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.

Chromatin state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type specific activity patterns, and for interpreting disease-association studies1-5. However, the computational challenge of learning chromatin state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise making it inaccessible to the wider scientific community. To address this challenge, we have developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large-scale functional datasets, and visualizing the resulting genome-wide maps of chromatin state annotations.

ChromHMM: automating chromatin-state discovery and characterization

The laboratory mouse shares the majority of its protein-coding genes with humans, making it the premier model organism in biomedical research, yet the two mammals differ in significant ways. To gain greater insights into both shared and species-specific transcriptional and cellular regulatory programs in the mouse, the Mouse ENCODE Consortium has mapped transcription, DNase I hypersensitivity, transcription factor binding, chromatin modifications and replication domains throughout the mouse genome in diverse cell and tissue types. By comparing with the human genome, we not only confirm substantial conservation in the newly annotated potential functional sequences, but also find a large degree of divergence of sequences involved in transcriptional regulation, chromatin state and higher order chromatin organization. Our results illuminate the wide range of evolutionary forces acting on genes and their regulatory regions, and provide a general resource for research into mammalian biology and mechanisms of human diseases.

A comparative encyclopedia of DNA elements in the mouse genome

The proper activities of enhancers and gene promoters are essential for coordinated transcription within a cell. Although diverse methodologies have been developed to identify enhancers and promoters, most have tacitly assumed that these elements are distinct. However, studies have unexpectedly shown that regulatory elements may have both enhancer and promoter functions. Here we review these results, focusing on the factors that determine the promoter and/or enhancer activity of regulatory elements. We discuss emerging models that define regulatory elements by accessible DNA and their non-mutually-exclusive abilities to drive transcription initiation (promoter activity) and/or to enhance transcription at other such regions (enhancer activity).

Determinants of enhancer and promoter activities of regulatory elements.

Genomic analyses of cancer genomes have largely focused on mutations in protein-coding regions, but the functional importance of alterations to non-coding regions is becoming increasingly appreciated through whole-genome sequencing. This Review discusses our current understanding of non-coding sequence variants in cancer — both somatic mutations and germline variants, and their interplay — including their identification, computational and experimental evidence for functional impact, and their diverse mechanisms of action for dysregulating coding genes and non-coding RNAs.

Role of non-coding sequence variants in cancer

The histone modification state of genomic regions is hypothesized to reflect the regulatory activity of the underlying genomic DNA. Based on this hypothesis, the ENCODE Project Consortium measured the status of multiple histone modifications across the genome in several cell types and used these data to segment the genome into regions with different predicted regulatory activities. We measured the cis-regulatory activity of more than 2000 of these predictions in the K562 leukemia cell line. We tested genomic segments predicted to be Enhancers, Weak Enhancers, or Repressed elements in K562 cells, along with other sequences predicted to be Enhancers specific to the H1 human embryonic stem cell line (H1-hESC). Both Enhancer and Weak Enhancer sequences in K562 cells were more active than negative controls, although surprisingly, Weak Enhancer segmentations drove expression higher than did Enhancer segmentations. Lower levels of the covalent histone modifications H3K36me3 and H3K27ac, thought to mark active enhancers and transcribed gene bodies, associate with higher expression and partly explain the higher activity of Weak Enhancers over Enhancer predictions. While DNase I hypersensitivity (HS) is a good predictor of active sequences in our assay, transcription factor (TF) binding models need to be included in order to accurately identify highly expressed sequences. Overall, our results show that a significant fraction (-26%) of the ENCODE enhancer predictions have regulatory activity, suggesting that histone modification states can reflect the cis-regulatory activity of sequences in the genome, but that specific sequence preferences, such as TF-binding sites, are the causal determinants of cis-regulatory activity.

/pdf/high-throughput-functional-testing-of-encode-segmentation-28ps0srovw.pdf

High-throughput functional testing of ENCODE segmentation predictions

We investigated how interactions between pluripotency transcription factors (TFs) affect cis-regulation. We created hundreds of synthetic cis-regulatory elements (CREs) comprised of combinations of binding sites for pluripotency TFs and measured their expression in mouse embryonic stem (ES) cells. A thermodynamic model that incorporates interactions between TFs explains a large portion (72%) of the variance in expression of these CREs. These interactions include three favorable heterotypic interactions between TFs. The model also predicts an unfavorable homotypic interaction between TFs, helping to explain the observation that homotypic chains of binding sites express at low levels. We further investigated the expression driven by CREs comprised of homotypic chains of KLF4 binding sites. Our results suggest that KLF homologs make unique contributions to regulation by these CREs. We conclude that a specific set of interactions between pluripotency TFs plays a large role in setting the levels of expression driven by CREs in ES cells.

/pdf/interactions-between-pluripotency-factors-specify-cis-4xuyxtgbdd.pdf

Interactions between pluripotency factors specify cis-regulation in embryonic stem cells

The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genome-wide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.

https://www.pnas.org/content/pnas/113/47/E7428.full.pdf

Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

Cis-regulatory modules contain multiple transcription factor (TF)-binding sites and integrate the effects of each TF to control gene expression in specific cellular contexts. Transposable elements (TEs) are uniquely equipped to deposit their regulatory sequences across a genome, which could also contain cis-regulatory modules that coordinate the control of multiple genes with the same regulatory logic. We provide the first evidence of mouse-specific TEs that encode a module of TF-binding sites in mouse embryonic stem cells (ESCs). The majority (77%) of the individual TEs tested exhibited enhancer activity in mouse ESCs. By mutating individual TF-binding sites within the TE, we identified a module of TF-binding motifs that cooperatively enhanced gene expression. Interestingly, we also observed the same motif module in the in silico constructed ancestral TE that also acted cooperatively to enhance gene expression. Our results suggest that ancestral TE insertions might have brought in cis-regulatory modules into the mouse genome. The gene-battery model posits transposable elements (TEs) may becis-regulatory elements to control gene expression. Here, mouse-specific TEs are shown as binding sites for multiple collaborating transcription factors in embryonic stem cells, and act as cis-regulatory modules in synergistic fashion.

/pdf/functional-cis-regulatory-modules-encoded-by-mouse-specific-1s1ee19je1.pdf

Functional cis-regulatory modules encoded by mouse-specific endogenous retrovirus

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High-throughput functional testing of ENCODE segmentation predictions

Interactions between pluripotency factors specify cis-regulation in embryonic stem cells

Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

Functional cis-regulatory modules encoded by mouse-specific endogenous retrovirus