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University of Cambridge > Department of Oncology > Computational Biology Group > Research

Research into DNA copy number changes

The accurate and high resolution mapping of (often large) DNA copy number abberations (CNA) has become an important tool by which to gain insight into the mechanisms of tumourigenesis. Additionally, in recent years, fine regions of copy number variation (CNV) have been linked to disease. Our research then looks at methodology for identifying DNA copy number changes on a range of scales.


Outline

the wave artefact
Showing relative copy number from a number of arrays (y-axis) by genomic location (x-axis). The wave is apparent in the vertical banding.

We have developed a novel method (BioHMM) for segmenting array comparative genomic hybridisation data into states with the same underlying copy number. By utilizing a heterogeneous hidden Markov model, BioHMM incorporates relevant biological factors (e.g. the distance between adjacent clones) in the segmentation process. BioHMM is available as a feature in our Bioconductor package snapCGH for the processing of aCGH data. Because the package is compatible with limma, it allows smooth transitions between preprocessing, normalisation and segmentation. snapCGH also provides facilities for cross-sample comparisons.

In collaboration with the Sangar Institute, we estimated the proportion of variation in gene expression that could be attributed to CNVs and developed novel calling methods for CNVs. We identified a 'wave' artefact that affects aCGH data and demonstrated a method for, and the benefits of, removing that artefact. We investigated approaches for incorporating pedigree information in CNV calling algorithms, and demonstrated methodology for estimating CNV calling performance from replicate experiments.

In collaboration with clinical colleagues, we have looked at DNA copy number changes in relation to a number of cancers including breast and ovarian.

Selected publications of ours in this area
  • Marioni JC, Thorne NP, Tavaré S. BioHMM: a heterogeneous hidden Markov model for segmenting array CGH data. Bioinformatics, 2006 22(9):1144-6. Epub 2006 Mar 13 [pubmed]
  • Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C, Thorne N, Redon R, Bird CP, de Grassi A, Lee C, Tyler-Smith C, Carter N, Scherer SW, Tavaré S, Deloukas P, Hurles ME, Dermitzakis ET. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 2007 Feb 9;315(5813):848-53 [pubmed]
  • Marioni JC, Thorne NP, Valsesia A, Fitzgerald T, Redon R, Fiegler H, Andrews TD, Stranger BE, Lynch AG, Dermitzakis ET, Carter NP, Tavaré S, Hurles ME. Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization. Genome Biol. 2007 Oct 25;8(10):R228 [pubmed]
  • Lynch AG, Marioni JC, Tavaré S. Numbers of Copy-Number Variations and False-Negative Rates Will Be Underestimated If We Do Not Account for the Dependence between Repeated Experiments. Am J Hum Genet. 2007 Aug;81(2):418-20 [pubmed]
  • Marioni JC, White M, Tavaré S, Lynch AG. Hidden copy number variation in the HapMap population, PNAS 105 (29) 10067-10072 [pubmed]
  • Chin SF, Teschendorff AE, Marioni JC, Wang Y, Barbosa-Morais NL, Thorne NP, Costa JL, Pinder SE, van de Wiel MA, Green AR, Ellis IO, Porter PL, Tavaré S, Brenton JD, Ylstra B, Caldas C. High-resolution array-CGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer. Genome Biol. 2007 Oct 9;8(10):R215 [pubmed]