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GenoGAM / Genome-wide Generalized Additive Model
Offers methods dedicated to ChIP-Seq factorial design experiments modelization. GenoGAM is an R package employing generalized additive models to perform statistical analysis of genome-wide data. It provides base-level and region-level significance testing with controlled type I error rate. Additionally, the software is able to fit generalized additive models (GAMs) to very long longitudinal data such as whole chromosomes at base-pair resolution.
BEADS / Bias Elimination Algorithm for Deep Sequencing
Allows correction of nucleotide composition bias, mappability variations and differential local DNA structural effects in deep sequencing data. BEADS is a three-step normalization scheme that estimates and corrects for data set-specific biases. The software can unmask real binding patterns in ChIP-seq data and remove systematic biases present in high-throughput sequencing data. BEADS normalization can assist in detecting genome copy number variations, where bias in the distribution of mapped sequence reads could mask or enlarge differences in copy number.
Provides a framework for the de novo detection of differentially bound genomic regions. csaw uses a window-based strategy to summarize read counts across the genome. It exploits existing statistical software to test for significant differences in each window. Finally, it clusters windows into regions for output and controls the false discovery rate properly over all detected regions. The csaw package can handle arbitrarily complex experimental designs involving biological replicates. It can be applied to both transcription factor and histone mark datasets, and, more generally, to any type of sequencing data measuring genomic coverage. csaw performs favorably against existing methods for de novo DB analyses on both simulated and real data.
Aims to detect differences in histone mark profiles between samples with significant genomic discrepancies. HMCan-diff is designed to analyze ChIP-seq data to detect changes in histone modifications between two cancer samples of different genetic backgrounds, or between a cancer sample and a normal control. HMCan-diff explicitly corrects for copy number bias, and for other biases in the ChIP-seq data, which significantly improves prediction accuracy compared to methods that do not consider such corrections.
Detects differential binding in a quantitatively principled way by formally testing hypothesis of non-differential binding at each putative binding site. DBChIP assigns uncertainty measure (P-values) to each finding, and thus, proper error rate control can be achieved. Furthermore, when there are more than two conditions for comparison (K>2), DBChIP can be used to detect pairwise differences after the detection of overall differential binding. Moreover, DBChIP does not rely on a specific sequencing platform and can accommodate data from Illumina, SOLiD and other platforms.
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.
DIME / Differential Identification using Mixtures Ensemble
A package for identification of biologically significant differential binding sites between two conditions using ChIP-seq data. DIME considers a collection of finite mixture models combined with a false discovery rate (FDR) criterion to find statistically significant regions. This leads to a more reliable assessment of differential binding sites based on a statistical approach. In addition to ChIP-seq, DIME is also applicable to data from other high-throughput platforms.
A quantitative method for comparing two biological ChIP-seq samples. The method employs a new global normalization method: nonparametric empirical Bayes (NEB) correction normalization, utilizes pre-defined enriched regions identified from single-sample peak calling programs, uses statistical methods to define differential enriched regions, then defines binding (histone modification) pattern information for those differential enriched regions. QChIPat was tested on a benchmark data: histone modifications data used by ChIPDiffs. It was then applied on two study cases: one to identify differential histone modification sites for ChIP-seq of H3K27me3 and H3K9me2 data in AKT1-transfected MCF10A cells; the other to identify differential binding sites for ChIP-seq of TCF7L2 data in MCF7 and PANC1 cells.
normR / normR obeys regime mixture rules
A tool for normalization and difference calling in ChIP-seq data. normR performs normalization and difference calling simultaneously to identify genomic regions enriched by the ChIP-procedure. In addition, normR enables the comparison between ChIP-seq data obtained from different conditions allowing for unraveling genomic regions that change their association with the ChIP-target. Lastly, normR is capable to differentiate multiple regimes of enrichment, i.e. broad domains and sharp peaks.
An algorithm for the computational inference of combinatorial chromatin state dynamics across an arbitrary number of conditions. ChromstaR uses a multivariate Hidden Markov Model to determine the number of discrete combinatorial chromatin states using multiple ChIP-seq experiments as input and assigns every genomic region to a state based on the presence/absence of each modification in every condition. chromstaR is a versatile computational tool that facilitates a deeper biological understanding of chromatin organization and dynamics.
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