AIMC Topic: Regulatory Sequences, Nucleic Acid

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Deep learning-based enhancement of epigenomics data with AtacWorks.

Nature communications Mar 8, 2021
ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks...
Brain Animals Deep Learning Humans Regulatory Sequences, Nucleic Acid Chromatin Chromatin Immunoprecipitation Sequencing Mice Epigenomics Leukocytes
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Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.

Nature communications Dec 1, 2020
Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression leve...
Drosophila melanogaster Regulatory Sequences, Nucleic Acid Bacteria RNA, Messenger Evolution, Molecular Gene Expression Regulation Base Sequence Saccharomyces cerevisiae Animals Deep Learning Mice Humans
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Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network.

BMC bioinformatics Nov 7, 2020
BACKGROUND: Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory w...
Cell Line Area Under Curve Promoter Regions, Genetic Humans Neural Networks, Computer ROC Curve Regulatory Sequences, Nucleic Acid
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Deep learning of immune cell differentiation.

Proceedings of the National Academy of Sciences of the United States of America Sep 25, 2020
Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular a...
Hematopoiesis Regulatory Sequences, Nucleic Acid Chromatin Transcription Factors Databases, Genetic Gene Expression Regulation Humans Animals Deep Learning Mice
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Frequency spectra characterization of noncoding human genomic sequences.

Genes & genomics Aug 31, 2020
BACKGROUND: Noncoding sequences have been demonstrated to possess regulatory functions. Its classification is challenging because they do not show well-defined nucleotide patterns that can correlate with their biological functions. Genomic signal pro...
Genome, Human Regulatory Sequences, Nucleic Acid Algorithms Genomics Nucleotides Humans Machine Learning
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Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information.

PloS one Jul 23, 2020
With advances in sequencing technology, a vast amount of genomic sequence information has become available. However, annotating biological functions particularly of non-protein-coding regions in genome sequences without experiments is still a challen...
Genomics Sequence Analysis, DNA DNA Genome Regulatory Sequences, Nucleic Acid Software Animals Humans Mice Neural Networks, Computer
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Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Nature communications Jul 15, 2020
Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such d...
Phenotype Molecular Sequence Annotation Binding Sites Sequence Analysis, DNA Gene Knockout Techniques Genome, Bacterial High-Throughput Nucleotide Sequencing Ribosomes Datasets as Topic Deep Learning Regulatory Sequences, Nucleic Acid Computational Biology Escherichia coli
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AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU.

BMC bioinformatics Oct 7, 2019
BACKGROUND: The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the b...
Humans Cell Line Chromosome Mapping Software Regulatory Sequences, Nucleic Acid Deep Learning Epigenomics
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Deep learning for DNase I hypersensitive sites identification.

BMC genomics Dec 31, 2018
BACKGROUND: The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources availab...
Regulatory Sequences, Nucleic Acid Deoxyribonuclease I Humans Models, Biological Deep Learning Arabidopsis Computational Biology Oryza
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Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

BMC bioinformatics May 31, 2018
BACKGROUND: In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. T...
Gene Expression Regulation Humans Regulatory Sequences, Nucleic Acid Machine Learning Neural Networks, Computer Deep Learning Genomics
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