sarks

Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains


Bioconductor version: Release (3.19)

Suffix Array Kernel Smoothing (see https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797), or SArKS, identifies sequence motifs whose presence correlates with numeric scores (such as differential expression statistics) assigned to the sequences (such as gene promoters). SArKS smooths over sequence similarity, quantified by location within a suffix array based on the full set of input sequences. A second round of smoothing over spatial proximity within sequences reveals multi-motif domains. Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.

Author: Dennis Wylie [aut, cre]

Maintainer: Dennis Wylie <denniscwylie at gmail.com>

Citation (from within R, enter citation("sarks")):

Installation

To install this package, start R (version "4.4") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("sarks")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("sarks")
sarks-vignette PDF R Script
Reference Manual PDF
NEWS Text
LICENSE Text

Details

biocViews DifferentialExpression, FeatureExtraction, GeneExpression, GeneRegulation, MotifDiscovery, RNASeq, Software, Transcriptomics
Version 1.16.0
In Bioconductor since BioC 3.11 (R-4.0) (4.5 years)
License BSD_3_clause + file LICENSE
Depends R (>= 4.0)
Imports rJava, Biostrings, IRanges, utils, stats, cluster, binom
System Requirements Java (>= 1.8)
URL https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797 https://github.com/denniscwylie/sarks
Bug Reports https://github.com/denniscwylie/sarks/issues
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package sarks_1.16.0.tar.gz
Windows Binary sarks_1.16.0.zip (64-bit only)
macOS Binary (x86_64) sarks_1.16.0.tgz
macOS Binary (arm64) sarks_1.16.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/sarks
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/sarks
Bioc Package Browser https://code.bioconductor.org/browse/sarks/
Package Short Url https://bioconductor.org/packages/sarks/
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