ALBERT

Description: Motivation: There is a growing number of studies generating matched Illumina Infinium HumanMethylation450 and gene expression data, yet there is a corresponding shortage of statistical tools aimed at their integrative analysis. Such integrative tools are important for the discovery of epigenetically regulated gene modules or molecular pathways, which play key roles in cellular differentiation and disease. Results: Here, we present a novel functional supervised algorithm, called Functional Epigenetic Modules (FEM), for the integrative analysis of Infinium 450k DNA methylation and matched or unmatched gene expression data. The algorithm identifies gene modules of coordinated differential methylation and differential expression in the context of a human interactome. We validate the FEM algorithm on simulated and real data, demonstrating how it successfully retrieves an epigenetically deregulated gene, previously known to drive endometrial cancer development. Importantly, in the same cancer, FEM identified a novel epigenetically deregulated hotspot, directly upstream of the well-known progesterone receptor tumour suppressor pathway. In the context of cellular differentiation, FEM successfully identifies known endothelial cell subtype-specific gene expression markers, as well as a novel gene module whose overexpression in blood endothelial cells is mediated by DNA hypomethylation. The systems-level integrative framework presented here could be used to identify novel key genes or signalling pathways, which drive cellular differentiation or disease through an underlying epigenetic mechanism. Availability and implementation: FEM is freely available as an R-package from http://sourceforge.net/projects/funepimod . Contact: andrew@picb.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The standard paradigm in omic disciplines has been to identify biologically relevant biomarkers using statistics that reflect differences in mean levels of a molecular quantity such as mRNA expression or DNA methylation. Recently, however, it has been proposed that differential epigenetic variability may mark genes that contribute to the risk of complex genetic diseases like cancer and that identification of risk and early detection markers may therefore benefit from statistics based on differential variability. Results: Using four genome-wide DNA methylation datasets totalling 311 epithelial samples and encompassing all stages of cervical carcinogenesis, we here formally demonstrate that differential variability, as a criterion for selecting DNA methylation features, can identify cancer risk markers more reliably than statistics based on differences in mean methylation. We show that differential variability selects features with heterogeneous outlier methylation profiles and that these play a key role in the early stages of carcinogenesis. Moreover, differentially variable features identified in precursor non-invasive lesions exhibit significantly increased enrichment for developmental genes compared with differentially methylated sites. Conversely, differential variability does not add predictive value in cancer studies profiling invasive tumours or whole-blood tissue. Finally, we incorporate the differential variability feature selection step into a novel adaptive index prediction algorithm called EVORA (epigenetic variable outliers for risk prediction analysis), and demonstrate that EVORA compares favourably to powerful prediction algorithms based on differential methylation statistics. Conclusions: Statistics based on differential variability improve the detection of cancer risk markers in the context of DNA methylation studies profiling epithelial preinvasive neoplasias. We present a novel algorithm (EVORA) which could be used for prediction and diagnosis of precursor epithelial cancer lesions. Availability: R-scripts implementing EVORA are available from CRAN ( www.r-project.org ). Contact: a.teschendorff@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: There is a growing perception that long non-coding RNAs (lncRNAs) modulate cellular function. In this study, we analyzed the role of the lncRNA HOTAIR in mesenchymal stem cells (MSCs) with particular focus on senescence-associated changes in gene expression and DNA-methylation (DNAm). HOTAIR binding sites were enriched at genomic regions that become hypermethylated with increasing cell culture passage. Overexpression and knockdown of HOTAIR inhibited or stimulated adipogenic differentiation of MSCs, respectively. Modification of HOTAIR expression evoked only very moderate effects on gene expression, particularly of polycomb group target genes. Furthermore, overexpression and knockdown of HOTAIR resulted in DNAm changes at HOTAIR binding sites. Five potential triple helix forming domains were predicted within the HOTAIR sequence based on reverse Hoogsteen hydrogen bonds. Notably, the predicted triple helix target sites for these HOTAIR domains were also enriched in differentially expressed genes and close to DNAm changes upon modulation of HOTAIR . Electrophoretic mobility shift assays provided further evidence that HOTAIR domains form RNA–DNA–DNA triplexes with predicted target sites. Our results demonstrate that HOTAIR impacts on differentiation of MSCs and that it is associated with senescence-associated DNAm. Targeting of epigenetic modifiers to relevant loci in the genome may involve triple helix formation with HOTAIR .