DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
We present a physics-based machine learning approach to predict in vitro transcription
factor binding affinities from structural and mechanical DNA properties directly derived from …
factor binding affinities from structural and mechanical DNA properties directly derived from …
Expanding the repertoire of DNA shape features for genome-scale studies of transcription factor binding
Uncovering the mechanisms that affect the binding specificity of transcription factors (TFs) is
critical for understanding the principles of gene regulation. Although sequence-based …
critical for understanding the principles of gene regulation. Although sequence-based …
TFBSshape: an expanded motif database for DNA shape features of transcription factor binding sites
Abstract TFBSshape (https://tfbsshape. usc. edu) is a motif database for analyzing structural
profiles of transcription factor binding sites (TFBSs). The main rationale for this database is …
profiles of transcription factor binding sites (TFBSs). The main rationale for this database is …
A unified approach for quantifying and interpreting DNA shape readout by transcription factors
Transcription factors (TF s) interpret DNA sequence by probing the chemical and structural
properties of the nucleotide polymer. DNA shape is thought to enable a parsimonious …
properties of the nucleotide polymer. DNA shape is thought to enable a parsimonious …
You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems
Visual query systems (VQSs) empower users to interactively search for line charts with
desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite …
desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite …
A comprehensive review of computational prediction of genome-wide features
There are significant correlations among different types of genetic, genomic and epigenomic
features within the genome. These correlations make the in silico feature prediction possible …
features within the genome. These correlations make the in silico feature prediction possible …
Predicting double-strand DNA breaks using epigenome marks or DNA at kilobase resolution
Double-strand breaks (DSBs) result from the attack of both DNA strands by multiple sources,
including radiation and chemicals. DSBs can cause the abnormal chromosomal …
including radiation and chemicals. DSBs can cause the abnormal chromosomal …
ThreaDNA: predicting DNA mechanics' contribution to sequence selectivity of proteins along whole genomes
J Cevost, C Vaillant, S Meyer - Bioinformatics, 2018 - academic.oup.com
Motivation Many DNA-binding proteins recognize their target sequences indirectly, by
sensing DNA's response to mechanical distortion. ThreaDNA estimates this response based …
sensing DNA's response to mechanical distortion. ThreaDNA estimates this response based …
[HTML][HTML] Quantitative measurement and thermodynamic modeling of fused enhancers support a two-tiered mechanism for interpreting regulatory DNA
MAH Samee, T Lydiard-Martin, KM Biette, BJ Vincent… - Cell reports, 2017 - cell.com
Computational models of enhancer function generally assume that transcription factors (TFs)
exert their regulatory effects independently, modeling an enhancer as a" bag of sites." These …
exert their regulatory effects independently, modeling an enhancer as a" bag of sites." These …
Bayesian Markov models improve the prediction of binding motifs beyond first order
W Ge, M Meier, C Roth, J Söding - NAR Genomics and …, 2021 - academic.oup.com
Transcription factors (TFs) regulate gene expression by binding to specific DNA motifs.
Accurate models for predicting binding affinities are crucial for quantitatively understanding …
Accurate models for predicting binding affinities are crucial for quantitatively understanding …