Software for nucleosome positioning prediction & modelling

Below is a list of computational tools for predicting nucleosome positioning. This collection is part of NucPosDB, a manually curated database of experimental nucleosome maps in vivo, cfDNA and computational tools related to nucleosome positioning

Jump to: NucPosDB front page | stable nucleosomes of the human genome | experimental nucleosome maps in vivo | experimental cfDNA datasets | tools for analysis of nucleosome mapping experiments | tools for prediction of nucleosome maps from DNA sequence | tools for analysis of sequenced cfDNA

**How to cite: Shtumpf M., Piroeva K.V., Agrawal S.P., Jacob D.R., Teif V.B. (2022). NucPosDB: a database of nucleosome positioning in vivo and nucleosomics of cell-free DNA. Chromosoma 13119-28 | open access article


DescriptionFeature selectionInterface/installation
ICM Web: ICM Web allows users to assess nucleosome stability and fold any sequence of DNA into a 3D model of chromatin (Sereda & Bishop, 2010; Stolz & Bishop, 2010). The model is displayed in the visual browser JSmol or can be downloaded. ICM takes a DNA sequence and generates (i) a nucleosome energy level diagram, (ii) coarse-grained representations of free DNA and chromatin and (iii) plots of the helical parameters (Tilt, Roll, Twist, Shift, Slide and Rise) as a function of position. featuresweb interface
FineStr: Single-base-resolution nucleosome mapping server (Gabdank et al, 2010; Trifonov, 2010). The analysis is performed using the probe based on the 117-bp DNA bendability matrix derived from C. elegans. The authors suggested the universality of this pattern for other species., 10-bp periodicity, k-mersweb interface
iNuc-PhysChem: Identifying nucleosomal or linker sequences from physicochemical properties (Chen et al, 2012). The algorithm identifies nucleosomal sequences by incorporating twelve physicochemical properties defined elsewhere, such as A-philicity, base stacking, B-DNA twist, bendability, bending stiffness, DNA denaturation energy, Z-DNA potential. The model was trained on data from H. sapiens, C. elegans and D. melanogaster., k-mers, empirical featuresweb interface, local install
iNuc-PseKNC: A sequence-based predictor for nucleosome positioning in genomes with pseudo k-tuple nucleotide composition (Guo et al, 2014). This is another software package from the developers of iNuc-PhysChem. Here, the samples of DNA sequences were formulated using six basic DNA local structural properties trained on datasets from H. sapiens, C. elegans and D. melanogaster., k-mers, empirical featuresweb interface
LeNup: Learning Nucleosome positioning from DNA sequences with improved convolutional neural networks. LeNup is a Python based open-source package based on convolutional neural networks to predict nucleosome positioning in H. sapiens, C. elegans, D. melanogaster as well as S. cerevisiae genomes, trained on benchmark datasets. install
Mapping_CC: Displays the nucleosome predictions based on the DNA dinucleotide correlation pattern. This algorithm was initially associated with one of the first high-throughput genome-wide nucleosome maps in Yeast (Ioshikhes et al, 2006). An updated version is available at, 10-bp periodicitylocal install
MOSAICS: Methodologies for Optimization and Sampling in Computational Studies (Minary & Levitt, 2014; Krawczyk, 2018). Perl scripts and a precompiled package to perform training-free atomistic prediction of nucleosome occupancy based on all-atom force field calculations. The effect of DNA methylation can be taken into account., empirical featureslocal install
NP-BERT: A Two-Staged BERT Based Nucleosome Positioning Prediction Architecture for Multiple Species. A machine learning model pre-trained on 12 nucleosome positioning datasets in human. Described in Fahzil et al., 2023. Code available at machine learninglocal install
NucEnerGen: Nucleosome energetics predictions based on high throughput sequencing (Locke et al, 2010). It utilizes dynamic programming to calculate allowed nucleosome configurations and the Percus equation to infer sequence-dependent energies from the experimental occupancy profiles., 10-bp periodicity, k-merslocal install
NucleosomeDensity Predict the nucleosome density in yeast with deep neural network. Described in Routhier et al., 2020. Using convolutional network (CNN) to predict the nucleosome density in yeast using the DNA sequence as input. learning, CNNlocal install
nuMap: A web application implementing the YR and W/S schemes to predict nucleosome positioning (Alharbi et al, 2014). The methodology is based on the sequence-dependent anisotropic bending, which dictates how DNA is wrapped around a histone octamer. This application allows users to specify a number of options such as schemes and parameters for threading calculation and provides multiple layout formats., 10-bp periodicityweb interface
NuPoP: Nucleosome Positioning Prediction Engine (Wang et al, 2008; Xi et al, 2010). NuPoP is built upon a duration hidden Markov model, in which the linker DNA length is explicitly modeled. NuPoP outputs the Viterbi prediction, nucleosome occupancy score (from backward and forward algorithms) and nucleosome affinity score. NuPoP has three formats including a web server prediction engine, a stand-alone Fortran program, and an R package. The latter two can predict nucleosome positioning for a DNA sequence of any length. http://nucleosome.stats.northwestern.edudi-/tri-nucleotides, 10-bp periodicity, empirical featuresweb interface, local install
Nu-OSCAR: Nucleosome-Occupancy Study for Cis-elements Accurate Recognition. It is devoted to identifying binding sites of known transcription factors, which further incorporates nucleosome occupancy around sites on promoter regions. The derivation of the algorithm is based on a biophysical view of interactions between protein factors and nucleosome DNA., 10-bp periodicityweb interface
nuScore: A nucleosome-positioning score calculator based on the DNA curvature properties (Tolstorukov et al, 2008). This software allows an important type of analysis, where a user enters many sequences to calculate the average nucleosome energy profile., 10-bp periodicity, empirical featuresweb interface
N-score: MATLAB and Python codes using a wavelet analysis based model for predicting nucleosome positions from DNA sequence (Yuan & Liu, 2008)., 10-bp periodicitylocal install
NXSensor: Prediction of nucleosome-excluding sequences based on DNA bending properties (Luykx et al, 2006). It takes as input DNA sequences in FASTA format, and outputs nucleosome-excluding or nucleosome favouring segments., 10-bp periodicity, empirical featuresweb interface
Segal Lab nucleosome positioning prediction (Field et al, 2008; Kaplan et al, 2009; Segal et al, 2006). Realized as a web server (allows analyzing a limited number of DNA sequences), and a stand-alone application which can be installed on a local cluster. It allows calculating nucleosome occupancy or nucleosome start site probability profiles of non-overlapping nucleosomes; alternatively, it is possible to calculate the net nucleosome formation energy profile. It uses machine learning for energy assignment based on the training datasets and dynamic programming to sample nucleosome configurations (similar to NucEnerGen, NuPoP and the algorithm of van Noort and co-authors)., 10-bp periodicity, k-mersweb interface, local install
Phase: A web server for prediction of the nucleosome formation probability based on (i) the 10-11 bp periodicities of dinucleotides and (ii) the typical pattern "linker-nucleosome-linker" defined by the authors (Levitsky et al, 2014)., 10-bp periodicityweb interface
RECON: A web server for prediction of the nucleosome formation potential learned from dinucleotide frequencies distribution for nucleosome positioning sequences (Levitsky, 2004; Levitsky et al, 1999)., 10-bp periodicityweb interface
SymCurv: A program for nucleosome positioning prediction (Nikolaou et al, 2010). It calculates the curvature of the DNA sequence and uses a greedy algorithm to parse the sequence in nucleosome-bound and nucleosome-free segments. featureslocal install
Schiessel Lab nucleosome positioning prediction. This resource contains software packages based on two types of algorithms: nucleosome mutation Monte Carlo (Eslami-Mossallam et al, 2016) and nucleosome positioning with Markov chains (Tompitak et al, 2017). The latter combines the "mutation Monte Carlo" method with dynamic programming similar to NucEnerGen, NuPoP and the algorithm of Segal and co-authors mentioned above. https://schiessellab.github.iodi-/tri-nucleotides, 10-bp periodicity, k-mers, empirical featureslocal install
Trifonov’s strong nucleosomes: Based on the discovery of strong nucleosome positioning sequences which are visually seen as regular arrays in genomic sequence (Nibhani & Trifonov, 2015; Salih et al, 2015), the program from Trifonov’s lab is finding a specific class of strongly positioned nucleosomes of the RR/YY and TA periodic types., 10-bp periodicityweb interface
van Noort Lab nucleosome positioning prediction (van der Heijden et al, 2012). This algorithm is based on dinucleotide distributions, but unlike other methods based on dinucleotide distributions it does not use machine learning and accounts only for the dinucleotide periodicity. In addition, this method uses dynamic programming to account for size exclusion and the Percus equation to assign nucleosome affinities (similar to NucEnerGen, NuPoP and the algorithms of Segal and co-authors and Schiessel and co-authors mentioned above)., 10-bp periodicityweb interface
G-Dash: Unites the Interactive Chromatin Modeling (ICM) tools with the Biodalliance genome browser and the JSMol molecular viewer to rapidly fold any DNA sequence into atomic or coarse-grained models of DNA, nucleosomes or chromatin. As a chromatin modeling tool, G-Dash enables users to specify nucleosome positions from various experimental or theoretical sources, interactively manipulate nucleosomes, and assign different conformational states to each nucleosome. As an informatics tool, data associated with 3D structures are displayed as tracks in a genome browser. Described in Li et a., 2018. interface
DescriptionFeature selectionInterface & installation