Transcript Exploring Williams-Beuren Syndrome using myGrid

Exploring Williams-Beuren
Syndrome using myGrid
R.D. Stevens,a H.J. Tipney,b C.J. Wroe,a T.M. Oinn,c
M. Senger,c P.W. Lord,a C.A. Goble,a A. Brass,a
M. Tassabehji b
a Department of Computer Science
b University of Manchester,
c European Bioinformatics Institute
University of Manchester
Academic Unit of Medical Genetics
Wellcome Trust Genome Campus
St Mary’s Hospital
Hinxton
Williams-Beuren Syndrome (WBS)
• Congenital disorder caused by
sporadic gene deletion
• 1/20,000 live births
• Effects multiple systems – muscular,
nervous, circulatory
• Characteristic facial features
• Unique cognitive profile
• Mental retardation (IQ 40-100,
mean~60, ‘normal’ mean ~ 100 )
• Outgoing personality, friendly nature,
‘charming’
• Haploinsuffieciency of the region
results in the phenotype
POM121
NOLR1
FKBP6T
GTF2IP
NCF1P
GTF2IRD2P
STAG3
PMS2L
Williams-Beuren Syndrome Microdeletion
7q11.23
C-tel
~1.5 Mb
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*
Patient deletions
WBS
SVAS
Physical Map
CTA-315H11
Chr 7 ~155 Mb
‘Gap’
CTB-51J22
GTF2IRD2
NCF1
GTF2I
B-tel
A-tel
A-mid
GTF2IRD1
CYLN2
RFC2
WBSCR5/LAB
WBSCR1/E1f4H
LIMK1
ELN
CLDN4
CLDN3
WBSCR21
STX1A
WBSCR14
WBSCR18
WBSCR22
TBL2
BCL7B
B-mid
C-mid
BAZ1B
B-cen
FZD9
A-cen
NOLR1
FKBP6
POM121
C-cen
Eicher E, Clark R & She, X An Assessment of the Sequence Gaps: Unfinished Business
in a Finished Human Genome. Nature Genetics Reviews (2004) 5:345-354
Hillier L et al. The DNA Sequence of Human Chromosome 7. Nature (2003) 424:157-164 Block A Block B Block C
Filling a genomic gap in Silico
1. Identify new, overlapping sequence of interest
2. Characterise the new sequence at nucleotide and amino acid
level
12181
12241
12301
12361
12421
12481
12541
12601
12661
12721
12781
acatttctac
cagtctttta
gaccatccta
gactaattat
taggtgactt
aggagctatt
ttcttataag
tggttaagta
tggcattaag
atccaatacc
taacccattt
caacagtgga
aattttaacc
atagatacac
gttgagcttg
gcctgttttt
tatatattct
tctgtggttt
tacatgacat
tacatccaca
cattaagctg
tctgtctcta
tgaggttgtt
tttagagaag
agtggtgtct
ttaccattta
ttttaattgg
ggatacaagt
ttatattaat
aaaacggatt
atattgtgca
tcactcccca
tggatttgcc
ggtctatgtt
agtcatacag
cactgtgatt
gacaacttca
gatcttaatt
tctttatcag
gtttttattg
atcttaacca
actatcacca
atctcccatt
tgttctggat
ctcaccaaat
tcaatagcct
ttaatttgca
ttagagaagt
tttttaaatt
atacacagtt
atgactgttt
ttttaaaatg
ctatcatact
ttcccacccc
attcatatta
ttggtgttgt
tttttagctt
ttttcctgct
gtctaatatt
attgatttgt
tgtgactatt
tttacaattg
taaaattcga
ccaaaagggc
tgacaatcaa
atagaatcaa
Cutting and pasting between numerous web-based services i.e.
BLAST, InterProScan etc
Filling a genomic gap in silico
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Frequently repeated – info rapidly added to public databases
Time consuming and mundane
Don’t always get results
Huge amount of interrelated data is produced – handled in
notebooks and files saved to local hard drive
Much knowledge remains undocumented:
Bioinformatician does the analysis
Advantages:
Specialist human intervention at every step, quick and easy access
to distributed services
Disadvantages:
Labour intensive, time consuming, highly repetitive and error prone
process, tacit procedure so difficult to share both protocol and
results
Why Workflows and Services?
Workflow = general technique for describing and enacting a process
Workflow = describes what you want to do, not how you want to do it
Web Service = how you want to do it
Web Service = automated programmatic internet access to applications
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Automation
– Capturing processes in an explicit manner
– Tedium! Computers don’t get bored/distracted/hungry/impatient!
– Saves repeated time and effort
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Modification, maintenance, substitution and personalisation
Easy to share, explain, relocate, reuse and build
Available to wider audience: don’t need to be a coder, just need to know
how to do Bioinformatics
Releases Scientists/Bioinformaticians to do other work
Record
– Provenance: what the data is like, where it came from, its quality
– Management of data (LSID - Life Science Identifiers)
myGrid
• E-Science pilot research project funded by EPSRC
www.mygrid.org.uk
www.mygrig.org.uk
• Manchester, Newcastle, Sheffield, Southampton, Nottingham, EBI
and RFCGR, also industrial partners.
• ‘targeted to develop open source software to support personalised
in silico experiments in biology on a grid.’
Which means….
Distributed computing – machines, tools, databanks, people
Personalisation
Provenance and Data management
Enactment and notification
A virtual lab ‘workbench’, a toolkit which serves life science
communities.
Workflow Components
Freefluo
Freefluo
Workflow
engine to run
workflows
Scufl Simple Conceptual Unified Flow Language
Taverna Writing, running workflows & examining results
SOAPLAB Makes applications available
Web Service
e.g. DDBJ BLAST
SOAPLAB
Web Service
Any Application
Williams Workflow
Plan
Query nucleotide
sequence
Pink: Outputs/inputs of a service
Purple: Tailor-made services
Green: Emboss soaplab services
Yellow: Manchester soaplab services
RepeatMasker
BLASTwrapper
GenBank Accession No
Promotor Prediction
URL inc GB identifier
Translation/sequence
file. Good for records
and publications
Identifies PEST seq
prettyseq
Sort for appropriate Sequences only
MW, length,
charge, pI, etc
pepstats
Predicts
cellular location
Identifies functional
and structural
domains/motifs
Hydrophobic
regions
GenBank Entry
epestfind
pscan
tblastn Vs nr, est,
est_mouse, est_human
databases.
Blastp Vs nr
Regulation Element Prediction
Amino Acid translation
Identifies
FingerPRINTS
Predicts Coiled-coil
regions
TF binding Prediction
pepcoil
Identify regulatory
elements in
genomic sequence
Seqret
Nucleotide seq (Fasta)
6 ORFs
RepeatMasker
Coding sequence
GenScan
BlastWrapper
SignalP
TargetP
PSORTII
restrict
sixpack
transeq
cpgreport
Restriction enzyme
map
CpG Island
locations and %
InterPro
ORFs
Pepwindow?
Octanol?
RepeatMasker
ncbiBlastWrapper
Repetitive elements
Blastn Vs nr, est
databases.
The Williams
Workflows
A
A: Identification of
overlapping sequence
B: Characterisation of
nucleotide sequence
C: Characterisation of
protein sequence
B
C
The Workflow Experience
Have workflows delivered on their promise? YES!
• Correct and Biologically meaningful results
• Automation
– Saved time, increased productivity
– Process split into three, you still require humans!
• Sharing
– Other people have used and want to develop the workflows
• Change of work practises
– Post hoc analysis. Don’t analyse data piece by piece receive
all data all at once
– Data stored and collected in a more standardised manner
– Results amplification
– Results management and visualisation
The Workflow Experience
• Activation energy versus Reusability trade-off
– Lack of ‘available’ services, levels of redundancy can be
limited
– But once available can be reused for the greater good of the
community
• Licensing of Bioinformatics Applications
– Means can’t be used outside of licensing body
– No license = access third-party websites
• Instability of external services
– Research level
– Reliant on other peoples servers
– Taverna can retry or substitute before graceful failure
• Shims
Shims
shim
(sh m) n. A thin, often tapered piece of material used to fill
gaps, make something level, or adjust something to fit
properly.
shimmed, shim·ming, shims
To fill in, level, or adjust by using shims or a shim.
• Explicitly capturing the process
• Unrecorded ‘steps’ which aren’t realised until attempting
to build something
• Enable services to fit together
Shims
‘I want to identify new sequences which overlap
with my query sequence and determine if they
are useful’
Sequence database entry
Fasta format sequence
Genbank format sequence
Sequence
i.e. last
known 3000bp
Mask
BLAST
Simplify and
Compare
Retrieve
Identify new sequences
and determine their degree
of identity
Lister
Old BLAST result
BLAST2
Alignment of full query
sequence V full ‘new’
sequence
The Biological Results
ELN
WBSCR28
WBSCR27
CLDN4
CLDN3
WBSCR21
STX1A
WBSCR22
WBSCR18
WBSCR24
WBSCR14
Four workflow cycles totalling ~ 10 hours
The gap was correctly closed and all known features identified
CTA-315H11
CTB-51J22
RP11-622P13
RP11-148M21
RP11-731K22
314,004bp extension
All nine known genes identified
Conclusions
• It works – a new tool has been developed which is being
utilised by biologists
• More regularly undertaken, less mundane, less error
prone
• Once notification is installed won’t even need to initiate it
• More systematic collection and analysis of results
• Increased productivity
• Services: only as good as the individual services, lots of
them, we don’t own them, many are unique and at a
single site, research level software, reliant on other
peoples services, licenses
• Activation energy
Future Directions
• Scheduling and Notification
• Portals
• Results visualisation
• Re-use: other genomic disorders, Graves Disease
Acknowledgments
• Dr May Tassabehji
• Prof Andy Brass
• Medical Genetics team at St Marys Hospital,
Manchester
• Wellcome Trust
www.mygrid.org.uk
myGrid
People
www.mygrid.org.uk
Core
Matthew Addis, Nedim Alpdemir, Tim Carver, Rich Cawley, Neil Davis, Alvaro
Fernandes, Justin Ferris, Robert Gaizaukaus, Kevin Glover, Carole Goble,
Chris Greenhalgh, Mark Greenwood, Yikun Guo, Ananth Krishna, Peter Li,
Phillip Lord, Darren Marvin, Simon Miles, Luc Moreau, Arijit Mukherjee, Tom
Oinn, Juri Papay, Savas Parastatidis, Norman Paton, Terry Payne, Matthew
Pockock Milena Radenkovic, Stefan Rennick-Egglestone, Peter Rice, Martin
Senger, Nick Sharman, Robert Stevens, Victor Tan, Anil Wipat, Paul Watson
and Chris Wroe.
Users
Simon Pearce and Claire Jennings, Institute of Human Genetics School of Clinical
Medical Sciences, University of Newcastle, UK
Hannah Tipney, May Tassabehji, Andy Brass, St Mary’s Hospital, Manchester, UK
Postgraduates
Martin Szomszor, Duncan Hull, Jun Zhao, Pinar Alper, John Dickman, Keith
Flanagan, Antoon Goderis, Tracy Craddock, Alastair Hampshire
Industrial
Dennis Quan, Sean Martin, Michael Niemi, Syd Chapman (IBM)
Robin McEntire (GSK)
Collaborators
Keith Decker