Enables analysis and visualization of the information content of genomic signals. MSR is a method, adapted from an image segmentation algorithm and inspired by multiscale approaches for classifying image texture patterns. The software enables global analysis of genomic data in an unbiased manner with respect to the spatial scales on which biological information is encoded. It was used to analyze measurements of transcription factor binding, covalent histone modifications and DNA methylation, as well as genomic annotations and sequence-derived data.
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Allows data-mining and visualization of next-generation sequencing (NGS) samples such as enrichment patterns of DNA-interacting proteins at functional genomic regions. ngs.plot has a built-in database of functional elements that facilitates the management of genomic coordinates for users. This software supports large sequencing data and is available through the Galaxy tool shed.
Enables computational reconstruction of regulatory circuitry from high-throughput data. ISMARA uses motif activity response analysis to identify the key regulators, i.e., transcription factors (TFs) and microRNAs (miRNAs) driving gene expression/chromatin state changes across the samples, the activity profiles of these regulators, their target genes, and the sites on the genome through which these regulators act. The stand-alone client application (called the ISMARA client) automates the process of pre-processing the user’s raw data on her/his own computer.
A computational model integrating Hi-C and histone mark ChIP-seq data to predict two important features of chromatin organization: chromatin interaction hubs and topologically associated domain (TAD) boundaries. HubPredictor accurately and robustly predicts these features across datasets and cell types. Cell-type specific histone mark information is required for prediction of chromatin interaction hubs but not for TAD boundaries. HubPredictor provides a useful guide for the exploration of chromatin organization.
Allows the integrative analysis of ChIP-chip/seq data across platforms and between laboratories. MM-ChIP proceeds by modeling the characteristic fragment size of the sequenced ChIP-DNA library for each individual data source. It uses then the 3’ direction to represent the protein-DNA interaction sites. Finally, a sliding window is used to score the significance of signal enrichment in the ChIP samples by measuring and comparing tags within the same windows between ChIP.
Examines epigenomic and transcriptomic next generation sequencing (NGS) data. Octopus-toolkit can be used for antibody- or enzyme-mediated experiments and studies for the quantification of gene expression. It can accelerate the data mining of public epigenomic and transcriptomic NGS data for basic biomedical research. This tool provides a private and a public mode: one to process the user’s own data, and the other to analyze public NGS data by retrieving raw files from the GEO database.
Allows detection of collaborative transcription factor pairs. MMARGE consists of a suite of software tools to analyze ChIP-seq, ATAC-seq, DNase I Hypersensitivity or other next generation sequencing (NGS) assays where genotyping or DNA sequence data is available. For performing, this tool needs two types of data: (1) genetic variation, and (2) high-throughput sequencing data (ChIP-seq, ATAC-seq, DNaseI-seq).
Assists in finding consensus peaks, determining accurate peak widths and resolving neighboring narrow peaks. JAMM is a peak finder that can integrate information from multiple replicates. It addresses replicate integration by looking at biological replicates as not being exactly reproducible and attempts to model their variability using information about their covariance in local enriched windows. This method is applicable to different types of datasets and can define accurate peak boundaries.
Provides a visualization tool available as plug-in for the Galaxy platform. CHROMATRA allows detailed yet concise presentations of data derived from ChIP-chip or ChIP-seq experiments by visualizing enrichment scores across genes or other genomic features while accounting for their length and additional characteristics such as gene expression. It integrates into typical analysis workflows and enables rapid graphical assessment and comparison of genome-wide data at a glance.
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.
A general methodological framework to rigorously combine the evidence of enriched regions in ChIP-seq replicates, with the option to set a significance threshold on the repeated evidence and a minimum number of samples bearing this evidence. Given a set of peaks from (biological or technical) replicates, the method combines the p-values of overlapping enriched regions: users can choose a threshold on the combined significance of overlapping peaks and set a minimum number of replicates where the overlapping peaks should be present. The method allows the "rescue" of weak peaks occuring in more than one replicate and outputs a new set of enriched regions for each replicate.
Permits users to identify allele specific binding of transcription factors. ABC controls for potential false positives resulting from biases. This method does not require phased haploid genomes. It can serve to call allele-specific transcription factor (TF) binding to the chromatin from normal or cancer samples. It can also be used to better understand the role of single nucleotide variants (SNV) associated with traits or disease by directly assessing their capacity to modulate TF binding to the chromatin.
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.
Calls peaks in ChIP-seq data with biological replicates. BinQuasi jointly models biological replicates by employing a generalized linear model framework. This program identifies peaks by using a one-sided quasi-likelihood ratio test in quasi-Poisson or quasi-negative binomial models. It can control false discovery rate (FDR) at the window-level and take into account that the windows may be correlated.
A general workbench for analysing regulatory sequence regions and discovering transcription factor binding sites and cis-regulatory modules. MotifLab can improve performance of binding site predictions by allowing users to integrate several motif discovery tools (including AlignAce, BioProspector, ChIPMunk, MEME, MotifSampler, Priority and Weeder) as well as different types of data, for instance phylogenetic conservation, epigenetic marks, DNase hypersensitive sites, ChIP-Seq data, positional binding preferences of transcription factors, TF-TF interactions, TF-expression and target gene expression.
Facilitates several typical operations related to the quantification of the sequencing signal in a set of genomic regions. compEpiTools provides a number of methods to score these data in regions of interest, leading to the identification of enhancers, lncRNAs, and RNAPII stalling/elongation dynamics. It allows a fast and comprehensive annotation of the resulting genomic regions, and the association of the corresponding genes with non-redundant GeneOntology terms.
A powerful tool that accurately identifies functional regions of interest (ROIs) on the genome where a genomic signal significantly deviates from the general genome-wide behavior. HATSEQ also includes a number of built-in post-analyses with which biological meaning can be attached to the detected ROIs in terms of gene pathways and de-novo motif analysis, and provides different visualizations and statistical summaries for the detected ROIs. In addition, HATSEQ has an intuitive graphic user interface that lowers the barrier for researchers to analyze their data without the need of scripting languages.
Enables to evaluate and correlate differential expression patterns from RNA sequencing and proteomics datasets. FunSys simplifies the differential expression analysis of transcriptome data and integrates these data with proteomics data, thus facilitating the analysis and identification of transcribed and translated genes. In addition, the reports and graphs generated by FunSys will assist users in analyzing their results and generating images for publication.
Allocates cells measured with single-cell RNA-seq to clones derived from low-coverage single-cell DNA-seq. clonealign is statistical method that constructs a phylogenetic tree using single-cell copy number measurements. It permits the discovery of clone-specific phenotypic properties in real cancer data. This tool is useful in the field of single cell biology including extensions for other multimodal approaches such as methylation-transcription and chromatin accessibility-transcription.
Allows user to create, reach and visualize read densities of sequencing-based data sets, from ChIP-Seq and RNA-Seq for instance.
A package to process multiple ChIP-seq BAM files and detect allele-specific events. BaalChIP computes allele counts at individual variants (SNPs/SNVs), implements extensive quality control steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples.
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