Format: Webinar

Speaker:

Tonia Korves, Ph.D.
Lead Data Scientist
Data and Human-Centered Solutions Innovation Center
MITRE Corporation

Abstract
As COVID-19 research rapidly escalated last year, we quickly built a platform to help biomedical experts track published research about potential therapeutics and vaccines. The platform includes a natural language processing pipeline that identifies scientific documents about SARS-CoV-2 and other viruses, particular drugs, and vaccine types, sorted by stages of research, and a dashboard called the COVID-19 Therapeutic Information Browser, available at covidtib.c19hcc.org. The comprehensive data from this platform enables us to characterize COVID-19 drug research over time and at scale, and potentially draw lessons that can inform future decisions. In this talk, we will present our natural language processing methods, the dashboard, and an analysis of trends in published COVID-19 drug research and clinical trials over the past year. We will also discuss other uses for this data, outstanding challenges, and other potential applications of this approach.

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Dear colleagues,

We’ll be hosting a special guest lecture by Prof. John Moult from UMD.

Abstract:
Computing the three-dimensional structure of a protein molecule from its amino acid
sequence is a long-standing grand challenge problem. Results from the recent Critical
Assessment of Structure Prediction (CASP14) experiment show that new deep-learning
methods have now provided a dramatic solution, with many computed structures
comparable, likely sometimes better, representations of in vivo protein structures to
those obtained with state-of-the-art experimental techniques of crystallography and
cryo-electron microscopy. These models have already demonstrated an ability to solve
problematic crystal structures, and the results suggest the methods will be successfully
applied to other areas of structural biology and more generally. This is the first solution
of a serious scientific problem by AI, and it will not be the last.
In this talk I’ll describe how the protein modeling field arrived at this point, what sort of
methods were used, characteristics of the computed structures, and some potential
further applications.

Bio:
John Moult is a Fellow at the Institute for Bioscience and Biotechnology Research and
Professor in the Department of Cell Biology and Molecular Genetics at the University of
Maryland. He is co-founder and Chair of CASP (Critical Assessment of Protein structure
Prediction), an organization that conducts large-scale experiments in protein structure
modeling, and joint founder of CAGI, a sister organization for advancing genome
interpretation. He is an ex-crystallographer turned computational biologist. His research
interests include the relationship between genetic variation and human disease, disease
mechanisms, protein structure, and different ways of doing science. (BSc Physics, University of London 1965, D.Phil Molecular Biophysics, University of Oxford 1970)

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Description: This course is an introduction to scientific computing in Julia for those who have some experience programming in other languages. Basic concepts of Julia and its scientific stack will be introduced. We will compare syntax and data structures in Julia with those of other scientific computing languages, and demo how to install Julia packages and how to run simple Julia scripts on Biowulf.

Expected knowledge: Some programming experience in a scientific computing language (Matlab, Python, R). Familiarity with the Linux/Unix command line.

Instructor: Antonio Ulloa (NIH HPC Staff)

The class is free but registration is required.
You can register at https://hpc.nih.gov/nih/classes/

Please contact staff@hpc.nih.gov with any questions about the NIH HPC Systems

Dr. Wang will present virtually. Register now and join us via Webex.

Researchers continue to face major challenges when comparing diverse single-cell RNA sequencing (scRNA-seq) data sets, because these data often are generated by different technologies from a variety of laboratories.

In this webinar, Dr. Charles Wang will address the need for guidelines to help choose algorithms for more accurate biological interpretations of varied data types acquired by different platforms.

Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, Dr. Wang compared different scRNA-seq platforms and several methods (preprocessing, normalization, and batch-effect correction) at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq data set characteristics (e.g., sample and cellular heterogeneity, the platform used, etc.) were critical in determining the optimal bioinformatics method. However, reproducibility across centers and platforms was high when appropriate bioinformatics methods were applied.

These findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.

Speaker:

Charles Wang, M.D., Ph.D., M.P.H.
Dr. Charles Wang is a professor at the Loma Linda University School of Medicine and director of the Center for Genomics. Dr. Wang was the director of Clinical Transcriptional Genomics Core at Cedars-Sinai Medical Center; associate professor of medicine at the David Geffen School of Medicine at the University of California-Los Angeles; and director of the Functional Genomics Core at City of Hope. He is the recipient of several awards, including the American Association for Cancer Research–Bristol-Myers Squibb Young Investigator Award.

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

Individuals with disabilities who need sign language interpreters and/or reasonable accommodation to participate in this event should contact Eve Shalley (240-276-5194, eve.shalley@nih.gov), and/or the Federal TTY Relay number (1-800-877-8339) at least 5 days in advance of the event.

Register/Join

Researchers continue to face major challenges when comparing diverse single-cell RNA sequencing (scRNA-seq) data sets, because these data often are generated by different technologies from a variety of laboratories.

In this webinar, Dr. Charles Wang will address the need for guidelines to help choose algorithms for more accurate biological interpretations of varied data types acquired by different platforms.

Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, Dr. Wang compared different scRNA-seq platforms and several methods (preprocessing, normalization, and batch-effect correction) at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq data set characteristics (e.g., sample and cellular heterogeneity, the platform used, etc.) were critical in determining the optimal bioinformatics method. However, reproducibility across centers and platforms was high when appropriate bioinformatics methods were applied.

These findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.

Presenter:

Charles Wang, M.D., Ph.D., M.P.H.
Dr. Charles Wang is a professor at the Loma Linda University School of Medicine and director of the Center for Genomics. Dr. Wang was the director of Clinical Transcriptional Genomics Core at Cedars-Sinai Medical Center; associate professor of medicine at the David Geffen School of Medicine at the University of California-Los Angeles; and director of the Functional Genomics Core at City of Hope. He is the recipient of several awards, including the American Association for Cancer Research–Bristol-Myers Squibb Young Investigator Award.

About the Data Science Seminar Series:

The CBIIT Data Science Seminar Series presents talks from innovators in the research and informatics community. Follow the conversation on Twitter with @NCIDataSci and #DataSciSeminar.

To see upcoming speakers or view recordings from past presentations, visit the CBIIT Data Science Seminar Series website.

Individuals with disabilities who need sign language interpreters and/or reasonable accommodation to participate in this event should contact Eve Shalley (240-276-5194, eve.shalley@nih.gov), and/or the Federal TTY Relay number (1-800-877-8339) at least 5 days in advance of the event.

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Presenter: George Emanuel PhD, Co-founder Vizgen, Director of Technology and Partnerships.

Abstract: Biological systems are comprised of numerous cell types, intricately organized to form functional tissues and organs. Cell atlas initiatives with single-cell RNA sequencing have begun to characterize cell types based on their RNA expression profiles. However, the tissue organization is lost when cells are dissociated for single-cell sequencing, making it difficult to study how the cellular heterogeneity is contributing to the function of the tissue.

This talk introduces a technology which enables in situ profiling of the spatial organization of intact tissue with genomic-scale throughput. It permits spatial profiling of hundreds of thousands of cells with high accuracy and reproducibility through combinatorial labeling, sequential imaging, and error-robust barcoding. The talk will highlight various, including mapping GPCR expression across the mouse brain, identifying rare blood cells by measuring millions of PBMCs, and characterizing the immune landscape and microenvironment of a human colon cancer tumor.

Biography: Dr. Emanuel is trained as a biophysicist at Harvard University in the lab of Dr. Xiaowei Zhuang. For the past decade he has worked on the development of highly multiplexed RNA fluorescence in situ hybridization-based technologies. George is a scientific cofounder at Vizgen, where he is currently Director of Technology and Partnerships.

Register 

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.

Tumor heterogeneity and plasticity provide a driving force for tumor progression and metastasis as well as treatment response. Tumor heterogeneity can be explained by the same mechanisms that govern the developmental programs of an organism and share many of the properties observed in cellular differentiation and physiological processes including stem cell biology and epithelial-mesenchymal transition (EMT). In this seminar series, I will talk about biological basis of tumor heterogeneity from the perspectives of three spatial levels: tissue, cell, and molecule. Tumor heterogeneity is often viewed at tissue level, which is determined by cellular heterogeneity and ultimately determined by gene regulatory networks consisting of genome sequences, transcription factors, signaling molecules, and epigenetic information. The sources of tumor heterogeneity include intrinsic developmental programs, tumor microenvironment, and stochastic processes. I will describe gene regulatory network and mathematical modeling and discuss how mathematical modeling can help understand the sources of heterogeneity as well as the initiation and dynamics of cellular states and epigenetic memory. I will describe differential equations and quasi-potential as a mathematic tool to quantify Waddington’s epigenetic landscape and predict trajectory of cancer cell evolution and treatment response.

Speaker: Maxwell Lee

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Meeting number (access code): 157 260 1880
Meeting password: UEuBV2P2Z$3

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Dear Colleagues,

As a continuation of the NHGRI seminar series focused on the “Bold Predictions for Human Genomics by 2030,” please join us for the third seminar in the series on Monday, April 12 from 3:00 – 4:30 pm ET. This seminar will focus on Bold Prediction #3: The general features of the epigenetic landscape and transcriptional output will be routinely incorporated into predictive models of the impact of genotype on phenotype. Dr. Tom Gingeras of Cold Spring Harbor Laboratory and Dr. Tuuli Lappalainen of Columbia University and the NY Genome Center will use this prediction as an aspirational theme for their talks, highlighting their own research in the context of that theme and speculating about the next decade in their research 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.

To access the Zoom Webinar, please register at: https://nih.zoomgov.com/webinar/register/WN_vq4qZJD7Sl6roqHA1DNMHg

Closed captioning will be provided. Individuals with disabilities who need reasonable accommodation to participate in this event should contact Susan Vasquez (Susan.Vasquez@nih.gov; 301-503-9790) and/or the Federal Relay (1-800-877-8339) in advance of the seminar.

All the best,

Eric

Eric Green, M.D., Ph.D.
Director, National Human Genome Research Institute National Institutes of Health

Workshop Registration

Dear NIH colleagues,

You are invited to participate in the Machine Learning in Genomics: Tools, Resources, Clinical Applications, and Ethics Workshop, held virtually on Tuesday, April 13 – Wednesday, April 14, 2021.

Information about the agenda, speakers and registration can be found on the workshop webpage.

The meeting is organized and hosted by the NHGRI Data Science Working Group, and will kick off with a welcome message from NHGRI Director, Dr. Eric Green.

This meeting is free and open to anyone who registers.

Please direct any questions to natalie.kucher@nih.gov and sean.garin@nih.gov.

Sincerely,

Shurjo Sen (on behalf of the Organizing Committee)

Register

Session Description

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.

Registration: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=ec198bc69667ba131397fb48e0c9b9708

Presenter: Christian Zinser PhD Head of Bioinformativs at Precigen Bioinformatics Germany
Description: This presentation will give you an overview of the Genomatix Genome Analyzer (GGA) functionalities and data background.

The GGA is Genomatix’s integrated solution for the analysis of Next Generation Sequencing (NGS) data, gene regulation, and pathway analysis. It includes a comprehensive genome annotation and data visualization, accessible in an intuitive web-based GUI.

The biological background data consisting of annotation and gene network data provided by ElDorado plus the transcription factor knowledge contained in MatBase lets researchers analyze and interpret their experimental results in a unique biological context for 26 different species. Differential expression analysis, gene network and pathway generation, regulatory frameworks, literature analysis and binding site motif definition are only a few of the tasks that can be performed.

POC: Daoud Meerzaman

Registration

Session Description
Animal models are used to study the development and progression of diseases and to test new treatments. Model organisms are a subset of research organisms that serve as a proxy for understanding human biology. This introductory class will cover techniques on locating biomedical research articles, patents, NIH-funded research projects, genetic information, and print and electronic books related to animal models and model organisms. The class will also discuss the differences between animal models, research organisms, and model organisms, and will review requirements and resources for the NIH Model Organism Sharing Policy.

Presenter:

Michael Kelly, Ph.D.
Scientist III, Team Lead
Single Cell Analysis Facility (CCR)
Cancer Research Technology Program

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Additional Connection information:
Meeting number (access code): 160 374 7017
Meeting password: jeGepe9E7$2

Tap to join from a mobile device (attendees only)
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Registration is required. Register at this link.

Before the workshop: We will use Google Colab for this workshop. Colab allows us to write and execute Python code through the browser. A Google Account is required for using Colab. If you do not have one, please create a Google Account before the workshop. For technical assistance, contact NCI technical support at https://service.cancer.gov/ncisp.

NOTE: A one-hour help session will be offered on April 23, 11 AM – 12 PM: Getting Started with Google Colab

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Registration

Session Description
QIAGEN’s CLC Genomics Workbench enables researchers to analyze NGS data without the use of command line and is a powerful tool for determining differential expression and for variant calling. In this workshop students will explore the usage of CLC Genomics Workbench for taking raw NGS data and performing QC, processing, statistical analyses, and visualizations. The first part of the training will focus on the identifying differentially expressed genes from RNA-seq and how those results can be passed to Ingenuity Pathway Analysis (IPA) for biological interpretation. The second half of the training will provide insight into analysis of DNA-seq data for variant detection and will introduce how Ingenuity Variant Analysis (IVA) can be used to prioritize those variant findings.

Registration: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=e51f19be5660107b4b92118dc48be1781

Presenter: Kevin Kendal PhD CEO of MacVector

Description: This workshop will focus on the analysis of Next Generation Sequencing data. It will cover alignment/assembly of NGS data to one or more reference sequences for (e.g.) RNA expression analysis, SNP detection and/or sequence confirmation as well as de novo assembly of short read (Illumina or IonTorrent) and/or long read (Sanger, PacBio or Oxford Nanopore) data for both modest sequences, like plasmids, and for entire genomes. We will also look at how you can use MacVector to identify and extract subsets of paired-end reads from large datasets so that you can focus on just those of interest to your project.

POC: Daoud Meerzaman

Register

Session Description
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.

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Register

Session Description
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).

Register

Session Description
QIAGEN’s CLC Genomics Workbench enables researchers to analyze Next Generation Sequencing (NGS) data without the use of command line and is a powerful tool for processing microbial data. In this workshop students will explore how the Microbial Genomics Module can be utilized for taxonomic profiling of sample microbiomes using both amplicon and whole metagenome sequencing data, including generating statistical results and visualizations. The workflows for de-novo genome assembly and annotation will also be presented.

Meeting Link: https://btep.ccr.cancer.gov/classes/variant-2/

Register Here

Presenter: Justin Lack (NIAID CBR)

1.Experimental Design Considerations in Variant Analysis

i. Germline vs Somatic

ii. WGS vs WES

iii. Sample sizes and statistical power

2. WGS/WES Pipelines at NIH

i. Pipeline Performance

ii. Using Pipeliner for internal and external data

3. Variant QC, Annotation and Downstream Analysis

i. Variant QC and data correction

ii. Variant annotation and analysis tools

4.  Structural variation and multi-omic integration

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Register

Session Description
This virtual hands-on workshop explores the fundamentals of machine learning using MATLAB. The participants will be introduced to machine learning techniques to quickly explore data, use classification and regression apps to interactively train, compare and tune a model, and optimize the model using hyperparameter tuning. The participants will also learn how to get started with deep learning in MATLAB for data preparation, design, simulation, and deployment of deep neural networks. This is an introductory level class.

Register

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

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Register Now

Faculty: Eliezer M. Van Allen, MD – Harvard University/Dana-Farber Cancer Institute/BROAD; 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

Register

Session Description
DNASTAR offers software solutions for molecular biology, structural biology, and genomics. In this class, attendees will be presented with an overview of the applications within the Lasergene Suite. The instructor will use the latest software version and demonstration data to provide tutorials for various workflows, including: cloning and primer design, Sanger sequence alignment, protein structure analysis, and next generation sequence assembly and analysis. Individual meetings after the training session can be scheduled to provide help with specific projects. To schedule an individual meeting, please email the instructor.

Register

Session Description
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.

Register

Session Description
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

Session Description
Animal models are used to study the development and progression of diseases and to test new treatments. Model organisms are a subset of research organisms that serve as a proxy for understanding human biology. This introductory class will cover techniques on locating biomedical research articles, patents, NIH-funded research projects, genetic information, and print and electronic books related to animal models and model organisms. The class will also discuss the differences between animal models, research organisms, and model organisms, and will review requirements and resources for the NIH Model Organism Sharing Policy.

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Register

Session Description
This training will provide an introduction to RNA-seq data analysis followed by tutorials showing the use of popular RNA-seq analysis packages and preparing participants to independently run basic RNA-Seq analysis for expression profiling. The exercise will run on the Galaxy platform using Illumina paired-end RNA-seq data.

Participants will have a chance to: run quality control check on sequencing data; align the sequencing reads to a reference genome; generate alignment statistics and check mapping quality; measure abundance of transcripts; perform differential expression analysis; and visualize the output of RNA-seq analyses.

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Register Now

Faculty:  Shannon McWeeney, PhD – Oregon Health & Science University; NCI Cancer Moonshot DRSN

Moderator:  Santosh Putta, PhD

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: Dana Pe’er, PhD – Memorial Sloan Kettering Cancer Center; NCI Cancer Moonshot HTAN
Moderator: Daniel Wells, PhD – Immunai

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: 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

Register Now

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