r-make
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 star, tophat2, bowtie2, bedtools, fastx-toolkit, samtools, rseqc, and others.
g-make
Description: g-make is a pipeline that processes next-generation DNA sequencing data, including whole genome and exome-captured sequencing data. g-make takes raw FASTQ files as input, aligns them using BWA, and then processes the aligned reads through the picard and GATK pipelines. In addition, g-make prints out plots and statistics for a variety of quality control metrics, including percentage of mapped reads, number of read duplicates, nucleotide frequencies, base quality-scores, GC content distribution, percent error, estimated insert size, average sequencing depth per chromosome, and percent of whole genome covered. Managed by make, g-make enables massive parallelization of intricate analyses with minimal user intervention.
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 star, tophat2, bowtie2, bedtools, fastx-toolkit, samtools, rseqc, and others.
g-make
Description: g-make is a pipeline that processes next-generation DNA sequencing data, including whole genome and exome-captured sequencing data. g-make takes raw FASTQ files as input, aligns them using BWA, and then processes the aligned reads through the picard and GATK pipelines. In addition, g-make prints out plots and statistics for a variety of quality control metrics, including percentage of mapped reads, number of read duplicates, nucleotide frequencies, base quality-scores, GC content distribution, percent error, estimated insert size, average sequencing depth per chromosome, and percent of whole genome covered. Managed by make, g-make enables massive parallelization of intricate analyses with minimal user intervention.