Detects motifs in large scale chromatin-immunoprecipitation (ChIP) data. Trawler is a program that can be run according two different manners: (i) a standalone version providing a pipeline that generates position weight matrices (PWMs) from the extraction and clustering of over-represented motifs; and (ii) a web application supplying the possibility to submit sequences in both FASTA or BED format, to rank predicted motifs by conservation score as well as to produce a set of background sequences.
A clustering and visualization tool that enables the interactive exploration of genome-wide data. It is intended to "spark" insights into genome-scale data sets. The approach utilizes data clusters as a high-level visual guide and supports interactive inspection of individual regions within each cluster. The cluster view links to gene ontology analysis tools and the detailed region view connects to existing genome browser displays taking advantage of their wealth of annotation and functionality.
A cross-platform desktop application developed for interactive visualization, exploration and clustering of epigenomic data such as ChIP-seq experiments. ChAsE is designed and developed in close collaboration with several groups of biologists and bioinformaticians with a focus on usability and interactivity. Data can be analyzed through k-means clustering, specifying presence or absence of signal in epigenetic data, and performing set operations between clusters. Results can be explored in an interactive heat map and profile plot interface and exported for downstream analysis or as high quality figures suitable for publications.
Classifies peaks with a functional analysis of their shapes. FunChIP is an R package which consists in a series of methods for the GenomicRanges class, which sequentially add metadata columns to the GRanges, and ultimately assign each enriched region to a cluster. It assigns each peak to a cluster, defined by the functions approximating their shapes. It includes also a versatile visualization function, which automatically separates the peaks according to the clustering, and plots their shapes.
Identifies pairwise ungapped local alignments between Position-specific Frequency Matrices (PFMs). m2match determines if a custom matrix corresponds to a novel identified motif. It can compare a motif generated from newly identified binding sites for a characterized transcription factor. This software implements a local ungapped alignment algorithm that also finds non-redundant set of Familial Binding Profiles by clustering a given collection of motifs.
A standalone for exhaustive pattern detection in ChIP profiling data. CATCHprofiles is built upon a computationally efficient implementation for the exhaustive alignment and hierarchical clustering of ChIP profiling data. The tool features a graphical interface for examination and browsing of the clustering results. CATCHprofiles requires no prior knowledge about functional sites, detects known binding patterns ‘‘ab initio’’, and enables the detection of new patterns from ChIP data at a high resolution, exemplified by the detection of asymmetric histone and histone modification patterns around H2A.Z-enriched sites. CATCHprofiles’ capability for exhaustive analysis combined with its ease-of-use makes it an invaluable tool for explorative research based on ChIP profiling data.
Allows to calculate biclusters. ChromBiSim is based on binarized data matrix of gene expression data sets. It can run on any size of data including whole genome histone modification profiles irrespective of the cell type and organism. The tool extracts local patterns and gives exact histone combinatorics present at various genomic locations excluding those modifications which are not present at those locations.
Allows clustering, alignment and clustering of epigenomic marks. DGW is based on an algorithm that enables construction of robust speech recognizers undeterred by the variability in pitch and speed of enunciation. The software can align genomic landmarks such as transcription start sites (TSSs) and first splicing sites (FSSs) on real epigenomic data from the ENCODE project. The software can be useful for exploratory data analysis of high throughput epigenomic data sets.
To fully understand a gene's function, it is essential to develop probabilistic models on multiple ChIP-Seq profiles for deciphering the combinatorial gene transcription. To this end, we propose FullSignalRanker for regression tasks on ChIP-Seq data. The proposed method is compared with other existing methods on ENCODE ChIP-seq datasets, demonstrating its regression and classification ability. The results suggest that FullSignalRanker is the best-performing method for recovering the signal ranks on the promoter and enhancer regions. In addition, FullSignalRanker is also the best-performing method for peak sequence classification.
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