AIMC Topic: Nucleotide Motifs

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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...
Humans Deep Learning Protein Binding Computational Biology Transcription Factors DNA Gene Expression Regulation K562 Cells Binding Sites Nucleotide Motifs
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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 ...
Software Protein Binding Genomics Transcription Factors Support Vector Machine Sequence Analysis, DNA DNA DNA-Binding Proteins Nucleic Acid Conformation Protein Array Analysis Binding Sites Nucleotide Motifs
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A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data.

Nucleic acids research Aug 21, 2017
Characterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-s...
Humans Machine Learning Algorithms Reproducibility of Results Protein Binding Computational Biology RNA-Binding Proteins Mutation Internet High-Throughput Nucleotide Sequencing Gene Expression Regulation Base Sequence Binding Sites Nucleotide Motifs Binding, Competitive 3' Untranslated Regions
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