Deciphering the genomic regulatory code of enhancers is a key challenge in biology as this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of noncoding genome variation, and empower the generation of cell type-specific drivers for gene therapy. Here we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models. We apply this strategy to decipher the enhancer code in melanoma, a relevant case study due to the presence of distinct melanoma cell states. We trained and validated a deep learning model, called DeepMEL, using chromatin accessibility data of 26 melanoma samples across six different species.

IMAGE: MouseAge.Org provides tools for cross-species analysis, and provide correlations between health and appearance. Tuesday, 29th of August, 2017, Baltimore, MD - Insilico Medicine, Inc, a Baltimore-based next-generation artificial intelligence company, today announced its participation in the MouseAge.org The scientists from Insilico Medicine will collaborate with scientists from Harvard, Oxford, Youth Laboratories, the Biogerontology Research Foundation, and other institutions to enable scientists worldwide to derive more information from rodent studies, develop novel biomarkers of aging and various diseases in mice, develop tools for cross-species analysis, and provide correlations between health and appearance. The project campaign has been launched today at research crowdfunding platform Lifespan.io. The project was conceived by Vadim Gladyshev, Professor of Medicine at Brigham and Women's Hospital, Harvard Medical School, and Alex Zhavoronkov, CEO of Insilico Medicine.

Translating potential disease biomarkers between multi-species 'omics' experiments is a new direction in biomedical research. The existing methods are limited to simple experimental setups such as basic healthy-diseased comparisons. Most of these methods also require an a priori matching of the variables (e.g., genes or metabolites) between the species. However, many experiments have a complicated multi-way experimental design often involving irregularly-sampled time-series measurements, and for instance metabolites do not always have known matchings between organisms. We introduce a Bayesian modelling framework for translating between multiple species the results from 'omics' experiments having a complex multi-way, time-series experimental design. The underlying assumption is that the unknown matching can be inferred from the response of the variables to multiple covariates including time.