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Do you want to attract computational biologists to your project?
Do you want to attract computational biologists to your department?
With a dozen colleagues around the globe, we present "A field guide to cultivating computational biology"!
Read on: https://arxiv.org/abs/2104.11364
Do you want to attract computational biologists to your department?
With a dozen colleagues around the globe, we present "A field guide to cultivating computational biology"!
Read on: https://arxiv.org/abs/2104.11364
Before we begin: computational biology has made major contributions and the culture is healthier than ever. Still, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. So we focus on solutions here!
(NB: we use “computationalist” broadly to include stats, math, computer/data science, etc)
1. Respect collaborators’ research interests and motivations
Biologists aren't just a pair of hands and computationalists aren’t just running the numbers. Comp folks need to produce interesting methods and lead authorship roles to survive. Core facilities can do routine analyses.
Biologists aren't just a pair of hands and computationalists aren’t just running the numbers. Comp folks need to produce interesting methods and lead authorship roles to survive. Core facilities can do routine analyses.
2. Seek necessary input during project design and throughout the life cycle of the project
Engage with collaborators early and often. Computationalists’ insights can impact the biological Qs & design. Biologists’ insights can influence the algorithmic approach and interpretation.
Engage with collaborators early and often. Computationalists’ insights can impact the biological Qs & design. Biologists’ insights can influence the algorithmic approach and interpretation.
3. Provide and preserve budgets for computational biologists' work
Cutting your collaborators out of awarded grant budgets is shoddy behavior-it threatens the health of computational biology labs. Keep your commitments, esp. for contributors to grant writing and preliminary data!
Cutting your collaborators out of awarded grant budgets is shoddy behavior-it threatens the health of computational biology labs. Keep your commitments, esp. for contributors to grant writing and preliminary data!
4. Change publication conventions and perceptions
a. We either need scientists to respect 2nd/co-1st/middle authorships or burn the whole authorship ordering system down. Dismissing scientists with these kinds of papers mean destroying many computational biologists' careers.
a. We either need scientists to respect 2nd/co-1st/middle authorships or burn the whole authorship ordering system down. Dismissing scientists with these kinds of papers mean destroying many computational biologists' careers.
b. Agencies are starting to ask for more context in biosketches allowing education on publishing venues/conventions in an unfamiliar field, impact of software and data products. Publications are starting to insist on software and datasets being cited properly, ensuring credit.
5. Establish academic structures and review panels that reward the team science efforts employed in computational biology
a. Until culture changes, interdisciplinary grant/publication/career review panels can be a good solution so comput'l biologists are assessed by their peers.
a. Until culture changes, interdisciplinary grant/publication/career review panels can be a good solution so comput'l biologists are assessed by their peers.
b. Otherwise, some biologists dismiss methods-focused researchers, some computationalists dismiss applied, non-theoretical work. We don’t want interdisciplinary contributors to the progress of science to go “extinct”!
6. Develop and reward cross-disciplinary training and mentoring
Institutions and societies can provide courses and mentors that help all researchers bridge gaps and gain computational skills. Institutions can ensure wet-lab and dry-lab researchers are in close proximity.
Institutions and societies can provide courses and mentors that help all researchers bridge gaps and gain computational skills. Institutions can ensure wet-lab and dry-lab researchers are in close proximity.
7. Support computing and software dev infrastructure to empower computational biologists
It’s necessary! And expensive! Institutions don't tell biologists to buy & install cell culture hoods on their own; we need similar support for compute (whether local cluster or cloud).
It’s necessary! And expensive! Institutions don't tell biologists to buy & install cell culture hoods on their own; we need similar support for compute (whether local cluster or cloud).
8. Facilitate computationally-driven experimentation and data generation
Institutional core facilities can offer full service to biologists without wet-llab skills needing to create a dataset or validate a hypothesis.
Institutional core facilities can offer full service to biologists without wet-llab skills needing to create a dataset or validate a hypothesis.
9. Provide incentives and mechanisms to share open data to empower discovery through reanalysis
Facilitated by grants, publication venues, and funded data repositories, the community benefits when data is available for all for re-analysis as methods improve.
Facilitated by grants, publication venues, and funded data repositories, the community benefits when data is available for all for re-analysis as methods improve.
10. Consider infrastructural, ethical, and cultural barriers to clinical data access
Patient data is valuable; institutions and societies can play a role in developing policies and infrastructure for proper and ethical collection (esp avoiding demographic biases) and sharing.
Patient data is valuable; institutions and societies can play a role in developing policies and infrastructure for proper and ethical collection (esp avoiding demographic biases) and sharing.
Conclusion
Over time, we expect the distinction between wet and dry biologists may fade, as both are working toward a common goal of understanding biology. Until then, we hope these ideas create environments where computational biology can thrive!
Over time, we expect the distinction between wet and dry biologists may fade, as both are working toward a common goal of understanding biology. Until then, we hope these ideas create environments where computational biology can thrive!
And here are all the coauthors!
@DrAnneCarpenter
@GreeneScientist
@FertigLab
@gwaygenomics
@fabian_theis
@SuzanneSindi
@benilton
@USCSysBio_Lab
@marchionniLab
@carninci
@mixOmics_team
@jleePSOC
+ Michiel de Hoon and Jean Y.H. Yang
@DrAnneCarpenter
@GreeneScientist
@FertigLab
@gwaygenomics
@fabian_theis
@SuzanneSindi
@benilton
@USCSysBio_Lab
@marchionniLab
@carninci
@mixOmics_team
@jleePSOC
+ Michiel de Hoon and Jean Y.H. Yang
