A comparison of feature selection and classification methods in DNA methylation studies using the Illumina Infinium platform

Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods

DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina’s Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies.

Base resolution methylome profiling: considerations in platform selection, data preprocessing and analysis

Bisulfite treatment-based methylation microarray (mainly Illumina 450K Infinium array) and next-generation sequencing (reduced representation bisulfite sequencing, Agilent SureSelect Human Methyl-Seq, NimbleGen SeqCap Epi CpGiant or whole-genome bisulfite sequencing) are commonly used for base resolution DNA methylome research. Although multiple tools and methods have been developed and used for the data preprocessing and analysis, confusions remains for these platforms including how and whether the 450k array should be normalized; which platform should be used to better fit researchers' needs; and which statistical models would be more appropriate for differential methylation analysis. This review presents the commonly used platforms and compares the pros and cons of each in methylome profiling. We then discuss approaches to study design, data normalization, bias correction and model selection for differentially methylated individual CpGs and regions.

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets.

Epigenome-wide association study (EWAS) of BMI, BMI change and waist circumference in African American adults identifies multiple replicated loci

Obesity is an important component of the pathophysiology of chronic diseases. Identifying epigenetic modifications associated with elevated adiposity, including DNA methylation variation, may point to genomic pathways that are dysregulated in numerous conditions. The Illumina 450K Bead Chip array was used to assay DNA methylation in leukocyte DNA obtained from 2097 African American adults in the Atherosclerosis Risk in Communities (ARIC) study. Mixed-effects regression models were used to test the association of methylation beta value with concurrent body mass index (BMI) and waist circumference (WC), and BMI change, adjusting for batch effects and potential confounders. Replication using whole-blood DNA from 2377 White adults in the Framingham Heart Study and CD4+ T cell DNA from 991 Whites in the Genetics of Lipid Lowering Drugs and Diet Network Study was followed by testing using adipose tissue DNA from 648 women in the Multiple Tissue Human Expression Resource cohort. Seventy-six BMI-related probes, 164 WC-related probes and 8 BMI change-related probes passed the threshold for significance in ARIC (P < 1 × 10(-7); Bonferroni), including probes in the recently reported HIF3A, CPT1A and ABCG1 regions. Replication using blood DNA was achieved for 37 BMI probes and 1 additional WC probe. Sixteen of these also replicated in adipose tissue, including 15 novel methylation findings near genes involved in lipid metabolism, immune response/cytokine signaling and other diverse pathways, including LGALS3BP, KDM2B, PBX1 and BBS2, among others. Adiposity traits are associated with DNA methylation at numerous CpG sites that replicate across studies despite variation in tissue type, ethnicity and analytic approaches.

Variational Bayesian Matrix Factorization for Bounded Support Data

A novel Bayesian matrix factorization method for bounded support data is presented. Each entry in the observation matrix is assumed to be beta distributed. As the beta distribution has two parameters, two parameter matrices can be obtained, which matrices contain only nonnegative values. In order to provide low-rank matrix factorization, the nonnegative matrix factorization (NMF) technique is applied. Furthermore, each entry in the factorized matrices, i.e., the basis and excitation matrices, is assigned with gamma prior. Therefore, we name this method as beta-gamma NMF (BG-NMF). Due to the integral expression of the gamma function, estimation of the posterior distribution in the BG-NMF model can not be presented by an analytically tractable solution. With the variational inference framework and the relative convexity property of the log-inverse-beta function, we propose a new lower-bound to approximate the objective function. With this new lower-bound, we derive an analytically tractable solution to approximately calculate the posterior distributions. Each of the approximated posterior distributions is also gamma distributed, which retains the conjugacy of the Bayesian estimation. In addition, a sparse BG-NMF can be obtained by including a sparseness constraint to the gamma prior. Evaluations with synthetic data and real life data demonstrate the good performance of the proposed method.

An epigenetic map of age-associated autosomal loci in northern European families at high risk for the metabolic syndrome

BACKGROUND

The prevalence of chronic diseases such as cancer, type 2 diabetes, metabolic syndrome (MetS), and cardiovascular disease increases with age in all populations. Epigenetic features are hypothesized to play important roles in the pathophysiology of age-associated diseases, but a map of these markers is lacking. We searched for genome-wide age-associated methylation signatures in peripheral blood of individuals at high risks for MetS by profiling 485,000 CpG sites in 192 individuals of Northern European ancestry using the Illumina HM450 array. Subjects (ages 6-85 years) were part of seven extended families, and 73% of adults and 32% of children were overweight or obese.

RESULTS

We found 22,122 genome-wide significant age-associated CpG sites (P α=0.05 = 3.65 × 10(-7) after correction for multiple testing) of which 14,155 are positively associated with age while 7,967 are negatively associated. By applying a positional density-based clustering algorithm, we generated a map of epigenetic 'hot-spots' of age-associated genomic segments, which include 290 age-associated differentially methylated CpG clusters (aDMCs), of which 207 are positively associated with age. Gene/pathway enrichment analyses were performed on these clusters using FatiGO. Genes localized to both the positively (n = 241) and negatively (n = 16) age-associated clusters are significantly enriched in specific KEGG pathways and GO terms. The most significantly enriched pathways are the hedgehog signaling pathway (adjusted P = 3.96 × 10(-3)) and maturity-onset diabetes of the young (MODY) (adjusted P = 6.26 × 10(-3)) in the positive aDMCs and type I diabetes mellitus (adjusted P = 3.69 × 10(-7)) in the negative aDMCs. We also identified several epigenetic loci whose age-associated change rates differ between subjects diagnosed with MetS and those without.

CONCLUSION

We conclude that in a family cohort at high risk for MetS, age-associated epigenetic features enrich in biological pathways important for determining the fate of fat cells and for insulin production. We also observe that several genes known to be related to MetS show differential epigenetic response to age in individuals with and without MetS.

Promoter hypermethylation of tumour suppressor genes as potential biomarkers in colorectal cancer

Colorectal cancer (CRC) is a common malignancy and the fourth leading cause of cancer deaths worldwide. It results from the accumulation of multiple genetic and epigenetic changes leading to the transformation of colon epithelial cells into invasive adenocarcinomas. In CRC, epigenetic changes, in particular promoter CpG island methylation, occur more frequently than genetic mutations. Hypermethylation contributes to carcinogenesis by inducing transcriptional silencing or downregulation of tumour suppressor genes and currently, over 600 candidate hypermethylated genes have been identified. Over the past decade, a deeper understanding of epigenetics coupled with technological advances have hinted at the potential of translating benchtop research into biomarkers for clinical use. DNA methylation represents one of the largest bodies of literature in epigenetics, and hence has the highest potential for minimally invasive biomarker development. Most progress has been made in the development of diagnostic markers and there are currently two, one stool-based and one blood-based, biomarkers that are commercially available for diagnostics. Prognostic and predictive methylation markers are still at their infantile stages.

Epigenome-wide association studies (EWAS): past, present, and future

Just as genome-wide association studies (GWAS) grew from the field of genetic epidemiology, so too do epigenome-wide association studies (EWAS) derive from the burgeoning field of epigenetic epidemiology, with both aiming to understand the molecular basis for disease risk. While genetic risk of disease is currently unmodifiable, there is hope that epigenetic risk may be reversible and or modifiable. This review will take a look back at the origins of this field and revisit the past early efforts to conduct EWAS using the 27k Illumina methylation beadarrays, to the present where most investigators are using the 450k Illumina beadarrays and finally to the future where next generation sequencing based methods beckon. There have been numerous diseases, exposures and lifestyle factors investigated with EWAS, with several significant associations now identified. However, much like the GWAS studies, EWAS are likely to require large international consortium-based approaches to reach the numbers of subjects, and statistical and scientific rigor, required for robust findings.

On the potential of models for location and scale for genome-wide DNA methylation data

Background

With the help of epigenome-wide association studies (EWAS), increasing knowledge on the role of epigenetic mechanisms such as DNA methylation in disease processes is obtained. In addition, EWAS aid the understanding of behavioral and environmental effects on DNA methylation. In terms of statistical analysis, specific challenges arise from the characteristics of methylation data. First, methylation β-values represent proportions with skewed and heteroscedastic distributions. Thus, traditional modeling strategies assuming a normally distributed response might not be appropriate. Second, recent evidence suggests that not only mean differences but also variability in site-specific DNA methylation associates with diseases, including cancer. The purpose of this study was to compare different modeling strategies for methylation data in terms of model performance and performance of downstream hypothesis tests. Specifically, we used the generalized additive models for location, scale and shape (GAMLSS) framework to compare beta regression with Gaussian regression on raw, binary logit and arcsine square root transformed methylation data, with and without modeling a covariate effect on the scale parameter.

Results

Using simulated and real data from a large population-based study and an independent sample of cancer patients and healthy controls, we show that beta regression does not outperform competing strategies in terms of model performance. In addition, Gaussian models for location and scale showed an improved performance as compared to models for location only. The best performance was observed for the Gaussian model on binary logit transformed β-values, referred to as M-values. Our results further suggest that models for location and scale are specifically sensitive towards violations of the distribution assumption and towards outliers in the methylation data. Therefore, a resampling procedure is proposed as a mode of inference and shown to diminish type I error rate in practically relevant settings. We apply the proposed method in an EWAS of BMI and age and reveal strong associations of age with methylation variability that are validated in an independent sample.

Conclusions

Models for location and scale are promising tools for EWAS that may help to understand the influence of environmental factors and disease-related phenotypes on methylation variability and its role during disease development.

Genome-wide hypermethylation coupled with promoter hypomethylation in the chorioamniotic membranes of early onset pre-eclampsia

Pre-eclampsia is the leading cause of fetal and maternal morbidity and mortality. Early onset pre-eclampsia (EOPE) is a disorder that has severe maternal and fetal outcomes, whilst its etiology is poorly understood. We hypothesize that epigenetics plays an important role to mediate the development of EOPE and conducted a case-control study to compare the genome-wide methylome difference between chorioamniotic membranes from 30 EOPE and 17 full-term pregnancies using the Infinium Human Methylation 450 BeadChip arrays. Bioinformatics analysis tested differential methylation (DM) at CpG site level, gene level, and pathway and network level. A striking genome-wide hypermethylation pattern coupled with hypomethylation in promoters was observed. Out of 385 184 CpG sites, 9995 showed DM (2.6%). Of those DM sites, 91.9% showed hypermethylation (9186 of 9995). Over 900 genes had DM associated with promoters. Promoter-based DM analysis revealed that genes in canonical cancer-related pathways such as Rac, Ras, PI3K/Akt, NFκB and ErBB4 were enriched, and represented biological functional alterations that involve cell cycle, apoptosis, cancer signaling and inflammation. A group of genes previously found to be up-regulated in pre-eclampsia, including GRB2, ATF3, NFKB2, as well as genes in proteasome subunits (PSMA1, PMSE1, PSMD1 and PMSD8), harbored hypomethylated promoters. Contrarily, a cluster of microRNAs, including mir-519a1, mir-301a, mir-487a, mir-185, mir-329, mir-194, mir-376a1, mir-486 and mir-744 were all hypermethylated in their promoters in the EOPE samples. These findings collectively reveal new avenues of research regarding the vast epigenetic modifications in EOPE.

Comparisons of non-Gaussian statistical models in DNA methylation analysis

As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance.

A novel method for identification and quantification of consistently differentially methylated regions

Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called “supervised” methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative “unsupervised” approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.

DNA methylation is globally disrupted and associated with expression changes in chronic obstructive pulmonary disease small airways

DNA methylation is an epigenetic modification that is highly disrupted in response to cigarette smoke and involved in a wide spectrum of malignant and nonmalignant diseases, but surprisingly not previously assessed in small airways of patients with chronic obstructive pulmonary disease (COPD). Small airways are the primary sites of airflow obstruction in COPD. We sought to determine whether DNA methylation patterns are disrupted in small airway epithelia of patients with COPD, and evaluate whether changes in gene expression are associated with these disruptions. Genome-wide methylation and gene expression analysis were performed on small airway epithelial DNA and RNA obtained from the same patient during bronchoscopy, using Illumina's Infinium HM27 and Affymetrix's Genechip Human Gene 1.0 ST arrays. To control for known effects of cigarette smoking on DNA methylation, methylation and gene expression profiles were compared between former smokers with and without COPD matched for age, pack-years, and years of smoking cessation. Our results indicate that aberrant DNA methylation is (1) a genome-wide phenomenon in small airways of patients with COPD, and (2) associated with altered expression of genes and pathways important to COPD, such as the NF-E2-related factor 2 oxidative response pathway. DNA methylation is likely an important mechanism contributing to modulation of genes important to COPD pathology. Because these methylation events may underlie disease-specific gene expression changes, their characterization is a critical first step toward the development of epigenetic markers and an opportunity for developing novel epigenetic therapeutic interventions for COPD.

Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer

Background

Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments.

Results

ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle.

Conclusions

The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs. This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4^th most common of cancer in women.

ChAMP: 450k Chip Analysis Methylation Pipeline

The Illumina Infinium HumanMethylation450 BeadChip is a new platform for high-throughput DNA methylation analysis. Several methods for normalization and processing of these data have been published recently. Here we present an integrated analysis pipeline offering a choice of the most popular normalization methods while also introducing new methods for calling differentially methylated regions and detecting copy number aberrations.

AVAILABILITY AND IMPLEMENTATION

ChAMP is implemented as a Bioconductor package in R. The package and the vignette can be downloaded at bioconductor.org

Epigenetic analysis of neurocognitive development at 1 year of age in a community-based pregnancy cohort

Multiple studies show that molecular genetic changes and epigenetic modifications affect the risk of cognitive disability or impairment. However, the role of epigenetic variation in cognitive development of neurotypical young children remains largely unknown. Using data from a prospective, community-based study of mother-infant pairs, we investigated the association of DNA methylation patterns in neonatal umbilical cord blood with cognitive and language development at 1 year of age. No CpG loci achieved genome-wide significance, although a small number of weakly suggestive associations with Bayley-III Receptive Communication scales were noted. While umbilical cord blood is a convenient resource for genetic analyses of birth outcomes, our results do not provide conclusive evidence that its use for DNA methylation profiling yields epigenetic markers that are directly related to postnatal neurocognitive outcomes at 1 year of age.

Alcohol and the methylome: design and analysis considerations for research using human samples

BACKGROUND

A growing number of studies in human samples have sought to determine whether chronic alcohol use and alcohol use disorders (AUDs) may be associated with epigenetic factors, such as DNA methylation. We review the extant literature in light of some of the challenges that currently affect the design and interpretation of epigenetic research in human samples.

METHOD

A literature search was used to identify studies that have examined DNA methylation in relation to alcohol use or AUDs in human samples (through July 2013). A total of 22 studies were identified.

RESULTS

Associations with quantitative or diagnostic phenotypes of alcohol use or AUDs have been reported for several genes. However, all studies to date have relied on relatively small samples and cross-sectional study designs. Additionally, attempts to replicate results have been rare. More generally, research progress is hampered by several issues, including limitations of the technologies used to assess DNA methylation, tissue- and cell-specificity of methylation patterns, the difficulties of relating observed methylation differences at a given locus to a functional effect, and limited knowledge about the molecular mechanisms underlying the effects of alcohol on DNA methylation.

CONCLUSIONS

Although we share the optimism that epigenetics may lead to new insights into the etiology and pathophysiology of AUDs, the methodological and scientific challenges associated with conducting methylomic research in human samples need to be carefully considered when designing and evaluating such studies.

Review of processing and analysis methods for DNA methylation array data

The promise of epigenome-wide association studies and cancer-specific somatic DNA methylation changes in improving our understanding of cancer, coupled with the decreasing cost and increasing coverage of DNA methylation microarrays, has brought about a surge in the use of these technologies. Here, we aim to provide both a review of issues encountered in the processing and analysis of array-based DNA methylation data and a summary of the advantages of recent approaches proposed for handling those issues, focusing on approaches publicly available in open-source environments such as R and Bioconductor. We hope that the processing tools and analysis flowchart described herein will facilitate researchers to effectively use these powerful DNA methylation array-based platforms, thereby advancing our understanding of human health and disease.

An integrated workflow for DNA methylation analysis

The analysis of cytosine methylation provides a new way to assess and describe epigenetic regulation at a whole-genome level in many eukaryotes. DNA methylation has a demonstrated role in the genome stability and protection, regulation of gene expression and many other aspects of genome function and maintenance. BS-seq is a relatively unbiased method for profiling the DNA methylation, with a resolution capable of measuring methylation at individual cytosines. Here we describe, as an example, a workflow to handle DNA methylation analysis, from BS-seq library preparation to the data visualization. We describe some applications for the analysis and interpretation of these data. Our laboratory provides public access to plant DNA methylation data via visualization tools available at our "Next-Gen Sequence" websites (http://mpss.udel.edu), along with small RNA, RNA-seq and other data types.

A variational Bayes beta mixture model for feature selection in DNA methylation studies

An increasing number of studies are using beadarrays to measure DNA methylation on a genome-wide basis. The purpose is to identify novel biomarkers in a wide range of complex genetic diseases including cancer. A common difficulty encountered in these studies is distinguishing true biomarkers from false positives. While statistical methods aimed at improving the feature selection step have been developed for gene expression, relatively few methods have been adapted to DNA methylation data, which is naturally beta-distributed. Here we explore and propose an innovative application of a recently developed variational Bayesian beta-mixture model (VBBMM) to the feature selection problem in the context of DNA methylation data generated from a highly popular beadarray technology. We demonstrate that VBBMM offers significant improvements in inference and feature selection in this type of data compared to an Expectation-Maximization (EM) algorithm, at a significantly reduced computational cost. We further demonstrate the added value of VBBMM as a feature selection and prioritization step in the context of identifying prognostic markers in breast cancer. A variational Bayesian approach to feature selection of DNA methylation profiles should thus be of value to any study undergoing large-scale DNA methylation profiling in search of novel biomarkers.

Analysing and interpreting DNA methylation data

DNA methylation is an epigenetic mark that has suspected regulatory roles in a broad range of biological processes and diseases. The technology is now available for studying DNA methylation genome-wide, at a high resolution and in a large number of samples. This Review discusses relevant concepts, computational methods and software tools for analysing and interpreting DNA methylation data. It focuses not only on the bioinformatic challenges of large epigenome-mapping projects and epigenome-wide association studies but also highlights software tools that make genome-wide DNA methylation mapping more accessible for laboratories with limited bioinformatics experience.

A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data

Motivation: The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs.

Results: Here we propose a novel model-based intra-array normalization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a statistical distribution characteristic of type1 probes. The strategy involves application of a three-state beta-mixture model to assign probes to methylation states, subsequent transformation of probabilities into quantiles and finally a methylation-dependent dilation transformation to preserve the monotonicity and continuity of the data. We validate our method on cell-line data, fresh frozen and paraffin-embedded tumour tissue samples and demonstrate that BMIQ compares favourably with two competing methods. Specifically, we show that BMIQ improves the robustness of the normalization procedure, reduces the technical variation and bias of type2 probe values and successfully eliminates the type1 enrichment bias caused by the lower dynamic range of type2 probes. BMIQ will be useful as a preprocessing step for any study using the Illumina Infinium 450 k platform.

Availability: BMIQ is freely available from http://code.google.com/p/bmiq/.

Contact:a.teschendorff@ucl.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online