Industry Engagement sector update Gabriella Rustici

Bioinformatics Training Facility, School of the Biological Sciences

Activities so far

Appointed an Industry Engagement advisory committee

Confirmed members: •  Claus Bendtsen, AstraZeneca •  Mark Forster, Syngenta •  Samiul Hasan, GSK •  Wendy Filsell, Uniliver •  William Spooner, Eagle Genomics •  Audrey Kauffmann, Novartis

To be confirmed: •  Justin Powell, Takeda (not replied)

Activities so far

Circulated two surveys to:

•  help us understanding the bioinformatics-related training needs of industry and

•  consequently ensure that suitable training activities are developed and honed to target such needs.

One survey targeted bioinformaticians, the other one wet lab scientists

Surveys’ results

0

10

20

30

40

50

60

70

80

90

Bioinformaticians Wet lab

Large company Small-to-medium enterprise

0 10 20 30 40 50 60 70 80

Biogen Idec Bioindustry Park Silvano Fumero

Databiology DNAdigest.org

DNAnexus Dupont

EMD Serono (Merck Serono) Euformatics Oy

Genentech Ina Harrow Consulting

Instem Scientific LGC

Life Technologies - Thermo Fisher Lundbeck

MedImmune Novo Nordisk

Omixon Biocomputing LTD Redoxis AB

Roche Astellas Pharma Inc.

Bayer Healthcare Eli Lilly & Company

Heptares OP

Pfizer Inc. UCB

Unilever AstraZeneca

Bayer Bayer Pharma AG

NIBR Sanofi

Eagle Genomics Illumina

GlaxoSmithKline Novartis

Discipline of interest

0 10 20 30 40 50 60

Virology Toxicology

Systems biology Proteomics

Plant Sciences Oncology

Neurobiology Molecular Biology

Microbiology Medicine

Infectious diseases Immunology

Genomics/epigenomics Epidemiology

Drug development Computer Science

Computational chemistry Chemistry

Cell biology Biomedical Sciences

Bioinformatics Biochemistry/Biophysics

Bioanalytics

Bioinformaticians Wet lab

What databases do they use in their work?

0 10 20 30 40 50 60

Systems databases (e.g. BioModels, Reactome, KEGG,...)

Structures databases (e.g. PDBe)

Protein databases (e.g. Uniprot, Pfam, Intact,...)

Ontology resources (e.g. Gene Ontology,..)

No, I do not use databases

Literature services (e.g. Pubmed,...)

Gene expression databases (e.g. ArrayExpress, Gene Expression Omnibus,...)

DNA & RNA databases (e.g. Ensembl, 1000 genomes, UCSC,....)

Chemical biology databases (e.g. ChEMBL,..)

bioinformaticians

wet lab

Bioinformaticians: Software tools/Data mining software

0 2 4 6 8 10

cBioPortal Custom

Databiology Genedata

IGV Matlab

Omicsoft Pathway Studio

postgresql SQLite

SVM Spotfire

Arraystudio Expressionist

Weka Knime

Linguamatics R/Bioconductor

Data mining software

0 5 10 15 20

AffyMetrix

Ensembl

Oracle

Plink

SAS

Eclipse

Spotfire

cytoscape

R/Bioconductor

NGS tools

Software tools

0 10 20 30 40 50 60 70

Workflow tools (e.g. Galaxy)

No, I do not use any software

Gene set enrichment testing tools (e.g DAVID)

Next generating sequencing read alignment and assembly programs (e.g BWA, Bowtie,...)

Pathway & network analysis tools (e.g. Cytoscape, Biocarta, Ingenuity,...)

Data analysis environments (e.g. R/Bioconductor, Matlab,....)

Sequence alignment, similarity & homology tools (e.g. Blast, Clustal,...)

Microsoft software (e.g. Excel)

Wet lab scientists: analysis software

Wet lab scientists and statistics

6%

29%

59%

6%

How confident are you with statistics?

Very confident

Confident

Not so confident

I am not even sure of what statistics I need to know

34.0%

1.5%

32.4%

32.4%

No, I do not have any support. I am responsible for analyzing the data that I

generate.

No, the data analysis is carried out by someone else. I just receive a file with the

results.

Yes, occasionally I interact with a bioinformatician/statistician at my Institute,

particularly when I get stuck and I don’t know how to proceed.

Yes, I have a bioinformatician in the group that helps me to design experiments and

also provides support for the data analysis

Do you collaborate with a bioinformatician/statistician?

Programming experience/languages

0 5 10 15 20

Python R/BioConductor

Perl Java C++

Matlab Ruby

Javascript Unix bash

HTML MySQL PL/SQL

Scala sparql

SQL

Bioinformaticians - Programming languages

26.5%

73.5%

Wet lab – programming experience

Yes

No

Bioinformaticians: What competencies are crucial?

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Using and building ontologies

Using and applying standards

Version control tools

Retrieving and manipulating data from public repositories

Working with high-performance computing or cloud-based solutions

Modeling and warehousing of biological data

Programming

Integrating public and private data-sets

Use of scripting languages

Ability to use statistical analysis software packages

Data mining of large biological data-sets

Wet lab: what expertise would you like to acquire?

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%

If other, please specify

Data publishing skills (e.g. how do I publish my results?)

Scientific knowledge (e.g. how should I design my experiment to obtain meaningful results?)

Data manipulation skills (e.g. what software is more appropriate to analyze my data? How does a specific software work?)

Statistical knowledge (e.g. what statistics do I need to know to be able to analyze my data?)

Data visualization skills (e.g. how does my data look like? How do I interpret and present my data?)

Bioinformaticians: Which bioinformatics training would you most value in relation to your work?

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Basic computing skills

Data analysis skills

Programming skills

Statistical methodologies

Use of data standards in curation and/or data integration practices

Bioinformaticians: What topics would you like to see covered in future training activities?

0 5 10 15 20 25 30 35 40

Programming

HPC

Cloud solutions

Text mining

Data visualization

Drug development/discovery

New resources/latest technologies

Network analysis

Workflows

Statistics

Data integration

NGS analysis

What training format do they prefer?

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Face-to-face training courses on site

Face-to-face training courses off site

Online/eLearning Face-to-face combined with online

Bioinformaticians

Wet lab

Summary

•  Statistics is a major weakness, regardless of the user group – the topic is inadequately covered in undergraduate curricula and its teaching often approached theoretically rather than practically

•  Analysis of high-throughput data, primarily with popular data mining software, knowledge of programming languages (Python, R, Perl), data integration and network analysis are high priorities for bioinformaticians

•  Basic data manipulation, visualization and statistics are fundamental to wet-lab scientists. Lack of confidence in the use of statistical software that requires scripting.

•  Face-to-face is always popular but a lot of basic training can be delivered online

Various considerations

•  Collate all Industry use cases already available to define key competencies in Industry and disseminate these to all other sectors

•  Utilize this information to prioritize key training areas

•  Hold the first Advisory committee meeting within the next six months

•  Collaborations with other Elixir nodes:

•  ELIXIR-NL will use the same surveys to assess the training needs of industry; share the results and collate more information – planning to do this in collaboration with all Elixir nodes

•  Engage Industries beyond pharma?