Many facets of modern biological research involve the analysis of RNA to understand how cells control gene expression. These experiments involve powerful new sequencing methods that produce large data sets and require sophisticated bioinformatic analysis. In the Spring of 2016, faculty at the University of Colorado Anschutz Medical Campus were awarded $20 million to start the RNA Bioscience Initiative. To alleviate the bottleneck in analysis of these data sets, the RBI started the Informatics Fellows program and recruited postdoctoral fellows to develop and apply bioinformatic approaches and to educate other trainees on the use of these methods in their own research.
A unique feature of the program is that Fellows are card-carrying RNA biologists who have significant training at the lab bench. So while they no longer do “wet” experiments themselves, this background gives them a unique perspective on RNA-related research and has been instrumental in helping collaborators ask and answer fundamental questions in RNA biology.
It’s an exciting time to be an RNA biologist capable of analyzing large sequencing data sets. We discover something new every day in our collaborators’ data sets and are capitalizing on our unique capabilities to push the limits of RNA sequencing technologies.
The goal of the Informatics Fellows program is train the next generation of bioinformatic analysts who span the disciplines required for completing RNA sequencing experiments including design, execution, and analysis.
The Fellows divide their time between bioinformatic collaborations with other groups on campus, internal projects involving software and method development, and teaching.
Their training involves several important areas of bioinformatic collaboration:
Experimental design. RNA sequencing experiments are easily conceptualized but require appropriate design to ensure rigorous analysis. Design considerations include experimental variables (e.g., replicates and conditions), sample preparation (e.g., RNA type and source), and the sequencing platform (e.g., short or long reads).
Analytical software development. Once analyses are codified, we have worked to create and disseminate software for broad use in the community. Software development combines fundamental design principles with coding skills in Python, R, and C/C++ . Software developed in the program is available on our Github page.
Exploratory data analysis. RNA sequencing experiments often yield more questions than answers, and their biology backgrounds have been essential in enabling the Fellows to identify meaningful signals in complex data sets. These exploratory analyses combine data mining and statistics to enable thorough investigation. Deliverables are often fully reproducible dynamic documents that facilitate a collaborator’s own data exploration.
Education. Our educational mission is to broadly disseminate the analytical skills required to analyze RNA sequencing experiments, and the Fellows participate in several teaching activities. These efforts have been impactful across campus, with many graduate students, PRAs, postdocs, and principal investigators benefiting from the Fellows’ expertise.
The program was formed and is led by Jay Hesselberth, Ph.D., Associate Professor of Biochemistry and Molecular Genetics.
Currently the program has four fellows:
Kent Riemondy, Ph.D. did thesis work at the University of Colorado Boulder with Rui Yi studying the role of miRNA biogenesis and targeting in skin. Kent has been in the program for 3.01 years.
Austin Gillen, Ph.D. did thesis work at Northwestern University with Ann Harris studying miRNA regulation of CFTR. Austin has been in the program for 2.37 years.
Rui Fu, Ph.D. did thesis work at the University of California San Diego with Jens Lykke-Andersen studying the mechanism of nonsense-mediated mRNA decay. Rui has been in the program for 1.62 years.
Ryan Sheridan, Ph.D. did thesis work at the University of Colorado Anschtuz Medical Campus with David Bentley studying the role of backtracking in RNA Polymerase II proofreading. Ryan has been in the program for 4.47 months.
Collaborative projects with the Informatic Fellows are done through RBI pilot awards. We are not a service core, and so to facilitate fee-for-service projects we have established a relationship with CIDA to facilitate analysis of single-cell mRNA sequencing experiments. The cost of these analyses can be included in a grant budget. Please initiate a collaboration for more details.
We also provide letters of support to investigators for their grant applications that detail the Informatics Fellows program and broader RNA sequencing capabilities on campus. Please contact jay.hesselberth@gmail.com for details.
The Informatics Fellows also provide office hours to provide help with programming, software, pipelines, and experimental design questions. Please see our office hour page for additional details.
The fellows hold weekly RBI Office Hours on Thursday afternoons from 1:00-4:00 PM. Office hours allow investigators across campus to ask questions about the design, execution, and analysis of RNA-related sequencing experiments. The Office Hours have been instrumental in educating investigators across campus on the appropriate design, execution, and analysis of RNA sequencing experiments.
The fellows mentor summer students as part of the RBI Summer Internship Program.
The Fellows help create and teach several courses focused on intersection of RNA biology and bioinformatics.
IDPT 7810 006: Practical Biological Data Analysis in RStudio. This 2 week course teaches students to perform practical data analysis tasks in R Studio with the goal of teaching reproducible research practices in the context of routine experimentation.
MOLB 7900: Practical computational biology for biologists: Python This is a computational biology class aimed at biology PhD students. Topics covered include: basic practices for coding in Python; analysis of standard high-throughput genomic data to study the regulation of gene expression; integration of multiple datasets for genomic analysis; introduction to scientific computing in Python.
MOLB 7910: Practical computational biology for biologists: R This is a computational biology class aimed at biology PhD students. Topics covered include: basic practices for coding in R; analysis of standard high-throughput genomic data to study the regulation of gene expression; introduction to modeling gene expression; data visualization; how to communicate computational analysis/results.
The fellows are developing a short course on analysis of single-cell RNA sequencing experiments. This course will be offered on the Anschutz Medical Campus and will hopefully shift some of the workload of single-cell RNA-seq analysis into individual labs.
The fellows taught the Genome Analysis Workshop in Spring 2017. This semester-long course teaches the basics of shell programming, analysis in R, and scripting in Python.