Classes & Events

The ISB-CGC (Cancer Gateway in the Cloud) hosts data from programs such as The Cancer Genome Atlas Program (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) in Google BigQuery where it can be quickly analyzed using simple SQL or loaded into R and Python. As a cloud initiative and part of the Cancer Research Data Commons ISB-CGC provides many resources and funding to start processing and analyzing your own data in the cloud.

Please join us on Wednesday, May 22, 2024, when Dr. Elham Azizi from Columbia University will present "Machine Learning Dynamics in the Tumor Microenvironment." The presentation starts at 11:00 a.m. ET and ends at noon.
 
Dr. Azizi is an Assistant Professor of Cancer Data Research and Assistant Professor of Biomedical Engineering. She is also affiliated with the Department of Computer Science, Data Science Institute, and the Herbert Irving Comprehensive Cancer Center.

This seminar provides an overview of differential expression testing with Seurat. Topics to be covered include preparing data for differential gene expression, differential gene analysis between specific groups, differential gene analysis for cluster classification, SingleR for cell type annotation, and visualizing genes of interest.  

 

 

Are you attending the 2024 AMIA Clinical Informatics Conference? Join NCI Fellow, Austin Fitts, as he presents on the National Childhood Cancer Registry (NCCR) during the May 22 afternoon sessions. The NCCR links cancer registry data with harmonized real-world data for population-level research in childhood cancer. He will also share how NCCR’s harmonization process allows for more longitudinal studies and can serve as a model for similar data harmonization initiatives. There are future plans to publish the NCCR data model and make an initial harmonized data set available to the cancer research community through the upcoming NCCR Data Platform.

Session Title: Advancing the Usability of Healthcare Data

Austin Fitts, Pharm.D., is a post-doctoral fellow at NCI’s Surveillance Research Program. He completed his Doctor of Pharmacy degree from University of Mississippi School of Pharmacy in 2021. Dr. Fitts completed residencies in hospital pharmacy at North Mississippi Medical Center and pharmacy informatics at Vanderbilt University Medical Center. His current professional interests include pharmacy informatics, pediatric oncology, and pharmacoepidemiology.

Versioning enables researchers to track changes in coding projects. This Coding Club introduces Git (https://git-scm.com), an open-source software used to perform versioning on a personal computer. At the end of this class, participants will:

• Understand the importance of versioning
• Describe Git
• Know how to access Git
  • Be aware of resources that helps with Git installation on personal computer
  • Be aware of the availability of Git on Biowulf, the NIH high performance computing system
• Define repository
• Know the steps involved in the versioning process including
  • Initiating a new repository
  • Understanding the difference between tracked and untracked files
  • Excluding files from being tracked
  • Staging files with changes
  • Commiting changes and writing commit messages
  • Viewing commit logs
• Compare between versions of code
• Revert to a previous version of code

Meeting link: https://cbiit.webex.com/cbiit/j.php?MTID=m8d56b3aff91ddd2e6df839d05dda6a8f   Meeting number: 2319 013 9531 Password: dnAnqfP$642 Join by video system Dial 23190139531@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 013 9531

During this virtual conversation of the Cancer Moonshot Seminar Series, Dr. Kai Tan, an investigator with the Human Tumor Atlas Network, and Liz Salmi, a co-investigator and patient advocate with the Participant Engagement and Cancer Genome Sequencing Network, will discuss how Cancer Moonshot initiatives are advancing data sharing in a session moderated by Dr. Emily Boja, a branch chief at NCI, who provides programmatic leadership and support for data sharing.

Additional information and registration can be found at the Cancer Moonshot Seminar Series Registration Website.

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  

The symposium's goals are to explore the integration of AI in understanding and managing immunological data, foster a paradigm shift in how immunologists leverage AI to propel their research forward, and inform NIAID about existing AI resources, needs, and future directions to support DAIT. The symposium will:

• Present stimulating use cases covering AI for immunology, e.g., concrete examples where AI has already made significant contributions to immunology
• Identify near-term and long-term challenges and barriers, e.g., address current limitations and challenges facing the integration of AI in immunology
• Discuss the scientific and clinical opportunities empowered by the AI revolution, e.g., how it could revolutionize our understanding of the immune system, lead to groundbreaking treatments, and influence public health policy. 

This is a hybrid meeting where attendees can choose to attend in-person or via Zoom Government.

Speakers and Moderators who are part of this program are expected to attend in person.

In-person registration is required by Tuesday, May 21, 2024

https://web.cvent.com/event/b1808ba5-fb93-4bf9-a253-dc63938869a9/summary

For programmatic questions, please contact dait_ai_workshop@mail.nih.gov.

For meeting logistical questions, please contact Heather Leonard, Lumina Corps, at EventsNIAID@luminacorps.com.

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.

In this session, we will explore how machine learning can be used to analyze Whole Slide pathological Images (WSIs). We will showcase how to utilize the Frederick Research Computing Environment (FRCE) to speed up processing using GPUs. Having a foundational understanding of machine learning or deep learning could be advantageous. This session will be recorded, and materials will be posted on the ABCS training site and will also be shared with attendees a few days after the event.

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

This class provides an overview of options for customizing a ggplot graph. This class will focus on methods for creating small multiples, options for customizing a graph, and how to apply ggplot themes. You must have taken Data Visualization in R: ggplot class to be successful in this class.

By the end of this class, participants should be able to describe options for time series data, create a line plot in ggplot, learn how to facet a plot, demonstrate options for customizing the title and axis, and apply different ggplot themes.

Dear Colleagues,
 
As machine learning permeates across biomedical research, achieving optimal accuracy demands more than just model deployment. 
Join us for a webinar where we explore post-processing techniques designed to elevate the accuracy and efficiency of prediction models. Using interactive tools in MATLAB, we will evaluate machine learning models, refine predictions, and discuss how to apply these techniques to your work.

Key Takeaways:

• Gain a deeper understanding of the benefits of post-processing in optimizing your work
• Implement post-processing techniques to refine and enhance predictions
• Use interactive tools to streamline workflows and reduce manual coding time

For questions, contact Daoud Meerzaman or Kayla Strauss.

This talk will cover a scRNA-seq workflow available to NCI researchers on NIDAP. NIDAP, the NIH Integrated Data Analysis Platform, is a cloud-based and collaborative data aggregation and analysis platform that hosts user-friendly bioinformatics workflows. This platform allows researchers to use many of the open-source tools discussed in this seminar series without necessitating coding experience. This seminar will focus on the capabilities of this workflow for QC, annotation, and visualization of single-cell RNA-seq data.

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. 

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 training will introduce ChIP sequencing data analysis followed by a tutorial showing ChIP-seq analysis workflow.  This workshop will be taught by NCI staff and is open to NIH and HHS staff.

This class is a mixture of lecture and hands-on exercises. By the end of this class, students will be able to: independently run basic ChIP-seq analysis for peak calling, run quality control on ChIP-seq data, map raw reads to a reference genome, generate alignment statistics and check mapping quality, call peaks using MACS, annotate peaks, and visualize the enriched regions.

The CCR Genomics Core Facility is pleased to host a virtual technology workshop with EpiCypher on CUT&RUN library prep/sequencing

Presentation overview: The location of histone post-translational modifications and chromatin-associated proteins within the genome is an important consideration for many cancer researchers. Today, there are many assays available to map how this landscape shifts in disease states or in response to treatment—but which is the best one for your lab? In this seminar, we will explore CUT&RUN, a revolutionary epigenomic mapping tool that is quickly replacing ChIP-Seq for understanding the role of the epigenome in cancer research. Whether you’re a current CUT&RUN researcher looking to improve your experimental outcomes, a ChIP-Seq expert interested in new technologies, or a new user curious about how CUT&RUN can be used to profile your favorite epigenetic targets, this webinar will set you on the path to success!

For questions about this seminar please Liz Conner, CCR Genomics Core

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  

This webinar introduces SimBiology as a modeling environment for mechanistic pharmacokinetic (PK), pharmacodynamic (PD), and quantitative systems pharmacology (QSP) modeling and simulation. Participants will learn how to use the SimBiology Model Builder app to build a mechanistic model and how to use the SimBiology Model Analyzer app to calibrate the model to experimental data, as well as perform model predictions. 

This is an introductory-level class taught by MathWorks. No installation of MATLAB is necessary. 

Large language models (LLMs) are artificial intelligence (AI) algorithms that employ deep learning and extensive data sets to create new content. LLMs offer many possible applications in the biomedical field, such as the development of chatbots for use by clinicians, patients, and researchers. Join this roundtable discussion to learn about current use cases of LLMs at NIH. The program will begin with brief presentations by our panelists, followed by an open discussion:

Alicia Lillich, NIH Library 
Introduction to Large Language Models (LLMs)

Trey Saddler, NIEHS
ToxPipe: Semi-Autonomous AI Integration of Diverse Toxicological Data Streams

Mike A. Nalls, Ph.D., NIA
LLMs to Accelerate Tedious Tasks in Research

Nathan Hotaling, Ph.D., NCATS
Applications of Retrieval Augmented Generative AI to Scientific Discovery, Scientific Management, and Code Development and Maintenance at NCATS

Nicole Sroka, NLM
NLM GenAI Pilot: Customer Response Case Study

Steevenson Nelson, Ph.D., OD
Trans IRP Contract Tool (Updates)

Nick Asendorf, Ph.D., NHLBI
NHLBI Chat

Macros are ways to use code to substitute in a value, and using macros makes a code in SAS easier to read and edit, less prone to errors, and allows it to run more efficiently. This 90-minute advanced class will provide an in-depth look at using and writing macros in SAS. Topics covered in this class include macro function, using SQL and Data Step to create macro variables, indirect references to macro variables, defining and calling a macro, macro variable scope, conditional processing, and iterative processing. 

Join us for an engaging training session where we will examine the similarities and differences between machine learning and statistical differential gene expression (DGE) analysis using Qlucore Omics Explorer. Qlucore Omics Explorer is a point-and-click package available to NCI CCR scientists that enables visualization-based analysis of multi-omics data including RNA-seq, scRNA, proteomics, metabolomics, as well as enabling the use of machine learning to obtain a deeper understanding of data.
 
At the end of this class, participants will
 
·      Have a deeper understanding of RNA-seq data analysis and understand how to leverage both machine learning and statistical methods to obtain more comprehensive insights.
·      Know the different gene selection methods used by machine learning and statistical DGE analysis.
·      Know how integrating machine learning with DGE analysis can provide additional insights and enhance your research findings.
·      Be able to describe steps for applying machine learning to enhance insight extraction from RNA-seq data.
 
Experience using Qlucore Omics Explorer is not needed to attend. Submit a ticket with service.cancer.gov to get this software installed on personal computer.

Meeting information:

https://cbiit.webex.com/cbiit/j.php?MTID=m11ad78fb6a8303d5d72cffe7c9abfb3a 
Meeting number:
2307 302 4819

Join by video system
Dial 23073024819@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: 2307 302 4819

Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. This class will demonstrate integrated development and learning (IDE) platforms for learning Python, the fundamentals of Python coding, and why it is advantageous to develop these skills.  The session will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab.  It will also provide an overview of programming constructs needed to learn Python. Finally, this class will demonstrate why these skills can boost productivity, rigor, and transparency in reporting research findings. 

What are common statistical analyses for continuous data? Can you check whether your continuous outcome is normally distributed? What are the methods when the data are not normal? How do you model the outcome with multiple predictors in regression?

This is a two-hour lecture intended for those doing basic data analysis using R. Basic R programming is a pre-requisite for this course, as is knowledge of basic statistical concepts, such as mean and p-value from statistical hypothesis testing.  This class will be taught by the Clinical Center's Biostatistics and Clinical Epidemiology Service (CC/BCES).

The learning outcomes include: 

• calculating and displaying descriptive statistics, such as center and spread of distribution and boxplots 
• recognizing common continuous probability density functions
• estimating mean and confidence intervals for the center of normally and non-normally distributed data 
• hypothesis testing for one-sample and two-sample 
• linear regression 
• the F-distribution and one-way ANOVA

R code snippets will be shared during the lecture and within lecture notes. The class will be recorded, so you can go back to the material as you begin to do your own modeling. During the class, time will be devoted to explaining the concepts, and code snippets and output and references will be provided for in-depth material. 

Preclass Requirements: You must take the basic R programming and statistical inference – Part I classes as pre-requisite through the NIH Library or have acquired the equivalent knowledge elsewhere prior to registering for this class.

Statistical Software: We will be using R and RStudio for our statistical analysis. R is open source and free. There are versions for Mac OSX, Windows, and Linux. You can download it from https://cran.r-project.org/. Additionally, we will be using RStudio as a graphical interface for R. RStudio is free for everyone to download at https://posit.co/download/rstudio-desktop/. See above for pre-requisites in R programming.

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  

This in-person hands-on workshop will introduce the Ingenuity Pathway Analysis (IPA), which is available to access from the NIH Library. IPA can be used identify biological relationships, mechanisms, pathways, functions, and diseases most relevant to experimental datasets.

Upon completion of this workshop, participants will  be to able compare different groups at different time points and treatments, perform Analysis Match to compare user data with public data sources, and generate IPA Networks using genes and diseases of interest. 

Session 1 (IPA): 10:00 AM to 12:00 PM

In this session, participants will learn about bioinformatics resources from the NIH Library and learn how to perform pathway analysis using IPA.

Lunch: 12:00 PM to 12:45 PM

Lunch on your own

Session 2 (IPA): 1:00 PM to 2:30 PM

In this session, participants will extend the learning from Session 1 and learn how to mine IPA database for novel discoveries.

Session 3 (CLC): 2:30 PM to 4:00 PM

In this session, participants will learn about CLC Genomics Workbench, including a live demo of the basic features and main functionalities.

Note on Technology

Participants are expected to bring their own laptops to this training. NIH Staff using an NIH-laptop can easily connect to the staff Wi-Fi. If participants are bringing a personal laptop, they are restricted to using the NIH Public Wi-Fi. 

Registrants will receive an email with information and instructions to install and verify access to IPA 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.

In this in-person session, participants will have an opportunity to discuss their own research and use of Qiagen products with Qiagen scientists.

Note on Technology

Participants are expected to bring their own laptops to this training. NIH Staff using an NIH-laptop can easily connect to the staff Wi-Fi. If participants are bringing a personal laptop, they are restricted to using the NIH Public Wi-Fi. 

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  

During this webinar, participants will enhance their technical skills and proficiency with MATLAB by navigating online MATLAB resources designed to augment the learning experience and problem-solving capabilities, including documentation, examples, and community forums. In addition, this webinar will also present a preview of upcoming webinars, featuring cutting-edge topics and expert insights. 

This is an introductory-level class taught by MathWorks. No installation of MATLAB is necessary. 

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

The goal of Artificial Intelligence Resource (AIR) is to make AI tools available to Center for Cancer Research (CCR) investigators. The strength of AI is that algorithms can be trained to seek specific information that may be scientifically or clinically important.  AIR will mainly focus on “Computer Vision” which analyzes medical images, such as radiologic, digital pathology, video/endoscopy, and optical imaging among others.  Examples of potential projects include developing better screening, detection methods or predictive markers, or improving procedures among many others. Both clinical and laboratory-based imaging projects will be considered. Please refer to our ongoing projects and prior publications for more information.

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