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CHANCE / CHIP-seq ANalytics and Confidence Estimation
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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.
cnvCSEM / CNV-guided ChIP-Seq by expectation-maximization algorithm
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.
Coda
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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.
phantompeakqualtools
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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
SMARTcleaner / Switching Mechanism At the 5’ end of the RNA 27 Transcript cleaner
Recognizes and deletes artifact reads in both paired-end (PE) and single-end (SE) ChIP-seq data, leading to improved ChIP-seq results. SMARTcleaner removes the artifact reads arising from false priming and amplification of the SMART poly(dA) primers. It can prepare the files required for the cleaning process. This tool is adaptable to ChIP-seq analytic pipelines. This tool aims to improve in peak calling, and downstream data analysis and interpretation.
ABS filter
Obsolete
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.
caCORRECT / chip artifact CORRECTion
Obsolete
Aims to assist users in exploiting microarrays data. caCORRECT aims to improve downstream analysis, particularly in biomarker selection and translational bioinformatics. The application merges methods of normalization and multi-chip variance calculations based on technical or biological replicates to generate crisp representations of artifacts. It allows to both identify and remove errors in microarray experiments as well as describe experimental datasets and chips according to quality scores.
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