The DNA in eukaryotic cells is packed into the chromatin that is composed of nucleosomes. Positioning of the nucleosome core particles on the sequence is a problem of great interest because of the role nucleosomes play in different cellular processes including gene regulation.
Maps directly nucleosome centers. NuCMap is based on chemical modification of engineered histones. It locates nucleosome positions genome-wide on a map in unprecedented detail and accuracy and shows significantly stronger dinucleotide signals in nucleosome DNAs than MNase maps. The tool reveals novel aspects of the in vivo nucleosome organization that are linked to transcription factor (TF) binding, RNA polymerase pausing, and the higher order structure of the chromatin fiber.
Implements the YR and W/S schemes to predict nucleosome positioning at high resolution. This methodology is based on the sequence-dependent anisotropic bending, which dictates how DNA is wrapped around a histone octamer. nuMap allows users to specify a number of options such as schemes and parameters for threading calculation and provides multiple layout formats.
Allows users to identify nucleosome positioning in genomes. iNuc-STNC utilizes various extraction techniques to find and extract salient and nominal features from nucleosome positioning sequences. The extracted feature spaces were fed to three hypothesis learners PNN (probabilistic neural network), support vector machines (SVM) and k-nearest neighbor (KNN) in order to find the best one.
Allows users to assess nucleosome stability and fold sequences of DNA into putative chromatin templates. ICM generates a nucleosome energy level diagram, coarse-grained representations of free DNA and chromatin and plots of the helical parameters (Tilt, Roll, Twist, Shift, Slide and Rise) as a function of position. It uses an elastic model to automatically place nucleosomes. The tool can be used to y assemble models of chromatin that can be employed to rationalize biophysical data, especially spatial relations.
Predicts nucleosome energetics by using high throughput sequencing. NucEnerGen makes a prediction of nucleosome occupancy on any DNA sequence. It establishes that nucleosome occupancies can be explained by systematic differences in mono- and dinucleotide content between nucleosomal and linker DNA sequences, with periodic dinucleotide distributions and longer sequence motifs playing a minor role.
An algorithm based on down-sampling operation and footprint in wavelet. AWNFR identified regulatory regions more effectively and accurately than the previous approaches with the epigenome data in mouse embryonic stem cells and human lung fibroblast cells (IMR90). Based on the detected epigenomic landscapes, AWNFR classified epigenomic status and studied epigenomic codes.
Allows prediction of nucleosome positioning. LeNup was developed based on the convolutional neural networks (CNN). This method combines automatically learn the feature representation. The software was tested on benchmark datasets of human, worm, fly, and yeast genomes. Its performance was measured thanks to the Jackknife test. This tool employs a support vector machine (SVM) method to classify features of DNA fragment.
Identifies nucleosomal sequences by incorporating physicochemical properties into a 1788-Dimensional feature vector. iNuc-PhysChem was able to identify nucleosome positioning for an independent DNA segment extracted from the Saccharomyces cerevisiae genome. It can be used to classify nucleosomal and linker sequences in the human genome. The tool appears to be able to identify nucleosome in the whole genome.
Analyses nucleosome position data obtained with microarray-based approach. MLM is a classifier to distinguish between several kinds of patterns. It appears to be an important tool for the genome wide analysis of nucleosome position and function thanks to its capacities to identify distinct classes of nucleosomes. It allows a better representation of nucleosome position data and a significant reduction in computational time.
Predicts nucleosome position by explicitly modeling the linker DNA length. NuPoP is based on a duration Hidden Markov model (HMM). It predicts nucleosome occupancy and the most probable nucleosomes positioning map genome-wide. The user has to specify which species the genomic sequence is from. NuPoP outperforms the N-score method and the Markov model/thermodynamic equilibrium method in term of sensitivity.
Predicts nucleosome positions. SymCurv parses the sequence space into 146-nucleotide long, non-overlapping segments in a strictly hierarchical manner. It first calculates the curvature values and subsequently applying the symmetry constrains on the resulting curvature data. The tool is able to capture sequence constraints, which are related to structure in genomic regions where a functional predicted role is not supported by sequence conservation.
Integrative analyses of epigenetic data promise a deeper understanding of the epigenome. Epidaurus is a bioinformatics tool used to effectively reveal inter-dataset relevance and differences through data aggregation, integration and visualization.
0 - 0 of 0
1 - 2 of 2
Filters / Sort by
0 - 0 of 0