Classes & Events

Dear Colleagues,
  
This webinar will introduce TumorDecon, a computational tool that's at an early stage of contributing to the intersection of bioinformatics and oncology.
 
TumorDecon aims to simplify the complex nature of tumors by utilizing deconvolution algorithms to estimate the percentages of various immune cells from gene expression profiles of the bulk of cells.
During the presentation, the following will be discussed:
 
•    Basic overview of TumorDecon, touching upon how it processes transcriptomic data to offer a glimpse into the cellular composition of tumors.
•    Preliminary applications of TumorDecon, drawing from a few examples and datasets to illustrate its potential utility in research and possibly in clinical contexts.
•    Live demonstration of TumorDecon's software, aiming to provide a clear picture of how users can navigate and utilize the tool.
 
Audience engagement is encouraged to exchange ideas and discuss how tools like TumorDecon can be improved and might fit into the broader landscape of cancer research and treatment.

For questions contact Daoud Meerzaman or Kayla Strauss.

In this webinar, attendees will learn the basics of using the All of Us Researcher Workbench’s point-and-click research tools, including how to create a workspace, how to build a cohort of All of Us participants using the Cohort Builder, and more. 

Presenter: Dr. Chris Lord is a project manager at Vanderbilt University Medical Center that primarily assists with research support for the Data and Research Center (DRC), focusing on the User Support Hub, featured workspaces, and creating support materials for users. Additionally, he assists with the Help Desk, office hours, and user communications. Before joining the DRC in 2022, he received his Ph.D. from UCSD in cell biology and then was a postdoctoral fellow and research assistant professor at Vanderbilt University.

This is the third of five sessions about NIH’s All of Us Research Program and Researcher Workbench. Attendees are encouraged, but not required, to attend all sessions. Register for additional sessions below: 

• Session 4 - April 26: Introduction to Coding in the Researcher Workbench
• Session 5 - May 3: Resources to Support Researchers

For questions about this webinar series, contact Cindy Sheffield, cynthia.sheffield@nih.gov

 

Dear Colleagues,
  
In this webinar, you'll get an introduction to XNAT. XNAT is an open-source platform for managing, processing, and sharing medical imaging and related data in research settings.
 
You can use XNAT's flexible architecture and extensive customization options to streamline your workflows and collaborate effectively. It can also help to accelerate discoveries in neuroscience and medical imaging research.
 
You'll learn about key features and benefits of XNAT, including example use cases in oncology research. 

For questions contact Daoud Meerzaman or Kayla Strauss.

NCI CCR Liver Cancer Program Seminar Series

Dr. Praveen Sethupathy is professor of physiological genomics and chair of the Department of Biomedical Sciences at Cornell University. He is also the director of the Cornell Center for Vertebrate Genomics. He leads a research lab focused on genome-scale and molecular approaches to understanding the pathophysiology of animal and human diseases. Dr. Sethupathy received his B.A. from Cornell University and his Ph.D. in genomics from the University of Pennsylvania. After completing a postdoctoral fellowship at the National Human Genome Research Institute under the mentorship of NIH Director Dr. Francis Collins, he moved in 2011 to the University of North Carolina at Chapel Hill as an assistant professor in the Department of Genetics. The same year, he was selected by Genome Technology as one of the nation’s top 25 rising young investigators in genomics. In 2017, he returned to Cornell University. He has authored over 140 peer-reviewed publications in scientific journals such as the Proceedings of the National Academy of Sciences, Cell, and Science and has served as a reviewer for over 50 different journals. Dr. Sethupathy’s honors include a faculty merit award for outstanding teaching and mentoring, the prestigious American Diabetes Association Pathway to Stop Diabetes Research Accelerator (which is awarded to only three people per year), and the inaugural Boehringer Ingelheim Award for Excellence in Research Mentorship.

For more information about this event, please contact Dr. Anuradha Budhu.

Meeting ID: 160 688 6958
Passcode: 849963

Intended Audience

This webinar targets researchers interested in exploring genomic mutation analysis capabilities within the recently introduced NCI Genomic Data Commons (GDC) 2.0 platform. It aims to accommodate individuals interested in analyzing genes and mutations within a cancer disease type, irrespective of their prior genomics experience.

Abstract

GDC 2.0 adopts a "cohort-centric" approach, allowing users to construct custom sets of cases and conduct gene- and variant-level data analysis directly within their web browser. This webinar will demonstrate the new cohort-centric workflow, from cohort building to analyzing genes and mutations associated with a cohort. Additionally, participants may ask GDC experts questions and provide feedback on GDC 2.0.

Included Topics

• Utilizing the Cohort Builder to create custom cohorts for specific cancer disease types
• Employing the Mutation Frequency Tool to visualize the most frequently mutated genes within a cohort
• Using OncoMatrix to analyze the top mutated genes affected by high-impact mutations in a cohort
• Using ProteinPaint to explore mutations and their potential impact within protein coding regions of genes

Webex Information

• Meeting number (access code): 2306 971 7385
• Meeting password: TGwpjPf@283 (84975731 from phones and video systems)

Dr. Stephanie Harmon obtained her Ph.D. in medical physics at the University of Wisconsin–Madison and did her postgraduate training at Leidos Biomedical Research where she worked within the Molecular Imaging Branch and investigated computational biomarkers in various state-of-the-art prostate imaging modalities. In 2020, she became a staff scientist within the newly formed Artificial Intelligence Resource of the NCI.

Dr. Harmon’s research interests focus on computational approaches, including computer vision and artificial intelligence, for combining multiscale biological and clinicopathological data to model cancer and cancer-related outcomes. She investigates the functional relationship of imaging characteristics across biological scales and their role in phenotypic heterogeneity in various cancer types. Her research centers around the idea that the use of integrative biomarkers from various imaging scales could enhance the understanding of disease and the role of imaging in cancer diagnosis and treatment. Her long-term goal is to identify interpretable imaging-based biomarkers on histopathological images that can predict underlying molecular phenotypes and translate these findings to improve diagnosis, treatment, and patient outcomes in various cancers.

Dr. Harmon received many awards during her training, including the Prostate Cancer Foundation Young Investigator Award and the NCI Director’s Intramural Innovation Award.

Dr. Harmon has published over 75 peer-reviewed articles, including publications in the Journal of Nuclear Medicine, PLOS One, JAMA Oncology, Cancer Imaging, Radiology, American Journal of Pathology, Journal of Medical Imaging, Nature Medicine, Journal of Clinical Oncology, Clinical Cancer Research, and Nature Communications.

For more information, please contact Aniruddha Ganguly, Ph.D.

Meeting number (access code): 2319 301 4914

Meeting password: KpxUgxg$372

What’s the difference between “regular” statistics (i.e., what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study?

This is Part 1 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 1 will address the frequentist approach and will cover the concepts of hypothesis testing, confidence intervals, Type I and Type II errors, statistical power, and p-values. Technical details will be kept to an absolute minimum. The class will be taught by the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES).

Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees, and references will be provided for in-depth self-study.

You must register separately for Part 2 of this class series.

Individuals who need reasonable accommodations to participate should contact the NIH Interpreting Office directly at nih@ainterpreting.com, or the NIH Library Information Desk at 301-496-1080. Requests should be made at least five business days in advance in order to ensure availability.

Crunching of large data requires large resources. Exploration of holobiont metagenomes is considered a “big data” project and here I’ll be describing how I used the FRCE cluster to explore 794 metagenomes simultaneously to assess the biosynthetic capacity associated with lichens. This presentation will focus more on computational tools and techniques used to solve metagenomic problems. This presentation is aimed at beginner to intermediate users. A basic understanding of Bash and Slurm would be beneficial but not vital. This will be a hybrid event. This session will be recorded, and materials will be posted on the ABCS training site and also shared with attendees a few days after the event.

For additional details and questions, please contact Natasha Pacheco (natasha.pacheco@nih.gov), Advanced Biomedical Computational Science group, Frederick National Laboratory for Cancer Research.

Please join us on Wednesday, April 24, 2024, when Dr. Abhishek Jha, co-founder and CEO of Elucidata, will present " Data Quality for LLMs: Building a Reliable Data Foundation." The presentation starts at 11:00 a.m. ET and ends at noon.
 
If you use large language models (LLMs) in your cancer research, register for this seminar to hear Elucidata’s Dr. Abhishek Jha discuss how data quality impacts LLM performance.
 
A reliable foundation that is well annotated and accessible to an LLM plays a major role in the value of its results.
 
You’ll see examples of how LLM-powered artificial intelligence (AI) agents query across three versions of the same gene expression corpus with differing results, including:

•    unstructured data from the public repository Gene Expression Omnibus.
•    structured data from the Crowd Extracted Expression of Differential Signatures project.
•    clean, linked, and harmonized data.
 
Dr. Jha will use these examples to discuss how the different quality in these data sources impacts LLM performance.

This seminar provides an introduction to R in the context of single cell RNA-Seq analysis with Seurat.  In this seminar, attendees will learn about options for analyzing scRNA-Seq data, resources for learning R, how to import scRNA-Seq data, and how to create, examine, and access data stored in a Seurat object.  

Dear Colleagues,
  
In this webinar, you'll get an introduction to WebMeV. WebMeV aims to democratize bioinformatics analysis for biological sciences researchers.
 
The maturation of many bioinformatics processes in sequencing has enabled the standardization of protocols. This allows simple programmatic encapsulation of the analysis. New technology has also allowed increased accessibility and replication of the software environment itself.
 
WebMeV provides both transparency and reproducibility of the analysis code and build environment. It also provides an easy to use web-based graphical interface to count-based bioinformatics analyses of RNASeq, scRNASeq, and more.

For questions contact Daoud Meerzaman or Kayla Strauss.

 

What’s the difference between “regular” statistics (i.e., what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study? 

This is Part 2 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 2 will address the Bayesian approach and will cover the concepts of Bayes’ Theorem, prior and posterior distributions, and Bayes factor. Technical details will be kept to an absolute minimum. This class will be taught by the Clinical Center's Biostatistics and Clinical Epidemiology Service (CC/BCES). 

Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees, and references will be provided for in-depth self-study. 

You must register separately for Part 1 of this class series.

This lesson reviews many of the standard steps in a scRNA-Seq workflow: QC filtering, normalization, scaling, and clustering. 

The explosion of biomedical big data and information in the past decade or so has created new opportunities for discoveries to improve the treatment and prevention of human diseases. As such, the field of medicine is undergoing a paradigm shift driven by AI-powered analytical solutions. This talk explores the benefits (and risks) of AI and ChatGPT, highlighting their pivotal roles in revolutionizing biomedical discovery, patient care, diagnosis, treatment, and medical research. By demonstrating their uses in some real-world applications such as improving biomedical literature searches (Nature Biotechnology 2018; Nature 2020; Nature Genetics 2023), accelerating patient trial matching (TrialGPT, in collaboration with NCI clinicians), and assisting gene set analysis (GeneAgent, in collaboration with NCI researchers), we underscore the potential of AI and ChatGPT in enhancing clinical decision-making, personalizing patient experiences, and accelerating knowledge discovery.

Alternative Meeting Information: Meeting number: 2300 950 8025 Password: qiQsnDx?923 Join by video system Dial 23009508025@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: 2300 950 8025  

Macros are ways to use code to substitute in a value, and using macros makes a code in SAS easier to read, easier to edit, less prone to errors,  and often allows it to run more efficiently. This intermediate class will provide an overview of what is a macro and how macros work in SAS, including the macro facility. Examples of macro code will be made available to class participants for modification and later use.

Galaxy is a scientific workflow, data integration, data analysis, and publishing platform that makes computational biology accessible to research scientists that do not have computer programming experience. This workshop will introduce RNA-seq data analysis followed by tutorials showing the use of popular RNA-seq analysis packages and preparing participants to independently run basic RNA-Seq analysis for expression profiling. The hands-on exercises will run on the Galaxy platform using Illumina paired-end RNA-seq data. The workshop will be taught by NCI staff and is open to NIH and HHS staff.

If you’re a researcher, clinician, informaticist, commercial partner, or policy maker interested in cancer data science, register to attend this Cancer Data Exchange Summit.

You’ll have the opportunity to hear (and take part in) discussions around current opportunities and challenges with the following topics:

• How to use a patient’s data to determine their eligibility for clinical trials
• How to identify and develop data standards to detect immune-related adverse events
• Ways to enhance the efficiency and timeliness of the collection of cancer registry data
• Ways to support patient access, interoperability, and data sharing

You can also help identify cancer-specific elements; develop implementation guides; and define requirements to build large language models for extracting data.

The participant group will comprise researchers, clinicians, informatics/data scientists, patient advocates, standard-setting organizations (such as HL7/FHIR), policymakers, EHR vendors, and industry partners. Their collaborative efforts will focus on identifying current opportunities, challenges, and essential oncology-specific data requirements for the USCDI+ Cancer use cases (1) using real-world data to determine patient eligibility for clinical trials; (2) identifying immune-related adverse events; (3) enhancing the efficiency and timeliness of cancer registry data.

This seminar provides an overview of differential expression testing workflows with Seurat.

This one-hour session will introduce attendees to the world of Artificial Intelligence (AI) as we explore the fundamentals, applications, and ethical considerations of this transformative technology. Key topics will include machine learning, deep learning, data handling, and real-world AI applications across various industries. We'll delve into the ethical implications of AI and offer insights on how to become AI literate. Whether you're a seasoned professional or just starting your AI journey, this session will equip you with essential knowledge to navigate the AI landscape effectively and make informed decisions in our data-driven world.

This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of R programming language and RStudio. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services and Bioinformatics programs that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.

By the end of this class, students should be able to: list reasons for using R; describe the purpose of the RStudio Script, Console, Environment, and Plots panes; describe the various methods for finding help on R and RStudio; organize files and directories for a set of analyses as an R Project; define the following terms as they relate to R: object, assign, comment, call, function, and arguments; and assign values to objects in R.

Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Please bring your laptop with R and RStudio installed.

This in-person workshop will focus on data wrangling using tidy data principles. Tidy data describes a standard way of storing data that facilitates analysis and visualization within the tidyverse ecosystem. There will be a discussion of what makes data "tidy," and methods for reshaping your data using dplyr and tidyr functions. Prior to attending this class, you will need to have:

1. Installed R and RStudio
2. Taken the Introduction to R and RStudio class. If not, here are some resources for getting started:
   1. Introduction to R
   2. Introduction to RStudio
   3. Introduction to Scripts in RStudio

By the end of this class, attendees will be able to demonstrate how to describe the purpose of the dplyr and tidyr packages, select certain columns in a data frame, select certain rows in a data frame according to filtering conditions, and add new columns to a data frame that are functions of existing columns.

Note on Technology

The NIH Library has 24 pre-configured Windows laptops that you are welcome to use during this training on a first come, first served basis. You are also welcome to bring your own laptop (PC or Mac). NIH Staff bringing their own NIH-laptop can easily connect to the staff Wi-Fi. If participants are bringing a personal laptop, they are restricted to using the NIH-Guest-Network Wi-Fi.

Registrants will receive an email with information and instructions to install and verify access to R and RStudio before the class.  If you register the day before the class, you may not have time to download and properly install the necessary software. If you do not have the software installed, this training will be demo only.

Participants will learn how to develop artificial intelligence (AI) applications using MATLAB, even if they do not have a formal background in machine and deep learning. The goal of this course is to introduce tools and fundamental approaches for developing predictive models on biomedical signals. The course will cover the entire AI pipeline, from signal exploration to deployment, including: annotating time series biomedical signals automatically, creating deep learning models using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) for biomedical signal data, creating machine learning models for biomedical signal data, applying advanced signal pre-processing techniques for automated feature extraction, and automatically generating code for edge deployment of AI models.

This is an introductory level class. No installation of MATLAB is necessary.

Generalist repositories offer NIH researchers a flexible, trusted resource to share data for which there is no appropriate discipline specific repository as well as to share many other research outputs valuable for reproducibility and open science. This webinar, presented by participants of the NIH Generalist Repository Ecosystem Initiative (GREI) (Dataverse, Dryad, Figshare, Mendeley Data, Open Science Framework, Vivli, and Zenodo) will share generalist repository use cases and best practices for sharing and finding data in generalist repositories. It will describe how generalist repositories fit into the NIH data repository landscape for intramural researchers and can be part of meeting the new NIH Data Management and Sharing Policy requirements. It will present both the key common features of generalist repositories that meet the NIH desirable repository characteristics as well as the unique features of these repositories that make them suited to specific types of data. 

This course provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation. By the end of this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing.

This is an introductory two-part course for those who want to learn about research data management and sharing, or for those who are interested in a refresher. Part 1 of this training will cover understanding research data, how to manage research data, and how to work with data. Audience: Researchers, fellows, post-docs, and trainees. You must register separately for Part 2 of this class.

Qiagen CLC Genomics Workbench is a point-and-click bioinformatics software that runs on a personal computer and enables bulk RNA sequencing, ChIP sequencing, long reads, and variant analysis. This software is available to NCI scientists.

This hands-on training will guide participants through bulk RNA sequencing analysis using CLC Genomics Workbench. After the class, participants will be able to

• Import files and illumina reads
• Import and associate metadata with samples
• Download reference genome and annotation
• Obtain RNA sequencing expression counts and perform differential expression analysis
• Construct PCA and heatmap to visualize RNA sequencing data

 

To get the most of this hands-on session, please reach out to the NCI service desk (https://service.cancer.gov/ncisp) to get this software installed, preview the tutorial (https://resources.qiagenbioinformatics.com/tutorials/RNASeq-droso.pdf), and download the example dataset (http://resources.qiagenbioinformatics.com/testdata/RNA_Seq_Droso2.zip) prior to attending.

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=m07f826d16b67d3c3b8a86e275ebac5a5
Meeting number:
2300 281 6121
Password:
e7aEqhpy@34

Join by video system
Dial 23002816121@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: 2300 281 6121

The NIH Artificial Intelligence (AI) Symposium will take place on Friday, May 17th, 2024, in Masur Auditorium in Building 10 on the Bethesda NIH campus. This event is open to all NIH members - registration and abstract submission are now open https://forms.microsoft.com/g/4WpdBXcEu6

Biomedical science is in a technological revolution, driven by innovations in deep learning architecture and computational power. These cutting-edge techniques are being applied to every sub-field of the biological sciences, and with ground-breaking advancements arriving constantly it is challenging for researchers to stay up to speed on what is possible. This one-day 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.

Keynote speakers James Zou, Ph.D. (Stanford University) and Hari Shroff, Ph.D. (Janelia Research Campus) will share their research, and also participate in a Panel Discussion on the current and future potential of AI in biomedical sciences. There will also be short talks and posters from researchers on campus who are developing or using AI approaches.

The NIH AI Symposium is sponsored by NHLBI, in partnership with FAES. Registration and abstract submission are open to all NIH members, including experts in AI-related fields and novices interested in gaining more exposure.

Important dates:

March 15th - Abstract submission deadline

April 5th - Abstract notifications

May 3rd – Registration deadline

Sign language interpreting and CART services are available upon request to participate in this event. Individuals needing either of these services and/or other reasonable accommodations should contact Ryan O’Neill (oneillrs@nih.gov).

Questions can be directed to Lead Organizer Ryan O’Neill, Ph.D. (oneillrs@nih.gov).

This course provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation. By the end of this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing.

This is an introductory two-part course for those who want to learn about research data management and sharing, or for those who are interested in a refresher. During Part 2 of this training, participants will learn about sharing and archiving data. Audience: Researchers, fellows, post-docs, and trainees. You must register separately for Part 1 of this class.

An exciting opportunity at the intersection of the biomedical sciences and machine learning stems from the growing availability of large-scale multi-modal data (imaging-based and sequencing-based, observational and perturbational, at the single-cell level, tissue-level, and organism-level). Traditional representation learning methods, although often highly successful in predictive tasks, do not generally elucidate underlying causal mechanisms. Dr. Uhler will present initial ideas towards building a statistical and computational framework for causal representation learning and its applications towards identifying novel disease biomarkers as well as inferring gene regulation in health and disease.

Alternative Meeting Information: Meeting number: 2312 523 4308 Password: rgE4DbPX$65 Join by video system Dial 23125234308@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: 2312 523 4308  

NCI is launching the virtual Cancer AI Conversations series featuring multiple perspectives on timely topics and themes in artificial intelligence for cancer research! 

Each event features short talks from 2-4 subject matter experts offering diverse views on the session topic. These talks will be followed by a moderated panel discussion.

“Cancer AI Conversations” are bimonthly, 1-hour virtual events featuring timely topics related to the application of artificial intelligence in cancer research. Each event features short talks from 2-4 subject matter experts offering diverse perspectives on the session topic. 

All of the Cancer AI Conversations will be recorded and posted for future viewing.

This 90-minute course equips participants with essential knowledge and skills for effective interactions with Large Language Models (LLMs), such as ChatGPT. Explore the intricacies of prompt engineering and its pivotal role in optimizing the conversational capabilities of LLMs. Emphasizing best practices and practical applications, this course features live demonstrations and group discussion, and provides valuable skills for the effective use of LLMs. Attendees are encouraged to register for a free ChatGPT account prior to taking this class. 

The Brooks Lab developed a computational tool called FLAIR (Full-Length Alternative Isoform Analysis of RNA) to produce confident transcript isoforms from long-read RNA-seq data with the aim of alternative isoform detection and quantification. With an increase in the usage of long-read RNA-seq, there is a growing need for a systematic evaluation of this approach. We are part of an international community effort called the Long-read RNA-seq Genome Annotation Assessment Project (LRGASP) to perform such an evaluation. The Brooks Lab is extending FLAIR to incorporate sequence variation, RNA editing, and RNA modification in isoform detection as well as detection of complex gene fusions from long-read sequencing data.

  Meeting number: 2311 656 4503 Password: ySkM7uW6B$5 Join by video system Dial 23116564503@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: 2311 656 4503  

Dr. Irizarry will share findings demonstrating limitations of current
workflows that are popular in single cell RNA-Seq data analysis.
Specifically, he will describe challenges and solutions to dimension
reduction, cell-type classification, and statistical significance
analysis of clustering. Dr. Irizarry will end the talk describing some of his
work related to spatial transcriptomics. Specifically, he will describe
approaches to cell type annotation that account for presence of
multiple cell-types represented in the measurements, a common
occurrence with technologies such as Visium and SlideSeq. He will
demonstrate how this approach facilitates the discovery of spatially
varying genes. Meeting link: https://cbiit.webex.com/cbiit/j.php?MTID=m9dcd9ce21f4fa6b1a8e2d998a88c2c2b    Meeting number: 2317 712 9095 Password: gUKZzp3u76? Join by video system Dial 23177129095@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: 2317 712 9095  

CARD is a collaborative initiative of the National Institute on Aging and the National Institute of Neurological Disorders and Stroke that supports basic, translational, and clinical research on Alzheimer’s disease and related dementias. CARD’s central mission is to initiate, stimulate, accelerate, and support research that will lead to the development of improved treatments and preventions for these diseases.

Alternative Meeting Information:  Meeting number: 2310 497 7985 Password: mjPjjmi$473 Join by video system Dial 23104977985@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 497 7985  

Kerry Goetz, Ph.D.

Meeting number: 2302 034 0947 Password: juFCdpx$627 Join by video system Dial 23020340947@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: 2302 034 0947  

The Elemento lab combines Big Data analytics with experimentation to develop entirely new ways to help prevent, diagnose, understand, treat and ultimately cure disease. Our research involves routine use of ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning. We’re revolutionizing healthcare by developing innovative approaches to better predict, diagnose, treat, and prevent disease to improve clinical care for every patient.  

Alternative Meeting Information: Meeting number: 2319 759 4122 Password: Join by video system Dial 23197594122@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: 2319 759 4122  

Dr. Mardis is an internationally recognized expert in cancer genomics, with ongoing interests in the integrated characterization of cancer genomes, defining DNA-based somatic and germline interactions and RNA-based pathways, and immune microenvironments that lead to cancer onset and progression, specifically involving pediatric cancers. Most recently, her research has been oriented toward translational aspects of cancer genomics, specifically identifying how the cancer genome changes with treatment, including acquired resistance, the use of genomics in understanding immune therapy response, and the clinical benefit of cancer molecular profiling in the pediatric setting.

Alternative Meeting Information: Meeting number: 2312 714 2024 Password: GrddnZQ*248 Join by video system Dial 23127142024@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: 2312 714 2024  

Dr. O'Neill's research programs employ molecular genetics, genomics and computational approaches to study the mechanisms that maintain, and disrupt, genome stability with a particular focus on repetitive elements. Projects include studying: retroelement transcription and centromere function; novel small RNA biogenesis pathways; and global chromosome and genome changes during instability (such as in cancer and hybrid dysgenesis). In addition, we use a diverse set of rapidly evolving next generation sequencing (NGS) technologies and novel library preparation and computational methodologies for drafting and characterizing genome sequences in efforts to establish broad eukaryotic species as models for studying genome biology. Recently, Dr. O'Neill's lab has expanded their efforts towards applying broad NGS techniques to both model and non-model systems to understand the dynamic response of the genome to environmental queues, such as global warming.

Meeting number: 2315 524 3558 Password: JEexR5Jq@63 Join by video system Dial 23155243558@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: 2315 524 3558

Dr. Blackshaw's work examines the molecular basis of neuronal and glial cell fate specification and survival, focusing on characterizing the network of genes that control specification of different cell types within the retina and hypothalamus, two structures that arise from the embryonic forebrain.  The ultimate goal is to use insights gained from learning how individual cell types are specified to understand how these cells contribute to the regulation of behavior, and how they can be replaced in neurodegenerative disease.

Meeting number: 2312 437 6963 Password: bMrGtiA@933 Join by video system Dial 23124376963@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: 2312 437 6963  

David M. Reif, Ph.D., joined the NIEHS in 2022 as Chief of the Predictive Toxicology Branch (PTB) in the Division of Translational Toxicology (DTT). In this role, he will leverage expertise of the branch in data science, toxicogenomics, spatiotemporal exposures and toxicology, computational methods development, and new approach methods (NAMs) to advance predictive toxicology applications with partners across NIEHS, the interagency Tox21 Program and the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM).

Meeting number: 2318 207 2771 Password: 5DMpVr5Mt5@ Join by video system Dial 23182072771@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: 2318 207 2771  

The primary theme of Dr. Bult's personal research program is “bridging the digital biology divide,” reflecting the critical role that informatics and computational biology play in modern biomedical research. Dr. Bult is a Principal Investigator in the Mouse Genome Informatics (MGI) consortium that develops knowledge-bases to advance the laboratory mouse as a model system for research into the genetic and genomic basis of human biology and disease. Recent research initiatives in Dr. Bult's research group include computational prediction of gene function in the mouse and the use of the mouse to understand genetic pathways in normal lung development and disease.

Join information Alternative Meeting Information: Meeting number: 2309 763 3797 Password: GmUAeeZ@236 Join by video system Dial 23097633797@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: 2309 763 3797