1 - 14 of 14 results

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

Zerone

A ChIP-seq discretizer with built-in quality control. Zerone is powered by a hidden Markov model with zero-inflated negative multinomial emissions, which allows it to merge several replicates into a single discretized profile. To identify low quality or irreproducible data, we trained a support vector machine and integrated it as part of the discretization process. The result is a classifier reaching 95% accuracy in detecting low quality profiles. We also introduce a graphical representation to compare discretization quality and we show that Zerone achieves outstanding accuracy. Zerone is designed for large volume pipelines aiming to combine many ChIP-seq profiles with little human intervention. To this end, it is compatible with the standard BED, SAM/BAM, and GEM formats, it produces congruent window-based outputs, and it can process hundreds of experiments per day on average hardware.

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.

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.

BIDCHIPS / BIas Decomposition of CHIP-seq Signals

Obsolete
A framework to quantify the roles of different types of biases in influencing the genome-wide ChIP-seq signal using a large compendium of ENCODE datasets. Our model, along with the accompanying software package, has general applicability, yields a better ranking of peaks and a better estimate of the binding signal than competing methods, and has led to several other new insights including (1) background influences are greater at larger scales, (2) mappability and chromatin accessibility significantly influence the ChIP-seq signal, (3) transcription factor ChIP-seq signals have a higher proportion of non-binding influences compared to histone mark ChIP-seq signals, and (4) confounders need to be accounted for before measuring relationships between gene expression and ChIP-seq signals around TSSs. BIDCHIPS can be used to first build the background model corresponding to a ChIP-seq dataset, and then estimate the purified binding signal for a user-given set of genomic intervals (e.g., peaks).

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.