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NHGRI will host a new seminar series this year on the “Bold Predictions for Human Genomics by 2030” that are described in NHGRI’s “Strategic Vision for Improving Human Health at the Forefront of Genomics.” The July 12, 2021 seminar will focus on Bold Prediction #6: The regular use of genomic information will have transitioned from boutique to mainstream in all clinical settings, making genomic testing as routine as complete blood counts (CBCs). Dr. Jennifer Posey of Baylor College of Medicine and Dr. Katrina Armstrong of Massachusetts General Hospital & Harvard Medical School 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.

Speakers:

Attendees will learn how to use Partek Flow to identify cell subtypes using both gene and protein expression in a peripheral blood mononuclear cell (PBMC) sample using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) technology. CITE-Seq is a multimodal single cell phenotyping method. CITE-Seq data analysis starts with importing a count matrix file, followed by QC and filtering data. Clustering analysis and dimension reduction techniques are then used to visualize and identify subtypes of cells. Differential expression detection among different subtypes will be demonstrated, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Basic knowledge of Partek Flow is required.

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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

For more information and to register send email to staff@hpc.nih.gov

*** No appointments are necessary, and all problems are welcome.****

Registration: https://btep.ccr.cancer.gov/classes/ai_three/

Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m933690e1ccdbfd15ae3f75d1cbec3b95

Description: This talk will describe machine learning and deep learning methods to analyze bulk and single-cell RNA sequencing data, as well as deep learning models that integrate epigenetic data to decipher the regulatory networks underlying gene expression.

Presenter: Avantika Lal PhD, Senior Scientist | Deep Learning and Genomics | NVIDIA

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Faculty: Michael Angelo, MD, PhD – Stanford University, Moonshot Cancer Atlas

Moderator: Carsten Krieg, PhD – Medical University of South Carolina

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

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This webinar will focus on making controlled-access data (stored in NIH operated and supported repositories) more readily findable and accessible. It will consider the benefits of standardized vocabularies to address and describe a data set’s contents and a common language for informed consent that allows for consistent interpretation of allowable data uses. Discussion points will also address current issues with access to summary data and how best to make summary data and metadata available and accessible.

This webinar is a breakout session from the July 9 webinar, Streamlining Access to Controlled Data at NIH: Tackling Challenges and Identifying Opportunities. To learn more about this topic, including additional breakout sessions planned for July 2021, visit the Office of Data Science Strategy webpage

The T cell receptor (TCR) repertoire of the adaptive immune system needs broad diversity to recognize any possible pathogen yet must have strategic gaps so as not to attack self.  The potential diversity is so high that a naive model would predict two individuals should have essentially no TCRs in common.  Yet shared (“public”) TCRs exist!  I will present analysis of repertoire sequencing data showing that these shared sequences are due, in part, to somatic selection.  I will also discuss how somatic selection may play a role in the aging of the naive TCR repertoire, in a process analogous to the development of cancer.

Bio: Philip Johnson began his academic career at Harvard College, where he earned an A.B. in Biology and Computer Science with his senior thesis advised by George Church.  After working at NCBI for several years, he studied theoretical population genetics at UC Berkeley with Monty Slatkin where he earned his PhD in 2009.  From Berkeley, he went to Emory University for a postdoc modeling immune system dynamics with Rustom Antia in collaboration with Rafi Ahmed.  In 2015, he started his own group in the Biology Department at the University of Maryland where he merged his interests to study the evolutionary genetics of adaptive immune systems.

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.

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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.

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During this month’s NCI Cancer Research Data Commons (CRDC) Cancer Genomics Cloud (CGC)  webinar series, two Georgetown University staff members about leveraging the CGC in their data science courses and curriculum.

Building from their experience as trainers in the fields of bioinformatics and computational biology, Dr. Yuriy Gusev and Ms. Krithika Bhuvaneshwar will cover:

• approach and methodology for establishing their online data science course “Demystifying Big Biomedical Data: A User’s guide.”
• examples of graduate-level courses that leverage the CGC as a teaching platform in the Masters in Health Informatics and Data Science program at Georgetown University.

As one of the three Cloud Resources within the NCI CRDC, the 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 on the cloud.

Presenters:

• Yuriy Gusev, Ph.D.

Dr. Yuriy Gusev is an associate professor of bioinformatics and a bioinformatics Lead at the Georgetown University Innovation Center for Biomedical Informatics. Dr. Gusev is the director of the graduate program for a Masters in Health Informatics and Data Science and co-director of the informatics shared resource for the Lombardi Comprehensive Cancer Center at Georgetown University. He has over 20 years of experience in teaching and training in bioinformatics and computational biology at several academic centers in the U.S. He has developed several new curricula for graduate and undergraduate programs at Georgetown. He has also developed a successful massive open online course on EdX titled, “Demystifying Big Biomedical Data: A User’s Guide,” which attracted over 8,000 students from around the world.

• Krithika Bhuvaneshwar

Ms. Krithika Bhuvaneshwar is a research instructor faculty and curriculum coordinator for the Masters in Health Informatics and Data Science program and is also a Senior Bioinformatician at the Innovation Center for Biomedical Informatics, Georgetown University. She has helped organize training workshops in Elsevier Pathway Studio, Globus Genomics, systems biology, immuno-oncology, and — most recently — imaging informatics for faculty and staff at Georgetown University Medical Center.

Understanding the biology in transcription factor binding site data requires a comprehensive and integrated approach and must be supplemented with highly-curated data content from multiple sources. Genomatix is a computational biology company with a 22-year history of developing tools and data content for understanding the molecular mechanisms of eukaryotic gene expression. This two-day class will provide an easy-to-use bioinformatics software platform for the detailed analysis of promoters and transcription factor binding sites.

This two-day lecture and training workshop will introduce students to the rich applications for transcription factor binding site (TFBS) analysis using the Genomatix platform. This course is broken down into a morning and an afternoon session. NOTE: Registration and attendance for the day one session is required to attend the day two session.

The day one session will focus on promoter annotation, basic transcription factor binding site analysis, and comparative genomics. Covered topics: promoter annotation and alternative promoters; using tissue activity data to select promoters (programs: ElDorado, Gene2Promoter); TFBS analysis with additional lines of evidence (MatInspector, MatBase); promoter orthologs and analysis of conserved TFBSs (Common TFs, DiAlignTF).

The day two session will focus on regulatory cassettes and genome-wide analysis of ChIP-Seq data. Covered topics: analysis of conserved gene regulatory cassettes (FrameWorker, ModelInspector); TFBS and TFBS pattern overrepresentation in ChIP-Seq peaks (Overrepresented TFs); gene neighbor and promoter overlap annotation of ChIP-Seq peaks (Annotation & Statistics).

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Faculty: Evan Newell, PhD – Fred Hutchinson Cancer Institute

Moderator: Carsten Krieg, PhD – Medical University of South Carolina

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

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Participants will learn how to develop artificial intelligence (AI) applications focused on biomedical imaging datasets. The course will cover the entire AI pipeline from image exploration and labeling to development and deployment of predictive models on images using both machine learning and deep learning approaches. This includes annotating and exploring image datasets, exploring techniques for developing machine and deep learning models, optimizing the parameters of the deep networks, and automatically generating code for edge deployment of AI models.

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

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Faculty: Bing Zhang, PhD – BCM, NCI CPTAC

Moderator: Carsten Krieg, PhD – Medical University of South Carolina

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

Save the date, and plan to join us on September 9, 9:30 a.m.–3:00 p.m. for a free virtual Bioinformatics and Computational Biology Symposium presented by the NIH Library Bioinformatics Support Program. This one-day symposium will feature keynote lectures, presentations on bioinformatics techniques, and research talks from authors of bioinformatics proceedings.

The morning sessions will focus on epigenomics, protein structures, and proteomics. The afternoon topics will feature genomics and single cell sequencing. We hope this event will be inspiring for your research and computational studies.
More information about the event and registration will be forthcoming.

For questions, contact Li Jia, li.jia2@nih.gov.

Registration: https://btep.ccr.cancer.gov/classes/ai_four/

Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m5fa0e43ae167ed5ea3a77fb25d339a82

Description: In this talk, we will highlight two examples for building predictive models from multi modal data. The first example predicts dose response in cell lines based on drug and molecular features. The second example will show to combine pathology whole slide images and molecular features for cancer diagnosis and prognosis.

Presenters: George Zaki, Bioinformatics Manager, Strategic and Data Science Initiatives (SDSI), Frederick National Laboratory for Cancer Research (FNL), Pinyi Lu, Bioinformatics analyst, SDSI, FNL

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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

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