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

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

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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.

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(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 data before the class. Please bring your laptop with R and RStudio installed.

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