Description: r-make is inspired by Solexa’s original pipeline. Solexa’s original pipeline (and Illumina’s current implementation of it) is powered by make. make automates the building of large, complicated processes by traversing dependency chains, allowing for unguided parallelization by abstraction. r-make picks up where Illumina leaves off. Specifically, r-make is, Illumina, meet the latest sequencing analysis tools, including tophat2, bowtie2, bedtools, fastx-toolkit, samtools, rseqc, and others.
(DEPRECATED, see r-make) Description: PPBS is a toolkit for the rapid analysis and characterization of RNA sequencing data. It utilizes a hybrid code based on GNU parallel, awk, BEDtools, SAMtools, TopHat, and R.
Description: A moving-window, peak-finding algorithm for Methylated RNA Immunoprecipitation (MeRIP) data sets.
Description: methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods such as Agilent SureSelect methyl-seq. It can potentially handle whole-genome bisulfite sequencing data if proper input format is provided.
Description: Comprehensive DMR analysis based on bimodal normal distribution model and weighted cost function for regional methylation analysis optimization. It captures the regional methylation modification by taking the spatial distribution of CpGs into account for the enrichment DNA methylation sequencing data so as to optimize the definition of the empirical regions. Combined with the dependent adjustment for regional p-value combination and DMR annotation.
Publications: Sheng Li, Francine E. Garrett-Bakelman, Altuna Akalin, Paul Zumbo, Ross Levine, Bik L. To, Ian D. Lewis, Anna L. Brown, Richard J. D’Andrea, Ari Melnick, and Christopher E. Mason, “An optimized algorithm for detecting and annotating regional differential methylation,” BMC bioinformatics, vol. 14 Suppl 5, p. S10, 2013.
Description: methclone can efficiently analyzes genome-wide DNA methylation data to identify the epigenetic loci that harbor large changes in the clonality of the epigenetic alleles (epialleles). We quantify the changes using a composition entropy calculation (ΔS) and also introduce a new measure of global clonality shift, epialleles per million CpGs (EPM), which enables comparisons between different samples to gauge global changes in epiallelic diversity.
Scripts for data analysis in relapsed AML study
An R package for genomic feature analysis and annotation by extending bioconductor packages.
This program is designed to simplify all stages of predicting and analyzing CNVs, from running prediction algorithms and combining their results to visualizing the raw data and designing qPCR primers for confirmation