Bioinformatics Training & Education Program

Developing artificial intelligence (AI) schemes to assist the clinician towards enabling precision medicine approaches requires development of objective markers that are predictive of disease response to treatment or prognostic of longer-term patient survival.

The solutions being developed involve designing computational imaging features together with histology or molecular data for detailed tissue and disease characterization in vivo as well associated with patient outcomes. The key innovation in this approach lies in “handcrafting” unique tools that can capture biologically relevant and clinically intuitive measurements from routinely acquired imaging (MRI, CT, PET) or digitized images of tissue specimens.

Further, by conducting cross-scale associations between imaging, pathology, and -omics, we can not only “unlock” and integrate the information captured by these different, disparate data modalities, but also develop an interpretable and intuitive understanding of what drives their performance.

Specific problems addressed via the new computerized imaging markers we have developed through our NCI funded RadxTools project include:

(a) Predicting response to treatment to identify optimal therapeutic pathways

(b) Evaluating therapeutic response to guide follow-up procedures. Further examining how to account for differences between sites, scanners, and acquisition parameters to ensure generalizable performance of AI tools and computational imaging features, crucial for wider clinical translation and widespread adoption.

These will be discussed in the context of clinical applications in colorectal and renal cancers as well as digestive diseases.

This session of the BTEP Coding Club will showcase NIH HPC OnDemand, a new interface for on-demand access to HPC resources via a web browser. HPC OnDemand simplifies access to graphical sessions and GUI applications such as RStudio, VSCode, and Jupyter Lab. This session, featuring Jonathan Goodson, a Computational Scientist with the NIH HPC Systems and maintainer of HPC OnDemand, will provide everything you need to know to get started using HPC OnDemand today.

Please note: Registration is required to get the Meeting Link for this event. Please pre-register.

Aviv Regev is head of Genentech Research and Early Development. Formerly, Regev was Chair of the Faculty and Core Member at the Broad Institute of MIT and Harvard, Professor of Biology at MIT, and a Howard Hughes Medical Institute Investigator. She is founding co-chair of the Human Cell Atlas and a leader in deciphering molecular circuits that govern cells, tissues, and organs in health and their malfunction in disease. She has pioneered foundational experimental and computational methods in single-cell genomics, enabling greater understanding of cell and tissue functions. Regev is a member of the National Academy of Sciences, National Academy of Medicine, American Academy of Arts and Sciences, and a Fellow of the International Society of Computational Biology. Her many honors include the ISCB Overton and Innovator Prizes, Paul Marks Prize, Lurie Prize in Biomedical Sciences, Keio Medical Science Prize, HFSP Nakasone Award, and L'Oréal-UNESCO for Women in Science Award.

This event will not be recorded at the speakers' request.

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, which is the first of three lessons focused on "pathway analysis", introduces concepts, methods, tools, and databases related to functional enrichment and pathway analysis. This is NOT a hands-on lesson. 

Open to HHS Staff Only

The goal of the NCI RNA Biology Initiative is to establish a collaborative environment that promotes the swift exchange of information and expertise regarding the structure, function, and biological significance of RNA. Our objective is to leverage this collective knowledge to advance the development of novel diagnostics and therapies. This year’s symposium will focus on fostering synergistic collaborations among NCI and NIH intramural investigators, by highlighting and celebrating RNA research by the NIH intramural community.

While there will not be a poster session, there will be ample networking opportunities. We encourage you to join us and help foster collaborations among NCI and NIH intramural investigators.

Organized by the NCI RNA Biology Initiative this symposium will bring intramural experts in the field of RNA biology, with the focus on:

• RNA Processing
• RNA Structure and Mechanism
• RNA Stability and Translation
• RNA in Disease

DAVID (the Database for Annotation, Visualization and Integrated Discovery) provides a comprehensive set of functional annotation tools for investigators to understand the biological meaning behind large lists of genes acquired from high-throughput assays such as RNA-Seq, Proteomics, Microarray, etc. This hands-on lesson will show attendees how to use and interpret results from DAVID using an example gene list. 

AI Club is a weekly meeting that explores various topics relating to AI and deep learning in biomedical sciences, typically in a seminar, workshop, or journal club format. AI Club is intended to be accessible to experts and non-experts alike. We currently meet in the Building 10 Library Training Room on Mondays from 11 - 12. IT IS STRONGLY RECOMMENDED TO COME IN PERSON.

Reactome is a free, open-source, curated and peer-reviewed pathway database that includes bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge. This lesson will showcase the capabilities of Reactome in garnering biological meaning from an example gene list derived from differential expression results. 

AI Club is a weekly meeting that explores various topics relating to AI and deep learning in biomedical sciences, typically in a seminar, workshop, or journal club format. AI Club is intended to be accessible to experts and non-experts alike. We currently meet in the Building 10 Library Training Room on Mondays from 11 - 12. IT IS STRONGLY RECOMMENDED TO COME IN PERSON.

What to bring:  Laptop capable of connecting to internet via NIH wifi

Are you looking to expand the reach of your sequencing to enable what long read technologies can provide?  Please join Oxford Nanopore Technologies (ONT) bioinformatics specialists on a deep dive into getting the most from your long read sequencing data.

This workshop will cover the details of analyzing single-cell RNA sequencing data using our EPI2ME pipeline wf-single-cell. This workflow provides access to industry standard tools for primary processing of single-cell data including deconvolution, quality control, gene and transcript identification, and data visualization. Participants will learn about the EPI2ME software, pipeline details, and work with an ONT bioinformatics expert in a hands-on data analysis training exercise.

This in-person, hands-on workshop gives learners the opportunity to learn the Nanopore Single Cell RNA-Seq Analysis Software Workflow. Taught by Nanopore personnel, with assistance from the CCR Genomics Core and BTEP, this session will run in the morning. Space is limited. If your plans change and you cannot attend, please cancel your registration. There will be no hybrid option for this class, it is in-person only. 

Participants should bring an internet-enabled laptop to access the tools and datasets used for the training. 

Agenda:

9:00-9:15 – Check in & distribute materials

9:15-9:30 – Data analysis intro from Oxford Nanopore Technologies (MinKNOW/EPI2ME/other advanced tools)

9:30-10:00 – Introduction to Single Cell RNA-seq data analysis with Oxford Nanopore Technologies

10:00-11:15 – Hands on data analysis: Single Cell EPI2ME App, nextflow command line, and Biowulf HPC demo

11:15-12:00 – Downstream analysis with Seurat

12:00-12:30 – Closing and Q&A

What to bring:  Laptop capable of connecting to internet via NIH wifi

Are you looking to expand the reach of your sequencing to enable what long read technologies can provide?  Please join Oxford Nanopore Technologies (ONT) bioinformatics specialists on a deep dive into getting the most from your long read sequencing data. 

This workshop will cover the details of analyzing human whole genome sequencing data using our EPI2ME pipeline wf-human-variation. This workflow provides users with tools to perform alignment and variant calling for single nucleotide, structural,  and copy number variants as well as clinically relevant short tandem repeats, and cytosine methylation. Participants will learn about the EPI2ME software, pipeline details, and work with an ONT bioinformatics expert in a hands-on data analysis training exercise.

This in-person, hands-on workshop gives learners the opportunity to learn the Nanopore Human Variation Data Analysis Software Workflow. Taught by Nanopore personnel, with assistance from the CCR Genomics Core and BTEP, this session will run in the afternoon. Space is limited. If your plans change and you cannot attend, please cancel your registration. There will be no hybrid option for this class, it is in-person only. 

Participants should bring an internet-enabled laptop to access the tools and datasets used for the training. 

 1:30-1:45 – Check in & distribute materials

1:45-2:15 – Data analysis intro from ONT (MinKNOW/EPI2ME/other advanced tools)

2:15-2:45 – Introduction to the Human Variation (WGS) pipeline

2:45-4:00 – Hands on data analysis: human variation EPI2ME App, nextflow command line, and Biowulf HPC demo

4:00-4:30 – Review result

4:30-5:00 – Closing/Q&A

What to bring:  Laptop capable of connecting to internet via NIH wifi

For questions or to register, please contact Eric Troop (eric.troop@nanoporetech.com)

Are you looking to expand the reach of your sequencing to enable what long read technologies can provide?  Please join Oxford Nanopore bioinformatics specialists on a deep dive into getting the most from your long read sequencing data.  In conjunction with the Frederick National Lab for Cancer Research (FNLCR), we are offering an in-depth workshop focusing to give you the tools and know how to delve deeper and gain further insight to your biological systems.

Oxford Nanopore Technologies offers data analysis solutions in our EPI2ME software platform tailored to the analysis of long read DNA and RNA sequencing data  from ONT devices. The Single Cell focus will cover the details of analyzing single-cell RNA sequencing data using our EPI2ME pipeline wf-single-cell. This workflow provides access to industry standard tools for primary processing of single-cell data including deconvolution, quality control, gene and transcript identification, and data visualization.

The Human Variation focus will cover the details of analyzing human whole genome sequencing data using our EPI2ME pipeline wf-human-variation. This workflow provides users with tools to perform alignment and variant calling for single nucleotide, structural,  and copy number variants as well as clinically relevant short tandem repeats, and cytosine methylation.

For both, participants will learn about the EPI2ME software, pipeline details, and work with an ONT bioinformatics expert in a hands-on data analysis training exercise.

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.

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=m5343b74ef86d64ec9760d23504277b26 
Meeting number:
2310 788 0773
Password:
jpS62rwdH5*

Join by video system
Dial 23107880773@cbiit.webex.com
You can also dial 173.243.2.68 and enter your meeting number.
Join by phone
1-650-479-3207 Call-in number (US/Canada)
Access code: 2310 788 0773

AI Club is a weekly meeting that explores various topics relating to AI and deep learning in biomedical sciences, typically in a seminar, workshop, or journal club format. AI Club is intended to be accessible to experts and non-experts alike. We currently meet in the Building 10 Library Training Room on Mondays from 11 - 12. IT IS STRONGLY RECOMMENDED TO COME IN PERSON.

AI Club is a weekly meeting that explores various topics relating to AI and deep learning in biomedical sciences, typically in a seminar, workshop, or journal club format. AI Club is intended to be accessible to experts and non-experts alike. We currently meet in the Building 10 Library Training Room on Mondays from 11 - 12. It is strongly recommended to come in person. 

This one-day in-person NIH AI Symposium will bring together researchers from a broad range of disciplines to share their AI-related research, with the goal of disseminating the newest AI research, providing an opportunity to network, and to cross-pollinate ideas across disciplines in order to advance AI research in biomedicine. We welcome all NIH researchers who are interested in AI, from novices to experts.

Sponsored by NHLBI and the Office of Intramural Research. 

Keynote Speakers: 

• Dr. Alexander Rives, Co-founder and chief scientist at Evolutionary Scale, a company focused on applying machine learning and language models to biological systems, including the development of ESM3, a protein language model that enables the generation of novel proteins with potential applications for drug discovery and basic biological research. 
• Dr. Leo Anthony Celi, Senior Research Scientist at Massachusetts Institue of Technology (MIT) and Associate Professor of Medicine at Harvard Medical school, who has a broad range of interests including integrating clinical expertise with data science, using information technology to enhance healthcare in low- and middle-income countries, and considering the social impacts of AI research. 

About Event: Biomedical science is in the early phase of a technological revolution, driven in large part by innovations in deep learning neural network architecture and availability of computational power. These cutting-edge techniques are being applied to every sub-field of the biological sciences, and with novel ground-breaking advancements arriving every week it is challenging for researchers to stay up to speed on what is available and possible.

Please register and submit a poster abstract. Attendance is limited, so please register now to reserve your spot. 

Registration deadline: April 25, 2025
Abstract deadline: April 11, 2025