The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0387

Opportunities and obstacles for deep learning in biology and medicine.

The accurate transition from G1 phase of the cell cycle to S phase is crucial for the control of eukaryotic cell proliferation, and its misregulation promotes oncogenesis. During G1 phase, growth-dependent cyclin-dependent kinase (CDK) activity promotes DNA replication and initiates G1-to-S phase transition. CDK activation initiates a positive feedback loop that further increases CDK activity, and this commits the cell to division by inducing genome-wide transcriptional changes. G1-S transcripts encode proteins that regulate downstream cell cycle events. Recent work is beginning to reveal the complex molecular mechanisms that control the temporal order of transcriptional activation and inactivation, determine distinct functional subgroups of genes and link cell cycle-dependent transcription to DNA replication stress in yeast and mammals.

/pdf/control-of-cell-cycle-transcription-during-g1-and-s-phases-2rr6auuwtx.pdf

Control of cell cycle transcription during G1 and S phases

The pluripotent state of embryonic stem cells (ESCs) is produced by active transcription of genes that control cell identity and repression of genes encoding lineage-specifying developmental regulators. Here, we use ESC cohesin ChIA-PET data to identify the local chromosomal structures at both active and repressed genes across the genome. The results produce a map of enhancer-promoter interactions and reveal that super-enhancer-driven genes generally occur within chromosome structures that are formed by the looping of two interacting CTCF sites co-occupied by cohesin. These looped structures form insulated neighborhoods whose integrity is important for proper expression of local genes. We also find that repressed genes encoding lineage-specifying developmental regulators occur within insulated neighborhoods. These results provide insights into the relationship between transcriptional control of cell identity genes and control of local chromosome structure.

Control of Cell Identity Genes Occurs in Insulated Neighborhoods in Mammalian Chromosomes

The observation that many, if not all, functional enhancers generate non-coding enhancer RNAs (eRNAs) has raised critical questions regarding the potential biological roles of the enhancer transcription process and, indeed, of eRNAs. This article reviews fundamental insights into the inter-regulation of enhancers and promoters and discusses unresolved questions regarding the functional role of enhancers as transcription units in genome regulation.

Enhancers as non-coding RNA transcription units: recent insights and future perspectives

Native elongating transcript sequencing reveals human transcriptional activity at nucleotide resolution.

https://www.cell.com/molecular-cell/pdf/S1097-2765(11)00526-0.pdf

Commitment to a cellular transition precedes genome-wide transcriptional change

Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.

/pdf/an-algorithm-to-automate-yeast-segmentation-and-tracking-5b7sr1g3d5.pdf

An Algorithm to Automate Yeast Segmentation and Tracking

The genome of metazoan cells is organized into topologically associating domains (TADs) that have similar histone modifications, transcription level, and DNA replication timing. Although similar structures appear to be conserved in fission yeast, computational modeling and analysis of high-throughput chromosome conformation capture (Hi-C) data have been used to argue that the small, highly constrained budding yeast chromosomes could not have these structures. In contrast, herein we analyze Hi-C data for budding yeast and identify 200-kb scale TADs, whose boundaries are enriched for transcriptional activity. Furthermore, these boundaries separate regions of similarly timed replication origins connecting the long-known effect of genomic context on replication timing to genome architecture. To investigate the molecular basis of TAD formation, we performed Hi-C experiments on cells depleted for the Forkhead transcription factors, Fkh1 and Fkh2, previously associated with replication timing. Forkhead factors do not regulate TAD formation, but do promote longer-range genomic interactions and control interactions between origins near the centromere. Thus, our work defines spatial organization within the budding yeast nucleus, demonstrates the conserved role of genome architecture in regulating DNA replication, and identifies a molecular mechanism specifically regulating interactions between pericentric origins.

https://www.pnas.org/content/pnas/114/15/E3061.full.pdf

Umut Eser

Papers

Native elongating transcript sequencing reveals human transcriptional activity at nucleotide resolution.

Commitment to a cellular transition precedes genome-wide transcriptional change

An Algorithm to Automate Yeast Segmentation and Tracking

Form and function of topologically associating genomic domains in budding yeast

The Ground State and Evolution of Promoter Region Directionality.