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Discovers DNA motifs on protein binding microarray (PBM) data. kmerHMM is a computational pipeline for PBM motif discovery in which hidden markov models (HMMs) are trained to model DNA motifs, and Belief Propagation is used to elucidate multiple motif models from each trained HMM. The software model the dependence between adjacent nucleotide positions and can also deduce multiple binding modes for a given transcription factor (TF).
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.
ChIPOTle / Chromatin ImmunoPrecipitation On Tiled arrays
Performs peak detection in chromatin immunoprecipitation (ChIP)-chip signal. ChIPOTle was created for the analysis of ChIP-chip data obtained using tiled arrays, which allow to exploit both the 'single-tail' and 'neighbour effect'. The software uses a sliding window approach to identify potential sites of enrichment, and then estimates the significance of enrichment for a genomic region using a standard Gaussian error function. It also allows multiple correction testing, signal normalization, replication merging, and probe sorting.
BAC / Bayesian analysis of ChIP-chip
Detects transcription factor bound regions, which incorporate the dependence between probes while making little assumptions about the bound regions (e.g., length). BAC is robust to probe outliers with an exchangeable prior for the variances, which allows different variances for the probes but still shrink extreme empirical variances. Parameter estimation is carried out using Markov chain Monte Carlo and inference is based on the joint distribution of the parameters. Bound regions are detected using posterior probabilities computed from the joint posterior distribution of neighboring probes. It was showed that these posterior probabilities are well calibrated and can be used to obtain an estimate of the false discovery rate.
A computational tool to make tiling array analysis and to detect genomic loci that show hybridization patterns of interest. TileMap includes functions to (i) compute probe-level test-statistics according to the transcriptional or protein binding patterns specified by users; (ii) filter local repeats; and (iii) infer if a region is of interest or not by applying hidden markov model or moving window average (MA). Compared with previous tools, TileMap provides a flexible way to study tiling array hybridizations under multiple experimental conditions.
A package that allows the identification of regions enriched for transcription factor binding sites in ChIP-chip experiments on Affymetrix tiling arrays. rMAT is based on the popular MAT software. It has been written in C and R and provides an efficient implementation of the functionality of MAT as well as statistical normalization techniques not available in the original MAT. We show that these model refinements can improve normalization and increase power when detecting enriched regions.
Predicts biological contexts in which a transcription factor (TF) and its target genes are functionally active. ChIP-PED is a software which allows users to extend the scope of their ChIPx data to possibly novel biological systems without performing additional costly and time-consuming ChIPx experiments. Given a TF and its activated and repressed target genes defined using ChIPx and gene expression data in one or more biological contexts, the software searches for other contexts in which the TF is likely to be functionally active.
CATCHprofiles / Clustering and AlignmenT of ChIp profiles
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.
A package that facilitates the analysis of ChIP-chip experiments by providing functionality for data import, quality assessment, normalization and visualization of the data, and the detection of ChIP-enriched genomic regions. Ringo makes the construction of programmed analysis workflows easier, offers benefits in scalability, reproducibility and methodical scope of the analyses and opens up a broad selection of follow-up statistical and bioinformatics methods. Its functionality covers the complete primary analysis for ChIP-chip tiling microarrays, especially those from the company NimbleGen. The package's close integration with other Bioconductor packages opens up a multitude of subsequent analysis approaches.
Facilitates comparative analysis of ChIP-chip data across experiments and across different microarray platforms. Starr provides functions for data import, quality assessment, data visualization and exploration. It includes high-level analysis tools such as the alignment of ChIP signals along annotated features, correlation analysis of ChIP signals with complementary genomic data, peak-finding and comparative display of multiple clusters of binding profiles. It uses standard Bioconductor classes for maximum compatibility with other software. Moreover, Starr automatically updates microarray probe annotation files by a highly efficient remapping of microarray probe sequences to an arbitrary genome. Finally, Starr enables systematic assessment of binding behaviour for groups of genes that are alingned along arbitrary genomic features.
Aims to improve identification of transcription factor (TF) target gene from ChIP-seq and ChIP-chip data using publicly available gene expression profiles. ChIPXpress is an R package that uses regulatory information from the diverse cell types, tissues, and diseases in publicly available gene expression data (PED) to enhance functional TF target gene prediction from ChIPx data. The software was developed using two large compendiums of human and mouse gene expression profiles collected from GEO.
RAP / Rank Align PWM
Infers binding-site motifs from PBM data. RAP outperforms prior art both in predicting in vitro binding and in producing motifs similar to literature motifs. The method works in four phases: (i) ranking phase: rank all 8-mers by the average binding intensity of the probes in which they appear; (ii) align the top 500 8-mers to the top-scoring 8-mer using star alignment; (iii) use the aligned 8-mers to build a PWM. The core matrix is of length 8. In each column of the PWM, the nucleotide probabilities are calculated according to a weighted count in the corresponding column of the alignment; (iv) the matrix is extended to both sides according to the original probes that contain each of the aligned 8-mers.
ACME / Algorithms for Calculating Microarray Enrichment
A package based on a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". ACME does not rely on a specific array technology (although the array should be a "tiling" array), it is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. ACME is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory.
HATSEQ / Hypergeometric Analysis of Tiling-array and SEQuence data
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.
Allows visualization and analysis of data generated on Illumina array platforms. GenomeStudio is a data analysis tool that provides three modules: (1) Genotyping Module for the analysis of single nucleotide polymorphism (SNPs) and copy number variations (CNVs) data and detection of sample outliers, (2) Gene Expression Module for the detection of cytosine methylation at single-base resolution and identification of methylation signatures across the entire genome, and (3) Polyploid Genotyping Module for the analysis of polyploid organism genotyping data.
Designed for finding motifs whenever sequences are associated with a semi-quantitative measure of protein-DNA-binding affinity. RankMotif++ algorithm uses the microarray data to infer relative binding preferences of the Transcription factors (TF) for pairs of probes and learns a motif model that is consistent with these preferences. Ultimately, RankMotif++ is applicable for a wide variety of relationships between the TF-binding affinity and semi-quantitative readout of that affinity.
Sandcastle / Software for the Analysis and Normalisation of Data from ChIP-chip AssayS of Two or more Linked Experiments
An R package which allows for comparative analyses of data from multiple experiments by normalizing all datasets to a common background. Sandcastle contains a new normalisation procedure, enabling the extraction of previously irretrievable information from datasets, an enriched region detection algorithm, which removes the need for the application of a multiple testing correction, and a suite of tools for analysing the resulting data. This enables a new dimension of ChIP-chip analyses to be undertaken which may be applied to legacy, as well as new, datasets. The normalisation procedure has been validated with Q-PCR at a number of sites across the yeast genome and the EDM has been tested with a number of simulated datasets and compared to ChIPOTle, an existing peak detection method.
CARPET / Collection of Automated Routine Programs for Easy Tilling
Serves for the analysis of ChIP-chip and expression tiling data, both for standard and custom chip designs. CARPET is a program that merges ChIP-chip and expression tiling profile experiments to extract additional biological and functional meaning from the results. Its main assets are that (1) it provides a collection of coordinated programs directly accessible through the web; or (2) the integration of CARPET with the Galaxy2 environment is possible by users and allows the graphical visualization of results.
CoCo / ChIP-on-Chip online
A web-based tool dedicated to ChIP-on-chip data visualization, analysis and knowledge storage. To support target gene assignment, CoCo integrates diverse types of meta-data, including the genomic context around the transcription factor (TF)-bound regions, in situ hybridization data indicating the tissues where neighbouring genes are expressed, and expression profiling data indicating the response of surrounding genes to different perturbations. As a specialized tool, CoCo implements several features adapted to ChIP-on-chip data visualization and provides key requirements for the management of complex analysis projects. To visualize large numbers of experiments simultaneously, the CoCo display is designed to pack essential features together, rather than stacking data in sequential tracks as in the generic data displays offered by genome browsers such as GBrowse, UCSC or Ensembl.
TAAPP / Tiling Array Analysis Pipeline for Prokaryotes
Provides a computational pipeline which is tailored for prokaryotic tiling array data. TAAPP consists of two modules: the first module handles normalized data from single color arrays, identifies expressed probes and then joins them to generate transcriptionally active regions (TARs); the second module maps these identified TARs back to the existing genome annotation, facilitating identification of sRNA elements, gene expression, operon structures and antisense RNA. sRNA elements can be identified in the non-coding area of genome where no annotation is available on either strand whereas antisense RNA is usually identified on the opposite strand of any annotated gene/RNA. The design of TAAPP into two separate modules allows data from two color tiling arrays (after analysis into differentially expressed TARs) to be mapped onto the genome directly using the second module. The web interface is freely available for academic use.
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