Got this in an email from the Bioinformatics Core at the Genome Center. I saw a lot of these mistakes in an NSF Biodiversity panel that I served on earlier this year. One thing I disagree with is the notion that you cannot do bioinformatics on a personal computer. I do this all the time. Nevertheless, this is a great resource for the UC Davis crowd.
10 ways to ensure your next grant application fails:
1. Budget for data creation (e.g. sequencing) but not bioinformatics (analysis, storage, backup, dissemination)
2. Propose bioinformatics experiments that cannot answer your question
3. Design experiments with insufficient statistical power
4. Use modern omics and bioinformatics jargon incorrectly
5. Assign critical analysis procedures to untrained personnel
6. Propose to employ out-dated techniques
7. Explain your methods vaguely without attention to detail
8. Trivialize your study by not linking it to other information
9. Attempt to perform bioinformatics analyses on personal computers
10. Propose writing novel software without employing a professional programmer
The NIH RO1 deadline (among others) is fast approaching and in the month of September, grant planning in collaboration with the UCD Bioinformatics Core is 100% free*. Our typical grant preparation packet includes:
* A face-to-face meeting with the Ph.D. scientists of the Bioinformatics Core
* A letter of support from Dr. Ian Korf, Associate Director for Bioinformatics at the Genome Center, expressing our delight and enthusiasm for your awesome project
* A bioinformatics methods section written by experienced Bioinformatics Core analysts
* Figures to help your reviewers understand the importance of your study
* Cost estimates for anything from pilot studies to long-term projects
* Thorough inspection of experimental design by a professional statistician
Granting agencies are increasingly looking for proof of bioinformatics competency in awarding funds. Applications that don’t demonstrate bioinformatics capabilities within the labs of collaborators, or through other research facilities, will not score well. This includes the running of standard and innovative bioinformatics analysis pipelines as well as design, purchase, and maintenance of the requisite high performance computing hardware. The Bioinformatics Core at the UC Davis Genome Center has 10 years of experience analyzing large biological datasets, from microarrays through Illumina and PacBio sequences, in close coordination with the Genome Center’s other experimental core facilities. We have 6 years of experience teaching bioinformatics analysis to grad students, postdocs, and PIs. And we have 10 years of experience designing and running high performance computing resources, including ROCKS clusters and high-memory servers.
* Actually, it’s always free to talk with us at the beginning of a project. We’re here to help!