MOTIVATION: Modern single-cell omics data are key to unraveling the complex mechanisms underlying risk for complex diseases revealed by genome-wide association studies (GWAS). Phenotypic screens in model organisms have several important parallels to GWAS which the author explores in this essay. RESULTS: The author provides the historical context of such screens, comparing and contrasting similarities to association studies, and how these screens in model organisms can teach us what to look for. Then the author considers how the results of GWAS might be exhaustively interrogated to interpret the biological mechanisms underpinning disease processes. Finally, the author proposes a general framework for tackling this problem computationally, and explore the data, mechanisms, and technology (both existing and yet to be invented) that are necessary to complete the task. AVAILABILITY AND IMPLEMENTATION: There are no data or code associated with this article.

MOTIVATION: DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set. RESULTS: We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer. AVAILABILITY AND IMPLEMENTATION: ELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Functional annotation represents a key step toward the understanding and interpretation of germline and somatic variation as revealed by genome-wide association studies (GWAS) and The Cancer Genome Atlas (TCGA), respectively. GWAS have revealed numerous genetic risk variants residing in non-coding DNA associated with complex diseases. For sequences that lie within enhancers or promoters of transcription, it is not straightforward to assess the effects of variants on likely transcription factor binding sites. Consequently we introduce motifbreakR, which allows the biologist to judge whether the sequence surrounding a polymorphism or mutation is a good match, and how much information is gained or lost in one allele of the polymorphism or mutation relative to the other. MotifbreakR is flexible, giving a choice of algorithms for interrogation of genomes with motifs from many public sources that users can choose from. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within bioconductor. AVAILABILITY AND IMPLEMENTATION: https://github.com/Simon-Coetzee/MotifBreakR, www.bioconductor.org. CONTACT: [email protected].

METHODS: A new method was developed for identifying novel transcription factor regulatory targets based on calculating Local Affinity Density. Techniques from the signal-processing field were used, in particular the Hann digital filter, to calculate the relative binding affinity of different regions based on previously published in vitro binding data. To illustrate this approach, the complete genomes of Drosophila melanogaster and D.pseudoobscura were analyzed for binding sites of the homeodomain proteinc Tinman, an essential heart development gene in both Drosophila and Mouse. The significant binding regions were identified relative to genomic background and assigned to putative target genes. Valid candidates common to both species of Drosophila were selected as a test of conservation. RESULTS: The new method was more sensitive than cluster searches for conserved binding motifs with respect to positive identification of known Tinman targets. Our Local Affinity Density method also identified a significantly greater proportion of Tinman-coexpressed genes than equivalent, optimized cluster searching. In addition, this new method predicted a significantly greater than expected number of genes with previously published RNAi phenotypes in the heart. AVAILABILITY: Algorithms were implemented in Python, LISP, R and maxima, using MySQL to access locally mirrored sequence data from Ensembl (D.melanogaster release 4.3) and flybase (D.pseudoobscura). All code is licensed under GPL and freely available at http://www.ohsu.edu/cellbio/dev_biol_prog/affinitydensity/.