Tools & Data
Computational Analysis Tools & Additional Data
We developed a new method to calculate the electrostatic potential in the minor groove in a high-throughput manner for any length or number of sequences based on the data mining of results from solving the non-linear Poisson-Boltzmann equation for many DNA fragments with diverse sequences. To model DNA binding specificities of transcription factors using electrostatic potential, we included a statistical machine learning approach (MLR) that combines minor-groove electrostatic potential with DNA sequence features.
Chiu et al. Genome-wide prediction of minor-groove electrostatic potential enables biophysical modeling of protein-DNA binding.
Nucleic Acids Res. 45, 12565-12576 (2017)
Link to DNAphi web server
TFBSshape is a motif database for analyzing structural profiles of transcription factor binding sites (TFBSs). This new release includes new entries from the JASPAR and UniPROBE databases, methylated TFBSs derived from in vitro high-throughput binding assays and in vivo methylated TFBSs. The structural profiles for each TFBS entry now include 13 shape features and minor groove electrostatic potential for DNA and four shape features for methylated DNA. We designed new tools for the shape-based alignment of TFBSs, for the comparison of methylated and unmethylated shape profiles, and for the design of shape-preserving nucleotide mutations in TFBSs.
Chiu et al. TFBSshape: an expanded motif database for DNA shape features of transcription factor binding sites.
Nucleic Acids Res. 48, D246-255 (2020)
Link to TFBSshape database
DNAproDB is a database and structural analysis tool that offers data visualization, data processing and search functionality with which researchers can analyze, access and visualize structural data of DNA-protein complexes. The new release of DNAproDB supports any DNA secondary structure from typical B-form DNA to single-stranded DNA to G-quadruplexes. We have updated the structure of our data files to support complex DNA conformations. Support for chemically modified residues and nucleotides has been significantly improved along with the addition of new structural features and improved structural moiety assignment.
Sagendorf et al. DNAproDB: an expanded database and web-based tool for structural analysis of DNA-protein complexes.
Nucleic Acids Res. 48, D277-287 (2020)
Link to DNAproDB database
We developed a high-throughput method for predicting the effect of cytosine methylation on DNA shape and its subsequent influence on protein-DNA interactions. This approach overcomes the limited availability of experimental DNA structures that contain 5-methylcytosine.
Rao et al. Systematic prediction of DNA shape changes due to CpG methylation explains epigenetic effects on protein-DNA binding.
Epigenetics Chromatin 11, 6 (2018)
Link to methyl-DNAshape web server
DNAproDB is a database and web-based visualization tool which is intended to make structural analysis of DNA-Protein complexes easy. Here users can find a wealth of data on the structure of and interaction between DNA and proteins in complex for currently 2,568 structures contained in the PDB. This data can be used to analyze individual structures, or to generate large datasets by constructing queries on a set of structural and interaction features using the search form. Additionally, the users can upload their own structure using the upload form, and use the same processing and visualization tools for unpublished data.
Sagendorf et al. DNAproDB: an interactive tool for structural analysis of DNA-protein complexes.
Nucleic Acids Res. 45, W89-W97 (2017)
Link to DNAproDB web server
DNAshapeR is a software package implemented in the statistical programming language R that predicts DNA shape features in an ultra-fast, high-throughput manner from genomic sequencing data. The package takes either nucleotide sequence or genomic coordinates as input, and generates various graphical representations for visualization and further analysis. DNAshapeR further encodes DNA sequence and shape features as user-defined combinations of k-mer and DNA shape features. The resulting feature matrices can be readily used as input of various machine learning software packages for further modeling studies.
Chiu et al. DNAshapeR: an R/Bioconductor package for DNA shape prediction and feature encoding.
Bioinformatics 32, 1211-1213 (2016)
Link to DNAshapeR software package
GBshape provides DNA shape annotations of entire genomes. The database currently contains annotations for minor groove width, roll, propeller twist, helix twist and hydroxyl radical cleavage for 98 different organisms. Additional genomes can easily be added in the provided framework. GBshape contains two major tools, a genome browser and a table browser. The genome browser provides a graphical representation of DNA shape annotations along standard genome browser annotations.
Chiu et al. GBshape: a genome browser database for DNA shape annotations.
Nucleic Acids Res. 43, D103-109 (2015)
Link to GBshape database
Our new TFBSshape database disentangles the complex relationships between DNA sequence, its 3D structure, and protein-DNA binding specificity. The TFBSshape database augments nucleotide sequence motifs with heat maps and quantitative predictions of DNA shape features for 739 TF datasets from 23 different species.
Yang et al. TFBSshape: a motif database for DNA shape features of transcription factor binding sites.
Nucleic Acids Res. 42, D148-155 (2014)
Link to TFBSshape database
We developed a new method for predicting DNA shape in a high-throughput manner on a genome-wide scale. This approach predicts structural features (several helical parameters and minor groove width) for the entire yeast genome in less than one minute on a regular laptop. The prediction can be visualized as genome browser tracks and compared to other properties of the genome such as sequence conservation.
Zhou et al. DNAshape: a method for the high-throughput prediction of DNA structural features on a genomic scale.
Nucleic Acids Res. 41, W56-62 (2013)
Link to DNAshape web server
Additional Data
Rao et al. Systematic prediction of DNA shape changes due to CpG methylation explains epigenetic effects on protein-DNA binding.
Epigenetics & Chromatin In press (2018)
J. Li et al. Expanding the repertoire of DNA shape features for genome-scale studies of transcription factor binding.
Nucleic Acids Res. 45, 12877-12887 (2017)
Supplementary Information
T.P. Chiu et al. Genome-wide prediction of minor-groove electrostatic potential enables biophysical modeling of protein-DNA binding.
Nucleic Acids Res. 45, 12565-12576 (2017)
Supplementary Information
L. Yang et al. Transcription factor family-specific DNA shape readout revealed by quantitative specificity models.
Mol. Syst. Biol. 13, 910 (2017)
Supplementary Information
T. Zhou et al. Quantitative modeling of transcription factor binding specificities using DNA shape.
Proc. Natl. Acad. Sci. USA 112, 4654-4659 (2015)
Supplementary Information
N. Abe et al. Deconvolving the recognition of DNA sequence from shape.
Cell 161, 307-318 (2015)
Supplementary Information