FourCSeq is an R package dedicated to the analysis of (multiplexed) 4C sequencing data. The package provides a pipeline to detect specific interactions between DNA elements and identify differential interactions between conditions. The statistical analysis in R starts with individual bam files for each sample as inputs. To obtain these files, the package contains a python script (extdata/python/demultiplex.py) to demultiplex libraries and trim off primer sequences. With a standard alignment software the required bam files can be then be generated.
|Author||Felix A. Klein, EMBL Heidelberg|
|Date of publication||None|
|Maintainer||Felix A. Klein <firstname.lastname@example.org>|
|License||GPL (>= 3)|
addFragments: Add the restriction fragment information
addPeaks: Add peaks based on z-scores and adjusted p-values
addViewpointFrags: Add the information of the viewpoint fragments
combineFragEnds: Combine the counts of both fragment ends.
countFragmentOverlaps: Count fragment overlaps
countFragmentOverlapsSecondCutter: Count fragment overlaps when sequencing was performed from...
distFitMonotone: Fit the distance dependency
distFitMonotoneSymmetric: Fit the distance dependency
fc: FourC object with counts
fcf: FourC object with z-scores
findViewpointFragments: Find the fragments to which the viewpoint primers map.
getAllResults: FourCSeq analysis results
getDifferences: Detect differences
getNormalizationFactors: Get normalization factors for each fragment
getReferenceSeq: Function to read reference sequences
getZScores: Calculate z-scores using the residuals of the general trend...
normalizeRPM: Normalize count data to rpm
plotDifferences: Plot differences
plotFits: Plot fit results.
plotNormalizationFactors: Plot the estimated normalization factors.
plotZScores: Plot z-score results.
smoothCounts: Smooth the counts of neighboring fragments
smoothHitPerCent: Smooth the hits of neighboring fragments
writeTrackFiles: Write track files of an selected 'assay'