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Runs from: August 7, 2025 - September 4, 2025
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Total Classes: 4
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What You Will Learn: The BTEP Bioinformatics Summer Series is five classes that introduce novices and new NIH scientists the essentials for getting started with bioinformatics analysis of next generation sequencing (NGS) data. Topics include a broad survey of bioinformatics resources available to all scientists at NIH, an overview of the NIH High Performance
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08/07/2025 |
The BTEP Bioinformatics Summer Series is five classes that introduce novices and new NIH scientists the essentials for getting started with bioinformatics analysis of next generation sequencing (NGS) data. Topics include a broad survey of bioinformatics resources available to all scientists at NIH, an overview of the NIH High Performance Computing System (Biowulf), a discussion and comparison between programming languages R and Python, and tips for making data FAIR and data analyses reproducible. Register for each class individually by clicking on its respective link below. These classes are open to NIH audience only.
August 7, 2025 (Thursday, 1 PM – 2 PM): Introduction to Bioinformatics Resources will inform participants of software (commercial and open-source), self-learning tools, and resources for bioinformatics and data science.
August 14, 2025 (Thursday, 1 PM – 2 PM): Introduction to Unix and Biowulf will serve as a crash course for using the NIH Unix-based High Performance Computing system (Biowulf). Participants will learn to navigate through directories, work with files, and use bioinformatics applications that are installed on the system.
August 21, 2025 (Thursday, 1 PM – 2 PM): Overview of R and Python will discuss the benefits that these two popular programming languages can bring to a bioinformatics project. After this session, participants should be able to decide which language to use for a given data analysis.
September 4, 2025 (Thursday, 1 PM – 2 PM): Managing Data Analysis Projects using Jupyter Lab. This class will introduce participants to Jupyter Lab, a tool for maintaining data, code, output, and description of analyses all in one place, which facilitates transparency and reproducibility of data analysis.
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