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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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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.

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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.

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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.

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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.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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.