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Chromatin

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Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.

Nature methods
Charting an organs' biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehe...

Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging.

Methods in molecular biology (Clifton, N.J.)
Chromatin is highly structured, and changes in its organization are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluor...

InsuLock: A Weakly Supervised Learning Approach for Accurate Insulator Prediction, and Variant Impact Quantification.

Genes
Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, ...

DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.

Genomics, proteomics & bioinformatics
Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcript...

Chromatin interaction-aware gene regulatory modeling with graph attention networks.

Genome research
Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting noncoding genetic variation. Here, we present a new deep learning approach...

DNAcycP: a deep learning tool for DNA cyclizability prediction.

Nucleic acids research
DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simult...

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...

Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization.

Journal of molecular biology
In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in whole-genome mapping technologies have revealed the multiscale features of 3D genome organization that are intertwined with fundamental genome functions. Howeve...

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...