Neural-signature methods for structured EHR prediction.

Models that can effectively represent structured Electronic Healthcare Records (EHR) are central to an increasing range of applications in healthcare. Due to the sequential nature of health data, Recurrent Neural Networks have emerged as the dominant component within state-of-the-art architectures. The signature transform represents an alternative modelling paradigm for sequential data. This transform provides a non-learnt approach to creating a fixed vector representation of temporal features and has shown strong performances across an increasing number of domains, including medical data. However, the signature method has not yet been applied to structured EHR data. To this end, we follow recent work that enables the signature to be used as a differentiable layer within a neural architecture enabling application in high dimensional domains where calculation would have previously been intractable. Using a heart failure prediction task as an exemplar, we provide an empirical evaluation of different variations of the signature method and compare against state-of-the-art baselines. This first application of neural-signature methods in real-world healthcare data shows a competitive performance when compared to strong baselines and thus warrants further investigation within the health domain.

OGT prevents DNA demethylation and suppresses the expression of transposable elements in heterochromatin by restraining TET activity genome-wide.

The O- GlcNAc transferase OGT interacts robustly with all three mammalian TET methylcytosine dioxygenases. We show here that deletion of the Ogt gene in mouse embryonic stem cells (mESC) results in a widespread increase in the TET product 5-hydroxymethylcytosine (5hmC) in both euchromatic and heterochromatic compartments, with concomitant reduction of the TET substrate 5-methylcytosine (5mC) at the same genomic regions. mESC engineered to abolish the TET1-OGT interaction likewise displayed a genome-wide decrease of 5mC. DNA hypomethylation in OGT-deficient cells was accompanied by de-repression of transposable elements (TEs) predominantly located in heterochromatin, and this increase in TE expression was sometimes accompanied by increased cis -expression of genes and exons located 3' of the expressed TE. Thus, the TET-OGT interaction prevents DNA demethylation and TE expression in heterochromatin by restraining TET activity genome-wide. We suggest that OGT protects the genome against DNA hypomethylation and impaired heterochromatin integrity, preventing the aberrant increase in TE expression observed in cancer, autoimmune-inflammatory diseases, cellular senescence and ageing.

Simultaneous sequencing of genetic and epigenetic bases in DNA.

DNA comprises molecular information stored in genetic and epigenetic bases, both of which are vital to our understanding of biology. Most DNA sequencing approaches address either genetics or epigenetics and thus capture incomplete information. Methods widely used to detect epigenetic DNA bases fail to capture common C-to-T mutations or distinguish 5-methylcytosine from 5-hydroxymethylcytosine. We present a single base-resolution sequencing methodology that sequences complete genetics and the two most common cytosine modifications in a single workflow. DNA is copied and bases are enzymatically converted. Coupled decoding of bases across the original and copy strand provides a phased digital readout. Methods are demonstrated on human genomic DNA and cell-free DNA from a blood sample of a patient with cancer. The approach is accurate, requires low DNA input and has a simple workflow and analysis pipeline. Simultaneous, phased reading of genetic and epigenetic bases provides a more complete picture of the information stored in genomes and has applications throughout biomedicine.