Incentives and barries to sharing of sequencing data


Richard Neher
Biozentrum, University of Basel


slides at neherlab.org/201905_OneHealth.html

Why share data? (or not?)

Common arguments for research data:
  • Reproducibility/accountability/transparency
  • data reuse → bigger return on research funding
  • FAIR (findable, accessible, interoperable, reusable)


Public health/pathogen data:
  • Pathogen dynamics is only evident in comprehensive data sets
  • sharing needs to be timely to inform counter measures, not after pulication.
  • legal and privacy arguments often used to justify not sharing

Data sharing in public health emergencies

data owner ↔ public interest

Sequences record the spread of pathogens

images by Trevor Bedford

2013-2015 West African Ebola virus outbreak

By CDC Global - WHO in PPE
By Chris55
It took two years to have a detailed understanding of the outbreak. Why did this take so long?

Sharing of Ebola virus sequences

  • Baize et al.: Samples collected in March 2014, sequences in GenBank within a month
  • Gire et al.: released data as it was generated
  • Followed by a long gap...
  • Early insights into transmission dynamics are important
  • Sharing has to be immediate -- not upon publication
Yozwiak et al

Recurring problem

  • 2002 Severe acute respiratory syndrome (SARS)
  • 2003 H5N1 influenza outbreak. Some countries stopped sharing any data
  • 2013-2015 Ebola virus outbreak in West Africa

  • 2014-2016 Zika Virus outbreak: Controversies about attribution and reuse
  • 2014- H7N9 influenza outbreak: Controversies about attribution and reuse

Different disease -- different scientists and institutions.

→Lessons need to be relearned.

Barriers to data sharing: scientists

  • Privacy of study participants
  • Fear of being scooped/ensure maximal return
  • Secondary analysis perceived as freeloading: "data parasites"
  • Don't want to be second guessed
  • Release and curation is laborious
  • Sloppy records
Also see Smith et al, F1000 2016

Barriers to data sharing: organizations and governments

  • Economic consequences of outbreaks (tourism, agriculture, reputation)
  • Conflicts between high and low/middle income countries
  • Concerns about IP and commercial exploitation
  • Legislative/regulatory barriers
See also: van Panhius et al, BMC Public health 2014

Each variant of A/H3N2 influenza is globally distributed

There are no secrets to keep! The virus doesn't "belong" to any country.

But global prevalence and dynamics are important to know!

Overcoming Barriers

Open vs restricted sharing

  • Alternatives to GenBank: GenBank is "public domain", no requirement to credit data producers
  • GISAID/EpiFlu: sign-up and agree to terms and conditions
  • virological.org: Platform for sharing and discussing molecular epidemiology
  • Explicit data reuse terms
  • Outline planned projects in white-paper
  • Caveat: Very difficult to enforce...
See Bogner et al, 2006

Open sharing: GenomeTrakr/PulseNet

  • US federal and state public health labs
  • Several labs outside the US
  • Analysis is done by NCBI
  • All data on genbank -- immediately!
  • frequently updated automated analysis
See also Smith et al, F1000 2016

Make sharing easy and provide incentives!

Grubaugh et al, samples from Florida
Metsky et al, sample from the Caribbean

nextstrain.org

joint work with Trevor Bedford & his lab

code at github.com/nextstrain

Real-time influenza virus analysis with nextstrain

nextstrain.org

  • Global analysis provides context for new sequences
  • Once nextstrain became the largest collection of Ebola/Zika sequences, everybody wanted their sequences on nextstrain
  • We take care to highlight the scientists contributing data
  • Link to original source, rather than reshare
  • nextstrain.org/zika?f_authors=Metsky et al
Open source: github.com/nextstrain

Summary and outlook

  • To connect outbreaks and detect transmission, we need dense coverage
    → data needs to be shared
  • Sharing needs to be timely, metadata is critical
  • Sequence data has become a commodity -- alone rarely publication worthy
  • We need better ways to credit data producers
    → data citations
  • Wider acceptance of preprints should help
    → establishes priority and a citation hook

nextstrain.org

  • Trevor Bedford
  • Colin Megill
  • Pavel Sagulenko
  • Sidney Bell
  • James Hadfield
  • Wei Ding
  • Tom Sibley
  • Emma Hodcroft