Bioinformatics Training & Education Program

Please email staff@hpc.nih.gov for the meeting link.

All Biowulf users, and all those interested in using the systems, are invited to call in to our Virtual Walk-in Consult to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff to discuss the problem 1-on-1.  We'll try to address simpler issues on the spot and follow up on more complex questions after the session.

Updated Location: ATRF, Frederick MD, Main Auditorium

What to bring:  Laptop capable of connecting to internet via NIH wifi

For questions or to register, please contact Amy Stonelake (amy.stonelake@nih.gov)

Are you looking to expand the reach of your sequencing to enable what long read technologies can provide?  Please join Oxford Nanopore bioinformatics specialists on a deep dive into getting the most from your long read sequencing data.  In conjunction with the Frederick National Lab for Cancer Research (FNLCR), we are offering an in-depth workshop focusing to give you the tools and know how to delve deeper and gain further insight to your biological systems.

Oxford Nanopore Technologies offers data analysis solutions in our EPI2ME software platform tailored to the analysis of long read DNA and RNA sequencing data  from ONT devices. The Single Cell focus will cover the details of analyzing single-cell RNA sequencing data using our EPI2ME pipeline wf-single-cell. This workflow provides access to industry standard tools for primary processing of single-cell data including deconvolution, quality control, gene and transcript identification, and data visualization.

The Human Variation focus will cover the details of analyzing human whole genome sequencing data using our EPI2ME pipeline wf-human-variation. This workflow provides users with tools to perform alignment and variant calling for single nucleotide, structural,  and copy number variants as well as clinically relevant short tandem repeats, and cytosine methylation.

For both, participants will learn about the EPI2ME software, pipeline details, and work with an ONT bioinformatics expert in a hands-on data analysis training exercise.

Cardinal Bernardin Cancer Center Virtual Grand Rounds

Interested in learning about various cancer indicators, approaches for data integration, and how to leverage data to drive scientific discovery? Then join NCI CBIIT’s Acting Director, Dr. Jill Barnholtz-Sloan, for a conversation about her work using data science for cancer research.

She’ll address the following topics:

• Examples of research involving brain tumors
• How to select a data set and use various ones for your research
• The strengths and limitations of registry data
• The benefits of Cancer Research Data Commons cloud resources
• A comparison of cancer registry-based data sets
• The strengths and limitations of Real-World Data

This lesson will serve as a general introduction to R and RStudio. Attendees will explore the RStudio interactive development environment (IDE) and get started with R programming.  

This class will introduce beginners or those looking for a refresher to Jupyter Lab, a platform used to organize code and analysis steps in one place. Jupyter Lab can be easily installed or run in a web browser, and supports several languages such as R and Python. It provides a way to keep track of all steps in an analysis and a place for collaboration. This class will not be hands-on and is a demo only. Experience using or installation onto personal computer of Jupyter Lab is not needed to attend. This is for NIH audience only and meeting link will be sent upon registration.

Register at: https://cbiit.webex.com/weblink/register/re16ac11f4d295ca6f9c6cd061790316c

In this lesson, attendees will learn the most basic features of the R programming language. The focus will be on R syntax, R objects, and data types. 

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering this two-part online training for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. 

Part 1 is a  two-hour online training that will address the frequentist approach and will cover the concepts of hypothesis testing, confidence intervals, Type I and Type II errors, statistical power, and p-values. Technical details will be kept to an absolute minimum.    

By the end of this training, attendees will be able to:  

• Understand how to use statistical concepts to test hypotheses 

• Interpret the results of statistical tests 

• Make informed decisions about the significance of findings while considering the potential for errors in the analysis 

Attendees are not expected to have any prior knowledge to be successful in this training. Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. 

You must register separately for Part 2 of this class series.

AI Club is a weekly meeting that explores various topics relating to AI and deep learning in biomedical sciences, typically in a seminar, workshop, or journal club format. AI Club is intended to be accessible to experts and non-experts alike. We currently meet in the Building 10 Library Training Room on Mondays from 11 - 12. IT IS STRONGLY RECOMMENDED TO COME IN PERSON.

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a several trainings that cover general concepts behind statistics and epidemiology. These trainings will help participants better understand and prepare data, interpret results and findings, design and prepare studies, and understand the results in published literature. 

This four-hour online training will provide a brief review of the principles of epidemiology, outbreak investigations, implications in public health, key concepts and terms, and commonly used statistics in epidemiology (e.g., morbidity and mortality rates; incidence and prevalence; relative risk; odds ratio; sensitivity and specificity). Time will be devoted to questions from attendees and references will be provided for in-depth self-study. 

By the end of this training, attendees will be able to:  

• Define epidemiology and its key principles 

• Share the purpose and function of outbreak investigations 

• List common statistical methods in epidemiology 

• Describe when to use different statistical tests and measures 

Explain measures of association and confounding 

In this lesson, attendees will continue to learn basic features of the R programming language. The focus of this lesson will be vectors, one of the most common object types in R. You will learn why vectors are useful and how to create, modify, and export vectors.

In this talk, Dr. Stergachis will present data on using Fiber-seq and deaminase-assisted Fiber-seq (DAF-seq) to resolve the functional impact of both germline and somatic genetic variants, as well as to identify somatic epimutations that arise during normal human development and oncogenesis.

In this webinar, the participants will learn about application of machine learning methods, specifically geared towards predicting the toxicity of target molecules. They will gain insights into various machine learning techniques, fostering a comprehensive understanding of their application in this critical domain. Furthermore, attendees will acquire the skills to apply machine learning to their data, utilize applications to train artificial intelligence (AI) models for toxicity prediction, and effortlessly share the results with collaborators.

This is an introductory level class taught by MathWorks. No installation of MATLAB is necessary.

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering this two-part online training for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. 

This one-and-a-half-hour online training will address the Bayesian approach and will cover the concepts of Bayes’ Theorem, prior and posterior distributions, and Bayes factor. Technical details will be kept to an absolute minimum.    

By the end of this training, attendees will be able to:  

• Explain the fundamental concepts of Bayesian inference, including Bayes’ Theorem and its applications. 
• Describe the roles of prior and posterior distributions in Bayesian analysis. 
• Interpret the Bayes factor and its use in comparing statistical models. 

Attendees are not expected to have any prior knowledge to be successful in this training. Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. 

Part 1 is a pre-requisite for this class. You must register separately for Part 1 of this class series. 

This lesson will introduce data structures including data frames and show attendees how to import data into the R environment. 

AI Club is a weekly meeting that explores various topics relating to AI and deep learning in biomedical sciences, typically in a seminar, workshop, or journal club format. AI Club is intended to be accessible to experts and non-experts alike. We currently meet in the Building 10 Library Training Room on Mondays from 11 - 12. It is strongly recommended to come in person. 

This is the last lesson in Part 1 of Introductory R for Novices: Getting Started with R. This lesson will focus exclusively on working with data frames. Attendees will learn how to examine, summarize, and access data in data frames.  

In this one hour and half hour online training, attendees will apply deep learning to brain MRI images.  

By the end of this training, attendees will be able to: 

• Recognize multiple methods of generating models 
• Interrogate the models with explainability techniques, such as applying artificial intelligence (AI) to data, using apps to train AI models for prediction, and sharing results with collaborators. 

 This is an introductory-level training taught by MathWorks. No installation of MATLAB is necessary.  

This one-day in-person NIH AI Symposium will bring together researchers from a broad range of disciplines to share their AI-related research, with the goal of disseminating the newest AI research, providing an opportunity to network, and to cross-pollinate ideas across disciplines in order to advance AI research in biomedicine. We welcome all NIH researchers who are interested in AI, from novices to experts.

Sponsored by NHLBI and the Office of Intramural Research. 

Keynote Speakers: 

• Dr. Alexander Rives, Co-founder and chief scientist at Evolutionary Scale, a company focused on applying machine learning and language models to biological systems, including the development of ESM3, a protein language model that enables the generation of novel proteins with potential applications for drug discovery and basic biological research. 
• Dr. Leo Anthony Celi, Senior Research Scientist at Massachusetts Institue of Technology (MIT) and Associate Professor of Medicine at Harvard Medical school, who has a broad range of interests including integrating clinical expertise with data science, using information technology to enhance healthcare in low- and middle-income countries, and considering the social impacts of AI research. 

About Event: Biomedical science is in the early phase of a technological revolution, driven in large part by innovations in deep learning neural network architecture and availability of computational power. These cutting-edge techniques are being applied to every sub-field of the biological sciences, and with novel ground-breaking advancements arriving every week it is challenging for researchers to stay up to speed on what is available and possible.

Please register and submit a poster abstract. Attendance is limited, so please register now to reserve your spot. 

Registration deadline: April 25, 2025
Abstract deadline: April 11, 2025

This one-hour online training will cover the fundamentals, applications, and ethical considerations of Artificial Intelligence (AI). Attendees will explore key topics such as machine learning, deep learning, data handling, and real-world AI applications across various industries. The session will also delve into the ethical implications of AI and provide insights on becoming AI literate. Whether you're a seasoned professional or just starting your AI journey, this session will equip you with essential knowledge to navigate the AI landscape effectively and make informed decisions in our data-driven world.

By the end of this training, attendees will be able to: 

• Understand the core concepts of AI 
• Recognize the significance of ethical considerations in AI 
• Begin the journey toward AI literacy

Attendees are not expected to have any prior knowledge of AI to be successful in this training.