JAX Bioinformatics Training Program

The JAX Bioinformatics training program aims to build data science talent at JAX by providing training in scientific computing and data analysis, and to build instructional capacity with instructor training from The Carpentries.

We use existing curricula developed by The Carpentries to teach foundational programming and data analysis, and build our own custom curricula to target JAX research training needs and to feature JAX expertise. Foundational training includes R, Python, the Unix shell, and data management. Intermediate and advanced training features genetic mapping for complex cross designs such as the Diversity Outbred and other JAX Diversity mice strains,  image analysis, high performance computing,  RNA sequence analysis, and data wrangling and visualization.


Workshops are hands-on events that cover the core skills needed to be productive in a small research team. Short tutorials alternate with practical exercises, and most instruction is done via live coding. Instruction aims to “leave no learner behind.” Workshops are staffed by two or more instructors and helpers so that the teacher-to-student ratio (1:10) is optimal for learning. Most workshops are open to those from neighboring institutions.

Participants bring a laptop and must be physically present in order to participate. To ensure a high-quality experience for all participants and instructors, remote attendance is not generally accommodated.

To learn more about data science, visit Data Science at JAX.

Visit The Carpentries to learn more about this organization and their open source lesson materials. To browse our intermediate and advanced offerings, see the selections below. All curricula are open source and are great for self-study.

To find our full listing of events and current courses, please visit the Education Calendar

Bioinformatics Training Program

Image Analysis with Python

This material covers the basics of image analysis using Python and Jupyter Notebook. The material covers image file management, basic processing such as rescaling and filtering, and basic analytical techniques such as image segmentation and fluorescence colocalization.

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High Performance Computing

This introduction to high performance computing uses the Jackson Laboratory computing cluster. The goal of this lesson is to teach novice programmers to use powerful tools and computing resources, and to engage in best practices for using these resources.

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Quantitative Trait Mapping

This material provides training in the qtl2 package for R for QTL mapping in backcrosses, intercrosses, and complex crosses like the Diversity Outbred. 

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R for Data Science

This training follows R for Data Science by Hadley Wickham and Garrett Grolemund. The book features data wrangling, visualization and modeling in R.

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RNA Sequence Analysis

This material teaches differential expression analysis and variant annotation using R and BioConductor, and is available from the Bioconductor 2018 Workshop Compilation.


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Rigor and Reproducibility in Experimental Design

This lesson introduces the foundations of sound experimental design and the importance of rigor and reproducibility in experimentation. The goal of this lesson is to provide conceptual understanding, methods, and practice in planning and evaluating experiments.

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Machine Learning with Python

Machine learning (ML) extracts knowledge from data and focuses on prediction. ML learns from our data how to make decisions for future observations. It is widely used and common in everyday interactions – on Facebook, Google, Amazon, or your favorite automated teller machine (ATM). Equally important are applications in science, such as personalized cancer treatment, medical diagnoses, and drug discovery. ML is essential in data driven sciences.

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