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Lindsay A Brown,
Jeremy Hoog,
Suet-Feung Chin,
Yu Tao,
Abd Alnaser Zayed,
Koei Chin,
Andrew E Teschendorff,
John F Quackenbush, John C Marioni,
Samuel Leung,
Charles M Perou,
Torsten O Neilsen,
Matthew Ellis,
Joe W Gray,
Philip S Bernard,
David G Huntsman,
Carlos Caldas
Nature Genetics 08/2008; 40(7):806-7; author reply 810-2. · 35.53 Impact Factor
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Thomas A Down,
Vardhman K Rakyan,
Daniel J Turner,
Paul Flicek,
Heng Li,
Eugene Kulesha,
Stefan Gräf,
Nathan Johnson,
Javier Herrero,
Eleni M Tomazou, [......],
Marlis Herberth,
Kevin L Howe,
David K Jackson,
Marcos M Miretti, John C Marioni,
Ewan Birney,
Tim J P Hubbard,
Richard Durbin,
Simon Tavaré,
Stephan Beck
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ABSTRACT: DNA methylation is an indispensible epigenetic modification required for regulating the expression of mammalian genomes. Immunoprecipitation-based methods for DNA methylome analysis are rapidly shifting the bottleneck in this field from data generation to data analysis, necessitating the development of better analytical tools. In particular, an inability to estimate absolute methylation levels remains a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling. To address this issue, we developed a cross-platform algorithm-Bayesian tool for methylation analysis (Batman)-for analyzing methylated DNA immunoprecipitation (MeDIP) profiles generated using oligonucleotide arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). We developed the latter approach to provide a high-resolution whole-genome DNA methylation profile (DNA methylome) of a mammalian genome. Strong correlation of our data, obtained using mature human spermatozoa, with those obtained using bisulfite sequencing suggest that combining MeDIP-seq or MeDIP-chip with Batman provides a robust, quantitative and cost-effective functional genomic strategy for elucidating the function of DNA methylation.
Nature Biotechnology 08/2008; 26(7):779-85. · 29.50 Impact Factor
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ABSTRACT: Recently, the extent of copy number variation (CNV) throughout the genome has been shown to be far greater than previously thought. Further, it has been demonstrated that specific copy number variable regions (CNVRs) are associated with particular diseases, suggesting that these genetic variations may have an important biological role. Hence, calling CNVRs and subsequently classifying samples as "losses" or "gains" is of great interest. A number of papers have been published containing classifications of CNVs, and here we show how the presence of pedigree information can be used for assessing the performance of those classification methods. In this article, by examining CNV classifications made in the HapMap samples, we show that estimates of the number of false-positive classifications per individual made by current approaches can be determined. Moreover, commonplace technologies for determining the locations of CNVRs aggregate information across the maternal and paternal chromosomes at the locus of interest. Here, we show that copy number variation on each chromosome can be inferred and, in particular, we discuss the existence of a class of CNVs that are inevitably misclassified and give an estimate of their prevalence. Although our focus is not on the development of calling algorithms per se, we describe and provide an example of how our model might be incorporated into the initial classification procedure to produce more robust results. Finally, we discuss how this methodology might be applied to future studies to obtain better estimates of the extent of CNV across the genome.
Proceedings of the National Academy of Sciences 07/2008; 105(29):10067-72. · 9.68 Impact Factor
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The American Journal of Human Genetics 09/2007; 81(2):418-20; author reply 420-1. · 10.60 Impact Factor
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Suet F Chin,
Andrew E Teschendorff, John C Marioni,
Yanzhong Wang,
Nuno L Barbosa-Morais,
Natalie P Thorne,
Jose L Costa,
Sarah E Pinder,
Mark A van de Wiel,
Andrew R Green,
Ian O Ellis,
Peggy L Porter,
Simon Tavaré,
James D Brenton,
Bauke Ylstra,
Carlos Caldas
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ABSTRACT: The characterization of copy number alteration patterns in breast cancer requires high-resolution genome-wide profiling of a large panel of tumor specimens. To date, most genome-wide array comparative genomic hybridization studies have used tumor panels of relatively large tumor size and high Nottingham Prognostic Index (NPI) that are not as representative of breast cancer demographics.
We performed an oligo-array-based high-resolution analysis of copy number alterations in 171 primary breast tumors of relatively small size and low NPI, which was therefore more representative of breast cancer demographics. Hierarchical clustering over the common regions of alteration identified a novel subtype of high-grade estrogen receptor (ER)-negative breast cancer, characterized by a low genomic instability index. We were able to validate the existence of this genomic subtype in one external breast cancer cohort. Using matched array expression data we also identified the genomic regions showing the strongest coordinate expression changes ('hotspots'). We show that several of these hotspots are located in the phosphatome, kinome and chromatinome, and harbor members of the 122-breast cancer CAN-list. Furthermore, we identify frequently amplified hotspots on 8q22.3 (EDD1, WDSOF1), 8q24.11-13 (THRAP6, DCC1, SQLE, SPG8) and 11q14.1 (NDUFC2, ALG8, USP35) associated with significantly worse prognosis. Amplification of any of these regions identified 37 samples with significantly worse overall survival (hazard ratio (HR) = 2.3 (1.3-1.4) p = 0.003) and time to distant metastasis (HR = 2.6 (1.4-5.1) p = 0.004) independently of NPI.
We present strong evidence for the existence of a novel subtype of high-grade ER-negative tumors that is characterized by a low genomic instability index. We also provide a genome-wide list of common copy number alteration regions in breast cancer that show strong coordinate aberrant expression, and further identify novel frequently amplified regions that correlate with poor prognosis. Many of the genes associated with these regions represent likely novel oncogenes or tumor suppressors.
Genome biology 02/2007; 8(10):R215. · 6.63 Impact Factor
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John C Marioni,
Natalie P Thorne,
Armand Valsesia,
Tomas Fitzgerald,
Richard Redon,
Heike Fiegler,
T Daniel Andrews,
Barbara E Stranger,
Andrew G Lynch,
Emmanouil T Dermitzakis,
Nigel P Carter,
Simon Tavaré,
Matthew E Hurles
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ABSTRACT: Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined.
We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses.
Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.
Genome biology 02/2007; 8(10):R228. · 6.63 Impact Factor
