An interplay of experimental and computational methods is required to achieve a
comprehensive understanding of protein–RNA interactions. UV crosslinking and …

Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with
diseases and identification of disease-related lncRNAs is helpful for elucidating complex …

Deep learning has become a powerful paradigm to analyze the binding sites of regulatory
factors including RNA-binding proteins (RBPs), owing to its strength to learn complex …

Highlights•RBPs display a wide variety of modular or gapped sequence and structure
preferences.•Classical motif identification methods use two layers, motif and occupancy …

Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a
variety of tasks, including biological sequence classification. Available implementations …

Methods for the systematic study of subcellular RNA localization are limited, and their
development has lagged behind that of proteomic tools. We combined APEX2-mediated …

Spatial organization and gene expression of mammalian chromosomes are maintained and
regulated in conjunction with cell cycle progression. This is perturbed once cells enter …

Background Non-small-cell lung cancer (NSCLC) remains a highly prevalent and deadly
form of cancer, with efforts to better understand the molecular basis of the progression of this …

We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count
distribution from RNA sequence at single-nucleotide resolution. By training on up to a million …

The TFIID component, TAF7, has been extensively characterized as essential for
transcription and is critical for cell proliferation and differentiation. Here, we report that TAF7 …