Events by this organizer

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).

MATLAB provides two-way integration with many programming languages such as Python, which allows for greater collaboration between investigators. This webinar will cover how to call MATLAB from Python and how to call Python libraries from MATLAB. Attendees will learn how to use MATLAB’s Python integration to improve the compatibility and usability of their code. This webinar will be helpful for participants who have collaborators using Python with MATLAB, or for attendees who want to integrate MATLAB’s capabilities in a Python program. This session is for beginners; no software installation required.

The ISB Cancer Gateway in the Cloud(link is external) (ISB-CGC) offers multiple avenues for accessing and analyzing large-scale cancer datasets, including TCGA, TARGET, CPTAC and important references such as GENCODE and COSMIC. ISB-CGC users can process petabytes of data using complex workflows written in the language of their choice (including but not limited to CWL, WDL, Snakemake, Nextflow, etc). They can develop new analyses using SQL, Python, and R to mine data including gene expression, protein abundance, and somatic mutations in easily accessible and queryable tables. Updated interactive web tools at isb-cgc.org (link is external)allow cohort creation, data discovery, and exploration. In our cloud computing session, we will demonstrate common bioinformatic workflows using both Python and R while integrating various omic data types such as gene mutations, copy number, gene expression, methylation, and proteomics. We will show how this can be interactively and iteratively performed in the cloud using the ISB-CGC platform.  Attendees will receive hands-on training on optimizing analyses using “burstable” cloud tools, which enables the user to rapidly combine and interrogate their cancer datasets with those available at the Cancer Research Data Commons.

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.

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.

The Cancer Genomics Cloud (CGC)(link is external), powered by Seven Bridges, is an NCI-funded resource that provides a unified platform for cancer data analysis by co-localizing three components within the cloud: 1) large cancer datasets from TCGA, CPTAC and several others; 2) more than 500 bioinformatics tools and best-practice workflows for analyzing multi-omics data; and 3) the computational capabilities to do large-scale analyses. This hands-on session of CGC allows the participants to browse, query, and filter datasets of interest and bring their own data for collaborative analysis. The CGC also provides the flexibility to use private tools and the ability to complete reproducible and interactive data analyses (e.g., RStudio, Jupyter notebook). Currently, data analysis is supported in both Google and Amazon cloud environments. Altogether, the CGC is a network of findable, accessible, interoperable, and reusable (FAIR) datasets, workflows, and services which make cancer data analysis faster and more easily available for all.

FireCloud (Powered by Terra(link is external)) is a data analysis platform that contains a system of workspace functionalities centered on data sharing and analysis. Researchers can use FireCloud to perform a variety of analyses through two main mechanisms: batch workflow execution and interactive analysis (including data visualization). Workflow execution is achieved through a workflow management system called Cromwell, which is designed to be highly portable, capable of connecting to multiple computing platforms, and horizontally scalable. Interactive analysis is achieved through Jupyter notebooks or RStudio, which can be associated with a range of configurable runtime environments and incorporate various kernels and packages to support exploratory analyses of arbitrary scale and complexity.

In this hands-on session, we will introduce FireCloud to all attendees as a part of the NCI Cloud Resources. The participants will learn how to access key data sets such as TCGA and CPTAC and bring this data (or their own data) into a secure FireCloud workspace. Participants will also learn how to configure and launch workflows from their workspace and perform interactive analysis with applications such as Jupyter Notebooks, RStudio, and more. This session will also cover how billing operates in FireCloud and how to securely share the personal FireCloud workspace with collaborators.

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(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 class data before the class.

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.

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.