Quality assessment software tools | ChIP sequencing data analysis
The absence of a quality control (QC) system is a major weakness for the comparative analysis of genome-wide profiles generated by next-generation sequencing (NGS). This concerns particularly genome binding/occupancy profiling assays like chromatin immunoprecipitation (ChIP-seq) but also related enrichment-based studies like methylated DNA immunoprecipitation/methylated DNA binding domain sequencing, global run on sequencing or RNA-seq.
Preprocesses HiChIP data. hichipper is a standalone application including functions for: (i) bias-corrected peak calling; (ii) library quality controlling (QC); (iii) DNA loop calling. The software generates files that can be exploited for downstream analysis and visualization as well as a QC report delivering information such as the efficiency of proximity ligation enabling the assessment of library preparation.
Assists in ChIP-seq quality control and protocol optimization. CHANCE assesses the strength of immunoprecipitation (IP) enrichment to identify potentially failed experiments. It permits to identify insufficient sequencing depth, polymerase chain reaction (PCR) amplification bias in library preparation, and batch effects. It also identifies biases in sequence content and quality, as well as cell-type and laboratory-dependent biases in read density.
Uses convolutional neural networks to learn a mapping from suboptimal to high-quality histone ChIP-seq data. Coda uses a high-dimensional discriminative model to encode a generative noise process. The tool transfers information from generative noise processes to a flexible discriminative model that can be used to denoise new data. It has the potential to improve data quality at reduced costs. The Coda’s performance depends on the similarity of the noise distributions and underlying data distributions in the test and training sets.
Assists users to evaluate the quality of ChIP-seq. The NGS-QC database hosts quality scores for over 28,000 datasets, covering a variety of ChIP-sequencing and related assays performed from about 8 species (Homo sapiens: 54%; Mus musculus: 34%; D. melanogaster: 6%). It contains an algorithm designed to: (i) infer a set of global QC indicators (QCis); or to (ii) provide local QCis to judge the robustness of cumulative read counts in a particular region.
Accounts for GC-content bias separately for effects related to protein binding and differential non-specific binding. gcapc is based on a mixture model. It reduces false-positive peaks for any predefined bin and improves agreement across laboratories. This tool allows independent adjustments of the signal and background signals and thus circumvents the confounding challenge and can be incorporated into most current peak callers.
A package to process multiple ChIP-seq BAM files and detect allele-specific events. BaalChIP computes allele counts at individual variants (SNPs/SNVs), implements extensive quality control steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples.
Computes quick but highly informative enrichment and quality measures for ChIP-seq/DNase-seq/FAIRE-seq/MNase-seq data. It can also be used to obtain robust estimates of the predominant fragment length or characteristic tag shift values in these assays. Phantompeakqualtools can be used to (i) Compute the predominant insert-size (fragment length) based on strand cross-correlation peak; (ii) compute data quality measures based on relative phantom peak; (iii) call peaks and regions for punctate binding datasets
Assists in the analysis of ChIP-seq. CCAT is a general-purpose framework that provides a linear signal-noise model for ChIP-seq analysis with negative control. This application estimates the noise rate using control library and derived a library-swapping approach for false discovery rate (FDR) estimation. An option of bootstrapping is also included in this method.
Guides multi-read allocation by copy-number variations (CNVs). cnvCSEM is a flexible framework that takes advantage of the state-of-the-art multi-read allocation algorithms and incorporates CNV information parsimoniously. Data-driven simulation results showed that the software (i) increases multi-read allocation coverage, (ii) reduces allocation ambiguity in the segmental duplication regions (SDR) with only a marginal loss in accuracy, and (iii) improves the accuracy of the read-depth recovery.
Allows users to discretize ChIP-seq data. Zerone combines an arbitrary number of ChIP-seq replicates in a single discretized profile, where conflicts are resolved by maximizing the likelihood of the underlying statistical model. This program controls the quality of its output to detect potential anomalies. This tool produces congruent window-based outputs, and it can process hundreds of experiments per day on average hardware.
Provides a more sensitive signal-to-noise ratio (S/N) indicator than current methods both for point- and broad-source marks and is robust for various cell types and sequencing depth. SSP is a peak calling–free quality assessment tool for read enrichment in ChIP-seq data. It supplies a useful way to assess and obtain additional information about sample quality and traits for ChIP-seq analyses.
Provides tools for both low and high-level analysis of next generation, ultra-high throughput signature sequencing data. USeq is an R package that aims to support the development of next generation sequencing data analysis applications. The software contains more than 25 command line applications written in Java, including ChIP-Seq Apps, RNA-Seq Apps, RNA Editing Apps, Sam Alignment Apps, or Converter Apps.
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).
Measures Next-generation sequencing (NGS)/ChIP-seq experiment quality through global peak alignment comparison. COPAR can extract genomic features based on spectrum method for in-depth analysis of ChIPsequencing profiles. It is able to process mapped read file in BED format and output statistically sound results for diverse high-throughput sequencing (HTS) experiments.
Automates quality controls and data analyses on ChIP-seq and DNase-seq data. ChiLin generates comprehensive quality control reports that include comparisons with historical data derived from over 23,677 public ChIP-seq and DNase-seq samples (11,265 datasets) from eight literature-based classified categories. Therefore, ChiLin can be an attractive solution to rapidly process batches of ChIP-seq datasets in an automated manner with detailed QC reports.
Provides an assortment of approaches for ChIP-seq datasets analysis. echipp supplies various methods and functions for alignment, quality control and statistical. The application is a pipeline composed of a package coupled to multiple scripts and is able to handle various datasets including the large ones.
Analyzes next generation sequencing (NGS) data. Basepair is a web platform that permits to choose among high-quality workflows for RNA-Seq, ChIP-Seq, DNA-Seq, or ATAC-Seq. It provides public and fully automated workflows, but users can create custom private workflows. Every analysis offers a clutter-free report with important stats and figures. Workflows display all steps of the analysis to assist users to understand exactly how their data will be analyzed.
Identifies and reduces the bias of clonal amplification in allele-specific (AS) analysis of ChIP-seq data. ABS filter is an R package that filters out many of the likely false-positive sites and improves the overall reliability of the data. The software analyzes the read alignment distribution around heterozygous single nucleotide polymorphism (SNP) sites and removes highly clonal, low complexity sites. It can be useful for studies aiming to use ChIP-seq technology to identify AS molecular changes.
Allows users to count and eliminate biases within ChIP-seq signals. BIDCHIPS is a standalone software able to detect biases individually or jointly and is available through two different implementations: MATLAB or R. The program can be used to construct the background model for a ChIP-seq dataset, and then, for set of genomic intervals given by the user, to evaluate the purified binding signal.
Topics (10): ChIP-seq analysis, DNase-seq analysis, De novo sequencing analysis, Mus musculus, Homo sapiens, Heredodegenerative Disorders, Nervous System, Movement Disorders, Brain Diseases, Demyelinating Autoimmune Diseases, CNS, Autoimmune Diseases of the Nervous System