workshop - finding and accessing data - cambridge sept 28 2016 · we#are#always#lookingfor#data...
TRANSCRIPT
We are always looking for data
Finding and accessing human genomic data for research�
Cambridge, 28th of September 2016
Slides will be made available online
Tweets welcome #CamFindData
Outline of the day
-‐ Data sources and data access (Charlotte)-‐ Case study: University of Cambridge-‐ Coffee break-‐ Introduction to Repositive (Manuel)-‐ Hands-‐on session: searching for data-‐ Round up and closure
On-line tools used during the workshop
To ask questions during the presentation and answer questions:
go to slido.com
enter event code: 7234
We are on twitter: @manuelcorpas@repositiveio
Cambridge, 28th September 2016
Slides will be made available online J
Tweets welcome #CamFindData
1. What data are you looking for?
Join at slido.comwith the event code 7234
This workshop will focus on finding and accessing human genomic data.
… why would you be looking for genomic data for your research?
How much data do you need to publish a paper?
2001: 1 human genome
2012: 1000 Genomes (1092 genomes, since increased to ~2500)
2015: UK10K & deCODE (>100k induviduals) Cancer Genome Atlas ~11,000 genomesExAC consortium 65,000 exomes
?
Case studies
Raquel, PhD Student, London, UK.
Researching genes associated with rare eye disorders.
Problems:- Doesn’t know where to look for data.
- Doesn't know if data even exists.
“I gave up on finding the data -it was very time consuming and not proving fruitful – so I started focusing more on generating my own data.”
Mahantesh, Academic Researcher, Taipei, Taiwan.
Studying pharmacogenomics in cardiovascular epidemiology.
Problems:- Needs lots of data.- Knows it exists but struggles with getting access to it.
“Often it’s very hard to get the required number of cases and controls to carry out research in public health and epidemiology.”
Jana, Company Biocurator, Zurich, Switzerland.
Biocurating microarray and RNA-Seq data.
Problems:- Needs lots of data.- Lots of data out there but hard to filter down to ‘useful / relevant’ data.
“Many repositories don’t list the metadata details I need to know if a dataset is useful to me, I can waste a lot of time searching.”
What can I do?
PRO TIPS:
v Involve a statistician early on in your study design!
v Include more reference data in your analysis
v Search for collaborators who have the data you need
v Tell your colleagues and peers what type of data you have in your lab
v Use external sources of data….
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Large amounts of data, but not accessible
≈ .5PBSequence available
80+PBSequenced every year
WGS data available in public repos
Exponential growth rate
Under-utilised datahas huge potential for medical research
2. Data resources from around the world
Public repositories
• some you apply for access, especially if data contains clinical info or whole genome PID
• some are open access: GEO, SRA, PGP, OpenSNP, GigaDB, …
• some are consented for general research use, some have specific consent
How many data sources?
How many sources of human genomics data do you know
about?
Hundreds of data sources…but they aren’t easy to find!
http://tinyurl.com/plos-‐biology-‐repositiveFirst 30 data sources listed here:
1025 33 35
102
174
239
0
50
100
150
200
250
300
Jan-‐15 Mar-‐15 Jun-‐15 Sep-‐15 Dec-‐15 Mar-‐16 Jun-‐16
DATA is fragmented
11
155
22
4
4
7
780
05
1015202530354045
GB FI NL FR DE CH EE BE DK ES SI IE SE
0
5
10
15
20
25
30
35
CA MD MA WA NY TX AZ DC NJ NC PA UT TN CO IN FL LA VA IL ME OH MO MI SC OR
1
1 1
11
1
Data sources across the globeGEO location of 278 data sources analysed.
Found by tracking IP address of the source.
These include:
Ø Public Repositories
Ø Universities
Ø Companies
Ø BioBanks
Ø Research consortiums
It may be confusing
Data source content
Assay Types
Dedicated to…
Number of samples in Data sources
1
10
100
1000
10000
100000
1000000
Sample # (Log10)
Top 5:GEO (1.8M)PMI Cohort Program (1M)Auria Biopankki (1M)EGA (~0.6M)SRA (~0.5M)
Name: UK BiobankType of data: genotypingURL: http://biobank.ctsu.ox.ac.uk/crystal/gsearch.cgi
BioBanks as a source of genomic data
Name: ToMMo BiobankType of data: genotyping, WGSURL: https://ijgvd.megabank.tohoku.ac.jp/
Name: Diabetes Biobank BrusselsType of data: data (including genomic; not specified) and clinical samples on >20.000 diabetic patients and their first degree relatives. URL: http://www.diabetesbiobank.org/
Name: Dutch biobanks (dozens of them!)Type of data: multipleURL: http://bit.ly/1XxPA6W
Name: Auria Biobank FinlandType of data: There are roughly one million human biological samples stored in Auria Biobank, a considerable proportion of which are cancer samples. At the moment, there is only the catalogue of samples, no catalogue of data. In case a researcher needs to know what kind of data we have, he/she needs to contact us.URL: https://www.auriabiopankki.fi/?lang=en
More information about data sources
… in our recent paper:
http://tinyurl.com/plos-‐biology-‐repositive
3. Getting access to Restricted data
Benefits: • Strict governance• Individuals are protected• Review of consent• Applicant signs for full responsibility for governance
Disadvantages: • No control of data once access is given • High barrier for access – too high?
Data accessibility
Can download the data straight away or after logging in.
Need to apply for access to the data.
Has both Open and Restricted access data within one
repository.
Access type of 225 sampled data sources.
Often a long process
Bottlenecks: • Finding relevant and usable
data• Getting authorisation to
access data• Formatting data• Storing and moving data
We studied the problem with qualitative interviews followed by a survey of researchers in
human genetics
T. A. van Schaik et alThe need to redefine genomic data sharing: a focus on data accessibility, Applied & Translational Genomics, 2014 http://tinyurl.com/schaik-‐dnadigest
Often a long process
Researchers spend months trying find and access genomic data, and often choose to not access data at all
NIH / eRA Commons login
No
Yes
Organisation registered with eRA
Organisation has DUNS number
No
NoWrite research proposal
Yes+ 2-‐3 days
+ 1-‐2 weeks
+ 1 week
Yes
Submit proposal
+ 1-‐2 days
Access grantedFind/Download/Decrypt data
+ 1-‐4 weeks
Science…
+ 1-‐2 days
PRO Tip: If you use human genomic data, apply for the GRU datasets in dbGaP, one application – access to all the GRU datasets.
dbGaP application process
Blog Post:http://blog.repositive.io/how-‐to-‐successfully-‐apply-‐for-‐access-‐to-‐dbgap/
Sanger eDAM Account
No
Write research proposal
+ 1 hourYes
Submit proposal
+ 1-‐2 days
Access grantedFind/Download/Decrypt data
+ 2-‐7 days
Science…
+ 1-‐2 days
EGA application process
Blog Post:http://blog.repositive.io/how-‐to-‐successfully-‐apply-‐for-‐access-‐to-‐ega/
• Post doctoral researcher at University of Cambridge Medical School• Working on genetic inheritance and Cancer• Using NGS data and bioinformatics
• After searching for data online she decided to apply for:• 2 dbGaP datasets • 3 EGA datasets
Cambridge specific Case Study
Blog Post:Pending… will be on http://blog.repositive.io/
The Research Operations Office - will help you with the contracts (DTAs) and signatures.
• Has a designated individual who processes all dbGaPapplications as they all abide by NIH legal restrictions and regulations about how to handle the data once granted access. • For EGA applications, each DTA must get processed separately because there is no consensus for the ‘contracts’ between each dataset.
Cambridge specific Case Study
Blog Post:Pending… will be on http://blog.repositive.io/
The nominated IT director - will be specific to your department.
• They will need to confirm you can support the requirements of the DTA.• If the head of your departmental IT is not happy to sign – the head of IT for the University will be able to sign it off.
Cambridge specific Case Study
Blog Post:Pending… will be on http://blog.repositive.io/
Top Tips:Be prepared…
• Think about your storage space!
• Think about what sort of analysis and processing you are going to do with the data once you do have it. After such a long process, the approval could be too quick!!
• Designate time!
• Understand what you need before you start the application process!
• You only have 1 year!
Cambridge specific Case Study
Be prepared…
Think about your storage space! You need to know how big the data is that you are applying for – you may not be able to fit it on your given folder on a server. If not, where will you get the space?
Think about what sort of analysis and processing you are going to do with the data once you do have it. After such a long process, the approval could be too quick!!• This comes back to space again - how big will the data get once you have analysed and processed it? How will you deal with that?
Designate time!• Especially if this is your first application. Understanding the process, finding the right people, filling in the forms and waiting for approval takes time.
• If you are under time pressure you will get frustrated, and might not get the data in time.
Understand what you need before you start the application process!• Read the application help documentation.• Read the legal consents.• Read some blog posts on http://blog.repositive.io/
You only have 1 year!• You will have to renew your application and authorisation keys each year, to show you still want it and need access to it –this is far easier than the first time around, but still…
• Don’t waste it.
Cambridge specific Case Study
4. Not all data is restricted
Applying for access to restricted data is a hard and time consuming process.
Think about using open access data!
Make the (research) world a better place by sharing in return J
Best practices: Share in return!
• If you expect data to be available to you – you have to make your data available too!
• Encourage collaborations: power by numbers
1. Get credit – publish and make your data available2. Give credit – cite data sources3. Understand consent – for all uses of clinical data
Best practices
• Use all available tools to make your life easier: • Data publications ß visibility and citations for your data, e.g. GigaScience and Scientific Data
• Figshare, Zenodo, Dryad for sharing open access data
• PhenomeCentral, Matchmaker exchange for rare disease research
• Repositive for finding data across repositories and make your own data discoverable
Best practices: use the tools
• Digital consent: towards automatic processing of applications
• Dynamic consent and power to the patient, e.g. PatientsKnowBest
• Privacy-preserving access to datasets: preserving control and governance with data custodian, lower barrier for access
What the future holds
“Weakness: Involvement of non-academic beneficiaries is limited”
“Weakness: highly focused on academic activities, and lacks an advanced communication strategy”
“Weakness: limited exposure to non-academic partners & infrastructures”
Excellence
Impact
Implementation
“data accessibility is unclear!”
“data storage & access not considered”
Best practices: Plan into your grant proposals
“Strengths: extensive dissemination of data to the scientific community (open access, databases)”
“outreach activities to a broad audience”
“research software is freely available”
Impact:Best practices: Plan into your grant proposals
Best practices: Plan into your grant proposals
Workshop: Finding and accessing human genomic data for research
Manuel Corpas – September 28th 2016
We are always looking for data
Genetics, Cancer,
Rare diseaseresearch
We need access to the right data at the right time
DNA interpretation requires
lots of data
Data is not easy to find and access
FRAGMENTEDPoor visibility of available
genomic data
ADMIN BURDENHuge overhead to manage
data access
BAD CULTURELack of data sharing habits in
research culture
We are enabling best practices
MAKE DATA DISCOVERABLE
SIMPLIFY WORKFLOWS
CONTRIBUTE TOCOMMUNITY
DNAdigest and Repositive – Connecting the world of genomic datahttp://www.tinyurl.com/plos-‐biology-‐repositive
Connecting the world of genomic data
Live demo http://discover.repositive.io
Team presentation: 2 minutes1. Introduction üWhat data did you try to find and why?üHave you tried to search for this data before?
2. Methodsü The 5 main steps you took on Repositive to try and find this data.
3. ResultsüDid you find the data on Repositive?üWhat challenges did you encounter?
4. Conclusionü Sum up your experience in 1 sentence.
1 2 3 4 5
Feedback on the workshop
Bugs and feedback to: Charlotte at Repositive.io
Please leave your feedback on the workshop:
http://tinyurl.com/feedback280916
Thank you!