Classes & Events

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).  

Using deep learning to investigate regulatory silencers

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. 

You can register for Part 2 in this series via the link below: 

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

The P-value is a cornerstone notion in statistics that is often used as the main deciding factor in determining the conclusiveness and reliability of empirical findings. Despite its ubiquity as a data-analysis feature in biological sciences, the rigorous definition, study, and interpretation of P-values can be elusive for practitioners. In this lecture, I will de-mystify P-values and explain their proper use in statistical hypothesis testing and related methodologies. Our main goal will be to look beyond the simplistic “P ≤ 0.05” rule and learn to regard P-values as valuable analytic tools rather than a mere formality. Beginner level of statistical knowledge is expected, intermediate is preferred.

This session will be recorded, and all materials will be posted on our training website and shared with attendees a few days after the event. In addition, the Advanced Biomedical Computational Science (ABCS) group at Frederick National Lab (FNL) provides statistical analysis and consultation for NCI and FNL laboratories. ABCS also hosts virtual office hours every Wednesday from 12:00–1:00 p.m. ET on Teams. For more details, please contact Natasha Pacheco.

This fast-paced, 60-minute class puts two AI heavyweights—Chirp and ChatGPT—head to head. Through live demos, real prompts, and a few surprises, participants will see how each model writes, reasons, and responds when given the same challenges. Along the way, we’ll explore what each tool does best, where they stumble, and how to choose the right AI partner for different tasks. Expect practical takeaways, engaging examples, and a clearer sense of how to get smarter, better results from today’s most popular AI assistants.

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 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/training/introduction-data-wrangling-using-python-part-1-2

If you use AI tools even occasionally, you’ve probably spent more time than you’d like rewriting prompts, tweaking outputs, or trying to remember “that one prompt that worked.” This live, hands-on class shows you how to stop starting over. You’ll learn how to turn your best prompts into reusable, high-quality assets—stored and shared using the Microsoft 365 tools you already work in every day. In under two hours, you’ll learn practical prompt design techniques that work across tools like ChatGPT, Claude, and CHiRP, and how to organize them in Teams, SharePoint, Word, Excel, and Loop so they’re easy to find, reuse, and improve. The focus is real NIH work, responsible AI use, and immediately applicable skills. You’ll leave with ready-to-use templates, example prompts, and a clear system you can apply the same day to save time, improve results, and make AI a reliable part of your workflow—not an experiment you have to rethink each time.

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 three-hour online training will provide a review of study designs in biomedical research. This training will also cover details related to case studies/series, ecological, cross-sectional, case-control, and cohort studies, clinical trials, and other study designs and considerations. 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:  

• Describe two broad categories of study designs 

• Provide examples of descriptive and analytic studies 

• Explain the advantages and disadvantages of analytic studies 

• Understand the differences between observational and experimental studies 

• List other types of atypical study designs 

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. 

The Sequence Read Archive (SRA) is the largest publicly available repository of high-throughput sequencing data. With big data come big challenges, and that includes keeping the SRA sustainable while making sure that data is findable, accessible, interoperable and reusable. Following a brief introduction to the SRA and the expanse of data it holds, we will share best practices for accessing SRA data for your analyses and the various formats you may encounter. Finally, we will describe the SRA Lite file format, which is faster to download with the added advantage of shrinking the overall footprint of SRA. We will demonstrate the use of SRA Lite format in NCBI RNA-seq pipelines and related analyses, and offer appropriate NCBI resources to learn more and engage with us.

This 45-minute online training provides a high-level overview of recent developments in artificial intelligence (AI). Each session highlights emerging trends, tools, and use cases in the evolving AI landscape, with an emphasis on practical relevance and responsible use. Whether you're just getting started or looking to stay current, this training offers timely insights in a concise format.  

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

• Summarize key trends and developments in AI 

• Identify new tools, capabilities, or applications relevant to their work 

• Describe considerations for ethical and responsible use of AI technologies 

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

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

The Replication Gap: Moving NIH Beyond Computational Reproducibility

The National Cancer Institute (NCI)'s Office of Data Sharing (ODS) kicked off a monthly webinar series for the data jamboree project teams to share their findings, lessons learned, and future directions in January 2026. In conjunction with its 3rd Annual Symposium at the end of September 2025, the ODS launched its inaugural data jamboree event using pediatric and AYA cancer data, aiming to facilitate interdisciplinary collaborations and enhance the usage and utility of pediatric cancer data. In 1.5 days, 23 interdisciplinary project teams formed to tackle specific problems in broad project categories, including enhancing data interoperability, building cohorts, developing or validating informatics tools and pipelines, evaluating data quality and AI-readiness, integrating data, and developing tutorials and educational materials.

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.

• how cross-modal data modeling uses one data type (like imaging) to fill in gaps in another data type (like genomics).
• ongoing multi-modal modeling efforts in spatial omics, digital pathology, and radiology.
• how multi-modal modeling is anticipated to help us better understand disease biology and improve healthcare practices.

Multi-modal modeling can empower researchers, like you, to model complex interactions among diverse biomedical data types (including omics and imaging). Attend this seminar and get a better understanding of how one modality influences another, facilitating in-silico exploration of disease mechanisms without the need for extensive and costly real-world data collection

In this talk, Dr. Carey will describe how Bioconductor approaches new challenges in supporting open method development and reproducible
analyses in genomic data science. He will discuss aspects of the project that bear on education in cancer epidemiology and
computational cancer genomics, and on emerging topics in software and data engineering for scalable omics analyses.

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

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.  

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.

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 proteomics analysis workflow starting from data import through performing QC, constructing visualizations (ie. PCA, heatmap, volcano, box, and violin plots),and conducting GSEA. 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. 

This one-hour online training, provided by a presenter from SAS, introduces the basics of accessing SAS 9.4 tools and setting up your environment.  

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

• Load data using SAS Studio or Enterprise Guide 

• Run simple programs using SAS Studio or Enterprise Guide 

• Generate reports using SAS Studio or Enterprise Guide 

• Describe technical aspects, such as understanding libraries, managing data sets, and using core SAS procedures for analysis 

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

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 six-hour online training will describe the basic concepts for using common statistical tests such as Chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, logistic regression, and survival analysis. 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:  

• Explain the importance of study design and hypothesis 

• Describe types of data and their distributions 

• List examples of statistical tests for analyzing continuous data 

• List examples of statistical tests for analyzing dichotomous or categorical data 

• Understand differences in regression methods 

• Identify nonparametric tests and when to use them 

The first part of the class will be 10:00 a.m. to 12:00 p.m. EST followed by a break from 12:00-1:00 p.m. The class resumes at 1:00 p.m. and concludes at 5:00 p.m. 

ChatGPT 101 training is part 1 of a three-part series.  

This one-hour online training led by OpenAI experts will cover the fundamentals of using ChatGPT Enterprise effectively in your daily NIH workflows. Attendees will learn to navigate the ChatGPT interface, implement practices for prompt writing, and utilize key features, such as working with files, search functions, and content drafting in Canvas. The training will also demonstrate real-world use cases for improving productivity and highlight security and compliance features tailored for NIH staff. 

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

• Use ChatGPT Enterprise’s foundational features, including Working with documents, Search, and Canvas. 
• Apply effective prompt strategies to generate accurate, useful outputs for NIH-specific tasks. 
• Understand best practices to help ensure responsible use of generative AI tools like ChatGPT. 

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

Denoising for Light Microscopy using Deep Learning

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.

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.

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). 

An Artificial Intelligence-based Pipeline for Drosophila Behavioral Analysis

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. 

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

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.

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.

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.

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.

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.

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 steps for spatial transcriptomics analysis including QC, exploratory analysis, batch effect removal, integration of spatial and gene expression information, as well as differential expression and pathway analysis.  No prior experience or access to Partek Flow is required. Attendance is limited to NIH staff.