The interplay between chromatin, transcription factors and genes generates complex
regulatory circuits that can be represented as gene regulatory networks (GRNs). The study …

Understanding how chromatin is folded in the nucleus is fundamental to understanding its
function. Although 3D genome organization has been historically difficult to study owing to a …

As a data-driven science, genomics largely utilizes machine learning to capture
dependencies in data and derive novel biological hypotheses. However, the ability to extract …

Determining how chromosomes are positioned and folded within the nucleus is critical to
understanding the role of chromatin topology in gene regulation. Several methods are …

Predicting the impact of noncoding genetic variation requires interpreting it in the context of
three-dimensional genome architecture. We have developed deepC, a transfer-learning …

Enhancer RNA (eRNA) is a type of noncoding RNA transcribed from the enhancer. Although
critical roles of eRNA in gene transcription control have been increasingly realized, the …

Mapping chromatin loops from noisy Hi-C heatmaps remains a major challenge. Here we
present DeepLoop, which performs rigorous bias correction followed by deep-learning …

Higher-order genome organization and its variation in different cellular conditions remain
poorly understood. Recent high-coverage genome-wide chromatin interaction mapping …

To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations
have been actively conducted for a long time, and a large number of achievements have …

Interactions between regulatory elements are of crucial importance for the understanding of
transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has …