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Chromatin

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Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in i...

DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences.

Briefings in bioinformatics
Identifying the function of DNA sequences accurately is an essential and challenging task in the genomic field. Until now, deep learning has been widely used in the functional analysis of DNA sequences, including DeepSEA, DanQ, DeepATT and TBiNet. Ho...

AgeAnno: a knowledgebase of single-cell annotation of aging in human.

Nucleic acids research
Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell le...

DeepZ: A Deep Learning Approach for Z-DNA Prediction.

Methods in molecular biology (Clifton, N.J.)
Here we describe an approach that uses deep learning neural networks such as CNN and RNN to aggregate information from DNA sequence; physical, chemical, and structural properties of nucleotides; and omics data on histone modifications, methylation, c...

Deep Learning on Chromatin Accessibility.

Methods in molecular biology (Clifton, N.J.)
DNA accessibility has been a powerful tool in locating active regulatory elements in a cell type, but dissecting the combinatorial logic within these regulatory elements has been a continued challenge in the field. Deep learning models have been show...

DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach.

Bioinformatics (Oxford, England)
MOTIVATION: Promoter-centered chromatin interactions, which include promoter-enhancer (PE) and promoter-promoter (PP) interactions, are important to decipher gene regulation and disease mechanisms. The development of next-generation sequencing techno...

Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

Nucleic acids research
Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Inter...

CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

Bioinformatics (Oxford, England)
MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure of cells. Although variou...

DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator.

Bioinformatics (Oxford, England)
MOTIVATION: The importance of chromatin loops in gene regulation is broadly accepted. There are mainly two approaches to predict chromatin loops: transcription factor (TF) binding-dependent approach and genomic variation-based approach. However, neit...

seqgra: principled selection of neural network architectures for genomics prediction tasks.

Bioinformatics (Oxford, England)
MOTIVATION: Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their con...