Format: Webinar

This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of RStudio, including how to import data, create scripts, and run code. 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; create a script in R and run code from a script and from the console; define the following terms as they relate to R: object, assign, call, function, and arguments; assign values to objects in R; learn about R objects; describe the major R data types; understand how to create and work with R vectors; describe the steps for importing data into R.

Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the data before the class. Please bring your laptop with R and RStudio installed.

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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 R 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.

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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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QIAGEN’s Ingenuity Variant Analysis (IVA) 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. IVA allows you to interrogate variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. This workshop will focus on how to use IVA to upload datasets, efficiently use different filters within variant analysis to identify causal variants, and export data. The class will also review recent IVA updates.

The Human Gene Mutation Database (HGMD) database provides quick access to known published gene lesions responsible for human inherited disease. This session will explore how to query genes and mutations in HGMD and how to view the curated content.

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MacVector is a powerful but easy to use Macintosh application that lets you document, analyze and manipulate DNA and Protein sequences. A simple graphical interface lets you generate beautiful plasmid maps, design primers, assemble ABI and NGS sequences, perform local and internet database searches based on sequence similarity or keyword, align sequences using a wide variety of options, replicate cloning experiments with restriction enzymes, Gateway or Gibson assembly approaches and much, much more. This presentation will introduce new users to the capabilities of MacVector while allowing existing users the opportunity to learn about the new functionality in MacVector 17.5 and our upcoming 18.0 release. In addition, we will walk though a number of common workflows where you are sure to learn a variety of tips and tricks to speed up and simplify working with sequence data.

Presenter: Kevin Kendall Field Application Scientist at MacVector

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This webinar is intended for cancer researchers who are interested in learning more about the data that is available in the NCI Genomic Data Commons (GDC).The GDC is a unified repository that enables data sharing across cancer genomic studies in support of precision medicine.
The GDC provides access to clinical and genomic data from 65+ projects through supported cancer research programs and organizations that contribute data to the GDC. This webinar will provide an overview of the programs and projects that provide data to the GDC and review the data types from these projects that are made available through GDC data access tools.

Speakers:
Ms. Sharon Gaheen, GDC Technical Project Manager, Leidos Biomedical Research Inc.
Dr. Biju Issac, Scientific Lead, Leidos Biomedical Research Inc.

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R markdown offers tools to generate and update reports automatically—including figures, tables, mathematical equations and code. Instead of copying tables and figures into another document, you can update reports quickly and easily at the click of a button whenever you have new data to share. Join this workshop to see a demonstration and learn how to use it!

Presenter:
Randall Johnson, PhD, Bioinformatics Analyst/Technical Project Manager at Frederick National Laboratory for Cancer Research (FNL) and organizer of the Bioinformatics Users Forum

Participants will learn how to develop artificial intelligence (AI) applications using MATLAB, even if they do not have a formal background in machine and deep learning. The goal of this course is to introduce tools and fundamental approaches for developing predictive models on biomedical signals. The course will cover the entire AI pipeline, from signal exploration to deployment, including: annotating time series biomedical signals automatically, creating deep learning models using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) for biomedical signal data, creating machine learning models for biomedical signal data, applying advanced signal pre-processing techniques for automated feature extraction, 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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Data preprocessing and compound identification are two important steps for making sense of mass spectrometry-based untargeted metabolomics data. In this webinar, Dr. Xiuxia Du will demonstrate the software tool “ADAP” and the online resource “ADAP-KDB,” which were developed by her research group.
ADAP extracts compound information from untargeted LC-MS and GC-MS data through data preprocessing. It carries out a sequence of computational steps, including peak picking, peak grouping, alignment, and spectral deconvolution. Dr. Du will describe the principles of the computational algorithms that underlie these steps.
ADAP-KDB is a spectral knowledge base that uses information from publicly available data repositories (such as the NIH’s Metabolomics Data Repository) for prioritizing unknown compounds. It consists of a computational workflow for extracting prioritization information and an online portal that allows researchers to manage and search the knowledge base.
Registration for this webinar is required in advance.

Speaker:
Xiuxia Du, Ph.D.
Dr. Du is a professor at the Department of Bioinformatics and Genomics, within the College of Computing and Informatics at the University of North Carolina at Charlotte.

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Presenter: Dr. Sam Dougaparsad

Field Application Scientist It has been established that over half of all cancer types are driven by DNA Copy Number Variants (CNVs). In this presentation, we will demonstrate the use of Nexus Copy Number software for detection of CNVs from various microarray and NGS platforms and performance of cohort analysis. In particular, we will use data from The Cancer Genome Atlas (TCGA) that has been manually curated and available to NCI personnel through the NexusDB database to showcase some of the advanced statistical and visualization capabilities of Nexus Copy Number. This will include identification of recurrent aberrations in different cancer types and sub-populations, statistical comparison analysis between different sets of samples, identification of regions with high predictive power for survival analysis and many other features. Subsequent sessions will expand on integrating expression and sequence variant data into the analysis.

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The Sequencing Facility (https://ostr.cancer.gov/resources/fnl-cores/sequencing-facility) is a second and third generation high-throughput sequencing core established by the Center for Cancer Research (CCR). Sequencing Facility’s (SF’s) primary mission is to utilize high-throughput sequencing technologies to enrich cancer research and ensure that the NCI community can remain at the leading edge of next-generation sequencing technology. The SF offers sequencing services on Illumina, Pacific Biosciences, and Oxford Nanopore platforms, as well as Single Cell and Genomics Mapping technology. These platforms have complementary strengths and can be used separately or in a combined approach to answer many genomics questions.

Seminar Schedule:

9:00 AM – 9:10 AM: Introduction to CCR-Sequencing Facility
Bao Tran, SF Director

9:10 AM – 9:40 AM: Overview of Illumina Sequencing Production Laboratory
Jyoti Shetty, SF Illumina Laboratory Manager

9:40 AM – 10:00 AM: PacBio: Technology and Applications
Caroline Fromont, SF Pacbio Laboratory

10:00 AM – 11:15 AM: New and Upcoming Sequencing Technologies at SF – New Solutions for Old Problems
Monika Mehta, SF R&D Manager

11:15 AM – 12:00 PM: Overview of Data Analysis Workflows and Deliverables for Key NGS Applications at SF
Yongmei Zhao, SF Bioinformatics Manager

12:00 PM – 12: 30 PM: Q & A Section
All Presenters

Questions/Comments and/or suggestions may be directed to Bao Tran at 301-360-3460 or tranb2@mail.nih.gov.

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The NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteogenomics. The next TEAG forum will focus on NCI resources generated through the CPTAC pipeline that are available for conducting proteomic research.

CPTAC Tumor Characterization Program and Data Resources
Ana I. Robles, Ph.D.
Office of Cancer Clinical Proteomics Research (OCCPR)
National Cancer Institute

CPTAC Pipeline Components and the Assay Portal
Mehdi Mesri, M.Med.Sci., Ph.D.
OCCPR
National Cancer Institute

Proteomic Pipeline Support and NCI Collaborations
Tara Hiltke, Ph.D.
OCCPR
National Cancer Institute

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In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class to assist researchers in writing a manuscript. Participants will become familiar with statistical issues that can arise as well as recommendations to prevent them. At the end of the class, participants should have a good sense of what to do and not to do when writing the statistical sections of a manuscript. Most of the examples will be related to clinical research; however, anyone can benefit from the tips shared. Plenty of time will be devoted for questions, and references will be provided for more in-depth self-study.
This class complements, How to Write a Research Paper Parts 1 & 2, as it specifically covers key points in writing the statistical portion of a manuscript – particularly for clinical research. Attendance to How to Write a Research Paper is not required to benefit from this class.

Instructor(s) — External Vendor:
Paul Wakim

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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Learn about data science education resources available through NIH and NCI, as well as online resources to learn about the data lifecycle, data management, data analysis and visualization, and other data science topics.
For more information about the webinars or to pre-register, contact Joelle Mornini. Check the Research Data Sources page on the Scientific Library website to learn more about research data online resources.

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Presenter:
Christina Curtis, Ph.D., M.Sc.
Dr. Christina Curtis is an Associate Professor in the Departments of Medicine (Oncology) and Genetics in the School of Medicine at Stanford University where she leads the Cancer Computational and Systems Biology Group and is Co-Director of the Molecular Tumor Board at the Stanford Cancer Institute. The Curtis laboratory is focused on the development and application of innovative experimental, computational, and analytical approaches to improve the diagnosis, treatment, and early detection of cancer. Dr. Curtis will be presenting on next generation sequencing and tumor initiation and progression.

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 – IGV download (the UCSV browser is web browser based).

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Presenter: Dr. Han Liang

Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to evaluate biomarkers and mechanisms underlying sensitivity and resistance to cancer therapy. The MD Anderson Cancer Center platform currently contains ~500 protein markers, covering all major signaling pathways. The Cancer Proteome Atlas (TCPA) we developed (http://tcpaportal.org) is the sole dedicated bioinformatics resource for RPPA data. Currently, it contains two analytic platforms: one contains >8000 patient samples; and the other contains >1500 cell line samples. We are developing the third component focusing on adaptive RPPA response given drug treatments. We have built an integrated data portal that contains user-friendly analytic and visualization tools for a broad biomedical community to utilize these data.To learn more please visit: https://tcpaportal.org/tcpa/
The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website https://itcr.cancer.gov/

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Learn how to locate data sharing policies for NIH, other public and private research funders, and journal publishers, and find templates for creating data management plans through DMP Tool.
For more information about the webinars or to pre-register, contact Joelle Mornini. Check the Research Data Resources page on the Scientific Library website to learn more about research data online resources.

New computational opportunities and challenges have emerged within the cancer research and clinical application fields, as the size, source and complexity of cancer datasets have grown. Simultaneously, advances in computational capabilities, with exceptional growth in AI and deep learning, are reaching unprecedented scales.

This Sixth Computational Approaches for Cancer Workshop 2020 (CAFCW20) will bring together a wide-range of individuals including clinicians, cancer biologists, mathematicians, data scientists, computational scientists, engineers, developers, thought leaders and others with an interest in advancing the use of computation to better understand, diagnose, treat and prevent cancer. As an interdisciplinary workshop, the sharing of insight and challenges fosters collaborations and future innovations accelerating progress in computationally and data-driven cancer research and clinical applications.

High-performance computing (HPC) has been and will continue to be a key component of cancer research. Industry, academic and government interest is demonstrably high with ongoing commitments, new announcements, advances and new opportunities involving cancer and computing. One need only review recommendations provided by the National Cancer Moonshot Blue Ribbon Panel to confirm the increasingly visible and critical role computing and HPC in particular will play in accelerating cancer research objectives. As HPC-related efforts from projects funded through the 21st Century Cures Act begin to mature, the workshop will provide an ongoing avenue for new computational approaches involving HPC at all scales to be shared with the growing community.

The Computational Approaches for Cancer workshop series originated in early 2015, following observations that the topic of cancer was already pervasive at the SC conference, yet no venue at SC existed to bring the key community together. The response has been favorable for the first five workshops with over 80 participants in each of the first two years, expanding to an estimated 150 attendees at SC17 and at room capacity in SC18 and SC19. Enthusiasm for the workshop continues to grow with many ideas and challenges shared, collaborations envisioned and needs identified. The successful call for papers in SC17 resulted in proceedings published for Open Access in BMC Bioinformatics, a growing number of submissions in SC18 and a record number of submissions in SC19. At SC19, the best paper award was given to a team who has progressively presented their work at the series of SC Computational Approaches for Cancer workshops.

Questions? Contact cafcw@nih.gov

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The goal of this meeting is to critically address the wealth of new data generated by bulk and single-cell molecular, imaging and computational approaches that are increasingly revealing how the genome folds to faithfully accommodate gene expression programs and cell fate decisions. The goal is also to advance an understanding of how transcriptional enhancers function, how to separate cause and effect, and to identify critical questions that will guide future research directions.

We are inviting current thought leaders in the field from the U.S. and Europe in order to promote intellectual discourse among colleagues on this subject. We anticipate a highly stimulating and interactive meeting and a published report to follow.

Sessions Include:

• Enhancer genetics.
• Enhancers in development.
• Enhancer biophysics.
• Enhancer in disease.
• Enhancers and ncRNA.

Registration is required in order to receive the WebEx link.

REASONABLE ACCOMMODATION
Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Romi Sawhney, at 240-760-6400, and/or the Federal Relay Service (1-800-877-8339). Requests should be made at least two business days in advance of the event.

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Learn how to find data standards through DCC Disciplinary Metadata, FAIRsharing.org, and NIH Common Data Elements (CDEs). For more information about the webinars or to pre-register, contact Joelle Mornini. Check the Research Data Resources page on the Scientific Library website to learn more about research data online resources.

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The goal of this meeting is to critically address the wealth of new data generated by bulk and single-cell molecular, imaging and computational approaches that are increasingly revealing how the genome folds to faithfully accommodate gene expression programs and cell fate decisions. The goal is also to advance an understanding of how transcriptional enhancers function, how to separate cause and effect, and to identify critical questions that will guide future research directions.

We are inviting current thought leaders in the field from the U.S. and Europe in order to promote intellectual discourse among colleagues on this subject. We anticipate a highly stimulating and interactive meeting and a published report to follow.

Sessions Include:

• Enhancer genetics.
• Enhancers in development.
• Enhancer biophysics.
• Enhancer in disease.
• Enhancers and ncRNA.

Registration is required in order to receive the WebEx link.

REASONABLE ACCOMMODATION

Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Romi Sawhney, at 240-760-6400, and/or the Federal Relay Service (1-800-877-8339). Requests should be made at least two business days in advance of the event.

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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 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.

Instructor: Paul Wakim (External Vendor)

Artificial Intelligence (AI) and Machine Learning (ML) are hot topics today, but are already part of our everyday lives. This class will introduce the basic principles of AI and ML and the current state of this field. The instructor will share examples of AI used in common applications and health research. AI limitations and ethical considerations will also be discussed. This course is designed for individuals new to the topic or for those who need a refresher.

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Speaker: Alicia Lillich (NIH STAFF)

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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.

Speaker: Paul Wakim (External Vendor)

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Learn how to find NIH data repositories and other repositories through Re3Data. Learn about resources for locating datasets by searching across data repositories, searching individual repositories, and searching data publications. For more information about the webinars or to pre-register, contact Joelle Mornini. Check the Research Data Resources page on the Scientific Library website to learn more about research data online resources.

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Learn about the NCI cancer research data ecosystem, as well as repositories and data portals for cancer research data. For more information about the webinars or to pre-register, contact Joelle Mornini. Check the Research Data Resources page on the Scientific Library website to learn more about research data online resources.

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In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This two-part lecture series will help participants to better understand and prepare data, interpret results and findings, design and prepare studies, and better understand the results in published literature.
Part 1 will address considerations for the choice of statistical tests including the importance of study design and hypothesis, type of data and their distributions, and provide a brief review of important statistical features. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.
Although you may attend only one part of this series, attending both parts will give you a better understanding of the most used statistical tests in the biomedical literature. You must register separately for Part 2 of this class series.

Instructor(s) — External Vendor:
Ninet Sinaii

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In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This two-part lecture series will help participants to better understand and prepare data, interpret results and findings, design and prepare studies, and better understand the results in published literature.
Part 2 will describe the basic concepts for using common statistical tests such as Chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, logistic regression, and some nonparametric tests. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.
Although you may attend only one part of this series, attending both parts will give you a better understanding of the most used statistical tests in the biomedical literature. You must register separately for Part 1 of this class series.

Instructor(s) — External Vendor:
Ninet Sinaii