1 - 32 of 32 results

ChIPpeakAnno

Facilitates batch annotation of enriched peaks identified from ChIP-seq, ChIP-chip, cap analysis of gene expression (CAGE) or any experiments resulting in a large number of enriched genomic regions. The binding sites annotated with ChIPpeakAnno can be viewed easily as a table, a pie chart or plotted in histogram form, i.e., the distribution of distances to the nearest genes for each set of peaks. ChIPpeakAnno also offers functionalities for determining the significance of overlap between replicates or binding sites among transcription factors within a complex, and for drawing Venn diagrams to visualize the extent of the overlap between replicates. Furthermore, it includes functionalities to retrieve sequences flanking putative binding sites for PCR amplification, cloning, or motif discovery, and to identify Gene Ontology (GO) terms associated with adjacent genes.

Epiviz

An interactive visualization tool for functional genomics data. Epiviz supports genome navigation like other genome browsers, but allows multiple visualizations of data within genomic regions using scatterplots, heatmaps and other user-supplied visualizations. Epiviz sets a precedent for genomic data analysis collaborative workflows by enabling reproducible and shareable steps, and allowing custom user code to be dynamically incorporated, while guaranteeing the security and integrity of user data.

MuSERA / Multiple Sample Enriched Region Assessment

A broadly useful standalone tool for both interactive and batch analysis of combined evidence from enriched regions (ERs) in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context; they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser.

ChAsE / Chromatin Analysis and Exploration

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.

Epigenomix

A package to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification. Epigenomix is based on a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays. We propose quantile normalization for ChIP-seq data and a Bayesian mixture approach involving a mixture of mixtures and distributions of different type (normal and exponential) to classify transcripts based on a measure for the correlation between histone modifications and gene transcription.

SUPERmerge

Allows the analysis and annotation of coverage islands within individual read alignment (BAM) files of histone modification ChIPseq datasets harboring broad chromatin domains. SUPERmerge allows flexible regulation of a variety of read pileup parameters, thereby revealing how read islands aggregate into areas of coverage across the genome and what annotation features they map to within individual biological replicates. It can be useful for investigating low sample size ChIP-seq experiments.

Seq2pathway

An R/Python wrapper for pathway (or functional gene-set) analysis of genomic loci, adapted for advances in genome research. Seq2pathway associates the biological significance of genomic loci with their target transcripts and then summarizes the quantified values on the gene-level into pathway scores. It is designed to isolate systematic disturbances and common biological underpinnings from next-generation sequencing (NGS) data. Seq2pathway offers Bioconductor users enhanced capability in discovering collective pathway effects caused by both coding genes and cis-regulation of non-coding elements.

ChIPseeker

Annotates ChIP-seq data analysis. ChIPseeker supports annotating ChIP peaks and provides functions to visualize ChIP peaks coverage over chromosomes and profiles of peaks binding to TSS regions. Comparison of ChIP peak profiles and annotation are also supported. Moreover, it supports evaluating significant overlap among ChIP-seq datasets. Currently, ChIPseeker contains 15,000 bed file information from GEO database. These datasets can be downloaded and compare with user's own data to explore significant overlap datasets for inferring co-regulation or transcription factor complex for further investigation.

CATCHprofiles / Clustering and AlignmenT of ChIp profiles

Obsolete
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.

Heat*seq

A web-tool that allows genome scale comparison of high throughput experiments (ChIP-seq, RNA-seq and CAGE) provided by a user, to the data in the public domain. Heat*seq allows users to contextualise their sequencing data with respect to vast amounts of public data in a few minutes without requiring any programming skills. Heat*seq currently contains over 12,000 experiments across diverse tissues and cell types in human, mouse and drosophila. Heat*seq displays interactive correlation heatmaps, with an ability to dynamically subset datasets to contextualise user experiments. High quality figures and tables are produced and can be downloaded in multiple formats.

DChIPRep

Compares profiles of enrichment in histone modifications around classes of genomic elements, e.g. transcription start sites (TSS). DChIPRep tests for differential enrichment at each nucleotide position of a metagene/metafeature profile and determines positions with significant differences in enrichment between experimental groups. DChIPRep provides two plotting functions to represent and inspect the final results of the analysis. The yeast case study demonstrates DChIPRep’s favourable performance when compared to a pipeline inspired by the csaw-package for differential binding analysis.

ePIANNO / ePIgenomics ANNOtation tool

Helps users to explore the associations between protein-binding event, disease-associated genomic variants, and information of general populations by annotating the ChIP-seq datasets. ePIANNO is a web server that combines SNP information of populations (1000 Genomes Project) and gene-disease association information of GWAS (NHGRI) with ChIP-seq (hmChIP, ENCODE, and ROADMAP epigenomics) data. ePIANNO has a user-friendly website interface allowing researchers to explore, navigate, and extract data quickly.

geneXtendeR

An R/Bioconductor package for histone modification ChIP-seq analysis. geneXtendeR is designed to optimally annotate a histone modification ChIP-seq peak input file with functionally important genomic features (e.g., genes associated with peaks) based on optimization calculations. It extends the boundaries of every gene in a genome by some genomic distance (in DNA base pairs) for the purpose of flexibly incorporating cis-regulatory elements, such as enhancers and promoters, as well as downstream elements that are important to the function of the gene (relative to an epigenetic histone modification ChIP-seq dataset). geneXtendeR computes optimal gene extensions tailored to the broadness of the specific epigenetic mark (e.g., H3K9me1, H3K27me3), as determined by a user-supplied ChIP-seq peak input file. As such, geneXtendeR maximizes the signal-to-noise ratio of locating genes closest to and directly under peaks.

PTHGRN / Post-Translational Hierarchical Gene Regulatory Network

An open web server to unravel relationships among PTMs, TFs, epigenetic modifications and gene expression. PTHGRN accepts three input: PPI, ChIP-seq binding peaks of TFs and epigenetic modifications, and gene expression data. The server provides the PPI data of human, mouse, rat, drosophila and Caenorhabditis elegans obtained from public databases BioGRID, STRING, Dip, HPRD et al. ChIP-based binding data of TF or epigenetic modifications were derived from ENCODE and modENCODE projects. Alternatively, users can submit their own datasets. Up- and down-regulated genes expression data should be separated during submission procedure.

Seqinspector

Facilitates the functional annotation and discovery of transcription factor binding sites on promoters of co-expressed transcripts, signals from ChIP-seq experiments and any other set of genomic coordinates sharing a common trait. The presented web resource includes a large collection of publicly available ChIP-seq and RNA-seq experiments (>1300 tracks) performed on transcription factors, histone modifications, RNA polymerases, enhancers and insulators in humans and mice. Over-representation is calculated based on the coverage computed directly from indexed files storing ChIP-seq data (bigwig). Therefore, seqinspector is not limited to pre-computed sets of gene promoters. The tool can be used to identify common gene expression regulators for sets of co-expressed transcripts (including miRNAs, lncRNAs or any novel unannotated RNAs) or for sets of ChIP-seq peaks to identify putative protein-protein interactions or transcriptional co-factors.

FullSignalRanker

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.

LPCHP / Linear predictive coding histone profile

Allows the capture and comparison of ChIP-seq histone profiles. LPCHP can be used as an alternative to read intensities, its utility may extend beyond ChIP-seq to other next-generation sequencing (NGS) applications. It can be used in identification of enhancer or regulatory regions in the genome. The tool is robust against changes in p, including cases where it was customized to dataset. LPCHP can identify commonalities between different histone modifications.

CASSys / ChIP-seq data Analysis Software System

Obsolete
An integrated, user-friendly software system, spanning all steps of ChIP-seq data analysis. CASSys supersedes the laborious application of several single command line tools. CASSys provides functionality ranging from quality assessment and -control of short reads, over the mapping of reads against a reference genome (readmapping) and the detection of enriched regions (peakdetection) to various follow-up analyses. The latter are accessible via a state-of-the-art web interface and can be performed interactively by the user. The follow-up analyses allow for flexible user defined association of putative interaction sites with genes, visualization of their genomic context with an integrated genome browser, the detection of putative binding motifs, the identification of over-represented Gene Ontology-terms, pathway analysis and the visualization of interaction networks.