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Regulatory Sequences, Nucleic Acid

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Deciphering the regulatory syntax of genomic DNA with deep learning.

Journal of biosciences
An organism's genome contains many sequence regions that perform diverse functions. Examples of such regions include genes, promoters, enhancers, and binding sites for regulatory proteins and RNAs. One of biology's most important open problems is how...

EPI-Mind: Identifying Enhancer-Promoter Interactions Based on Transformer Mechanism.

Interdisciplinary sciences, computational life sciences
MOTIVATION: Enhancer-Promoter Interactions (EPIs) is an essential step in the gene regulation process. However, the detection of EPIs by traditional wet experimental techniques is time-consuming and expensive. Thus, computational methods would be ver...

Deep learning modeling mA deposition reveals the importance of downstream cis-element sequences.

Nature communications
The N-methyladenosine (mA) modification is deposited to nascent transcripts on chromatin, but its site-specificity mechanism is mostly unknown. Here we model the mA deposition to pre-mRNA by iM6A (intelligent mA), a deep learning method, demonstratin...

LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome.

Mathematical biosciences and engineering : MBE
DNase I hypersensitive sites (DHSs) are a specific genomic region, which is critical to detect or understand cis-regulatory elements. Although there are many methods developed to detect DHSs, there is a big gap in practice. We presented a deep learni...

Deep learning-assisted genome-wide characterization of massively parallel reporter assays.

Nucleic acids research
Massively parallel reporter assay (MPRA) is a high-throughput method that enables the study of the regulatory activities of tens of thousands of DNA oligonucleotides in a single experiment. While MPRA experiments have grown in popularity, their small...

Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques.

BMC bioinformatics
BACKGROUND: Cis-regulatory regions (CRRs) are non-coding regions of the DNA that fine control the spatio-temporal pattern of transcription; they are involved in a wide range of pivotal processes such as the development of specific cell-lines/tissues ...

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...

NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding the mechanisms of gene expression regulation. Although deep convolutional neural networks (CNNs) have achieved great success in predicting cis-regulator...

LegNet: a best-in-class deep learning model for short DNA regulatory regions.

Bioinformatics (Oxford, England)
MOTIVATION: The increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep-learning approaches for modeling DNA regulatory grammar.