Classes & Events

Whether you are measuring mRNA expression, protein expression, DNA methylation, expressed miRNAs, protein binding to DNA or RNA, etc., you will likely end up with a list of genes or gene products from which you would like to derive functional relationships. In the -omics world, functional enrichment analysis is an umbrella term encompassing approaches used to derive biological / functional meaning from gene lists. This lesson, which is the first of three lessons focused on "pathway analysis", introduces concepts, methods, tools, and databases related to functional enrichment and pathway analysis. This is NOT a hands-on lesson. 

Please note: Registration is required to get the Meeting Link for this event. Please pre-register.

The Human Tumor Atlas Network (HTAN) is a National Cancer Institute (NCI)-funded initiative to construct 3-dimensional atlases of the dynamic cellular, morphological, and molecular features of human cancers as they evolve from precancerous lesions to advanced disease. (Cell April 2020).

This tutorial will demonstrate how to access HTAN single cell expression data directly from ISB-CGC BigQuery tables. It will then show how to perform a CellTypist analysis in Python.

This webinar is part of a series of Human Tumor Atlas Network (HTAN) presentations. Please see the calendar for other events in this series. 

Join Dr. Caroline Chung as she discusses the multifaceted process of integrating AI into healthcare, emphasizing the critical role of context at every stage. You will be equipped with insights and strategies for successful AI development and implementation that is context-aware and patient-centric.

Dr. Chung will talk about bridging the gap between technical innovation and clinical feasibility and utility. Namely, she will discuss:

• understanding the nuances of data collection.
• algorithm design.
• regulatory compliance, quality, and safety.
• infrastructural constraints.

Open to HHS Staff Only

The goal of the NCI RNA Biology Initiative is to establish a collaborative environment that promotes the swift exchange of information and expertise regarding the structure, function, and biological significance of RNA. Our objective is to leverage this collective knowledge to advance the development of novel diagnostics and therapies. This year’s symposium will focus on fostering synergistic collaborations among NCI and NIH intramural investigators, by highlighting and celebrating RNA research by the NIH intramural community.

While there will not be a poster session, there will be ample networking opportunities. We encourage you to join us and help foster collaborations among NCI and NIH intramural investigators.

Organized by the NCI RNA Biology Initiative this symposium will bring intramural experts in the field of RNA biology, with the focus on:

• RNA Processing
• RNA Structure and Mechanism
• RNA Stability and Translation
• RNA in Disease

DAVID (the Database for Annotation, Visualization and Integrated Discovery) provides a comprehensive set of functional annotation tools for investigators to understand the biological meaning behind large lists of genes acquired from high-throughput assays such as RNA-Seq, Proteomics, Microarray, etc. This hands-on lesson will show attendees how to use and interpret results from DAVID using an example gene list. 

The NCI Rising Scholars: Cancer Research Seminar Series is an opportunity to highlight the research and the important contributions made by NCI-supported postdoctoral fellows and early career investigators at NCI laboratories and NCI-funded institutions nationwide.

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.

Reactome is a free, open-source, curated and peer-reviewed pathway database that includes bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge. This lesson will showcase the capabilities of Reactome in garnering biological meaning from an example gene list derived from differential expression results. 

Please note: Registration is required to get the Meeting Link for this event. Please pre-register.

The Human Tumor Atlas Network (HTAN) is a National Cancer Institute (NCI)-funded initiative to construct 3-dimensional atlases of the dynamic cellular, morphological, and molecular features of human cancers as they evolve from precancerous lesions to advanced disease. (Cell April 2020).

This session will provide an overview of accessing Human Tumor Atlas Network (HTAN) data on the Cancer Genomics Cloud (CGC) and demonstrate how to process and analyze these datasets using scalable pipelines and interactive apps.

This webinar is part of a series of Human Tumor Atlas Network (HTAN) presentations. Please see the calendar for other events in this series. 

This one hour online training introduces participants to the tools and techniques for analyzing and quantifying microscopy images using MATLAB’s low-code algorithms. Participants will learn how to preprocess images, segment regions of interest, and extract quantitative data from whole slide images. The session demonstrates resources for automating workflows, enhancing reproducibility, and handling large datasets typical in microscopy studies. Practical examples, such as cell counting, measuring fluorescence intensity, and detecting morphological changes, will be provided. This training is ideal for researchers and data scientists involved in image-based analysis in biology, medicine, or materials science.

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

• Preprocess images, including filtering, background subtraction, and contrast enhancement, to prepare for analysis
• Perform image segmentation to identify regions of interest, such as cells or organelles, using techniques like thresholding and edge detection
• Quantify key metrics from microscopy images, such as cell count, fluorescence intensity, and area measurements
• Automate image analysis workflows to handle large datasets efficiently and reproducibly

No prior experience with MATLAB is required, but basic familiarity with image processing is recommended.

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.

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

Are you looking to expand the reach of your sequencing to enable what long read technologies can provide?  Please join Oxford Nanopore Technologies (ONT) bioinformatics specialists on a deep dive into getting the most from your long read sequencing data.

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

This in-person, hands-on workshop gives learners the opportunity to learn the Nanopore Single Cell RNA-Seq Analysis Software Workflow. Taught by Nanopore personnel, with assistance from the CCR Genomics Core and BTEP, this session will run in the morning. Space is limited. If your plans change and you cannot attend, please cancel your registration. There will be no hybrid option for this class, it is in-person only. 

Participants should bring an internet-enabled laptop to access the tools and datasets used for the training. 

Agenda:

9:00-9:15 – Check in & distribute materials

9:15-9:30 – Data analysis intro from Oxford Nanopore Technologies (MinKNOW/EPI2ME/other advanced tools)

9:30-10:00 – Introduction to Single Cell RNA-seq data analysis with Oxford Nanopore Technologies

10:00-11:15 – Hands on data analysis: Single Cell EPI2ME App, nextflow command line, and Biowulf HPC demo

11:15-12:00 – Downstream analysis with Seurat

12:00-12:30 – Closing and Q&A

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

Are you looking to expand the reach of your sequencing to enable what long read technologies can provide?  Please join Oxford Nanopore Technologies (ONT) bioinformatics specialists on a deep dive into getting the most from your long read sequencing data. 

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

This in-person, hands-on workshop gives learners the opportunity to learn the Nanopore Human Variation Data Analysis Software Workflow. Taught by Nanopore personnel, with assistance from the CCR Genomics Core and BTEP, this session will run in the afternoon. Space is limited. If your plans change and you cannot attend, please cancel your registration. There will be no hybrid option for this class, it is in-person only. 

Participants should bring an internet-enabled laptop to access the tools and datasets used for the training. 

 1:30-1:45 – Check in & distribute materials

1:45-2:15 – Data analysis intro from ONT (MinKNOW/EPI2ME/other advanced tools)

2:15-2:45 – Introduction to the Human Variation (WGS) pipeline

2:45-4:00 – Hands on data analysis: human variation EPI2ME App, nextflow command line, and Biowulf HPC demo

4:00-4:30 – Review result

4:30-5:00 – Closing/Q&A

 

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

For questions or to register, please contact Eric Troop (eric.troop@nanoporetech.com)

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.

Agenda: • 1:00-1:15 – Check in/Registration/Distribute materials • 1:15-1:45 – Data analysis intro from ONT (MinKNOW/EPI2ME/other advanced tools) • 1:45-2:15 – Introduction to Single Cell RNA-seq data analysis with ONT • 2:15-2:45 – Hands on data analysis: wf-single-cell (EPI2ME app) • 2:45-3:15 – Introduction to Human WGS data analysis with ONT • 3:15-3:45 – Hands on data analysis: wf-human-variation (EPI2ME app) • 3:45-4:00 – Closing and Q&A

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.

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=m5343b74ef86d64ec9760d23504277b26 
Meeting number:
2310 788 0773
Password:
jpS62rwdH5*

Join by video system
Dial 23107880773@cbiit.webex.com
You can also dial 173.243.2.68 and enter your meeting number.
Join by phone
1-650-479-3207 Call-in number (US/Canada)
Access code: 2310 788 0773

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 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. 

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. 

Please note: Registration is required to get the Meeting Link for this event. Please pre-register.

The Human Tumor Atlas Network (HTAN) is a National Cancer Institute (NCI)-funded initiative to construct 3-dimensional atlases of the dynamic cellular, morphological, and molecular features of human cancers as they evolve from precancerous lesions to advanced disease. (Cell April 2020).

This tutorial will demonstrate how to perform spatial analysis on HTAN single cell data identifying local cell neighborhoods directly with built in BigQuery functionality.

This webinar is part of a series of Human Tumor Atlas Network (HTAN) presentations. Please see the calendar for other events in this series. 

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.