This document presents a comparative evaluation of Galaxy and Ruffus-based scripting workflows for DNA sequencing analysis pipelines. The research aims to identify the optimal workflow system by implementing DNA-seq analysis pipelines in both Galaxy and Ruffus and benchmarking their performance. Literature on existing workflow systems is reviewed. The document outlines the research objectives, design, methodology, and requirements for the DNA-seq analysis pipeline use case. Preliminary results indicate pros and cons of each approach, with further analysis of performance metrics still needed.

1. A Comparative Evaluation Among Galaxy And Ruffus-based
Scripting Workflows For DNA-seq Analysis Pipeline workflow
Supervisor: Prof Alan Christoffels Mentor: Peter van Heusden

2. Brief Background
Human genome sequencing data analysis, such as secondary analysis is by far the
most computationally intensive. This is due to the
 Size of files that must be manipulated,
 Complexity of determining optimal alignment for millions of reads to the human
reference genome, and
 Subsequently utilisation of the pipelines for variant calling and genotyping.

3. Literatures Review
This research focus on a comparative evaluation among different workflow
systems, but most specific on Galaxy and Ruffus-scripting program. There
exist a number of workflow management systems for use in bioinformatics.
Framework’s like Galaxy (Goecks et al., 2010), Arvados (Zaranek, 2013) or
Mobyle (Neron et al., 2009) have been developed mainly for application in the
life science (Marc and Uif, 2013). Some workflow systems utilise high
computing resources and lack data provenance and management. Others tend
to be CPU bound and memory intensive.

4. Research Aims
 To identify the optimal workflow system for analysis NGS data among
Galaxy, and a collection of scripts.
 To present a community resource system that can allow users of a workflows
system to decide on which workflows to use for analysis NGS data analysis
with less time consumption.
 To obtain detailed task-level performance metrics, and present it graphically.

5. Research Objectives
 Implementation of the DNAseq Galaxy workflow and ruffus-script pipeline.
 Graphical representation of Galaxy and ruffus-script pipeline
 Benchmarking the performance of Galaxy workflow, and ruffus-script
using one of the linux benchmarking tools
 Performance metric tabulation (e.g runtime, memory usage,etc) among
Galaxy and a collection of scripts pipeline.

6. Research Design and Methodology
Researchers at SANBI carried out the assembly and functional annotation of a
relatively large dataset with 400 Mycobacterium tuberculosis genome using reference
based assembly approach. Since it is more challenging to gather all the analysis steps
within a single graphical user interface, we use Galaxy and ruffus-scripting pipeline to
build an automated workflow for DNA-seq variant calling pipeline that run efficiently
on SANBI-HPC clusters.

7. Data and Requirement Analysis
 This project required and uses DNA-seq data for its analysis steps to carried out
the comparison evaluation.
 Concrete design implementation such as Galaxy and Ruffus-Scripting based
workflow system was used as design methodology stylish
Follows was a: USE CASE: DNA-seq Analysis Pipeline for this experiment

8. DNA-seq Analysis Requirements Design
Mate fixed reads per
sample
(matefixed.sorted.bam)
Reference
Genome
(Fasta)
Raw Reads
(fastq.gz)
Trim and Align
(trimmomatic +
(NovoAlig,BWA-mem)
Trimmed QC
reads
R1/R2.Fastq.pai
red.gz
Aligned reads
per samples
(sorted.bam)
Aligned reads duplicates
marked per samples
(sorted.dup.bam)
Realigned reads per
sample
(realigned.sorted.bam
)
Indel realigner
(GATK + Picard)
Samples
Metrics
(Picard)
Fixmate
(Picard)
Mark as
duplicates
(Picard)
Aligned reads duplicates
marked per samples
(sorted.dup.bam)
Recalibration
(GATK)
aligned reads
recalibrated per sample
(sorted.dup.recal.bam)
SNP and INDEL
BCF/FilterBCF
(Samtools mpileup +
bcftools + vcfutils
varfilter)
Bcf per region per
samples
Vcf per sample
(sample. *.vcf)
Filter
(perl script)
Filtered vcf per
sample
(Nfiltered.vcf)
Flag mappabillity
(vcftools )
vcf with mappability
flag per sample
(…mil.vcf)
SNP ID
ANNOTATION
(SNPEFF)
vcf variant
annotated with
custom annotation
(…SNPid.vcf)
SNPeff
Annotation:effect
prediction tool
(SNPeff)
Vcf SNPs annotated as
high/moderate/low
impact
(snpid.snpeff.vcf)
Final Report
(Hmtl)
End
Start

9. On a farm far, far away, in the countryside of Hong
Kong,Ruffus the shy-creature was born.
This banded krait images was from Wikimedia.

10. Ruffus is a shy creature, and pretends to be a
cobra or a banded krait by putting up its red tail
and ducking its head in its coils when startled.

11. But they did not know that Ruffus
had a secret......
Pipeline
Workflow
Scripting

12. Ruffus: Pipeline Flowchart
This project uses ruffus framework. Pipeline was created using object oriented
method, and was manipulated directly. This project use task objects instead of
via decorators.

13. Program Name Pipeline Runner State Util
Main
Configuration
Logger Stages
Error_4_DRMMAPipeline_Config
JobScripts
Ruffus: Script-Based Implementation Design

14. Main Script Module
 Build the pipeline workflow
 Run or print the workflow
 End the DRMAA session

15. Configuration Module
Uses config-option that is written in
Yaml, where users can supplies input

16. Logger Module
Allow instant and concurrency logging facility

17. Pipeline Module
 Integrate all stages by constructing stages.
 Build the pipeline workflow together

18. Runner Module
 Set DRMMA to virtual python environment
 Run the pipeline workflow stges (Locally or Cluster)

19. Stage Module
 Individual stages of this project implemented,
 And functions from input files to output files
 Integrated Run-Stage functions that:
 Providing state parameter for Runner
 Access to pipeline module, config option, DRMMA & Logger

20. Galaxy: DNA-seq Analysis Pipeline Workflow
System
 This project customize Galaxy to run jobs on a cluster, in specifically, virtual local
job runner on cluster.
 Galaxy uses shared filesystem between its application server and the cluster nodes.
 Galaxy frontend application runs on a single server as usual, however tools are
run on cluster nodes instead.
 Galaxy uses distributed resources manager (DRM) at the backend to implement
the Distributing Resource Management Application API (DRMAA) interface.

21. Galaxy Project: Main Component of Operating System
architecture Design
For this project, Galaxy was configure and organized using the following virtual
layers: driver, core components, and high-level tools.
Hypervisor (Virtual Hardware)
Platform ( SANBI Physical Hardware )
Physical Hardware Drivers
DriversCores
Administrator tools Users OS
Blocks
Memory+Dish Size
+CPU
SanbiInfrastructureManagement
PhysicalTools

22. Galaxy: DNA-seq Analysis Workflow
Implementation

23. Results: Pipeline Comparison Pros
The following summaries the merit of using another of the workflow:
 Jobs are submitted via the python-drmaa
 DNA-seq Pipeline Case Study
 Uses SUN Grid Engine (SGE) as runner configuration manager.
 Pipelines can be created on the fly
 Multiple Tasks can share the same python function
 Reuse and reproducible common sub-pipelines
Shared sub pipelines can be created from discrete python modules and joined
together as needed. Bioinformaticists may have “mapping”, “aligning”, “variant-
calling” sub-pipelines etc.

24. Ruffus-Script Based Galaxy Project
Only based on Python Galaxy allows integration of different
programming languages
Task cannot be stop Task can be pause and restart using
the History refresh button
Only tested on SGE, using drmaa as
job submission.
Galaxy application server host processes
can only be configure in the DRM as a
submit host despite different Scheduler
engine
Prone to quick syntax errors where
tasks will blow up at any instance of
ambiguity in any particular pipeline
analsysis steps
Galaxy application server and worker
nodes must run the same version of
Python
Ruffus normal process-based allows
multiple local and distributing jobs
submission.
Shared filesystem and absolute
pathname are part of Galaxy workflow
system limitations
Pipeline Comparison Cons

25. Further Results
This project characterising the execution profile among Galaxy
and Ruffus-based scripting. Though, the performance
comparisons strategies are yet to be explored. However, the project
experimental use case was tested. The results statistics are yet
to be collected and graph-out. Results including:
 Analysis construction performance,
 Task start and end execution time,
 Pipeline Runtime,
 Disk and CPU usage summaries, and
 Memory Usage.

26. Future Work
 To complete the Advance DNA-seq analysis on Galaxy with
SANBI custom tool: NovoCraft tools
 To deploy sanbi-ruffus scripting based pipeline experiment on
Docker platform
 A graphical representation of comparison evaluation and
performance metric analysis using High chart and collectl
util tool on cluster.

27. References
 TERKHORN nov 2011. Strength and weaknesses of sub-workflow interoperatbility.
Ekblom, r. & Galindo, j. 2011. Applications of next generation sequencing in molecular ecology of non-
model organisms. Heredity (edinb.), 107, 1-15.
 Goecks, j., Nekrutenko, a., Taylor, j. & Galaxy, t. 2010. Galaxy: a comprehensive approach for
supporting accessible, reproducible, and transparent computational research in the life sciences. Genome
biol., 11, r86.
 Haas, b. J., Papanicolaou, a., Yassour, m., Grabherr, m., Blood, p. D., Bowden, j., Couger, m. B., Eccles,
d., Li, b., Lieber, m., Macmanes, m. D., Ott, m., Orvis, j., Pochet, n., Strozzi, f., Weeks, n., Westerman,
r., William, t., Dewey, c. N., Henschel, r., Leduc, r. D., Friedman, n. & Regev, a. 2013. De novo
transcript sequence reconstruction from rna-seq using the trinity platform for reference generation
and analysis. Nat. Protoc., 8, 1494-512.
 Leo Goodstadt 2010, Ruffus: a lightweight Python library for computational pipelines.
Bioinformatics 26(21): 2778-2779
 Alexander Wait Zaranek. (2013). Lightning: the first component of the Arvados project to be
(re)written in "Go". Zenodo. 10.5281/zenodo.7133
 Bernie Pope, Clare Sloggett, Gayle Philip, Matthew Wakefield , Complexo a pipeline for calling variants

28. People Contributions to this Project:
• Zahra
• Hocine
• Combat-TB Teams
THANK YOU !!!!