RNA-Seq sample

Load the RNA-Seq sample workflow into your current working directory.

library(systemPipeRdata)
genWorkenvir(workflow="rnaseq")
setwd("rnaseq")

Run workflow

Next, run the chosen sample workflow systemPipeRNAseq (PDF, Rnw) by executing from the command-line make -B within the rnaseq directory. Alternatively, one can run the code from the provided *.Rnw template file from within R interactively.

Workflow includes following steps:

  1. Read preprocessing
    • Quality filtering (trimming)
    • FASTQ quality report
  2. Alignments: Tophat2 (or any other RNA-Seq aligner)
  3. Alignment stats
  4. Read counting
  5. Sample-wise correlation analysis
  6. Analysis of differentially expressed genes (DEGs)
  7. GO term enrichment analysis
  8. Gene-wise clustering

ChIP-Seq sample

Load the ChIP-Seq sample workflow into your current working directory.

library(systemPipeRdata)
genWorkenvir(workflow="chipseq")
setwd("chipseq")

Run workflow

Next, run the chosen sample workflow systemPipeChIPseq_single (PDF, Rnw) by executing from the command-line make -B within the chipseq directory. Alternatively, one can run the code from the provided *.Rnw template file from within R interactively.

Workflow includes following steps:

  1. Read preprocessing
    • Quality filtering (trimming)
    • FASTQ quality report
  2. Alignments: Bowtie2 or rsubread
  3. Alignment stats
  4. Peak calling: MACS2, BayesPeak
  5. Peak annotation with genomic context
  6. Differential binding analysis
  7. GO term enrichment analysis
  8. Motif analysis

VAR-Seq sample

VAR-Seq workflow for single machine

Load the VAR-Seq sample workflow into your current working directory.

library(systemPipeRdata)
genWorkenvir(workflow="varseq")
setwd("varseq")

Run workflow

Next, run the chosen sample workflow systemPipeVARseq_single (PDF, Rnw) by executing from the command-line make -B within the varseq directory. Alternatively, one can run the code from the provided *.Rnw template file from within R interactively.

Workflow includes following steps:

  1. Read preprocessing
    • Quality filtering (trimming)
    • FASTQ quality report
  2. Alignments: gsnap, bwa
  3. Variant calling: VariantTools, GATK, BCFtools
  4. Variant filtering: VariantTools and VariantAnnotation
  5. Variant annotation: VariantAnnotation
  6. Combine results from many samples
  7. Summary statistics of samples

VAR-Seq workflow for computer cluster

The workflow template provided for this step is called systemPipeVARseq.Rnw (PDF, Rnw). It runs the above VAR-Seq workflow in parallel on multiple computer nodes of an HPC system using Torque as scheduler.

Ribo-Seq sample

Load the Ribo-Seq sample workflow into your current working directory.

library(systemPipeRdata)
genWorkenvir(workflow="riboseq")
setwd("riboseq")

Run workflow

Next, run the chosen sample workflow systemPipeRIBOseq (PDF, Rnw) by executing from the command-line make -B within the ribseq directory. Alternatively, one can run the code from the provided *.Rnw template file from within R interactively.

Workflow includes following steps:

  1. Read preprocessing
    • Adaptor trimming and quality filtering
    • FASTQ quality report
  2. Alignments: Tophat2 (or any other RNA-Seq aligner)
  3. Alignment stats
  4. Compute read distribution across genomic features
  5. Adding custom features to workflow (e.g. uORFs)
  6. Genomic read coverage along transcripts
  7. Read counting
  8. Sample-wise correlation analysis
  9. Analysis of differentially expressed genes (DEGs)
  10. GO term enrichment analysis
  11. Gene-wise clustering
  12. Differential ribosome binding (translational efficiency)
Jump to: next_page