1. Wallenberg Advanced Bioinformatics Infrastructure (WABI)
Directors
KAW Scientific Advisory Board, Dec 12, 2017
Björn Nystedt
Joint Head of Facility Bioinformatics Long-term Support (WABI)
Gunnar von Heijne
Siv Andersson
Managers
Björn Nystedt
Pär Engström

2. Human WGS grows faster than Moore’s law
Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, et al. (2015) Big Data: Astronomical or Genomical?. PLoS Biol 13(7): e doi: /journal.pbio

3. Data is cheap, analysis is not
Cost
Cost
Bioinformatics analyses
Computing
Data
Data
Year
Year
“Per base”
“Per project”
Data scientists
Data

4. Bioinformatics know-how as infrastructure
“The scientific community has failed to craft attractive career paths for those who do the analyses it increasingly requires. Institutions and funding bodies must carve out a viable place for bioinformaticians who focus on collaborations, and reward them for their abilities to navigate the myriad demands of multidisciplinary projects.”

5. SciLifeLab national service
SciLifeLab platforms
SciLifeLab national service VR
National Genomics Infrastructure
Diagnostics Development
National Bioinformatics Infrastructure
Sweden
SNIC
Single-cell omics
Bengt Persson
Computer resources free for Swedish researchers
SciLifeLab
Data Center
Johan Rung

6. NBIS – The Bioinformatics platform at SciLifeLab
Platform board
Director and central staff
WABI
35% of NBIS
75% of national project support
Monthly platform management meetings
Support
(Project service)
Infrastructure
(Community service)
Peer-review (WABI)
Fee-for-service
Training

7. NBIS – Support, Tools, and Training
250+ consultations a year
800 projects a year running on national super-computers, flexibly allocated based on needs
Continuous investigations
of future compute environments
200+ software and databases
maintained by application experts
20+ courses with 400+ PhD students/post-docs per year
Guidance and help with data publishing and open science
100+ research projects per year supported with advanced data analyses
Complex workflows like the Cancer Analysis Workflow (CAW)

8. Research Council evaluation
“[NBIS..] is crucial to the future competitiveness of Sweden in data-driven life sciences research, and is helping to keep Sweden in the European forefront in the area.”
“NBIS is probably the largest genuinely national and fully established bioinformatics infrastructure in Europe.” The Swedish Research Council, 2017 Overall score: 7/7 (“Outstanding”) Scientific impact: 7/7 (“Outstanding”)

9. [WABI]
We aim to ensure that qualitatively excellent projects are not stalled due to difficulties in recruiting experienced bioinformaticians, and we strive to enable high-quality basic research in a reproducible manner.

10. National Proposals Evaluation Committee
The WABI model
Scientific value
Feasibility
Involvement
National Proposals Evaluation Committee
3 times per year
1-2 months to decision
Accept ~20 projects per year
feasibility
priority
Web portal
Facility management
application
time allocation
Research project
Hands-on scientist
Support staff
500h effective time; average ~2 years active involvement
Hands-on involvement from the research group is mandatory
Staff 100% support (not driving own research)
Co-authors according to normal contribution criteria

11. The Proposals Evaluation Committee
Ulf Pettersson
Uppsala University
Erik Kristiansson
Chalmers
Cecilia Williams
KTH
Mauno Vihinen
Lund University
Jan Larsson
Umeå University
Peter Söderqvist
Linköping University
Tanja Slotte
Stockholm University
Pär Ingvarsson
SLU
Mattias Rantalainen
Karolinska Institutet
Erik Larsson
Gothenburg University

12. The team Genetics and epigenetics Population and evolutionary genomics
Mixed competence 6 sites
Technical and biological skills
Average 8 years post PhD
1 physical + 1 video monthly
Cancer genomics
Omics integration
Metagenomics
Reproducibility
Transcriptomics and proteomics
Deep learning
scRNA
Cloud computing
Alumni:
DeCode (Island) 10X Genomics (USA)
IBM (Sweden)
Novo Nordisk (UK)
Method development
Spatial transcriptomics

13. Project proposals 2013 - 2016 400+ project proposals from 255 PI:s
POPULAR
NATIONAL
400+ project proposals from 255 PI:s
80 granted projects (acceptance rate: 20%)
30% of all applying PIs granted a project
30 species, 20 data types
MULTIDISCIPLINARY

14. Projects average ~2 years + 1 year to publication
WABI build-up
Staff
Projects average ~2 years + 1 year to publication
10 FTE
4 FTE
6+2 FTE

15. Publications
2016
Nature Science Cell Stem Cell Genome Research Nature Communication Nature Communications eLife Briefings in Bioinformatics PLOS Genetics Oncotarget Bioinformatics RNA Biology Scientific Reports Int. J. of Cardiology
Genes, Chromosomes and Cancer
Molecular Ecology Resources BMC Evolutionary Biology J. of the American Heart Association

16. User evaluations

17. Dopamine neuronal lineages
Åsa Björklund
Research paper
Public interactive app to explore the data
Knowledge transfer
Single-Cell Analysis Reveals a Close Relationship between Differentiating Dopamine and Subthalamic Nucleus Neuronal Lineages
Predictive Markers Guide Differentiation to Improve Graft Outcome in Clinical Translation of hESC-Based Therapy for Parkinson’s Disease
Thomas Perlman
Developmental biology
Kee et al. (2017)
Cell Stem Cell 20:29-40
Cell Stem Cell 20:29–40
Cell Stem Cell 20:135–148
2 additional articles in preparation

18. Speciation in action TE TE Selection signature area
Johan Reimegård
Selection signature area
Selection signature area
C. graniflora
Experimental hybrid
Recent divergence
(50,000 y)
C. rubella
No phenotype diff
Phenotype diff
Genomic areas with selective signatures show allele-specific gene expression for flowers in the hybrid
Altered cis-regulation drives phenotypic diversity!
Implications of TE insertions and siRNA-mediated methylation TE TE
24nt siRNA
Tanja Slotte
Plant evolutionary genomics
Steige et al. (2015, 2017)
Mol Biol Evol 32:
PNAS 114:

19. Reproducible Research
Leif Wigge, Rasmus Ågren, Per Unneberg

20. Recent data waves 40% of our active projects
Eukaryotic scRNA/protein/DNA projects
Perlmann, Dopamine neurons
Muhr, Neural stem cells
Simón, Newt limb regeneration
Castelo-Branco, Oligodendrocyte lineages
Samakovlis, Lung epithelium
Pietras, Fibroblast in breast cancer
Göritz, Pericytes in wound healing
Adameyko, Nervous system origin
Dahl, Neurological disorders
Spalding, Hetereogeneity in fat tissue
Petroupolos, In vitro embryos
Mjösberg ,Innate lymphoid cells
Kasper, Hair follicles ,
Andersson, Pancreatic beta cells
scRNA/scProtein
Landegren, Neuronal developmen
scDNA
Frisén, Lineage tracing
Human WGS projects
(8 SciLifeLab National Projects)
Gyllensten, SweRef
Sullivan, Schizophrenia
Lindstrand, StructVar
Eriksson, Somatic mutations
Å Johansson, Complex traits
E Johansson, Colon cancer
Syvänen, ALL
Andersson, Infant ALL
Martinsson, Neuroblastoma
Fernö, Breast cancer
Rosenquist, CLL
Wadelius, ADR
Technical development leading to a major scale-up in data generation should be complemented by a strategy for bioinformatics competence in the research community

21. Thank you for listening!
Supported groups