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CEAS / Cis-regulatory Element Annotation System
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A software tool designed to characterize genome-wide protein-DNA interaction patterns from ChIP-chip and ChIP-Seq data. CEAS provides summary statistics on ChIP enrichment in important genomic regions such as individual chromosomes, promoters, gene bodies or exons, and infers the genes most likely to be regulated by the binding factor under study. CEAS also enables biologists to visualize the average ChIP enrichment signals over specific genomic regions, particularly allowing observation of continuous and broad ChIP enrichment that might be too subtle to detect from ChIP peaks alone.
Allows analysis of enrichment-based epigenomic data. Repitools is an R package that provides several functions for quality assessment, visualization, summarization and statistical analysis of epigenomics experiments. The software utilizes aroma.affymetrix and several Bioconductor packages for various preprocessing steps. It includes for instance BayMeth for quantifying methylation. It was tested on Affymetrix and Nimblegen tiling microarrays and Illumina Genome Analyzer sequencing data.
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 Python library designed to analyze and visualize data from disparate file formats and experimental protocols (ChIP-seq, RNA-seq, RNA immunoprecipitation and sequencing (RIP-seq), etc.) in a flexible and interactive manner. There are four core functional capabilities that will be described in more detail below: (i) a unified interface for accessing genomic data by interval across multiple file formats, (ii) integration with existing Python tools for working with tabular data and extension of these tools specifically for use with genomic data, such as those keyed by feature ID, (iii) a persistent mapping between feature ID and its genomic coordinates and (iv) extension of existing plotting and visualization tools to aid in the presentation of genome-wide data.
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.
Computes tag density across functional features in parallel using multicores and a grid engine and to interactively explore it in a graphical user interface. iTagPlot allows you to stratify features by defining groups based on biological function and measurement, summary statistics and unsupervised clustering. iTagPlot uses an annotated list of genomic features in the BED format coupled with BED or BAM files of mapped reads to generate a tag density plot of the given feature with flanking upstream and downstream regions, the length of which is predetermined by the user.
A software package that allows for simple exploration, clustering and visualization of high-throughput sequencing experiments. fluff contains three command-line tools to generate publication-quality figures in an uncomplicated manner using sensible defaults. Genome-wide data can be aggregated, clustered and visualized in a heatmap, according to different clustering methods. This includes a predefined setting to identify dynamic clusters between different conditions or developmental stages. Alternatively, clustered data can be visualized in a bandplot. Finally, fluff includes a tool to generate genomic profiles. As command-line tools, the fluff programs can easily be integrated into standard analysis pipelines.
Visualizes next-generation sequencing (NGS) signals and sequence motif densities along genomic features using average plots and heatmaps. It can also calculate sequence motif density profiles from reference genome. SeqPlots is useful both for exploratory data analyses and preparing replicable, publication quality plots. Other features of the software include collaboration and data sharing capabilities, as well as ability to store pre-calculated result matrixes, that combine many sequencing experiments and in-silico generated tracks with multiple different features.
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