New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

Epigenetic association studies have been carried out to test the hypothesis that environmental perturbations trigger cellular reprogramming, with downstream effects on cellular function and phenotypes. There have now been numerous studies of the potential molecular mediators of epigenetic changes by epigenome-wide association studies (EWAS). However, a challenge for the field is the interpretation of the results obtained. We describe a second-generation EWAS approach, which focuses on the possible cellular models of epigenetic perturbations, studied by rigorous analysis and interpretation of genomic data. Thus refocused, epigenetics research aligns with the field of functional genomics to provide insights into environmental and genetic influences on phenotypic variation in humans.

Associating cellular epigenetic models with human phenotypes

We developed Lisa (http://lisa.cistrome.org/) to predict the transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on the input gene sets, Lisa first uses histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosted the performance of imputed TR cistromes and outperformed alternative methods in identifying the perturbed TRs.

/pdf/lisa-inferring-transcriptional-regulators-through-262nhrod26.pdf

Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.

In genome editing with CRISPR–Cas9, transgene integration often remains challenging. Here, we present an approach for increasing the efficiency of transgene integration by homology-dependent repair (HDR). CtIP, a key protein in early steps of homologous recombination, is fused to Cas9 and stimulates transgene integration by HDR at the human AAVS1 safe harbor locus. A minimal N-terminal fragment of CtIP, designated HE for HDR enhancer, is sufficient to stimulate HDR and this depends on CDK phosphorylation sites and the multimerization domain essential for CtIP activity in homologous recombination. HDR stimulation by Cas9–HE, however, depends on the guide RNA used, a limitation that may be overcome by testing multiple guides to the locus of interest. The Cas9–HE fusion is simple to use and allows obtaining twofold or more efficient transgene integration than that with Cas9 in several experimental systems, including human cell lines, iPS cells, and rat zygotes.

/pdf/ctip-fusion-to-cas9-enhances-transgene-integration-by-2aus32dqpm.pdf

CtIP fusion to Cas9 enhances transgene integration by homology-dependent repair.

Chromatin immunoprecipitation followed by next-generation DNA sequencing (ChIP-seq) is a widely used technique for identifying transcription factor (TF) binding events throughout an entire genome. However, ChIP-seq is limited by the availability of suitable ChIP-seq grade antibodies, and the vast majority of commercially available antibodies fail to generate usable data sets. To ameliorate these technical obstacles, we present a robust methodological approach for performing ChIP-seq through epitope tagging of endogenous TFs. We used clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-based genome editing technology to develop CRISPR epitope tagging ChIP-seq (CETCh-seq) of DNA-binding proteins. We assessed the feasibility of CETCh-seq by tagging several DNA-binding proteins spanning a wide range of endogenous expression levels in the hepatocellular carcinoma cell line HepG2. Our data exhibit strong correlations between both replicate types as well as with standard ChIP-seq approaches that use TF antibodies. Notably, we also observed minimal changes to the cellular transcriptome and to the expression of the tagged TF. To examine the robustness of our technique, we further performed CETCh-seq in the breast adenocarcinoma cell line MCF7 as well as mouse embryonic stem cells and observed similarly high correlations. Collectively, these data highlight the applicability of CETCh-seq to accurately define the genome-wide binding profiles of DNA-binding proteins, allowing for a straightforward methodology to potentially assay the complete repertoire of TFs, including the large fraction for which ChIP-quality antibodies are not available.

/pdf/cetch-seq-crispr-epitope-tagging-chip-seq-of-dna-binding-6yjhgvh2nk.pdf

CETCh-seq: CRISPR epitope tagging ChIP-seq of DNA-binding proteins

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CETCh-seq: CRISPR epitope tagging ChIP-seq of DNA-binding proteins