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wavClusteR (version 2.6.2)

Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data

Description

The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq).

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Version

Version

2.6.2

License

GPL-2

Maintainer

Federico Comoglio

Last Published

February 15th, 2017

Functions in wavClusteR (2.6.2)

exportCoverage

Export coverage as BigWig track
exportSequences

Export cluster sequences for motif search analysis
getAllSub

Identify all substitutions observed across genomic positions exhibiting a specified minimum coverage
getMetaGene

Compute and plot metagene profile using identified clusters
estimateFDR

Estimate False Discovery Rate within the relative substitution frequency support by integrating PAR-CLIP data and RNA-Seq data
plotStatistics

Pairs plot visualization of clusters statistics
getMetaTSS

Compute and plot read densities in genomic regions around transcription start sites
wavClusteR-package

A comprehensive pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non- experimental sources by a non-parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq).
plotSizeDistribution

Plot the distribution of cluster sizes
plotSubstitutions

Barplot visualization of the number of genomic positions exhibiting a given substitution and, if model provided, additional diagnostic plots.
model

Components of the non-parametric mixture moodel fitted on Ago2 PAR-CLIP data
getMetaCoverage

Compute and plot distribution of average coverage or relative log-odds as metagene profile using identified clusters
annotateClusters

Annotate clusters with respect to transcript features
getHighConfSub

Classify substitutions based on identified RSF interval and return high confidence transitions
filterClusters

Merge clusters and compute all relevant cluster statistics
fitMixtureModel

Fit a non-parametric mixture model from all identified substitutions
getClusters

Identify clusters containing high-confidence substitutions and resolve boundaries at high resolution
readSortedBam

Load a sorted BAM file
getExpInterval

Identify the interval of relative substitution frequencies dominated by experimental induction.
exportHighConfSub

Export high-confidence substitutions as BED track
exportClusters

Export clusters as BED track