Events at this location

Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA’s knowledge & discovery tools to relate the most recent literature findings to your research. The demo session will mainly focus on biological interpretation of expression data but will also cover multi-omics analysis including variant data and phosphoproteomics.

Getting Started: fundamentals of IPA; overview of key features; search & pathway building; advanced search; building & editing pathways; using Build & Overlay tools. Dataset Analysis: data upload & analysis; interpretation of gene, transcript, protein & metabolite data; pathway analysis & canonical pathways; downstream effects & interpreting the heat map; causal regulators and their directional effect on genes, functions and diseases across multiple time points or doses; interpreting networks; comparison & multiple observations analysis; miRNA and isoform analysis; BioProfiler: define relationships between molecule activity and diseases/processes in the literature. Q & A session: A QIAGEN Ingenuity Field Applications Scientist will be available to answer individual questions or discuss specific projects.

In this session of the University of Alabama at Birmingham’s O’Neal Research Seminar Series, CBIIT’s Dr. Jill Barnholtz-Sloan will share how big data has provided insight on brain and central nervous system (CNS) tumors. Dr. Barnholtz-Sloan will particularly discuss:

• general cancer statistics vs. brain/CNS tumor statistics.
• statistics on brain tumor incidence (by age) and U.S. survival probabilities.
• general cause and risk factors for brain tumors, as well as environmental and genetic risk factors.
• the molecular basis of brain tumors.
• male and female sex differences as they relate to brain tumors (including treatment responses and adverse effects).

O’Neal Cancer Seminars are regular research seminars hosted by the O’Neal Comprehensive Cancer Center that feature a wide range of cancer-related topics.

Presenter:

Jill Barnholtz-Sloan, Ph.D.

Dr. Barnholtz-Sloan is the associate director for the Informatics and Data Science Program at NCI CBIIT and a senior investigator for NCI’s Division of Cancer Epidemiology and Genetics.

In this session of the University of Alabama at Birmingham’s Informatics Institute PowerTalk Seminar Series, CBIIT’s Dr. Jill Barnholtz-Sloan will emphasize how big data’s impact on cancer research has influenced cancer diagnosis, treatment choices, and prognosis.

Attend this seminar to hear Dr. Barnholtz-Sloan:

• touch on data-driven research limitations and how the NCI Cancer Research Data Commons (CRDC) has been designed as a data science platform to address said limitations.
• define the current CRDC repositories and highlight activities/functionalities pertaining to them.
• highlight how big data has successfully influenced research.

Presenter:

Jill Barnholtz-Sloan, Ph.D.

Dr. Barnholtz-Sloan is the associate director for the Informatics and Data Science Program at NCI CBIIT and a senior investigator for NCI’s Division of Cancer Epidemiology and Genetics.

We welcome members of the NIH to join us for a seminar event, covering multiplexed, direct digital gene expression profiling applications utilizing NanoString nCounter expression profiling technology as well as spatial biology discoveries generated using the GeoMx Digital Spatial Profiler and CosMx Spatial Molecular Imager. nCounter and GeoMx technologies are currently available for use on campus, and the NIH will be one of the first to adopt the CosMx technology.
Presented by Min Mo, PhD, Field Applications Scientist at NanoString Technologies and former Fellow at the NIH in Frederick.

Includes technology updates, including Immuno-Oncology, Oncology, Immunology, Metabolic Profiling content, the new CosMx Spatial Molecular Imager, and case study reviews.

Speaker:
Min Mo, PhD,
NanoString Field
Applications Scientist

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.

All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff to discuss the problem 1-on-1.  We’ll try to address simpler issues on the spot and follow up on more complex questions after the Zoom-In.

At the Zoom-In: You will initially join the  main lobby and triage area.  There, you can briefly describe your issue, and then will be invited to join a 1-on-1 breakout room with a staff member.

Once you are finished with your focused consultation you can return to the main meeting room if you have additional questions or topics to discuss.  Please

– mute when not speaking

– refrain from screen sharing until asked to do so in the breakout room

– screen share as you would in a public space with the understanding that other NIH HPC staff may join and view what you are sharing (i.e. look over your shoulder)

– be prepared to wait your turn if staff are already helping other users.

Please contact staff@hpc.nih.gov with any questions about the NIH HPC Systems

Meeting ID: 160 300 4510

Passcode: 908767

Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA’s knowledge & discovery tools to relate the most recent literature findings to your research. The demo session will mainly focus on biological interpretation of expression data but will also cover multi-omics analysis including variant data and phosphoproteomics.

Getting Started: fundamentals of IPA; overview of key features; search & pathway building; advanced search; building & editing pathways; using Build & Overlay tools. Dataset Analysis: data upload & analysis; interpretation of gene, transcript, protein & metabolite data; pathway analysis & canonical pathways; downstream effects & interpreting the heat map; causal regulators and their directional effect on genes, functions and diseases across multiple time points or doses; interpreting networks; comparison & multiple observations analysis; miRNA and isoform analysis; BioProfiler: define relationships between molecule activity and diseases/processes in the literature. Q & A session: A QIAGEN Ingenuity Field Applications Scientist will be available to answer individual questions or discuss specific projects.

In partnership with the National Cancer Institute (NCI) Cancer Moonshot^SM, the Society for Immunotherapy of Cancer (SITC) and the Big Data and Data Sharing Committee are excited to announce our second “SITC-NCI Computational Immuno-Oncology Webinar Series” throughout 2022.

These nine, hour-long webinars will feature a moderator and faculty speaker leading instruction on a range of topics that cover a host of computational challenges of analyzing and integrating diverse assay data across the spectrum of immuno-oncology. Meant for scientists early in their career or those who want to remain abreast of the latest technologies, the goal of this series is to help foster better communication concerning data science technologies and analyses between cancer immunotherapy researchers and clinicians in order to fuel translational immunotherapy research.

Join University of Colorado’s Dr. Arjun Krishnan as he discusses how machine learning and natural language processing can contribute to making public -omics data more accessible.

Dr. Krishnan will also present the recent work from his group, the Krishnan Lab, to address these two challenges that contribute to under-used data:

• Unstructured metadata: sample descriptions contain information about their source in the form of ambiguous plain text.
• Missing metadata: sample descriptions frequently miss key, basic pieces of information about their source.

The Krishnan Lab works in the areas of computational biology and biomedical data science at the University of Colorado Anschutz Medical Campus.

The Data Science Seminar Series presents talks from innovators in the cancer research and informatics communities both within and outside of NCI.

Speaker:

Arjun Krishnan, Ph.D.

Dr. Krishnan is an associate professor at the University of Colorado and a group leader at the Krishan Lab. He works on developing computational approaches to study the genetic basis of biomedical phenomena relevant to human health and disease. Dr. Krishnan is primarily interested in bridging the gap between large-scale genomic/clinical data and actionable biological insights using statistical and machine learning approaches.

DNASTAR offers software solutions for molecular biology, structural biology, and genomics. In this class, attendees will be presented with an overview of the applications within the Lasergene Suite. The instructor will use the latest software version and demonstration data to provide tutorials for various workflows, including: cloning and primer design, Sanger sequence alignment, protein structure analysis, and next generation sequence assembly and analysis. Individual meetings after the training session can be scheduled to provide help with specific projects. To schedule an individual meeting, please email the instructor.

Speaker:

Neil L. Kelleher, Ph.D.

Walter and Mary E. Glass Professor of Molecular Biosciences
Professor of Chemistry in the Weinberg College of Arts and Sciences
Professor of Medicine (Hematology & Oncology) in the Feinberg School of Medicine
Director, Chemistry of Life Processes Institute & Northwestern Proteomics
Northwestern University

Dr. Kelleher received his Ph.D. from Cornell University and completed his postdoctoral training from Harvard Medical School. He then joined the faculty at the University of Illinois at Urbana-Champaign where he established research program in proteomics. In 2010, he joined the faculty at the Northwestern University.
Dr. Kelleher’s Team is built around expertise in technology development for complex mixture analysis using Fourier-Transform Mass Spectrometry for targeted applications in proteomics and metabolomics. His lab leverages top-down proteomics, which is the analysis of intact proteins for precise localization of post-translational modifications to advance the understanding of chromatin and cancer biology.

Dr. Kelleher is a pioneer of the top-down approach and the Human Proteoform Project (HPfP), a global research initiative to weigh every protein in the human body, 250,000 proteoforms (specific molecular form of a gene product) in 4,000 different cell types. This will enable dramatic increases in the speed and efficiency by which investigators can identify higher-value protein-based markers of disease including cancer, and spur game-changing advances in biomedical research, drug development and human health.

Dr. Kelleher developed the ProSight PTM software platform that allows identification and characterization of both intact proteins and peptides. This is the only proteomics software that allows the user to search their tandem MS data against proteome warehouses containing the known biological complexity present in UniProt (a resource of protein sequence and functional information).

Dr. Kelleher has co-authored over 300 articles in peer-reviewed journals including Science, Nature Methods, PNAS, Cancer Discovery, Mol. Cell. Proteomics, J. Cell Biol., and J Biol Chem.

Dr. Kelleher received many awards including Biermann Medal (American Society for Mass Spectrometry), Pfizer Award in Enzyme Chemistry (American Chemical Society, DBC), Presidential Early Career Award in Science and Engineering, National Science Foundation CAREER Award, Burroughs Wellcome Award in the Pharmacological Sciences, and American Society of Mass Spectrometry Research Award.

Meeting number (access code): 2309 555 7341
Meeting password: 4qrPy7wAm*2

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.

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.

We invite you to join us for the Neuro-Oncology Visiting Scholar Lecture scheduled for next week by Eytan Ruppin, M.D. Ph.D:

Next Generation Transcriptomics-based Precision Oncology

Eytan Ruppin received his M.D. and Ph.D. (Computer Science) from Tel-Aviv University where he has served as a professor of Computer Science & Medicine since 1995, conducting computational multi-disciplinary research spanning a wide variety of topics, including neuroscience, evolutionary computation, natural language processing, machine learning and systems biology. He joined the University of Maryland in July 2014 as a Computer Science professor and director of its center for bioinformatics and computational biology (CBCB), before joining the NCI in January 2018, where he co-founded and is Chief of its Cancer Data Science Lab. Studying cancer metabolism, his lab has been involved in identifying the first metabolic synthetic lethal cancer drug target and in the discovery of the link between urea cycle dysregulation and response to immunotherapy, among others. His recent research focus is on developing new approaches for transcriptomics-based precision oncology, which are now moving into clinical prospective testing. Dr. Ruppin is a member of the editorial board of Molecular Systems Biology and a fellow of the International Society for Computational Biology (ISCB). He has recently received the NCI Director award (2022) and the Delano Award for Computational Biosciences (2023) for his contributions for advancing transcriptomics-based precision oncology. Dr. Ruppin is also a co-founder of a few startup companies involved in precision medicine and cancer drug discovery.

Meeting number:2319 071 3551

The size and complexity of data necessary to derive meaningful scientific and clinical insights are advancing at an unprecedented rate.  At the core of this complexity is an ever-expanding array of technologies, instrumentation, and analytic workflows, the outputs of which must be brought together and co-analyzed.  Aggregating and harmonizing disparate data types into a functional model that is useable by diverse subject matter experts and supports low code/no code multimodal analysis is key to harnessing this complexity. The NIH Integrated Data Analysis Platform (NIDAP) aims to accelerate basic, translational, and clinical research by addressing this challenge.  In this talk, Dr. Chitwood will describe how the Palantir Foundry platform serves as the NIDAP hub and demonstrate how interoperability with numerous NCI data systems and computational resources supports the development of enterprise-wide and group-specific capabilities custom data configurations that enable users to access and analyze multimodal data rapidly.

Speaker:

Dr. Patrick Chitwood from Palantir Technologies. Dr. Chitwood will describe how the Palantir Foundry platform serves as the NIDAP hub and demonstrate how interoperability with numerous NCI data systems and computational resources supports the development of both enterprise-wide capabilities and group-specific custom data configurations which enable users to rapidly access and analyze multimodal data.

An overview of the JPSurv webtool to analyze survival data by year of diagnosis and estimate calendar years when changes in survival have occurred. Covers topics such as the JPsurv model, trend survival measures in the survival and probability of death scales, and applications. A live demonstration is provided using the JPSurv webtool application.

Speakers:

  Angela Mariotto, PhD

Chief, Data Analytics Branch

National Cancer Institute

  Theresa Devasia, PhD

Mathematical Statistician, Data Analytics Branch

National Cancer Institute

The goal of this webinar series is to enhance understanding within the NCI and the research community of opportunities for targeting fusion oncoproteins through emerging chemoproteomic methods. This presentation will focus on computational tools and strategies for rational degrader design to target fusion oncoproteins.

Speaker:

Huan Rui, Ph.D.

Senior Scientist, Computational Chemistry

Dr. Rui is a senior scientist at Amgen specializing in computational chemistry. Dr. Rui currently leads the computational efforts for the induced proximity platform at Amgen, where her research interests include using molecular dynamics and free energy simulations to study biological systems and applying machine-learning techniques to augment physics-based modeling.

We invite you to join in an inspiring conversation between a thought leader from the Cancer Moonshot and creative visualization expert and opportunity to engage with new colleagues from other fields in a discussion on the frontiers of cancer data visualization.  Additional information can be found at the DataViz + Cancer website.

Speakers:

  Benjamin Stokes, Ph.D.

Associate Professor
American University
Civic media scholar, neighborhood game designer
Director of The Playful City Lab
Chief Advisor of interactive media for the Peabody Awards

  Eric Holland, M.D., Ph.D.

Senior VP and Director
Human Biology Division at Fred Hutch
Physician-scientist who develops multidisciplinary approaches to address the molecular basis of brain tumors and new approaches to their treatment

This is an introductory two-part course that focuses on data and project management using R and RStudio. RStudio makes it possible to work on a complete research project in a more efficient, integrated, and organized manner. RStudio also connects with Git and Github, and learners will have a chance to experiment with this integration and understand its advantages for collaboration and version control. Some familiarity or experience in R and RStudio is recommended but not required. Participants are encouraged to install R, RStudio and create a GitHub account before the course so that they can follow along with the instructor. Participants must register separately for Part 2 of this class.

This is an introductory two-part course that focuses on data and project management using R and RStudio. RStudio makes it possible to work on a complete research project in a more efficient, integrated, and organized manner. Participants will learn how to create reproducible documents that combine code, analysis, and narrative. This intermediate-level course is designed to be relevant to students from different disciplines. Some familiarity or experience in R and RStudio is recommended but not required. Participants are encouraged to install R, RStudio and create a GitHub account before the course so that they can follow along with the instructor. Participants must register separately for Part 1 of this class.

The call for better data and evidence for decision-making has become very real as evidenced by the Federal Data Strategy, as well as the passage of both the Foundations of  Evidence-based Policymaking Act (Evidence Act) and the CHIPS+ Act.  The challenge to be addressed is finding out not just what data are produced but how they are used – in essence, to build an Amazon.com for data -so that both governments and researchers can quickly find the data and evidence they need. To paraphrase Lee Platt’s aphorism about HP – “If researchers knew what researchers know, they would be three times more productive”

This talk will provide an overview of a massive effort over the past five years which has been focused on finding out how data are being used, to answer what questions, and find out who are the experts, by mining text documents that are hidden in plain sight – in the text of scientific publications, government reports and public documents.

Just as with Amazon, the results are enormously powerful.  The pilot, which is sponsored by agencies such as  NSF’s National Center for Science and Engineering Statistics (NCSES) and the Department of Education’s National Center for Education Statistics (NCES) – has generated a prototype API and a dashboard that can be used – so that, for example, agencies can document dataset use for Congress and the public, program managers can identify investment opportunities rapidly and researchers can more easily build on existing knowledge rather than redoing things from scratch.

Speaker:

Dr. Julia Lane is a Professor at the NYU Wagner Graduate School of Public Service.  She is founder or co-founder of many data initiatives that have served the public good, including the Longitudinal-Employer Household Dynamics Program at the Census Bureau; the Star Metrics/UMETRICS program that led to the establishment of the Institute for Research on Innovation and Science at the University of Michigan; the New Zealand Integrated Data Infrastructure, which holds data from across various sectors; the NORC Data Enclave supporting research access to confidential data; the Patentsview project to increase the usability of patent data; and the Coleridge Initiative to use data more effectively in government decision-making. She currently serves on the Advisory Committee on Data for Evidence Building and the National AI Research Resources Task Force. Her most recent paper(link is external) was published in Nature, and used UMETRICS data.

October’s Containers and Workflow Interest Group (CWIG) webinar series.

Presenters:
Mike Callaghan, Cloud Customer Experience Account Lead, Google
Dave Belardo, Customer Engineer, Google
Mike Callaghan, Customer Engineer, Deloitte Consulting

Meeting number (access code): 2318 931 3839

Meeting password: YQbjmZ3U32@

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.

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.

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.

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 seminar series showcases research from different Cancer Moonshot initiatives that support the 10 recommendations of the Blue Ribbon Panel Report. These presentations will inform the community about the progress of Cancer Moonshot–funded projects, provide outreach related to Cancer Moonshot projects, enhance discussions and collaborations related to Cancer Moonshot research, and promote the sharing of data from Cancer Moonshot initiatives.

Presenter:

Speaker: Zeynep Gümüş Ph.D., Icahn School of Medicine at Mt. Sinai

This seminar series showcases research from different Cancer Moonshot initiatives that support the 10 recommendations of the Blue Ribbon Panel Report. These presentations will inform the community about the progress of Cancer Moonshot–funded projects, provide outreach related to Cancer Moonshot projects, enhance discussions and collaborations related to Cancer Moonshot research, and promote the sharing of data from Cancer Moonshot initiatives.

Speaker: Heidi Hanson, Ph.D., Oak Ridge National Laboratory