1 - 34 of 34 results

cERMIT / conserved Evidence-Ranked Motif Identification Tool

Allows motif identification. cERMIT is designed to analyze current large genomic regulatory datasets such as those from ChIPchip or ChIP-seq experiments. The software makes use of the complete data without the need to pre-define or infer thresholds. It can take different data as evidence for regulatory interactions, and can optionally utilize orthologous sequences from related species to restrict the search to co-occurring motifs.


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.


Provides access to a set of useful tools performing common ChIP-Seq data analysis tasks. The ChIP-Seq Web Server also includes positional correlation analysis, peak detection, and genome partitioning into signal-rich and signal-poor regions. The server also provides access to hundreds of publicly available data sets such as ChIP-seq data, RNA-seq data (i.e. CAGE), DNA-methylation data, sequence annotations (promoters, polyA-sites, etc.), and sequence-derived features (CpG, phastCons scores).


An extensible environment for both building and running end-to-end analysis workflows with automated report generation for a wide range of next-generation sequencing (NGS) applications. Its unique features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software on local computers and computer clusters. A flexible sample annotation infrastructure efficiently handles complex sample sets and experimental designs. To simplify the analysis of widely used NGS applications, systemPipeR provides pre-configured workflows and reporting templates for RNA-seq, ChIP-seq, VAR-seq and Ribo-seq.

PRI-CAT / Plant Research International ChIP-seq analysis tool

Allows to manage and analyse ChIP-seq experiments. PRI-CAT focuses on plants and particularly on Arabidopsis CHIP-seq analysis. It uses the ratio between the number of mapped reads in experimental and control data sets to returns score values per nucleotide position. The tool allows the user to fairly compare experiments thank to the ratios used are less dependent on the different number of reads sequenced in each experiment.


Enables broad and analysis of ChIP-seq data. ChIPseeqer is a comprehensive computational framework that includes: (1) gene-level annotation of peaks, (2) pathways enrichment analysis, (3) regulatory element analysis, using either a de novo approach, known or user-defined motifs, (4) nongenic peak annotation (repeats, CpG islands, duplications, published ChIP-seq datasets), (5) conservation analysis, (6) clustering analysis, (7) visualization tools, (8) integration and comparison across different ChIP-seq experiments.


Automates the processing and analysis of several commonly used Next Generation Sequencing (NGS) datasets including: ChIP-seq, RNA-seq, Global Run On sequencing (GRO-seq), micrococcal nuclease footprint sequencing (MNase-seq), DNase hypersensitivity sequencing (DNase-seq), and transposase-accessible chromatin using sequencing ATAC-seq datasets. CIPHER provides an analysis mode that accomplishes complex bioinformatics tasks such as enhancer prediction. It supplies functions to integrate various NGS datasets together.

ChiPSeqFPro / ChIP-Seq Full Processing

Automates processing of a collection of ChIPSeq or ATAC-Seq data starting from the gzip compressed fastq files. ChIPSeqFPro is a pipeline that performs the quality control using FastQC, mapping to the human genome or mouse using BWA mapper. It then converts sam to bam using samtools view, creates statistics on bam files using samtools flagstat, peak calling with MACS. It creates high resolution bigwig files from bam files using a custom script bam2bigwig that invokes bedtools bamtobed and UCSC scripts, bedItemOverlapCount, bedGraphToBigWig and fetchChromSizes.


Aims to perform basic analysis and interpretation of datasets with minimal effort. Bio-Docklets is an approach for abstracting the complex data operations of multi-step, bioinformatics pipelines for Next Generation Sequencing (NGS) data analysis. It also run a large number of pipeline instances for concurrent analysis of multiple datasets. It enables easy access to NGS data analysis pipelines for non-bioinformatics experts, on any computing environment whether a laboratory workstation, university computer cluster, or a cloud service provider.


A free service that provides access to RNA-Seq and ChIP-Seq analysis tools for studying infectious diseases. The site makes available thousands of pre-indexed genomes, their annotations, and the ability to stream results to the bioinformatics resources VectorBase, EuPathDB, and PATRIC. The site also provides a combination of experimental data and metadata, examples of pre-computed analysis, step-by-step guides, and a user interface designed to enable both novice and experienced users of RNA-Seq data.


A set of 20 ChIP-seq analysis software modules implemented in the Kepler workflow system; most (18/20) were also implemented as standalone, fully functional R scripts. The set consists of four full turnkey pipelines and 16 component modules. The turnkey pipelines in Kepler allow data provenance tracking. Implementation emphasized use of common R packages and widely-used external tools (e.g., MACS for peak finding), along with custom programming. ChIPSeqWorkflows presents comprehensive solutions and easily repurposed code blocks for ChIP-seq analysis and pipeline creation. Tasks include mapping raw reads, peakfinding via MACS, summary statistics, peak location statistics, summary plots centered on the transcription start site (TSS), gene ontology, pathway analysis, and de novo motif finding, among others.


A workflow system for laboratories with the need to analyze data from multiple NGS projects at a time. QuickNGS takes advantage of parallel computing resources, a comprehensive back-end database, and a careful selection of previously published algorithmic approaches to build fully automated data analysis workflows. QuickNGS considerably reduces the barriers that still limit the usability of the powerful NGS technology and finally decreases the time to be spent before proceeding to further downstream analysis and interpretation of the data.

GeMSE / GenoMetric Space Explorer

Allows analysis and visualization of interval-based genomic data. GeMSE implements a set of abstractions for data analysis, exploration and visualization. The software supports primitives for data explorations spanning from select, sort, and discretize clustering, and pattern extraction. It enables interactive analytics (IA), an approach suggested for evaluating processing results and for designing and adapting next-generation sequencing (NGS) data analysis pipelines.


A completely automated procedure for ChIP-seq data analysis, starting from raw read quality control, through read mapping, peak detection and annotation, and including comprehensive DNA sequence motif analysis. Among Crunch's novel features are a Bayesian mixture model that automatically fits a noise model and infers significantly enriched genomic regions in parallel, as well as a Gaussian mixture model for decomposing enriched regions into individual binding peaks. Moreover, Crunch uses a combination of de novo motif finding with binding site prediction for a large collection of known regulatory motifs to model the observed ChIP-seq signal in terms of novel and known regulatory motifs, extensively characterizing the contribution of each motif to explaining the ChIP-seq signal, and annotating which combinations of motifs occur in each binding peak.


A python toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis. You write a high level configuration file specifying your inputs and analysis parameters. This input drives a parallel pipeline that handles distributed execution, idempotent processing restarts and safe transactional steps. The goal is to provide a shared community resource that handles the data processing component of sequencing analysis, providing researchers with more time to focus on the downstream biology.