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 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.

Qiagen Ingenuity Pathway Analysis (IPA) is a point-and-click software that enables scientists to discern how genomic, transcriptomic, proteomic, and metabolomic changes influence molecular biology pathways and networks. This software is available to NCI investigators. Submit a ticket with NCI computing help desk (https://service.cancer.gov/ncisp) to get it installed on personal computer. In this Qiagen scientist led training, participants will learn conduct path analysis from bulk RNA sequencing differential expression results using this software. Experience using or installation of IPA is not required for participation. This class is a demonstration and not hands-on. Attendance is restricted to NIH staff.

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.

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.

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). 

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. 

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

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.

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.