Published in Science, this paper describes PromoterAI, a new tool from the team at Illumina Artificial Intelligence Laboratory. It’s a deep learning algorithm that identifies potential disease-causing variants within “promoter” sequences in the human genome. This algorithm will empower clinical researchers to better understand the causes of rare genetic diseases and cancer, and drive the discovery of novel therapeutic targets in biobank-scale cohorts.

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In this study published in Nature, researchers in Spain and Germany used targeted single-cell methylome sequencing analysis to map DNA methylation at CpG sites across the genome in hematopoietic cells in mice and primary human bone marrow samples. Their results identified stochastic DNA methylation changes that provide insights enabling identification of age-related expansions in clonal cell populations, indicating the need for further investigation of age-related decline in clonality.

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Research teams from UK and Spain collaborated to develop chromosomal instability (CIN) signatures which could potentially be used to guide therapy selection of chemotherapy. Using these CIN signatures, tumors with resistance to these cytotoxic agents could be identified allowing for selection of an alternative therapy. In this paper, the authors show feasibility of using CIN signatures as biomarkers on targeted-capture gene panel sequencing of tumor combined with shallow whole-genome sequencing (sWGS) of plasma showing promise to advance precision use of cytotoxics and improve overall health outcomes.

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