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

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Documentation for package ‘wavClusteR’ version 2.32.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

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).
annotateClusters	Annotate clusters with respect to transcript features
estimateFDR	Estimate False Discovery Rate within the relative substitution frequency support by integrating PAR-CLIP data and RNA-Seq data
exportClusters	Export clusters as BED track
exportCoverage	Export coverage as BigWig track
exportHighConfSub	Export high-confidence substitutions as BED track
exportSequences	Export cluster sequences for motif search analysis
filterClusters	Merge clusters and compute all relevant cluster statistics
fitMixtureModel	Fit a non-parametric mixture model from all identified substitutions
getAllSub	Identify all substitutions observed across genomic positions exhibiting a specified minimum coverage
getClusters	Identify clusters containing high-confidence substitutions and resolve boundaries at high resolution
getExpInterval	Identify the interval of relative substitution frequencies dominated by experimental induction.
getHighConfSub	Classify substitutions based on identified RSF interval and return high confidence transitions
getMetaCoverage	Compute and plot distribution of average coverage or relative log-odds as metagene profile using identified clusters
getMetaGene	Compute and plot metagene profile using identified clusters
getMetaTSS	Compute and plot read densities in genomic regions around transcription start sites
model	Components of the non-parametric mixture moodel fitted on Ago2 PAR-CLIP data
plotSizeDistribution	Plot the distribution of cluster sizes
plotStatistics	Pairs plot visualization of clusters statistics
plotSubstitutions	Barplot visualization of the number of genomic positions exhibiting a given substitution and, if model provided, additional diagnostic plots.
readSortedBam	Load a sorted BAM file
wavClusteR	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).