AIMC Topic: Transcription Factors

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Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.

Nucleic acids research Jan 11, 2021
Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise ...
Humans Deep Learning Genome-Wide Association Study Organ Specificity Molecular Sequence Annotation Binding Sites Linkage Disequilibrium Genetic Diseases, Inborn Chromatin Immunoprecipitation Histone Code Transcription Factors Causality Epigenome Datasets as Topic Polymorphism, Single Nucleotide Epigenomics Models, Genetic
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iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.

Nucleic acids research Jan 8, 2021
Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their ass...
Transcriptome Gene Regulatory Networks Transcription Factors Escherichia coli Machine Learning Software Gene Expression Profiling Internet Knowledge Bases Gene Expression Regulation, Bacterial Transcription, Genetic Staphylococcus aureus Bacillus subtilis Databases, Genetic Bacterial Proteins
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Prediction of condition-specific regulatory genes using machine learning.

Nucleic acids research Jun 19, 2020
Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has bec...
Datasets as Topic Stress, Physiological Promoter Regions, Genetic Gene Expression Regulation, Plant RNA-Seq Gene Regulatory Networks Transcription Factors Genes, Regulator Genes, Plant Arabidopsis Proteins Arabidopsis Machine Learning Gene Expression Profiling Deep Learning Single-Cell Analysis
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Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.

Nucleic acids research Sep 5, 2019
The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-re...
K562 Cells Binding Sites Nucleotide Motifs Computational Biology Transcription Factors DNA Gene Expression Regulation Humans Deep Learning Protein Binding
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AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

Nucleic acids research Jun 4, 2019
ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'targ...
Protein Binding Computational Biology Transcription Factors Sequence Analysis, DNA High-Throughput Nucleotide Sequencing Binding Sites Chromatin Immunoprecipitation Humans Machine Learning Algorithms Reproducibility of Results
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Discovering epistatic feature interactions from neural network models of regulatory DNA sequences.

Bioinformatics (Oxford, England) Sep 1, 2018
MOTIVATION: Transcription factors bind regulatory DNA sequences in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn the cis-regulatory grammars encoded in regulatory DNA sequences associated with transcription...
Neural Networks, Computer Protein Binding Genomics Transcription Factors Sequence Analysis, DNA DNA High-Throughput Nucleotide Sequencing Binding Sites Chromatin
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DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants.

Nucleic acids research Jun 20, 2018
The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifying variants that disrupt TF binding and lead to human diseases remains a great challenge. To address ...
Regulatory Sequences, Nucleic Acid DNA-Binding Proteins Gene Expression Regulation Binding Sites Regulatory Elements, Transcriptional Computational Biology Transcription Factors DNA Humans Neural Networks, Computer Algorithms
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DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding.

Bioinformatics (Oxford, England) Oct 1, 2017
MOTIVATION: Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of ...
Support Vector Machine Sequence Analysis, DNA DNA DNA-Binding Proteins Protein Binding Genomics Transcription Factors Nucleotide Motifs Nucleic Acid Conformation Protein Array Analysis Binding Sites Software
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FARNA: knowledgebase of inferred functions of non-coding RNA transcripts.

Nucleic acids research Mar 17, 2017
Non-coding RNA (ncRNA) genes play a major role in control of heterogeneous cellular behavior. Yet, their functions are largely uncharacterized. Current available databases lack in-depth information of ncRNA functions across spectrum of various cells/...
Humans Transcription Factors MicroRNAs RNA, Long Noncoding Knowledge Bases Databases, Nucleic Acid
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First steps in automatic summarization of transcription factor properties for RegulonDB: classification of sentences about structural domains and regulated processes.

Database : the journal of biological databases and curation Jan 1, 2017
RegulonDB, http://regulondb.ccg.unam.mx.
Serine Endopeptidases Escherichia coli K12 Databases, Bibliographic Regulon Transcription Factors Support Vector Machine Protein Domains
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