Bioinformatics Training & Education Program

Qlucore Omics Explorer is a desktop-based point-and-click software with built-in machine learning capabilities. It enables RNA sequencing (bulk and single cell), proteomics and metabolomics analysis. This software is available for NCI CCR scientists upon submitting a ticket at https://service.cancer.gov/ncisp. In this demonstration-only class, Qlucore scientist will illustrate the use of regression approaches to identify correlation between gene and protein expression. Experience using or installation of this software is not required for attendance. Participation is restricted to NIH staff.

This one-hour online training will provide a high-level overview of Python coding concepts, as well as some of the integrative development environments (IDEs, such as Jupyter notebooks) used for Python coding. Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. The training will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab. This overview training will demonstrate how these skills can boost productivity, rigor, and transparency in reporting research findings.  

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

• Recognize four freely available IDEs for python coding 

• Identify fundamental components of python code 

• Understand how and why notebooks support rigor and transparency in analysis 

Attendees are not expected to have any prior knowledge of python coding or the IDEs to be successful in this training.  

If you choose to follow along with Google Colab or Jupyter Notebooks, these IDEs should be installed and ready to go. Code will be provided during the training for this option.

Join us for the first session of a two-session demonstration on using the Google Cloud Platform (GCP) to run a bulk RNA-Seq workflow with a prokaryotic data set. The first session (Aug 11) will include read trimming, quality control, mapping, and quantification of differential gene expression. In the second session (Aug 13) we will demonstrate using AI to create visualizations of the results.

What’s the secret to great AI results? Great prompts. This hands-on class teaches you how to craft clear, specific, and effective instructions for Copilot and other AI tools. Practice real-world examples and get a toolkit of reusable prompt templates you can start using right away.

This lesson introduces the principles of differential gene expression (DEG) analysis. Participants will learn about common normalization strategies and gain a conceptual understanding of the statistical frameworks used by widely adopted DEG tools, including limma, edgeR, and DESeq2, with an emphasis on their assumptions, strengths, and appropriate use cases. This is not a hands-on lesson.

Join us for a quick tour of a “day in the life” with Microsoft 365 Copilot. In this 90-minute overview, see how M365 Copilot helps you manage emails, prep for meetings, and create documents effortlessly in Outlook, Teams, Word, Excel, and PowerPoint. Boost your productivity and make every day easier! Imagine starting your day with a clear inbox, joining meetings fully prepared, and creating polished documents in record time with the help of M365 Copilot. Join us to see how Copilot transforms everyday tasks into effortless productivity!

This one-hour and thirty minute online training is part one of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation.   

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

• Understand data management best practices   

• Become familiar with data management tools  

• Have a solid knowledge of the resources, enabling data sharing  

During Part 2, attendees will learn about sharing and archiving data. You must register separately for Part 2 of this training. This training is introductory, no prior knowledge required.  

“Illumina Connected Multiomics” is a multi-omics and multi-modal analysis tool that can help you calculate statistics and create interactive visualizations, regardless of your bioinformatics experience level.

•  Learn how you can use ICM to explore data, make insights, and accelerate discovery.
• Get an overview of the ICM software and its workflows.
• Discover the tools in the Illumina informatics ecosystem that help you streamline data analysis and maintenance.

Join us for the second of two sessions on using the Google Cloud Platform (GCP) to run a bulk RNA-Seq workflow with a prokaryotic data set. The first session (Aug 11) will include read trimming, quality control, mapping, and quantification of differential gene expression. In the second session (Aug 13) we will demonstrate using AI to create visualizations of the results.

This hands-on lesson demonstrates how to perform DEG analysis using the iDEP web platform on Biowulf. Participants will learn how to upload data, configure analysis settings, and interpret key outputs such as quality control plots, volcano plots, heatmaps, and differential expression tables to identify biologically meaningful gene expression changes.

This hour and half online training is part two of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation.  

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

• Have a solid knowledge of the resources, enabling data sharing  

• Understand how data is archived and preserved  

Part 1 of this training covers understanding research data, how to manage research data, and how to work with data. During Part 2, attendees learn about sharing and archiving data. This training is introductory, no prior knowledge required.  

You must register separately for Part 1 of this training. 

This hour and half online training led by Anthropic will cover the fundamentals of using Claude effectively in your daily NIH workflows. Attendees will learn to navigate the Claude interface, apply best practices for prompt writing, and utilize key features such as working with documents, Projects, and Artifacts. The training will also demonstrate real-world use cases relevant to NIH staff for improving productivity, and highlight security and responsible-use considerations tailored for federal environments. 

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

• Navigate the Claude interface and use foundational features, including working with documents, Projects, and Artifacts. 

• Apply effective prompting strategies to generate accurate, useful outputs for NIH-specific tasks. 

• Identify everyday NIH use cases and understand best practices for responsible use of generative AI tools like Claude. 

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

In this 90-minute session, discover how M365 Copilot can transform the way you work in Teams. Learn how to summarize long chat threads, extract key action items from meeting notes, videos, and quickly find the information you need—without endless scrolling or searching. We’ll explore real-world scenarios for streamlining meetings, accelerating teamwork, and making data-driven decisions with ease. By the end, you’ll be ready to put M365 Copilot to work as your AI-powered partner in productivity.

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 introduces concepts, methods, tools, and databases related to functional enrichment and pathway analysis. This is NOT a hands-on lesson.  

You will learn how to analyze ChIP-Seq data using Galaxy, a user-friendly web-based platform that makes bioinformatics accessible to researchers with little or no programming experience.

 To get you started, Drs. Chunhua Yan and Qingrong Chen will:

• Introduce the fundamentals of ChIP-Seq data analysis.

• Demonstrate commonly used ChIP-Seq analysis tools and workflows in Galaxy.

• Prepare you to run a basic ChIP-Seq analysis workflow in Galaxy for transcription factor binding site identification.

During the self-paced hands-on exercises, you will analyze Illumina ChIP-Seq data in Galaxy and learn how to:

• Perform quality control on raw ChIP-Seq data.

• Map sequencing reads to a reference genome.

• Generate alignment statistics and assess mapping quality.

• Identify enriched binding regions, or peak calling, using Model-based Analysis of ChIP-Seq (MACS).

• Annotate identified peaks.

• Visualize enriched genomic regions.

Whether you're new to bioinformatics or looking to strengthen your ChIP-Seq data analysis skills, this workshop will give you practical experience and confidence to perform your own analyses—no programming required.

The goals of the Infectious Agents and Cancer Epidemiology Research series are to: - Highlight emerging and cutting-edge research related to infection-associated cancers that could be applied to cancer epidemiology; - Share scientific knowledge about technologies and methods that may enhance and facilitate infection-associated cancer epidemiology research; and - Foster cross-disciplinary discussions on infectious agents and cancer epidemiology.

Partek Flow is a point-and-click platform for building analysis workflows for Next Generation Sequences (NGS), including DNA, bulk and single-cell RNA, spatial transcriptomics, ATAC, and ChIP, helping scientists avoid the steep learning curve of code-based NGS analysis. This class is demonstration-only. Starting from single cell RNA expression matrix, Illumina scientist will illustrate how to conduct QC, perform cell type classification, obtain differential expression results, and generate visualizations. No prior experience or access to Partek Flow is required. Attendance is limited to NIH staff.

Are you interested in spatial proteomics? Join this webinar as NCI grantee, Dr. Brooke Fridley, walks you through the analysis methods and statistical tools her lab has built to analyze spatial proteomic data sets and what that has revealed about immune cells behavior in ovarian cancer tumors. During the webinar, you will hear about these NCI-funded R-based analysis tools:

• • spatialTIME: Visualize and analyze the spatial architecture of the tumor immune microenvironment with multiplex immunofluorescence data.
• • mxfda: Perform functional data analysis for spatial single cell data.
• • scSpatialSim: Simulate single-cell molecular spatial patterns without requiring reference data sets.
• • BTIME: Assess the relationship between specific cell populations and a predictor or exposure variable by fitting Bayesian hierarchical beta-binomial models to the data.

Whether you study cancer biology or focus on data science, this webinar offers practical tools and insights you can apply to your work. NCI’s Informatics Technology for Cancer Research (ITCR) program has also funded Dr. Fridley’s and her lab’s work to develop analytical to study the tumor microenvironment leveraging spatial transcriptomics.

This practical session explores the pathway and enrichment analysis options available within iDEP. While reviewing the range of supported analyses, the lesson focuses on pre-ranked Gene Set Enrichment Analysis (GSEA), guiding participants through gene ranking strategies, execution of GSEA in iDEP, and interpretation of enrichment plots, leading-edge genes, and pathway-level results.

Learning Objectives

1.        Understand the different elements of CRF planning and best practices for data archiving, audit-readiness, and reuse.

2.        Understand the requirements and expectations for data management and sharing plans in NIH-DIR.

3.        Understand and be able to incorporate data naming and data documentation best  practices.

Claude 201 is part 2 of a two-part series. 

This hour and half online training led by Anthropic will dive deeper into intermediate and advanced strategies for maximizing Claude in NIH workflows. Building on the fundamentals from Claude 101, this training will focus on structured and multi-step prompting, working effectively with longer documents and datasets, and using Projects to organize ongoing work and build reusable context. Attendees will also learn how to integrate Claude into specialized NIH tasks and optimize outputs for research, administrative, and policy workflows. 

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

• Use structured and multi-step prompting techniques to handle complex tasks and improve output quality. 

• Work effectively with documents, longer-form content, and data inside Claude to support research and analysis workflows. 

• Set up and use Projects to organize ongoing work, build reusable context, and collaborate on NIH-specific initiatives. 

Attendees are expected to be familiar with the basic functions of Claude to be successful in this training (gained by attending Claude 101, attending another relevant training, and/or using Claude previously). 

This two-day national symposium addresses recent advances in the field and should be an exciting forum for discussion and debate on the current understanding of cancer immunology in the era of omics and artificial intelligence.

Confirmed Speakers:

• Grégoire Altan-Bonnet, NCI
• Avinash Bhandoola, NCI
• Remy Bosselut, NCI
• Mary Carrington, NCI
• Leah Cook, NCI
• Amiran Dzutsev, NCI
• Donna Farber, Columbia University
• Paul François, Université de Montréal
• Romina Goldszmid, NCI
• Timothy Greten, NCI
• Peng Jiang, NCI
• Yann LeCun, New York University
• Lichun Ma, NCI
• Bali Pulendran, Stanford School of Medicine
• Barbara Reherman, NIDDK
• Eytan Ruppin, Cedars-Sinai Medical Center
• Eldad Shulman, Cedars-Sinai Medical Center
• Naomi Taylor, NCI
• Giorgio Trinchieri, NCI
• John Tsang, Yale University
• Roxane Tussiwand, NCI 
• Golnaz Vahedi, University of Pennsylvania School of Medicine
• Roberto Weigert, NCI
• Ramnik Xavier, Harvard University
• Li Yang, NCI
• Chen Zhao, NCI
• Marlies Meisel, University of Pittsburgh School of Medicine
• Rosandra Kaplan, NCI

Main Topics

• DATA SCIENCE AND DEEP LEARNING IN CANCER IMMUNITY
• TUMOR MICROENVIRONMENT
• MICROBIOME AND CANCER
• T CELLS IN CANCER IMMUNITY

This one-hour online training provides researchers with an overview of online resources for locating research datasets, data repositories, and data publications for data sharing and re-use. Participants will learn search strategies for locating datasets through federated data search portals and generalist data repositories, including directories for locating discipline-specific and institutional data repositories. An overview of key issues to consider when re-using datasets or when locating a data repository for sharing and preservation purposes will be discussed. 

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

• Locate different types of data repositories and datasets 

• Identify issues to consider with data repositories 

• Discuss how data repositories can improve reproducibility

• Identify issues to consider when re-using datasets 

• Describe guidelines and resources for citing datasets 

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

General Schedule of Events

https://researchfestival.nih.gov/2026/general-schedule-events-0

Claude 101 is part 1 of a two-part series. 

This hour and half online training led by Anthropic will cover the fundamentals of using Claude effectively in your daily NIH workflows. Attendees will learn to navigate the Claude interface, apply best practices for prompt writing, and utilize key features such as working with documents, Projects, and Artifacts. The training will also demonstrate real-world use cases relevant to NIH staff for improving productivity, and highlight security and responsible-use considerations tailored for federal environments. 

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

• Navigate the Claude interface and use foundational features, including working with documents, Projects, and Artifacts. 

• Apply effective prompting strategies to generate accurate, useful outputs for NIH-specific tasks. 

• Identify everyday NIH use cases and understand best practices for responsible use of generative AI tools like Claude. 

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

Partek Flow is a point-and-click platform for building analysis workflows for Next Generation Sequences (NGS), including DNA, bulk and single-cell RNA, spatial transcriptomics, ATAC, and ChIP, helping scientists avoid the steep learning curve of code-based NGS analysis. In this demonstration-only class, an Illumina scientist will show a bulk ATAC-sequencing workflow starting from FASTQ files through peak and motif detection as well as comparison of peaks found across samples. No prior experience or access to Partek Flow is required. Attendance is limited to NIH staff.