Bioinformatics Training & Education Program

Denoising for Light Microscopy using Deep Learning

NCI's Office of Data Sharing (ODS) will continue a monthly webinar series for jamboree project teams to share their findings, lessons learned, and future directions in April!

This talk will provide an overview of plot/graph types, use cases, pitfalls, best practices, and possible extensions. This will be a high-level talk, but example R code will be provided as a reference. No prior experience is required.

This one and a half-hour online training covers the basic principles of FAIR (Findable, Accessible, Interoperable, Reusable) data and why it is important to make your data FAIR.  

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

• Define FAIR data   

• Explain what purpose FAIR data serves 

• Apply FAIR data principles to make data findable, accessible, interoperable, and reusable 

This is an introductory level training.

Speaker will: 

• Address how robust AI models in digital pathology are limited not only due to the scarcity of large-scale, annotated data sets but also due to the lack of clear framework for clinical translation. • exemplify a deep learning model that can generate synthetic, but plausible, histological stains from unstained tissue.
• Highlight how NIH exploratory grants (e.g., NIH R21) have made it possible for a feasible technology to become a clinically validated, risk-probability tool (i.e., prognostic model)
• Give perspective on both emerging challenges and standards for validating and approving these complex, AI-enabled medical devices intended for clinical use.

All problems and concerns are welcome, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. The meeting connection details are emailed to all Biowulf users the week of the consult.

Email staff@hpc.nih.gov for meeting link.

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! 

This webinar will explore the data reuse journey, providing perspective from a researcher who successfully conducted secondary analyses using existing datasets. Guest speaker Ravi Mathur, PhD, of RTI International, is a researcher specializing in biomedical data science, real-world data analysis, and cloud-based research platform use.

The ability to measure gene expression levels for individual cells (vs. pools of cells) and with spatial resolution is crucial to address many important biological and medical questions, such as the study of stem cell differentiation, the discovery of cellular subtypes in the brain, and cancer diagnosis and treatment. Single-cell transcriptome sequencing (RNA-Seq) allows the high-throughput measurement of gene expression levels for entire genomes at the resolution of single cells. Spatially-resolved transcriptomics further allows the measurement of gene expression levels along with the location of the RNA molecules within a tissue. Transcriptomics exemplifies the range of issues one encounters in a data science workflow, where the data are complex in a variety of ways, questions are not always clearly formulated, there are multiple analysis steps, and drawing on rigorous statistical principles and methods is essential to derive meaningful and reliable biological results. 

In this talk, Dr. Dudoit will provide a survey of statistical questions related to the analysis of single-cell transcriptome sequencing data to investigate the differentiation of stem cells in the brain, including, exploratory data analysis, expression quantitation, cluster analysis, and the inference of cellular lineages. She will also address differential expression analysis in spatial transcriptomics.

The NLM Colloquia on Biomedical Data Science and Computational Biology Research is a series of scientific lectures featuring experts from across the bioinformatics community who present their research and discuss how it contributes to advancing biomedical discovery. This series is presented by NLM’s DIR a premier hub of innovation for computational biology and biomedical data science.

ChatGPT 102 training is part 2 of a three-part series.  

This one-hour online training led by OpenAI experts will dive deeper into intermediate features and strategies for maximizing ChatGPT Enterprise in NIH workflows. Building on the fundamentals from ChatGPT 101, this training will focus on intermediate features including Custom GPTs, Projects, Data Analysis, coding in Canvas, and Deep Research to enable broader value creation and collaboration with ChatGPT. Attendees will also learn how to integrate ChatGPT into specialized tasks and optimize outputs for NIH-specific use cases. 

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

• Create and customize GPTs and projects to serve as tailored assistants for NIH-specific initiatives and domains. 
• Utilize additional intermediate features including Data Analysis, coding in Canvas, and Deep Research, to handle complex tasks and collaborative workflows. 
• Implement best practices for integrating ChatGPT into broader NIH processes while maintaining compliance and security standards. 

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

The Replication Gap: Moving NIH Beyond Computational Reproducibility

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. 

Discover how Copilot can help you build compelling presentations faster than ever. During this 60-minute class, you’ll explore how to use simple, natural language prompts to generate high‑quality slide content, enhance your visuals, and craft engaging speaker notes. You’ll also learn how Copilot can streamline revisions, tighten your message, and adapt your presentation to different audiences with just a few quick commands. By the end, you’ll be able to create polished, professional PowerPoint decks with confidence—and in a fraction of the usual time.

Note: We highly recommend taking the Getting Started with AI Productivity Double Feature class prior to this. 

 If you have begun experimenting with AI tools to draft emails, summarize information, or brainstorm ideas, but are wondering where the big productivity gain are, this class is for you. The real productivity gains come when AI is used not just for single tasks but for structured workflows that guide complex work from start to finish. This class will show you how to design practical AI workflows for common NIH activities such as literature reviews, grant and application summaries, meeting-to-action tracking, portfolio analysis, and policy brief development. This fast-paced session will teach you how to break complex work into AI-assisted steps, build reusable prompts, create verification checkpoints, and ensure output remain accurate and responsible. By the end of the session, you will be able to transform AI from a helpful tool into a repeatable productivity system that supports research, program, and policy work at NIH. 

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!

Advanced ChatGPT training is part 3 of a three-part series. 

This one-hour online training, led by OpenAI experts, is for those who have completed the ChatGPT 101 and 102 trainings. The training will focus on leveraging two of ChatGPT Enterprise's most powerful features: Custom GPTs and Data Analysis. Attendees will learn how to create specialized GPTs tailored for specific NIH tasks and how to use the Data Analysis feature to upload, interpret, and visualize data sets for deeper insights. This training is designed to provide the skills needed to apply these advanced tools to complex, enterprise-level projects. 

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

• Build and deploy Custom GPTs tailored to specific NIH workflows. 
• Use the Data Analysis feature to upload, analyze, and visualize data. 
• Apply advanced techniques to solve complex problems using ChatGPT Enterprise. 

Attendees are expected to be able to utilize ChatGPT to be successful in this training.  

You can register for the other trainings in this series via the link(s) below:  

 ChatGPT 101

ChatGPT 102

Artificial Evolution with Artificial Intelligence

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! 

The Genomic Data Commons is releasing a new Correlation Plot Tool which provides a framework for correlating GDC molecular information (mutation, CNV, gene expression) with clinical and survival data. Using quick access features, researchers can compare mutation or CNV status of a gene with a clinical variable or survival, CNV and mutation for given genes, a gene's CNV with its expression, and gene expression level with survival. The tool assists in identifying statistically meaningful correlations between genomic variants and clinical phenotypes to uncover patterns that assist in enabling diagnostic and treatment discoveries. Join us for an overview and demonstration of the GDC Correlation Plot Tool, and associated data supporting correlative analysis.

All problems and concerns are welcome, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. The meeting connection details are emailed to all Biowulf users the week of the consult.

Email staff@hpc.nih.gov for the meeting link.

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering 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
• Describe methods for measuring risk
• Be familiar with screening and diagnostic accuracy indices and their differences
• Describe when to use relative risks and odds ratios
• Explain differences between confounding and interaction 

Join us for a day-long symposium exploring AI approaches in biomedical sciences, with the aim of sharing effective AI implementation strategies across NIH. 

Contact Lead Organizer Ryan O’Neill, PhD (oneillrs@nih.gov) for more info.

Sign language interpreting and CART services are available upon request to participate in this event. Individualsneeding either of these services and/or other reasonable accommodations should contact Lisa Bossert (lisa.bossert@nih.gov) by May 1.

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 single cell RNA sequencing analysis workflow starting from data import through performing QC, visualization, clustering (tSNE, UMAP, 3D PCA) and marker-based cell type identification. Experience using or installation of this software is not required for attendance. Participation is restricted to NIH staff.

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.

Recent advances in large language models (LLMs) have enabled powerful AI agents for biomedical research, yet their adoption in high-stakes settings remains limited by concerns about hallucination, opacity, and reliability. In this talk, I discuss how expert-curated domain knowledge can be used to help mitigate these challenges in general-purpose LLMs. Drawing on real-world systems and case studies such as GeneAgent (Nature Methods 2025), I will highlight design principles for building AI agents that are scientifically sound, interpretable, and suitable for biomedical research and clinical applications.

All problems and concerns are welcome, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. The meeting connection details are emailed to all Biowulf users the week of the consult.

Email staff@hpc.nih.gov for the meeting link.

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.

Dr. Bocks' research utilizes a synergistic "READ, LEARN, WRITE" framework that combines multi-omics profiling, deep learning, and high-throughput CRISPR screening to map, model, and program complex cellular functions. By integrating single-cell technologies to read epigenetic states with advanced neural networks to learn their regulatory circuits, he can systematically design and write new biological instructions into human cells. They successfully applied this integrated approach to optimize immunotherapy, using large-scale in vivo CRISPR screens to identify and validate gene knockouts that significantly boost the performance of CAR T cells against solid tumors.