI-/CIN-) CRCs exists. The prognosis of these patients is unclear. Furthermore, MSI and CIN are each associated with specific molecular changes, such as mutations in KRAS and BRAF, that have been associated with prognosis. It is not known which of MSI, CIN, and the specific gene mutations are primary predictors of survival. We evaluated the prognostic value (disease-free survival, DFS) of CIN, MSI, mutations in KRAS, NRAS, BRAF, PIK3CA, FBXW7, and TP53, and chromosome 18q loss-of-heterozygosity (LOH) in 822 patients from the VICTOR trial of stage II/III CRC. We followed up promising associations in an Australian community-based cohort (N=375). In the VICTOR patients, no specific mutation was associated with DFS, but in idually MSI and CIN showed significant associations after adjusting for stage, age, gender, tumor location, and therapy. A combined analysis of the VICTOR and community-based cohorts showed that MSI and CIN were independent predictors of DFS (for MSI, hazard ratio (HR)=0.58, 95% confidence interval (CI) 0.36-0.93, and P=0.021 for CIN, HR=1.54, 95% CI 1.14-2.08, and P=0.005), and joint CIN/MSI testing significantly improved the prognostic prediction of MSI alone (P=0.028). Higher levels of CIN were monotonically associated with progressively poorer DFS, and a semi-quantitative measure of CIN was a better predictor of outcome than a simple CIN+/- variable. All measures of CIN predicted DFS better than the recently described Watanabe LOH ratio. MSI and CIN are independent predictors of DFS for stage II/III CRC. Prognostic molecular tests for CRC relapse should currently use MSI and a quantitative measure of CIN rather than specific gene mutations.
Publisher: Oxford University Press (OUP)
Date: 24-07-2008
DOI: 10.1093/BIOINFORMATICS/BTN386
Abstract: Summary: Current genotyping algorithms typically call genotypes by clustering allele-specific intensity data on a single nucleotide polymorphism (SNP) by SNP basis. This approach assumes the availability of a large number of control s les that have been s led on the same array and platform. We have developed a SNP genotyping algorithm for the Illumina Infinium SNP genotyping assay that is entirely within-s le and does not require the need for a population of control s les nor parameters derived from such a population. Our algorithm exhibits high concordance with current methods and & % call accuracy on HapMap s les. The ability to call genotypes using only within-s le information makes the method computationally light and practical for studies involving small s le sizes and provides a valuable independent quality control metric for other population-based approaches. Availability: www.stats.ox.ac.uk/~giannoul/GenoSNP/ Contact: cholmes@stats.ox.ac.uk
Publisher: Public Library of Science (PLoS)
Date: 08-09-2011
Publisher: Springer Science and Business Media LLC
Date: 09-2010
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Christopher Holmes.