Assessing Galaxy's ability to express scientific workflows in bioinformatics

Introduction
Biological Sequence Analysis
Scientific workflow management systems
The Galaxy framework
Conclusion
Bibliography
References

Assessing Galaxy’s ability to express scientific
workflows in bioinformatics
Peter van Heusden and Alan Christoffels
South African National Bioinformatics Institute
University of the Western Cape
Bellville, South Africa

10th FASTAR/Espresso Workshop 2013 / 4-6 November 2013

Assessing Galaxy’s ability to express scientific workflows in bioinformatics

Introduction
Conclusion
Bibliography
References

What is bioinformatics?

Bioinformatics is the discipline of solving problems in biology and
medicine using computational resources.
Within bioinformatics, biological sequence analysis (BSA)
describes those analyses that “infer biological information from
sequence alone”. (Durbin, 1998)
Cost of biological sequence analysis has two parts:
1
2

Cost of acquiring sequence
Cost of analysing sequence



Introduction
Conclusion
Bibliography
References

Cost of acquiring sequence

(Wetterstrand, 2013)


Introduction
Conclusion
Bibliography
References

Cost of analysing sequence

The “sudden reliance on computation has created an ‘informatics
crisis’ for life science researchers: computational resources can
be difficult to use, and ensuring that computational experiments
are communicated well and hence reproducible is challenging”
(Goecks et al., 2010)

As cost of sequencing plummets analysis faces two challenges:
1

2

Growing data volume demands more sophisticated computational
approaches
Translating biological questions into computational workflows
remains difficult



Introduction
Conclusion
Bibliography
References

How do we do bioinformatics?

Given a set of protein sequences from species A, which genes
from species B produce similar proteins, and where are these
genes located on the genome of B?
Analysis proceeds (Stevens et al., 2001) using:
1
2

3

Collections of data objects
Transformers that generate new collections (e.g. transform
collection of proteins into collection of genome regions that they
match)
Filters (e.g. discard low quality matches to genome)



Introduction
Conclusion
Bibliography
References

How we do bioinformatics (2)

Data collections typically exist as (compressed) files
Bioinformatics tools typically are command line executables that
accept and generate files (often using ad-hoc formats)
Scripting languages (Perl, Python) used to compose workflows,
APIs often used for reading/writing file formats
1

2

Workflow enactment often involves manual steps and is closely
tied to execution environment
Workflow is not easily reproducible nor reusable



Introduction
Conclusion
Bibliography
References


Scientific workflow management systems (SciWMS) have been
proposed as an alternative to current script-based approaches to
analysis workflow.
SciWMSs “provide a high-level declarative way of specifying what
a particular in silico experiment modelled by a workflow is set to
achieve, not how it will be executed.” (Taverna project, 2009)
Workflow descriptions resemble dataflow languages (McPhillips
et al., 2009)



Introduction
Conclusion
Bibliography
References

The promise of SciWMSs

In addition to workflow specification, SciWMSs sometimes offer:
Types that model objects of scientific domain
Recording of provenance of data objects
Execution of scientific workflows on diverse computing
environments (desktop, cluster, grid, cloud)



Introduction
Conclusion
Bibliography
References

SciWMSs for bioinformatics

Many workﬂow systems have been proposed for use in
bioinformatics: Taverna, Kepler, Triana, Bioopera, Mobyle,
BiosFlow, bpipe
Some workﬂow features are also available in Galaxy



Introduction
Conclusion
Bibliography
References

Support for workflow patterns
Scientific Data Modelling
Workflow representation and use

What is Galaxy

Galaxy emerged in 2004/5 as a web interface to bioinformatics
tools and data
Galaxy is becoming common platform through which to “publish”
tools and data
More than 30 known public Galaxy servers
36 000 users on main public Galaxy server, 0.8 Pb of data



Introduction
Conclusion
Bibliography
References


Galaxy as an open-source project

Galaxy consists of c. 250 000 lines of (mostly Python) code
Core team includes 15 developers spread across 4 different
institutes
Development is open source and “out in the open” with code
hosted on BitBucket, development planning on Trello and mailing
lists



Introduction
Conclusion
Bibliography
References




Galaxy I

Introduction
Conclusion
Bibliography
References




Galaxy II

Introduction
Conclusion
Bibliography
References


Galaxy workflow management features

Galaxy allows composition of workflows defined as series of
tasks and related dataflow
Allows execution of workflows on local machine or via various job
schedulers
Data objects generated in Galaxy have associated provenance
information



Introduction
Conclusion
Bibliography
References


Limitations of Galaxy as a SciWMS

Limited support for scientific workflow patterns
Type refers to format of data items
Provenance is recorded as attribute of data files

Workflows are not first class objects
Analysis view focuses on individual datasets
Execution engine schedules tasks (with limited support for task
collections)

Galaxy can be enriched by drawing on prior research on
SciWMSs



Introduction
Conclusion
Bibliography
References


Scientific workflow patterns
Analysis of scientific workflows has yielded a set of design
patterns used in workflows (Yildiz et al., 2009)
Galaxy workflow language supports sequential dataflow, parallel
split and synchronisation
Tool definition language has recently been extended to support
multiple instances of task (not workflow) execution with a-priori
runtime knowledge
Tool authors can signal that input to tool can be split for parallel
execution
No interface between workflow authors and multiple instance
support

Support for cancel of individual task but not entire workflow
No support for triggering new thread of activity (restart)


Introduction
Conclusion
Bibliography
References


Scientific workflow patterns (2)

No support for exclusive choice (e.g. execute different dataflow
path based on different input)
No support for sub-workflows
Galaxy workflow language is “abstraction hating” (Green and
Petre, 1996)
Leads to workflow diagrams resembling bowl of spaghetti for
anything but the most simple cases



Introduction
Conclusion
Bibliography
References


The Galaxy type system

Galaxy types represent file types
File type does not map simply to semantics
Collection types are not supported, although some types are
“splittable” to allow parallel task execution
Workflow parameters are not supported via type system

Cannot guarantee that workflow is well-formed
Provenance recording is coarse-grained
What will happen if we update single element of input data
collection?



Introduction
Conclusion
Bibliography
References


Science questions vs execution plans
Type system could model scientific domain objects (e.g. protein
and nuceleotide sequences) but . . .
Bioinformatics tools do not support standard formats or support
standard formats with quirks
Not clear what information to save from tool output
Experienced bioinformaticists want opportunity to review “raw
output” to explore factors that underpin confidence in analysis

Need to support both recording and reporting of workflow output
Both recording “raw” output trace and reporting provenance of
scientific domain objects are necessary features for SciWMS



Introduction
Conclusion
Bibliography
References


Workflow execution in Galaxy
Internally workflows are expanded into collections of tasks at
execution time
Tasks are executed by backend classes: either local or via
scheduler
Execution parameters can be set by “dynamic job runners”
Allows e.g. resource requirements of job to be signalled to
scheduler
Configured using a combination of XML and Python code
maintained by Galaxy administrator

Workflow execution leaves no visible trace in the user interface
At runtime execution shows individual jobs running
Data objects are grouped by “history”, not associated with a
workflow

No support for re-execution of part of workflow


Introduction
Conclusion
Bibliography
References


Scope for workflow optimisation

Workflows are dataflow graphs (Johnston et al., 2004)
Knowledge of inputs and types can be used to plan execution
efficiently, e.g. pipeline tasks and exploit opportunities for
streaming
Collection of data objects and parameters sets can be exploited
for automatic parallel enactment of tasks and sub-workflows

Data collections and workflows provide structures for nesting of
provenance information
Knowledge of data provenance could facilitate lifecycle of data
products: kept for re-use or discarded as “intermediate products”



Introduction
Conclusion
Bibliography
References

Conclusion
Bioinformatics faces an “informatics crisis” as cost to generate
sequence has decreased while cost to compose or reproduce
analysis has remained high
Galaxy has emerged as a popular interface to bioinformatics tools
and data with workflow management features
Insight from prior research on SciWMSs suggests areas for
enhancement:
Support for additional workflow patterns
Extension of type system with support for biological types,
collections and parameter sets
Improvement of workflow execution through treating workflows as
first class objects with associated optimisation of execution and
provenance storage

Currently being pursued as a research agenda at SANBI


Introduction
Conclusion
Bibliography
References

Thanks

Workﬂows for biological sequence analysis are discussed
by the “Pipelines collaboration”
Research on SciWMS supported
by the MRC and Prof Christoffels
Professor Alan Christoffels



Introduction
Conclusion
Bibliography
References

Bibliography I
R. Durbin. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids.
Cambridge University Press, Apr. 1998. ISBN 9780521629713.
J. Goecks, A. Nekrutenko, J. Taylor, and T. G. Team. Galaxy: a comprehensive approach for
supporting accessible, reproducible, and transparent computational research in the life
sciences. Genome Biol, 11(8), 2010.
T. R. G. Green and M. Petre. Usability analysis of visual programming environments: a ‘cognitive
dimensions’ framework. Journal of Visual Languages and Computing, 7:131–174, 1996.
W. M. Johnston, J. R. P. Hanna, and R. J. Millar. Advances in dataflow programming languages.
ACM Computing Surveys, 36(1):1–34, Mar. 2004.
T. McPhillips, S. Bowers, D. Zinn, and B. Ludäscher. Scientific workflow design for mere mortals.
Future Generation Computer Systems, 25(5):541–551, May 2009.
R. Stevens, C. Goble, P. Baker, and A. Brass. A classification of tasks in bioinformatics.
Bioinformatics, 17(2):180–188, Feb. 2001.
Taverna project. Why use workflows?, 2009. URL

http://www.taverna.org.uk/introduction/why-use-workflows/.


Introduction
Conclusion
Bibliography
References

Bibliography II

K. Wetterstrand. DNA sequencing costs: Data from the NHGRI genome sequencing program
(GSP), 2013. URL http://www.genome.gov/sequencingcosts/.
U. Yildiz, A. Guabtni, and A. H. H. Ngu. Towards scientific workflow patterns. In Proceedings of
the 4th Workshop on Workflows in Support of Large-Scale Science, WORKS ’09, page
13:1–13:10, New York, NY, USA, 2009. ACM.



Assessing Galaxy's ability to express scientific workflows in bioinformatics

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Destacado

Destacado (11)

Similar a Assessing Galaxy's ability to express scientific workflows in bioinformatics

Similar a Assessing Galaxy's ability to express scientific workflows in bioinformatics (20)

Último

Último (20)

Assessing Galaxy's ability to express scientific workflows in bioinformatics