Scott Edmunds: GigaScience - a journal or a database? Lessons learned from the Genomics Tsunami

: a Journal or a Database?
(Lessons learned from the Genomics “Tsunami”)
Scott Edmunds
HUPO Congress 2011, Geneva
www.gigasciencejournal.com

BGI Introduction
Formerly known as Beijing Genomics Institute
Founded in 1999
Now the largest genomic organization in the world
Goal
Use genomics technology to impact the society
Make leading edge genomics highly
accessible to the global research community

Largest Sequencing Capacity in the World
Sequencers
137Illumina/HiSeq 2000
27LifeTech/SOLiD 4
16 AB/3730xl + 110 MegaBACEs
2 IlluminaiScan
Data Production
5.6 Tb / day
> 1500X of human genome / day
Multiple Supercomputing Centers
157 TB Flops
20 TB Memory
12.6 PB Storage

Mass spectrometry at BGI
QTRAP 5500, AB SCIEX
Orbitrap velos, Thermo Scientific
maXis Q-TOF, Bruker
ultraflex, Bruker

Products and Services Offered to Collaborators
Protein Profiling for any species
(tying in with 1000 PARGP)
Techniques:
Quantitative analysis
Post-translational modification
Target Proteomics
Metabolomics

“Trans-Omics”
Objective to integrate data from:
Genomics
Transcriptomics
Proteomics
Metabolomics

BGI Proteomics Dept Focus:
RAW MS data storage and analysis
Upstream analysis
“Large-scale” screening/quantitative analysis
Working on:
Automatic analysis pipelines/tools
Industrial usage/standards

Lessons Learned:
What went right?

Lessons Learned:
1. having a cool project helps…
Bill Clinton:
“We are here to celebrate the completion of the first survey of the entire human genome. Without a doubt, this is the most important, most wondrous map ever produced by human kind. “
“Today we are learning the language in which God created life.”

Lessons Learned:
2. Reproducibility is important…
Helped by stability of:
Platforms
Infrastructure
Standards
1st Gen
2ndGen

Lessons Learned:
3. Sharing is important…
V

Lessons Learned:
3. Sharing is important…
Bermuda Accords 1996/1997/1998:
Automatic release of sequence assemblies within 24 hours.
Immediate publication of finished annotated sequences.
Aim to make the entire sequence freely available in the public domain for both research and development in order to maximise benefits to society.
Fort Lauderdale Agreement, 2003:
Sequence traces from whole genome shotgun projects are to be deposited in a trace archive within one week of production.
Whole genome assemblies are to be deposited in a public nucleotide sequence database as soon as possible after the assembled sequence has met a set of quality evaluation criteria.
Toronto International data release workshop, 2009:
The goal was to reaffirm and refine, where needed, the policies related to the early release of genomic data, and to extend, if possible, similar data release policies to other types of large biological datasets – whether from proteomics, biobanking or metabolite research.

Benefits of Data-sharing
Sharing Detailed Research Data Is Associated with Increased Citation Rate.
Piwowar HA, Day RS, Fridsma DB (2007) PLoSONE 2(3): e308. doi:10.1371/journal.pone.0000308
Every 10 datasets collected contributes to at least 4papers in the following 3-years.
Piwowar, HA, Vision, TJ, & Whitlock, MC (2011). Data archiving is a good investment Nature, 473 (7347), 285-285 DOI: 10.1038/473285a

Rice v Wheat: consequences of publically available genome data.

The Ecoresponsive Genome of Daphnia pulexColbourne et al., Science4 February 2011:
200Mb Genome, 30,907 genes
Duplicated genes most responsive to ecological challenges

Daphnia Genome Consortium
wFleabase: Mar 2006
Genome release: July 2007
Genome Published: Feb 2011
>58 companion papers
https://daphnia.cgb.indiana.edu/Publications

Problems?
Flickr cc: opensourceway

Lessons Learned:
4. Need to manage expectations…
June 2000
Thomas Michael Dexter (Wellcome trust):
“Mapping the human genome has been compared with putting a man on the moon, but I believe it is more than that. This is the outstanding achievement not only of our lifetime, but in terms of human history”

Lessons Learned:
4. Need to manage expectations…
June 2010

Lessons Learned: 5. Data, data, data
Sequencing cost($ per Mbp)
Moore’s Law
~100,000X
Sequencing
Source: E Lander/Broad

Sequencing Output
Data
Storage
Moore’s/Kryders Law

Sequencing Output
Data
Publication
Dissemination?

Can we keep up?

Do we have models for long term funding?
Human Gene Mutation Database
Kyoto Encyclopedia of Genes and Genomes
?

Growing/widening user base.
3rd Gen sequencers: “Democratizing sequencing”
?

Curation, curation, curation?
?
The long tail of new “big-data” producers?

Are there now too many hurdles?
?

Are there now too many hurdles?
Technical: too large volumes
too heterogeneous
no home for many data types
too time consuming
Economic: too expensive, no long-term funding
Cultural: inertia
no incentives to share
unaware of how
?

Potential solutions?

Potential solutions: Better handling of data, data, data
Cloud?

Potential solutions: Better handling of data, data, data
What to save/what to throw away?
Better Compression?

Potential solutions: Better handling of metadata…
Cloud solutions?
Better tools for assessing data quality…

Potential Solutions:
New incentives/credit
Credit where credit is overdue:
“One option would be to provide researchers who release data to public repositories with a means of accreditation.”
“An ability to search the literature for all online papers that used a particular data set would enable appropriate attribution for those who share. “
Nature Biotechnology 27, 579 (2009)
Prepublication data sharing
(Toronto International Data Release Workshop)
“Data producers benefit from creating a citable reference, as it can later be used to reflect impact of the data sets.”
Nature461, 168-170 (2009)
?

Datacitation: Datacite and DOIs
Digital Object Identifiers (DOIs) offer a solution
Mostly widely used identifier for scientific articles
Researchers, authors, publishers know how to use them
Put datasets on the same playing field as articles

Dataset
Yancheva et al (2007). Analyses on sediment of Lake Maar. PANGAEA.
doi:10.1594/PANGAEA.587840

Datacitation: Datacite and DOIs
>1 million DOIs since Dec 2009
Central metadata repository to link with WoS/ISI
- finally can track and credit use!

How can we combine these?
Databases
?
Journals

Now taking submissions…
Large-Scale Data
Journal/Database
In conjunction with:
Editor-in-Chief: Laurie Goodman, PhD
Editor: Scott Edmunds, PhD
Assistant Editor: Alexandra Basford, PhD

Criteria and Focus of Journal/Database
Reproducibility/Reuse
Utility/Usability
Standards/Searchability/Scale/Sharing
Data publishing/DOI

Data publishing/DOI
Data hosting will follow standard funding agency and community guidelines.
DOI assignment available for submitted data to allow ease of findingand citing datasets, as well as for citation tracking.
Datasets tracked by WOS/ISI allowing additional metrics/credit for use.

Reproducibility/Reuse
BGI Cloud Computing resources for handling and analyzing large-scale data.
Integrated tools to promote more widespread access, viewing, and analysis of data.
Encourage and aid use of workflow systems for methods (e.g. submission of Galaxy XML files).

Special Series/Hub for cloud-based tools
Technical notes: test tools in the BGI-Cloud.
Tools + Test Data (BGI or user) in one place.
Aids reproducibility.
Aids reviewers (free)
Aids authors: visibility (pubmed, etc.) hosting (included/free offers)
–contact us: editorial@gigasciencejournal.com
Oledoeflickr cc

Standards/Searchability/Sharing
ISA-Tab compatibility to aid and promote best practice in metadata reporting.
Allsupporting data must be publically available.
Ask for MIBBI compliance and use of reporting checklists.
Part of the Biosharing network.

Our first DOI:
To maximize its utility to the research community and aid those fighting the current epidemic, genomic data is released here into the public domain under a CC0 license. Until the publication of research papers on the assembly and whole-genome analysis of this isolate we would ask you to cite this dataset as:
Li, D; Xi, F; Zhao, M; Liang, Y; Chen, W; Cao, S; Xu, R; Wang, G; Wang, J; Zhang, Z; Li, Y; Cui, Y; Chang, C; Cui, C; Luo, Y; Qin, J; Li, S; Li, J; Peng, Y; Pu, F; Sun, Y; Chen,Y; Zong, Y; Ma, X; Yang, X; Cen, Z; Zhao, X; Chen, F; Yin, X; Song,Y ; Rohde, H; Li, Y; Wang, J; Wang, J and the Escherichia coli O104:H4 TY-2482 isolate genome sequencing consortium (2011) Genomic data from Escherichia coli O104:H4 isolate TY-2482. BGI Shenzhen. doi:10.5524/100001 http://dx.doi.org/10.5524/100001
To the extent possible under law, BGI Shenzhen has waived all copyright and related or neighboring rights to Genomic Data from the 2011 E. coli outbreak. This work is published from: China.

“The way that the genetic data of the 2011 E. coli strain were disseminated globally suggests a more effective approach for tackling public health problems. Both groups put their sequencing data on the Internet, so scientists the world over could immediately begin their own analysis of the bug's makeup. BGI scientists also are using Twitter to communicate their latest findings.”
“German scientists and their colleagues at the Beijing Genomics Institute in China have been working on uncovering secrets of the outbreak. BGI scientists revised their draft genetic sequence of the E. coli strain and have been sharing their data with dozens of scientists around the world as a way to "crowdsource" this data. By publishing their data publicy and freely, these other scientists can have a look at the genetic structure, and try to sort it out for themselves.”

G10K Genomes Get DOI®s
doi:10.5524/100004

We want your data!
scott@gigasciencejournal.com
editorial@gigasciencejournal.com
@gigascience

Scott Edmunds: GigaScience - a journal or a database? Lessons learned from the Genomics Tsunami

by GigaScience, BGI Shenzhen on Sep 06, 2011

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