Bioinformatics Training & Education Program

Course: Bioinformatics for Beginners (B4B) 2026

Module 1: Unix and Biowulf

Lesson 3: Swarm Jobs, File Transfers & Downloading from the SRA

Learning Objectives:

Students will:

• Submit parallel jobs using swarm
• Transfer files between Biowulf and your local computer
• Understand the NCBI Sequence Read Archive (SRA)
• Download sequencing data

ACCREDITATION STATEMENT The Johns Hopkins University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. CREDIT DESIGNATION STATEMENT The Johns Hopkins University School of Medicine designates this live activity for a maximum of 1 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity. This regularly scheduled series has been approved for a 52-week cycle beginning September 3, 2025. POLICY ON PRESENTER AND PROVIDER DISCLOSURE It is the policy of the Johns Hopkins University School of Medicine that the presenter and provider globally disclose conflicts of interest and any discussion of unlabeled/unapproved uses of drugs or devices during their presentation(s). The Johns Hopkins University School of Medicine OCME has established policies in place that will identify and resolve conflicts of interest prior to this educational activity. Detailed disclosure will be made prior to presentation of the education. FULL DISCLOSURE POLICY AFFECTING CME ACTIVITIES As a provider approved by the Accreditation Council for Continuing Medical Education (ACCME), Johns Hopkins University OCME.

For reasonable accommodations please contact ccgrandrounds2@cc.nih.gov.

In The Digital Declutter Lab, you'll learn practical strategies for managing the information that lives outside your email. This session explores systems and strategies to organize files, streamline Teams conversations, improve collaboration, and create a sustainable digital workspace using Microsoft 365 tools.

 You'll also discover how AI assistants such as Copilot, Claude, Gemini, and CHIRP can help you categorize information, summarize content, surface what matters, and reduce digital clutter. Walk away with a personalized system that makes it easier to find what you need, stay focused, and work more efficiently every day.

During the self-paced hands-on exercise, you will have the chance to test out the Galaxy platform using Illumina paired-end exome sequencing data.

• introduction to exome sequencing data analysis

• show tutorials on how to use popular exome sequencing analysis  packages; and

• prepare to independently run basic exome sequencing analysis for variant detection.

• run quality control checks on sequencing data.

• align sequencing reads to a reference genome.

• generate alignment statistics and assess mapping quality.

• identify germline and somatic mutations.

• visualize exome sequencing data and variants.

 Whether you’re new to bioinformatics or looking to sharpen your exome sequencing data analysis skills, this workshop will equip you with practical tools and confidence to run your own analyses— no programming required.

Learn how to efficiently connect to, clean, transform, and combine data from multiple sources using Power Query. Through hands-on exercises and real-world examples, learners will discover how to automate repetitive data preparation tasks, improve data quality, and create reusable transformation workflows in Excel and Power BI. By the end of the course, participants will be able to streamline reporting processes, reduce manual effort, and prepare data for faster, more reliable analysis and decision-making.

This two-hour virtual roundtable discussion will explore the evolving role of artificial intelligence (AI) in research impact analysis. The program will begin with brief presentations by our panelists, followed by an open discussion. Research impact analysis uses quantitative and qualitative approaches to examine publications, citations, collaboration networks, and other indicators of scientific contribution. With the rapid advancement of generative AI and machine learning tools, new methods are emerging to enhance content analysis, trend identification, visualization, and interpretation of research outputs.  

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

• Describe how AI-driven techniques can enhance research impact analysis in the biomedical field 

• Identify emerging AI tools and methods for citation, content, and trend analysis 

• Provide examples of how AI-informed research impact analysis can support planning, evaluation, and reporting at NIH  

Attendees are not expected to have any prior knowledge of research impact analysis techniques or tools.  

Presenters:

• Joelle Mornini, NIH Library 
  Using ChatGPT to Create Visualizations
• Troy Zarcone, NIGMS  
  Surviving the AI Bubble: Staying on the Cutting Edge While Avoiding the Bleeding Edge
• Comfort Kai, OD 
  Lauren Oliveira Hashiguchi, NINR 
  Esther Yui, NINR  
  Research to Insights: Prompting AI to Summarize Impact
• Hua Ou, MD, Ph.D., OD 
  Lessons from Using Local LLMs (ChIRP) for Annual Portfolio Analysis
• James McClain, OD 
  Evan Ochsenfaber, OD 
  Overview of the All of Us Research Program's LLM Pipeline to Automatically Ingest, Authenticate, Categorize, and Synthesize Journal Publications by Researchers
• Vanessa Barnes, M.S., OD  
  More Than MeSH: AI-Powered Topic Mapping of Publications from the Kids First Program

This class introduces bulk RNA sequencing. At the end of the class, participants will become familiar with the objectives and limitations of bulk RNA sequencing. Further, an overview of experimental and analytical considerations including available compute resources at NIH for analyzing bulk RNA sequencing data will be provided. This session is not hands-on.

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

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

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

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

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

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

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

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.)

Dive into the dynamic world of the Cancer Genomics Cloud (CGC)! The CGC is built for success, no matter what your cloud or coding experience. We'll walk through how to access data and run basic analyses securely and reproducibly in the NIH-compliant CGC, with a review of advancements and innovations from the past year. Then we'll finish with an exclusive sneak peek into the exciting features lined up for 2026 and beyond. 

• Platform Overview - navigating your analysis in the cloud
• How to download data - including easy access to everything you're authorized to use through dbGaP
• Finding the right analysis tools no matter what your coding comfort
• A quick variant analysis run through

Join us and be part of the conversation to shape the future of cancer genomics research. Stay ahead, stay informed, and let's make 2026 a year of groundbreaking discoveries together!

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.

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).