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ERANGE / Enhanced Read Analysis of Gene Expression
A software tool for mapping and quantifying Mammalian transcriptomes by RNA-Seq. The functions of ERANGE are to (i) assign reads that map uniquely in the genome to their site of origin and, for reads that match equally well to several sites ('multireads'), assign them to their most likely site(s) of origin; (ii) detect splice-crossing reads and assign them to their gene of origin; (iii) organize reads that cluster together, but do not map to an already known exon, into candidate exons or parts of exons; and (iv) calculate the prevalence of transcripts from each known or newly proposed RNA, based on normalized counts of unique reads, spliced reads and multireads.
HOMER / Hypergeometric Optimization of Motif EnRichment
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Performs peak finding and downstream data analysis for next-generation sequencing analysis. HOMER affords several tools and methods to make use of ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and other types of functional genomics sequencing data sets. This software offers support to UCSC visualization, peaks annotation, quantification of transcripts and repeats or differential features, enrichment and expression.
MACS / Model-based Analysis for ChIP-Seq
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Analyzes data generated by short read sequencers. MACS is a standalone software dedicated to the forecasting of protein-DNA interaction sites from ChIP-Seq. The application is able to: (i) model the distance d and shifting tags by d/2 to enable the spatial resolution of the predicted sites; (ii) capture local biases in the genome by exploiting a dynamic λ local parameter and (iii) evaluate the false discovery rate (FDR) for each detected peak.
DANPOS / Dynamic Analysis of Nucleosome and Protein Occupancy by Sequencing
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Enables comparative analysis of nucleosome physical organization at single-nucleotide resolution. DANPOS is a bioinformatics pipeline allowing dynamic analysis of nucleosome position and occupancy. The software is useful for detecting functionally relevant dynamic nucleosomes, not only in promoters in yeast, but also in distal regulatory regions with more fuzzy nucleosomes, in complex genomes such as those of mammalian.
SICER / spatial clustering approach for the identification of ChIP-enriched regions
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Recognizes ChIP-enriched regions in histone modification data. SCIER is based on a mathematical theory for the score distribution in a genomic background model of random reads. It finds spatial clusters, large and small, unlikely to appear by chance. This tool is able to deal with the enrichment context of a local window in determining its significance. It assists users to reduce the sampling fluctuations in the control library.
F-Seq
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Creates a continuous tag sequence density estimation to identify biologically meaningful sites. F-Seq can summarize and display individual sequence data as an accurate and interpretable signal, allowing tag sequencing to identify specific sequence features like regions of open chromatin (DNase-seq) or transcription factor binding sites (ChIP-seq). Outputs results can be visualized through the UCSC Genome Browser.
ZINBA / Zero-Inflated Negative Binomial Algorithm
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Recognizes genomic regions enriched for sequenced reads across a wide spectrum of signal patterns and experimental conditions. ZINBA is based on a mixture regression approach that classifies genomic regions into three general components: background, enrichment, and an artificial zero count. It can preprocess data, determine significantly enriched regions, and refine boundary for more narrow sites.
Vancouver Short Read Analysis
A package for collating and searching across thousands of next-generation sequence (NGS) samples. Vancouver Short Read Analysis provides a database can be installed easily to rapidly access and store genetic variation information, compare data from any sequencing platform and perform aggregate analyses. The schema of the database makes rapid and insightful queries simple and enables easy annotation of novel or known genetic variations. Filtering can be done by utilizing annotations, matched pair datasets or datasets marked as non-cancer for separating polymorphisms from putative variants.
BRACIL / Binding Resolution Amplifier and Cooperative Interaction Locator
Improves binding site resolution and predicts cooperative interactions. BRACIL can study physical properties of DNA shearing from the ChIP-seq coverage. It incorporates motif discovery and is able to detect multiple sites in an enriched region with single nucleotide resolution, high sensitivity, and high specificity. The tool improves peak caller sensitivity, from less than 45% up to 94%, at a false positive rate <11% for a set of 47 experimentally validated prokaryotic sites.
Jmosaics
A probabilistic method for jointly analyzing multiple ChIP-seq datasets. jMOSAiCS (joint model-based one- and two-sample analysis and inference for ChIP-seq) is a probabilistic model for integrating multiple ChIP-seq datasets to identify combinatorial patterns of enrichment. The key components of jMOSAiCS are base models for the sequencing reads of each individual ChIP-seq experiment and a model that governs the relationship of enrichment among different samples. It facilitates joint analysis of multiple ChIP-seq datasets for both identifying enrichment patterns of a single TF across multiple conditions and characterizing enrichment patterns of multiple epigenomic marks in one or more conditions.
Octopus-toolkit
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.
EDD / Enriched Domain Detector
Allows discovery of broad genomic enrichment areas from ChIP-seq data. EDD is a genomic domain caller designed to detect megabase-size domains. The software enables quantitative analysis of ChIP-seq data for proteins widely distributed and with low-level enrichment on chromatin. It can discover genomic domains enriched in lamin A (LMNA) using new ChIP-seq data for LMNA. The main advantages of EDD are its sensitivity to the size of domains rather than the strength of enrichment at a particular site and its robustness against local variations.
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.
JAMM / Joint Analysis of NGS replicates via Mixture Model clustering
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.
BayesPeak
Provides a flexible implementation of the BayesPeak algorithm and is compatible with downstream BioConductor packages. The BayesPeak package introduces a new method for summarizing posterior probability output, along with methods for handling overfitting and support for parallel processing. It provides a Bayesian analysis, with advantages including allowance for overdispersion in read counts and a competitive genome-wide specificity and sensitivity. By anticipating peak structure, BayesPeak does not call peaks based on sheer numbers of reads without appropriate read formation.
PePr / Peak Prioritization pipeline
A ChIP-seq peak-calling and prioritization pipeline that uses a sliding window approach and models read counts across replicates and between groups with a negative binomial distribution. PePr empirically estimates the optimal shift/fragment size and sliding window width, and estimates dispersion from the local genomic area. Regions with less variability across replicates are ranked more favorably than regions with greater variability. Optional post-processing steps are also made available to filter out peaks not exhibiting the expected shift size and/or to narrow the width of peaks.
MSPC
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.
Pasha / Preprocessing of Aligned Sequences from HTS Analyses
An R package designed for processing aligned reads from chromatin-oriented high-throughput sequencing experiments. Pasha allows easy manipulation of aligned reads from short-read sequencing technologies (ChIP-seq, FAIRE-seq, MNase-seq...) and offers innovative approaches such as ChIP-seq reads elongation, nucleosome midpoint piling strategy for positioning analyses, or the ability to subset paired-end reads by groups of insert size that can contain biologically relevant information. It integrates several options allowing a seamless adaptation to various experimental setups. Additionally, the R package provides several tools for programmers that need to develop or integrate additional features.
PeakError
Computes the annotation error of peak calls. PeakError allows, after constructing a database of annotated regions that represent your visual interpretation of the peak locations in a ChIP-seq experiment, to compute the error of a peak calling model. PeakError proposes to create labels that encode an experienced scientist’s judgment about which regions contain or do not contain peaks. The labels can then be used as a gold standard to quantitatively train and test peak detection algorithms on specific data sets.
CSAR / ChIP-Seq Analysis in R
An R package for the statistical analysis of ChIP-seq experiments. CSAR calculates single-nucleotide read-enrichment values, taking the average size of DNA fragments subjected to sequencing into account. After normalization, sample and control are compared using a test based on the ratio test or the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutations. Computational efficiency is achieved by implementing the most time-consuming functions in C++ and integrating these in the R package.
BiSA / Binding Sites Analyser
Identifies genes located near binding regions of interest, genomic features near a gene or locus of interest and statistical significance of overlapping regions can also be reported. BiSA is a transcription factor DNA binding site analyser software for archiving of binding regions and easy identification of overlap with or proximity to other regions of interest. It is capable of reporting overlapping regions that share common base pairs; regions that are nearby; regions that are not overlapping; and average region sizes.
DROMPA / DRaw and Observe Multiple enrichment Profiles and Annotation
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Allows peak calling, visualization, quality check and Polymerase Chain Reaction (PCR) bias filtering of ChIP-seq data. DROMPA calls peaks by comparing the read distribution of the ChIP sample with that of the corresponding input sample. The software identifies peaks as bar graph protein-binding sites when the peaks are sharp (approximately 1 kbp) and when they are broad (approximately 1 Mbp). It can accept multiple mapped reads (reads mapped on multiple loci of the reference genome).
PeakRanger
A peak-caller package that works equally well on punctate and broad sites. PeakRanger can resolve closely-spaced peaks, has excellent performance, and is easily customized. It can be run in a parallel cloud computing environment to obtain extremely high performance on very large data sets. PeakRanger has above-average spatial accuracy in terms of identifying the precise location of binding events. PeakRanger also has excellent sensitivity and specificity in all benchmarks evaluated.
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