Bioinformatics Training & Education Program

This 90-minute online training led by Google experts will dive into intermediate features, including agent creation and code generation, with Gemini for Government. The training will focus on hands-on applications, including building a simple agent with Agent Designer, using vibe-coding for data analysis, and leveraging NotebookLM as a personal research assistant. Attendees will also learn how to create more sophisticated, data-driven infographics to communicate their findings. 

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

• Build a simple, custom agent using Agent Designer to automate a research-related task, such as monitoring new publications. 
• Generate Python scripts using natural language (vibe-coding) to clean and analyze data. 
• Utilize NotebookLM to upload source materials, ask targeted questions across documents, and organize research insights. 
• Create a data-driven infographic that transforms raw data into a compelling visual story. 

Attendees are expected to be familiar with the basic functions of Gemini to be successful in this training (gained by attending Gemini for Government 101), attending another relevant training, and/or using Gemini previously).  Gemini for Government can be accessed at: https://go.hhs.gov/gemini  

Experience a practical course for staff who work across large volumes of NIH content, helping you search internal knowledge, summarize complex material, pull out actions and themes, and turn scattered information into usable insights.

This class provides an overview of quality assurance and cleanup steps for bulk RNA sequencing data, necessities for downstream analyses. Participants will learn about tools for quality assessment and data cleanup as well as become familiar with interpreting results from these steps. This session is hands-on. Experience is not needed to attend. Those with personal Biowulf accounts are welcome to enroll. Participants who do not have access will be assigned to one of the 40 Biowulf student accounts to follow along with the hands-on exercises.

Join us for a quick tour of a “day in the life” with Microsoft 365 Copilot. In this 60-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!

 (Recommended after completing Getting Started with AI Productivity Double Feature.)

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.

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, Illumina scientist will illustrate how to obtain insights to regulation of gene expression from bulk RNA and ATAC sequencing data. No prior experience or access to Partek Flow is required. Attendance is limited to NIH staff.

This session introduces bulk RNA sequencing alignment, the process of finding where in the genome each sequencing read comes from. By the end of this class, participants will be able to describe tools used for aligning bulk RNA sequencing data as well as interpreting and visualizing output. This session is hands-on. Experience is not needed to attend. Those with personal Biowulf accounts are welcome to enroll. Participants who do not have access will be assigned to one of the 40 Biowulf student accounts to follow along with the hands-on exercises.

Jump into a course specially designed for clinical research teams, IRB-related stakeholders, and staff who support protocol development, consent materials, and study documentation. Discover scenarios such as drafting protocol sections, simplifying patient-facing language, searching prior protocol content, organizing review notes, and creating clearer meeting minutes and action trackers. This is especially relevant because your NIH work already includes AI-enabled support for the IRB protocol lifecycle, simplified consent forms, historical protocol search, and multilingual needs.

Qiagen’s CLC Genomics Workbench is a point-and-click software for analyzing multi-omics sequencing data including variant analysis, RNA sequencing, and ChIP sequencing. This class will demonstrate variant analysis using this software. Participants will be able to start from FASTQ files and generate as well as interpret variant information using CLC Genomics Workbench after this class. NCI scientists can use CLC Genomics Workbench through the NCI institutional license, just submit a ticket with service.cancer.gov to get it installed on personal computer.

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 90-minute online training led by Google experts is the capstone session for power users who want to push the boundaries of AI in biomedical research. This session showcases advanced agentic workflows and complex comparative analysis. The training will feature a demo on building sophisticated research assistant agents with Agent Designer and will demonstrate additional NotebookLM use cases for research. 

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

• Design a complex, multi-agent system in Agent Designer capable of automating a research sub-task, such as finding and comparing experimental protocols. 
• Apply advanced NotebookLM techniques to perform complex comparative analysis on diverse scientific data sources. 
• Develop strategies for using AI to analyze a portfolio of grants and publications to identify alignment with NIH strategic priorities. 

Attendees are expected to be able to independently utilize Gemini to be successful in this training.  Gemini for Government can be accessed at: https://go.hhs.gov/gemini  

This session of the Cancer AI Conversations will will explore methods for measuring and mitigating the risk of sensitive data leakage from AI systems, with a focus on protecting patient privacy while enabling the safe development, evaluation, and deployment of AI in cancer research.

Individuals with disabilities who need reasonable accommodation to participate in this event should contact Dr. Soumya Korrapati at soumya.korrapati@nih.gov. Requests should be made at least 5 days in advance of the webinar.

This class provides an overview of the gene estimation process in bulk RNA sequencing analysis. After attending, participants will be able to describe tools used in gene expression estimation and interpret results. This session is hands-on. Experience is not needed to attend. Those with personal Biowulf accounts are welcome to enroll. Participants who do not have access will be assigned to one of the 40 Biowulf student accounts to follow along with the hands-on exercises.

Join Copilot experts in this course for data analysts, epidemiology leads, informatics staff, and scientific program teams who need to turn complex information into usable insights. The course focuses on summarizing analyses, drafting findings, creating executive-ready narratives from dashboards or datasets, and improving discoverability of information across reports, portfolios, and governed data assets. This fits NHLBI and other NIH persona patterns that include epidemiologic analyses, cohort funding plans, and data-related stewardship and review.

Learn the core concepts of metatranscriptomics and discover how microbial gene expression data can be used to identify active microorganisms, understand community function, and address biological research questions.

This is part 1 of a two-day workshop presented by the Nephele Team, an interdisciplinary group with the Bioinformatics and Computational Biosciences Branch (BCBB) of the NIAID Office of Cyber Infrastructure and Computational Biology (OCICB) in the National Institute of Allergy and Infectious Diseases (NIAID).

This one-hour online training, is the first of a two-part series, which introduces participants to cleaning and exploring a patient health dataset using Python and pandas. Attendees will load tabular data, inspect structure and data types, summarize columns, and identify common data quality problems such as missing values, inconsistent formats, and duplicate records. They will then apply practical fixes, including standardizing height and weight units, parsing and normalizing dates of birth, splitting combined fields, and using Boolean masks to flag or correct implausible values.​

By the end of this session students will be able to:

• Import CSV data into pandas DataFrames and quickly understand column types, basic statistics, and overall data quality.​
• Identify duplicate or repeated patient records and decide whether to keep, correct, or remove them.​
• Detect and handle missing or inconsistent values using methods such as isna, fillna, filtering, and conditional replacement.​
• Standardize mixed formats (for example, heights with and without units, date strings in different formats, and numeric values embedded in text).​
• Create derived columns such as systolic and diastolic blood pressure, and use logical conditions to flag questionable or out-of-range values.​

Attendees are expected to have:

• Basic Python coding knowledge
• Familiarity with an IDE and loading script and data files into the IDE. (Colab, Jupyter Notebooks) 

Requirements: 

• Participants will receive a script file and data files prior to the training. These should be loaded and ready to use before the training session begins. 

This one-hour online training, the second session of the two-part series,  focuses on reshaping and enriching the cleaned patient dataset to prepare it for analysis and reporting. Attendees will practice splitting and recombining columns (for example, separating full names into first and last names), converting columns to appropriate data types, and engineering new fields such as outlier indicators and blood pressure status labels. The session also covers merging multiple tables (patient details, contact information, and subsets of records) and filtering or subsetting data to answer specific analytical questions.​

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

• Reshape and restructure data by splitting and combining columns, changing data types, and reordering or selecting relevant fields.​
• Engineer clinically useful features, including z-score–based outlier flags, hypertension indicators, and combined status columns for downstream models or dashboards.​
• Merge and join DataFrames using common keys (such as patient ID) to bring together core data with supplemental tables like contact information.​
• Filter and subset records based on multiple conditions (for example, patients with diabetes and abnormal blood pressure) to create analysis-ready datasets.​

Attendees are expected to have:

• To have attended Intro to Data Wrangling Using Python - Part 1 of the series
• Basic Python coding knowledge

Familiarity with an IDE and loading script and data files into the IDE. (Colab, Jupyter Notebooks) 

Requirements: 

• Participants will receive a script file and data files prior to the training. These should be loaded and ready to use before the training session begins. 

You can register for Part 1 in this series via the link below: 

https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/introduction-data-wrangling-using-python-part-1-2

Apply metatranscriptomic analysis methods using the Nephele platform, with guided instruction on workflow selection, data inputs, result interpretation, and best practices for reproducible analysis.

This is part 2 of a two-day workshop presented by the Nephele Team, an interdisciplinary group with the Bioinformatics and Computational Biosciences Branch (BCBB) of the NIAID Office of Cyber Infrastructure and Computational Biology (OCICB) in the National Institute of Allergy and Infectious Diseases (NIAID).

This series invites Principal Investigators, Senior Scientists, and Senior Clinicians to share cutting-edge research and developments in their fields. Each session includes a 20-30 minute presentation followed by a Q&A or journal club discussion, fostering deeper insights and scholarly exchange. Lunch is provided. Please note this event is only open to members of the NIH community.

Adaptive optics is a technology used in modern large astronomical telescopes for correcting aberrations in earth’s atmosphere. In this talk, I will describe how adaptive optics has transformed the field of retinal imaging, with examples of new clinical insights from studying patients with rare diseases at the NIH Clinical Center.

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.

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.

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.  

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. 

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.  

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.

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.

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). 

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.