Events at this location

Abstract:
Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. In this talk, I will present a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we model the problem as a variant of connected set cover and obtain a subnetwork of associated genes using integer linear program (ILP) optimization. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with responses for many drugs. We show that the identified modules provide important insights into drug action and can also be leveraged to suggest drug combinations.

Bio:
Dr. Yoo-Ah Kim is a staff scientist in the National Center for Biotechnology Information at National Institutes of Health (NCBI/NLM/NIH). Her current research focuses on algorithmic approaches in cancer network biology. Before joining NIH in 2008, she received her PhD degree in Computer Science from the University of Maryland, College Park in 2005 and was with the CSE department at the University of Connecticut, working on combinatorial optimization and graph algorithms.

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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 and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class.

Presenter:

Gary Patti, Ph.D.
Departments of Chemistry, Genetics, and Medicine
Washington University in St. Louis

It is well established that the metabolism of cancer cells is reprogrammed to support the demands of rapid proliferation. However, a comprehensive map of metabolic adaptations that occur as a result of malignant transformation has yet to be achieved. This talk will focus on the application of mass spectrometry-based metabolomics to broaden our understanding of metabolic alterations in cancer, with the ultimate goal of identifying biochemical liabilities that can be exploited therapeutically. To increase insight, data from multiple experimental paradigms of metabolomics will be described in detail, including (i) global, untargeted profiling, (ii) isotope-tracer analysis, and (iii) dose-response metabolomics. Dr. Patti will dedicate particular attention to computational resources available for data processing, such as those supported by the NIH Metabolomics Common Fund. Dr. Patti will also review the workflow covering metabolic profiling to drug selection and target validation in an imals and discuss opportunities for polypharmacology.

Event contacts: Krista Zanetti, zanettik@mail.nih.gov and Catherine Yu, catherine.yu@nih.gov

Abstract:
Data sharing is essential for the acceleration of science, but privacy concerns need to be addressed before clinical data can be properly shared for research. I will briefly introduce the main issues in clinical data sharing, as perceived by researchers and patients, and describe how a combination of privacy technology (i.e., methods that make it difficult to identify a specific patient whose data are going to be shared) and policy can help strike a balance between data utility for researchers and privacy protection for the patient and healthcare institutions.

Speaker:

Lucila Ohno-Machado, MD, PhD, MBA
Professor of Medicine
Chair, Department of Biomedical Informatics
Associate Dean for Informatics and Technology
University of California San Diego

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Abstract:
The NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative allows NIH to explore the use of cloud environments to streamline NIH data use by partnering with commercial providers. NIH’s STRIDES Initiative provides cost-effective access to industry-leading partners to help advance biomedical research. These partnerships enable access to rich datasets and advanced computational infrastructure, tools, and services. The first of two STRIDES webinars, this meeting will focus on the NIH STRIDES Initiative as a whole. In the meeting we will provide an overview of the benefits of STRIDES, as well as how individuals and organizations can engage with STRIDES. We’ll also detail a few of STRIDES’ early successes. https://datascience.nih.gov/strides

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CBIIT provides two managed cloud environments for NCI. Cloud One is a managed Amazon Web Services platform and in full production with FISMA Moderate ATO. Cloud Two is a managed Google Cloud Platform with FISMA Moderate ATO in progress. It is expected to be ready in Spring 2021.

Attend the January NCI IT Engagement Seminar Series to learn more about these NCI-managed cloud environments.

During this presentation, CBIIT’s IT Engineering Program Lead Sue Pan will cover the following discussion points :
•Cloud computing compared with on-premises computing models: Differences and advantages
•Intended usage of Cloud One and Cloud Two
•NIH STRIDES Initiative
•NCI IT cloud security models
•NCI IT cloud computing support services

Thank you,
Center for Biomedical Informatics and Information Technology (CBIIT)
National Cancer Institute

Please plan to attend the Earl Stadtman Investigator Program search seminar by:
Mikhail Kolmogorov, Ph.D.
University of California, San Diego

Dr. Kolmogorov’s research focus is bioinformatics. Particularly, he is interested in algorithms for genome assembly using long reads, which enable high-quality reconstruction of the human genome sequence. He also works on tools for comparative genomics and computational proteomics.
This seminar will be available via ZoomGov. See below for information on the ZoomGov session. For additional information on this seminar, please contact Nadia Nimley on 240.858.3612.

To view the schedule for all upcoming Earl Stadtman Investigator Program seminars, please visit: https://ccrod.cancer.gov/confluence/display/NIHStadt/

The Earl Stadtman Investigator search is a trans-NIH effort to attract a diverse group of talented early-career scientists pursuing interests across the biomedical research spectrum. Additional information on the program can be found at: https://irp.nih.gov/careers/trans-nih-scientific-recruitments/stadtman-tenure-track-investigators?cid=eb_govdel

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Speaker:
Noémie Elhadad, Ph.D.
Columbia University

In this talk, Dr. Elhadad will discuss how artificial intelligence and informatics improve patient-centered healthcare. She will show how patient-record summation tools can help clinicians make sense of seemingly overwhelming amounts of patient data at the point of care, and how “mHealth” tools can be used to help patients understand and manage healthcare decisions.

About the Data Science Seminar Series

The National Cancer Institute’s (NCI) Center for Biomedical Informatics and Information Technology (CBIIT) Data Science Seminar Series presents talks from innovators in the data science and cancer research communities.
To view upcoming speakers or view recordings for past presentations, visit Data Science Seminar Series.  Follow the conversation @NCIDataSci and #DataSciSeminar. For more information about NCI’s Center for Biomedical Informatics and Information Technology (CBIIT) visit: datascience.cancer.gov

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.

No appointments are necessary, and all problems are welcome.

Zoom URL: https://nih.zoomgov.com/j/1610653404?pwd=SmcvL3Q4djY1RzJ5ejNBRVBYQlBxdz09
Meeting ID: 161 065 3404
Passcode: 198109

Please observe the following etiquette/protocol when joining:
There will be a main room treated as a lobby and triage area. There, you can talk to one or a few staff members who will try to understand basic information about your issue. Then you may be invited to join to a staff member’s personal breakout room for more detailed 1:1 consultation. 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. Attendees are highly encouraged to
– mute when not speaking
– refrain from screen sharing unless asked to do so
– screen share as you would in a public space with the understanding that other NIH HPC users and 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

See you there!

Registration:
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Description:

This workshop will focus on the analysis of Next Generation Sequencing (NGS) data using the program MacVector. It will cover alignment/assembly of NGS data to one or more reference sequences for RNA expression analysis, and Single Nucleotide Polymorphism (SNP) detection and/or sequence confirmation. It will also cover de novo assembly of short read (Illumina or IonTorrent) and/or long read (Sanger, PacBio or Oxford Nanopore) data for both modest sequences and for entire genomes. Learn how to use MacVector to identify and extract subsets of paired-end reads from large data sets, enabling focus on just those related to your project.

Speaker: Dr. Kevin Kendal, Field Application Scientist

For questions, contact Dr. Daoud Meerzaman.

Please plan to attend the Earl Stadtman Investigator Program search seminar by:
Noam Auslander, Ph.D.
National Center for Biotechnology Information (NCBI), NIH

Dr. Auslander’s research focus is on designing techniques that make use of biological knowledge and developing computational methods to solve complex emerging problems in biology and cancer research.
This seminar will be available via ZoomGov. See below for information on the ZoomGov session. For additional information on this seminar, please contact Nadia Nimley on 240.858.3612.

To view the schedule for all upcoming Earl Stadtman Investigator Program seminars,
please click here.

The Earl Stadtman Investigator search is a trans-NIH effort to attract a diverse group of talented early-career scientists pursuing interests across the biomedical research spectrum. Additional information on the program can be found at: https://irp.nih.gov/careers/trans-nih-scientific-recruitments/stadtman-tenure-track-investigators?cid=eb_govde

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Overview

Are you interested in improving your machine or deep learning models? You often cannot be sure you’ve developed the best model without performing hyperparameter optimization. In this talk, we will explain what this crucial procedure is and how to perform it with only minimal effort using the CANDLE open-source software platform on NIH’s Biowulf supercomputer.

We will also provide an overview of what machine learning is, how it relates to deep learning, and how to get started!

Location:  Webex (https://bit.ly/3rSTk98)
Registration:   Not required
Presenter:  Andrew Weisman, PhD, High Performance Computing Analyst, Frederick National Laboratory for Cancer Research (FNLCR).
Questions?  Contact the NCI Data Science Learning Exchange
(NCIDataScienceLearningExchange@mail.nih.gov

Please register here to attend.

Webinar Presenter: Dr Tao Liu, Pacific Northwest National Laboratory

Join the Office of Cancer Clinical Proteomics Research (OCCPR) for our 2021 OCCPR webinar series! Hear from our Clinical Proteomic Tumor Analysis Consortium (CPTAC) scientists about their research and current projects.

Mass spectrometry (MS)-based proteomics enables the characterization of the human proteome at a genome scale. Recent advances in sample handling platforms and proteomic analysis strategies also allow for analysis of protein expression and phosphorylation in very small populations of cells, even single cells. These advances in single-cell proteomics hold great potential for improved understanding of biological heterogeneity underlying cancer for translational applications.
The CPTAC program is run by the OCCPR which aims to improve prevention, early detection, diagnosis, and treatment of cancer by bringing more understanding to the molecular underpinning of cancer through proteo-genome science and technology development and providing community resources (data and reagents).

To sign up for CPTAC updates click here.
For more information, please contact La’Toya Kelly.

Registration:
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Although much MacVector functionality is targeted at DNA analysis, a large number of features also exist for creating, annotating and analyzing protein sequences. This workshop will cover this functionality in depth. Topics include translating DNA into protein sequences, reverse-translating proteins into DNA and optimizing codon usage, analyzing protein sequences for active sites, and other steps in the analysis process. Dr. Kendal also will demonstrate search functions, including BLAST and local searches, and offer tips for extracting subsets of NGS reads that encode a specific protein.

Speaker: Dr. Kevin Kendal, Field Application Scientist

For questions, contact Dr. Daoud Meerzaman.

Presenter: Dr. Arjun Raj from the Perelman School of Medicine, UPenn.

Abstract:Anti-cancer therapies can often kill the vast majority of tumor cells but a few rare cells remain and grow despite treatment. Non-genetic variability has emerged as a potential contributor to this behavior. However, it remains unclear what drives this variability, and what the ultimate phenotypic consequences are. We have developed a set of new single cell barcoding technologies (Rewind and FateMap) that have enabled us to show how different types of variability can translate into different drug-resistant outcomes upon treatment with drugs. In particular, we found that even a genetically and epigenetically clonal population harbors enough latent variability to produce an entire ecosystem of different resistant cell types, and show preliminary evidence suggesting that these cell types can contribute to tumor development in distinct ways.

Meeting details:

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Thanks,
Sushant

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Partek Flow software aids in the analysis of next generation sequencing data: RNA, small RNA, and DNA. Uchenna Emechebe, a field application scientist at Partek, will show how to use the application to conduct Single Cell RNA-Seq data analysis, including how to import data, conduct quality checks, filter and normalize data, conduct cluster analyses, and visualize the results.

Agenda:
Presentation: Partek Flow Single Cell Solution Overview
Live Demo: Single Cell RNA-Seq Data Analysis and Visualization in Partek Flow
Search Portal and Public Data Repository for Single Cell analysis
Data Import
QA/QC
Data Filter and Normalization
Clustering Analysis
Dimension Reduction and Visualize in 2/3 D
Differential Expression

Speaker: Uchenna Emechebe, Field Application Scientist at Partek

For questions, contact Dr. Daoud Meerzaman

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In this training, the attendees will learn how advanced pathway and network biology algorithms from the Computational Biology Methods for Drug Discovery (CBDD) toolkit can be applied to a broad range of OMICs datasets without the need for scripting skills. The instructor will provide use cases including disease mechanism reconstruction, drug mechanism of action elucidation, target discovery, biomarker identification, and integration of omics datasets. This class would be useful to clinicians and researchers/scientists in digging deep on the association of diseases, biomarkers, and drugs.

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Data Wrangling in R is the third class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of manipulating, analyzing and exporting data with the R tidyverse. 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, students should be able to: describe the purpose of Tidyverse packages; select certain columns or rows in a data frame; describe the function of the pipe operator; add new columns to a dataframe that are functions of existing columns; use the split-apply-combine concept for data analysis; use summarize, group by, and count to split a data frame into groups of observations, apply summary statistics for each group, and then combine the results; describe the concept of a wide and a long table format and for which purpose those formats are useful; describe the function of key-value pairs; reshape a data frame using the gather commands from the tidyr package; export a data frame to a .csv file.

Students are encouraged to install R and RStudio  and dowload the class date  before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class.

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QIAGEN’s Ingenuity Variant Analysis (IVA) has been replaced by QIAGEN Clinical Insight Interpret – Translational (QCII-T), which combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based on published biological evidence and your knowledge of disease biology. QCII-T allows you to interrogate variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. This class will focus on how to use QCII-T to upload datasets, efficiently use different filtering mechanisms to identify causal variants, and export data. Participants will also review feature changes migrating from Ingenuity Variant Analysis to QCII-T.