Events at this location

The NIAID-funded International epidemiology Databases to Evaluate AIDS (IeDEA) consortium is a global HIV observational research network with seven geographic regions and over 350 participating HIV care and treatment clinics. The data scientists and data managers of IeDEA established the Harmonist Project to develop informatics-driven methodologies, data standards, and tools to catalyze IeDEA data sharing and scientific collaboration. In this presentation, we will share the process and outcomes, demos of generalizable tools built on REDCap and R Shiny, and potential uses for other large observational research networks.

Speakers:

• Stephany Duda, Ph.D.

Associate Professor of Biomedical Informatics

Vanderbilt University Medical Center

• Judy Lewis, Ph.D.

Lead Developer, Harmonist Data Toolkit

Adjunct Assistant Professor of Biomedical Engineering

Vanderbilt University Medical Center

While genomics initiatives have generated large amounts of data, gaining clinically relevant insights from this data is challenging. This symposium will showcase multi-omics approaches to functional characterization of candidate cancer drug targets. Researchers will discuss how deep data analyses and high-throughput approaches are reshaping our understanding of cancer biology and impacting clinical oncology.

The Symposium is organized by Cell Press (Cell and Cancer Cell) and the Cancer Target Discovery and Development (CTD²) Network—an NCI initiative for bridging the knowledge gap between large-scale genomic datasets and the underlying etiology of cancer development, progression, and metastasis. There is a government registration rate for NIH/NCI staff and content will be available online both live and via on-demand archive.

Dr. Daniela S. Gerhard, who sadly and suddenly passed away this summer, was the premier architect of this symposium and on a grander scale, the CTD² Network. In recognition and appreciation of Dr. Gerhard’s immense efforts and dedication to many of NCI’s genomics initiatives, the Symposium will feature a memorial session delivered by Drs. Louis Staudt and Stuart Schreiber.

As a continuation of the NHGRI seminar series focused on the “Bold Predictions for Human Genomics by 2030,” please join us for the eighth seminar in the series on Monday, October 4 from 3:00 – 4:30 pm ET. This seminar will focus on Bold Prediction #8: A person’s complete genome sequence along with informative annotations can be securely and readily accessible on their smartphone. Dr. Gillian Hooker of Concert Genetics and Dr. Michael Schatz of Johns Hopkins University will use this prediction as an aspirational theme for their talks, highlighting their own work in the context of that theme and speculating about the next decade in their areas. The talks will be followed by a moderated question-and-answer session. All seminars will be open to the public and recorded for posting on GenomeTV.

This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them.

Feel free to install the IGV browser in advance from here – https://software.broadinstitute.org/software/igv/download(link is external) (the UCSV browser is web browser based).

Dr. Pjotr Prins and Arun Isaac will present their Concise Common Workflow Language (CCWL). CCWL makes code reusable and adaptable across a variety of software and hardware environment while following FAIR+ (Findable, Accessible, Interoperable, Reusable, and Computable) principles. They will also discuss their prototype COVID-19 cloud setup, with a hands-on demonstration of the universal software deployment system Guix, part of the open-source operating system GNU.

CWL is of particular interest to cancer researchers because it provides a standardized machine-readable semantics model for running workflows on virtual environments. Attendees will learn how CCWL, as a CWL compiler, works with a package manager like GNU Guix to improve reproducibility and validation of bioinformatics workflows.

This webinar is part of the monthly Containers and Workflow Interest Group (CWIG) webinar series. CWIG brings together data scientists, bioinformaticians, computer scientists, and researchers to learn more about cloud computing and container technologies, workflows, and pipelines that could drive cancer data science.

The webinar series features a variety of presenters from across NIH, industry, and academia. Though cancer research is the focus of the series, unrelated data science and cloud computing topics are still welcome. In the last year, the CWIG webinar speakers have discussed:

• NIH cloud programs like the NCI Cloud Resources and NIH STRIDES.
• commercial cloud platforms for biomedical data storage and computing.
• pipelines and tools for deep learning and various omics analysis.

Speakers:

Pjotr Prins, Ph.D.

Dr. Prins is a bioinformatician at-large and assistant (coding) professor at the Department of Genetics, Genomics, and Informatics at the University of Tennessee Health Science Center. He is also the director of Genenetwork.org and writes critical software for genetics and pangenomics

Arun Isaac

Mr. Isaac is a doctoral student at the Department of Computational and Data Sciences, Indian Institute of Science. He regularly contributes to GNU Guix and is the author of guile-email, an email parser for Guile.

This course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies.

Speaker: Tali Mazor, Ph.D., Scientist, Knowledge Systems Group, Dana-Farber Cancer Institute

Tali Mazor, Ph.D., of the Dana-Farber Cancer Institute will discuss the functions and features of the cBioPortal for Cancer Genomics. This open-source software platform offers an interactive tool for exploring large-scale cancer genomics data sets through a user-friendly interface. Using cBioPortal, researchers can integrate genomic and clinical data and have access to a suite of visualization and analysis options, including cohort/patient-level visualization, mutation visualization, survival analysis, and alteration enrichment analysis.

The cBioPortal for Cancer Genomics is an open-source software platform that enables interactive, exploratory analysis of large-scale cancer genomics data sets with a user-friendly interface. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including cohort and patient-level visualization, mutation visualization, survival analysis and alteration enrichment analysis. Features of the portal include OncoPrints, a compact graphical representation of alterations in multiple genes across a cohort, mutational diagrams that show locations and frequencies of mutations in a single gene, subgroup definition and comparison, Kaplan-Meier survival curves, plots that allow the visualization of correlation between different data types (e.g. the correlation between DNA copy number and mRNA expression for a gene of interest), among others. To facilitate interpretation, the cBioPortal also integrates data from several leading knowledgebases and computational resources.

This webinar will introduce basic exploratory, analytic and visualization features of the cBioPortal, as well as several advanced features, including:
– Exploring data with study view
– Running and modifying queries
– OncoPrints, mutation diagrams, survival analysis, enrichment analysis
– Resources integrated into the cBioPortal
– Working with different data types, including mutations, copy number, mRNA expression and protein levels
– Onco Query Language
– Resources to get additional help

MATLAB provides two-way integration with many programming languages such as Python, which allows for greater collaboration between investigators. This webinar will cover how to call MATLAB from Python and how to call Python libraries from MATLAB. Attendees will learn how to use MATLAB’s Python integration to improve the compatibility and usability of their code. This webinar will be helpful for participants who have collaborators using Python with MATLAB, or for attendees who want to integrate MATLAB’s capabilities in a Python program. This session is for beginners; no software installation required.

Register for the 2021 Clinical Proteomics Tumor Analysis Consortium (CPTAC) Virtual Scientific Symposium to hear CPTAC investigators share their latest discoveries in the field of cancer proteogenomics, cancer research, and data analysis tools.

In addition to scientific talks on tumor biology and translational studies, there will be demonstrations of these CPTAC-developed data analysis tools:

• FragPipe Computational Pipeline (for comprehensive analysis of mass spectrometry-based proteomics data)
• ProTrack-Kinase Activity Portal (for querying/visualizing/downloading kinase activity scores of multiple cancer types)
• BayesDebulk (analysis method for inferring cell-type composition in bulk tissue using proteogenomics data)

The CPTAC program is a national effort coordinated through NCI’s Office of Cancer Clinical Proteomics Research to accelerate the understanding of cancer biology through the marriage of large-scale proteome and genome analysis, or proteogenomics. Proteogenomics allows CPTAC researchers to paint a more detailed picture of tumor carcinogenesis and progression, micro-environments, and immune landscapes. This information can then be leveraged therapeutically to improve patient care.

Please plan to attend the Physician-Scientist Early Investigator Program (PEIP) seminar by:

Nicholas Rydzewski, M.D.

University of Wisconsin

Dr. Rydzewski is currently Chief Resident in the Radiation Oncology Residency Program at the University of Wisconsin. He is deeply committed to advancing the use of artificial intelligence and liquid biopsies to better inform precision cancer therapy approaches for patients before and following radiation. This is a significant and timely arena for translational research with great promise to advance the discipline of Radiation Oncology over the years to come.

Presenter:

Neville Sanjana, Ph.D.
Assistant Professor of Biology
Neuroscience and Physiology
NYU School of Medicine and Core Faculty Member
New York Genome Center

Forward genetic screens using CRISPR (clustered regularly interspaced short palindromic repeats)–associated nucleases like Cas9 are a powerful tool to pinpoint genes involved in disease. Initial screens capitalized on genome-scale libraries to perturb nearly all protein-coding genes in the human genome to examine therapeutic resistance and gene essentiality in cancer cell lines.

Dr. Sanjana’s lab has further developed the CRISPR screening toolbox in several new directions, including in vivo screens to understand drivers of lung metastasis, saturation mutagenesis of noncoding regions to identify functional elements that drive chemotherapeutic resistance in melanoma, and screens that dissect complex interactions between tumor cells and primary immune cells in cancer immunotherapy. Recently, they developed the first RNA-targeting CRISPR screens in melanoma and combined CRISPR perturbations of chromatin modifiers with single-cell measurements of chromatin accessibility. Taken together, these new frontiers expand the potential of CRISPR screens for fundamental genomic discovery, gene regulation, and therapeutic development to overcome drug resistance.

The ISB Cancer Gateway in the Cloud(link is external) (ISB-CGC) offers multiple avenues for accessing and analyzing large-scale cancer datasets, including TCGA, TARGET, CPTAC and important references such as GENCODE and COSMIC. ISB-CGC users can process petabytes of data using complex workflows written in the language of their choice (including but not limited to CWL, WDL, Snakemake, Nextflow, etc). They can develop new analyses using SQL, Python, and R to mine data including gene expression, protein abundance, and somatic mutations in easily accessible and queryable tables. Updated interactive web tools at isb-cgc.org (link is external)allow cohort creation, data discovery, and exploration. In our cloud computing session, we will demonstrate common bioinformatic workflows using both Python and R while integrating various omic data types such as gene mutations, copy number, gene expression, methylation, and proteomics. We will show how this can be interactively and iteratively performed in the cloud using the ISB-CGC platform.  Attendees will receive hands-on training on optimizing analyses using “burstable” cloud tools, which enables the user to rapidly combine and interrogate their cancer datasets with those available at the Cancer Research Data Commons.

This course will introduce you to Google Cloud’s big data and machine learning functions. You’ll begin with a quick overview of Google Cloud and then dive deeper into its data processing capabilities.

Prerequisites

Objectives

Audience

Course Outline

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.

Presenter:

Trevor Bedford, Ph.D.
Associate Professor
Vaccine and Infectious Disease Division
Human Biology Division
Herbold Computational Biology Program
Fred Hutchinson Cancer Research Center

Genomic epidemiology has enabled critical insights during the COVID-19 pandemic.  At the forefront of these insights has been SARS-CoV-2’s remarkable potential for adaptive evolution.  Dr. Bedford will discuss the evolutionary dynamics of SARS-CoV-2 with a focus on the emergence of variant of concern and variant of interest viruses.  He will characterize mutational patterns in these variant viruses and chart their spread across geographies.  He also will provide a larger perspective on genomic surveillance, projected future viral circulation patterns, and strategies for ongoing pandemic management.

Dr. Bedford is an associate professor in the vaccine and infectious disease division, the human biology division, and the Herbold Computational Biology Program at Fred Hutch.  The Bedford lab studies the rapid spread and evolution of viruses, including those that cause COVID-19, influenza, Ebola, and Zika.  Bedford’s visual representations of viral family trees are used to show how the fate of dangerous outbreaks is often determined by the genetics of the infectious agent, human behavior, and geography.

Link now to https://videocast.nih.gov/ical.ics?live=43795 to add this to your Outlook calendar.  Continuing Medical Education (CME) credits will be available; the code will be announced at the start of the lecture.

Register Now

Faculty: Joao Xavier, PhD – Memorial Sloan Kettering Cancer Center; NCI CSBC

Moderator: Matthew J. Reilley, MD – University of Virginia

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member

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.

Learn how to create and deploy containerized applications on Google Kubernetes Engine (GKE). This course features a combination of lectures, demos, and hands-on labs to help you explore and deploy solution elements —including infrastructure components like pods, and containers.

Prerequisites

Objectives

Audience

Course Outline

Wrapping up its final webinar of 2021, the NCI Cancer Research Data Commons (CRDC) Cancer Genomics Cloud (CGC) welcomes Cold Spring Harbor Laboratory fellow Dr. Pascal Belleau. He will share his findings from recent work on the CGC, typing the Human Leukocyte Antigen (HLA) class II complex in 11,000 patient samples from The Cancer Genome Atlas (TCGA) data set.

Through his study, Dr. Belleau investigated the presence and patterns of the HLA class II complex across 33 tumor types. The HLA class II complex is a molecular component linked to the body’s immune response. Understanding how this molecular complex differs across cancer types can give us insight into how it may mediate the immune system’s response to cancer.

As one of the three Cloud Resources within the NCI CRDC, the Seven Bridges’ CGC provides researchers access to a wide variety of data sets, a catalog of tools to analyze and visualize the data directly from the browser, and scalable computational resources to perform large scale-analysis in the cloud.

Speaker:

Pascal Belleau, Ph.D.

Dr. Belleau is a computational biology postdoctoral fellow at the Quantitative Biology Department of Cold Spring Harbor Laboratory.

The Cancer Genomics Cloud (CGC)(link is external), powered by Seven Bridges, is an NCI-funded resource that provides a unified platform for cancer data analysis by co-localizing three components within the cloud: 1) large cancer datasets from TCGA, CPTAC and several others; 2) more than 500 bioinformatics tools and best-practice workflows for analyzing multi-omics data; and 3) the computational capabilities to do large-scale analyses. This hands-on session of CGC allows the participants to browse, query, and filter datasets of interest and bring their own data for collaborative analysis. The CGC also provides the flexibility to use private tools and the ability to complete reproducible and interactive data analyses (e.g., RStudio, Jupyter notebook). Currently, data analysis is supported in both Google and Amazon cloud environments. Altogether, the CGC is a network of findable, accessible, interoperable, and reusable (FAIR) datasets, workflows, and services which make cancer data analysis faster and more easily available for all.

Please join us for a discussion followed by question and answer session on the topic of “R or Python, Which Should I Learn?”.

We will be joined by Andrew Weisman of the Frederick National Laboratory for Cancer Research, Strategic Data Science Initiative (FNLCR, SDSI) and new BTEP Trainer Joe Wu to discuss the utility of R and Python for bioinformatics and data science analyses. You will gain insight into which of these 2 programming languages might be more appropriate for the types of analyses you want to perform. For example:

• Are there bioinformatics or genomics specialized tools in R? (Bioconductor). In Python? (BioPython)
• What kinds of data visualization can I do in R and how do I do them?
• Should I use Python for Machine Learning?
• Can I run Python and R on my local machine?
• Should I use Python or R on NIH High Performance Unix Cluster Biowulf?
• What are Integrated Data Environments (IDE) for R and Python? Why should I use them and how do I access them?
• How do I upload my data into Python? Into R?
• Where should I go next to learn more about R and Python?

FireCloud (Powered by Terra(link is external)) is a data analysis platform that contains a system of workspace functionalities centered on data sharing and analysis. Researchers can use FireCloud to perform a variety of analyses through two main mechanisms: batch workflow execution and interactive analysis (including data visualization). Workflow execution is achieved through a workflow management system called Cromwell, which is designed to be highly portable, capable of connecting to multiple computing platforms, and horizontally scalable. Interactive analysis is achieved through Jupyter notebooks or RStudio, which can be associated with a range of configurable runtime environments and incorporate various kernels and packages to support exploratory analyses of arbitrary scale and complexity.

In this hands-on session, we will introduce FireCloud to all attendees as a part of the NCI Cloud Resources. The participants will learn how to access key data sets such as TCGA and CPTAC and bring this data (or their own data) into a secure FireCloud workspace. Participants will also learn how to configure and launch workflows from their workspace and perform interactive analysis with applications such as Jupyter Notebooks, RStudio, and more. This session will also cover how billing operates in FireCloud and how to securely share the personal FireCloud workspace with collaborators.

This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. 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 program 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, participants should be able to: define R data frames; characterize how to inspect data frames; list the major methods for describing the content and structure of data frames; illustrate how to index and subset a data frame; describe how to use comparison operators on a data frame; discuss R factors; describe how to convert R factors; describe how to rename factors; discuss options for dealing with missing data in R; and describe how to save data in R.

Participants are encouraged to install R(link is external) and RStudio(link is external) before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class.

Register Now

Faculty: Joao Xavier, PhD – Memorial Sloan Kettering Cancer Center; NCI CSBC

Moderator: Matthew J. Reilley, MD – University of Virginia

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member

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.

Register Now

Faculty: Yi Xing, PhD – Children’s Hospital of Pennsylvania/University of Pennsylvania; NCI Cancer Moonshot IOTN

Moderator: Kellie N. Smith, PhD – Johns Hopkins School of Medicine

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member