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Course Catalog

Below are topic or subject areas taught by the NIH Library. Click the topic to see a list of upcoming classes or other related content. To view our full training catalog, visit the library training calendar. We are open to your feedback and suggestions related to our training program. Please suggest a class if you do not see it listed.

NIH Library classes are taught in-person in the NIH Library training rooms, Building 10, Clinical Center, near the South Entrance or virtually. In addition to classes, self-paced online tutorials are available through a variety of vendors and our library staff.

 

  • This training will provide an introduction to ChIP sequencing data analysis followed by a tutorial showing ChIP-seq analysis workflow and preparing participants to independently run basic ChIP-seq analysis for peak calling using a "point and click" approach on Galaxy platform. The hands-on exercise will run on a Galaxy platform using ChIP-seq data.  Participants will have a chance to: run quality control on ChIP-seq data; map raw reads to a reference genome; generate alignment statistics and check mapping quality; call peaks using MACS; annotate peaks; visualize the enriched regions.

    Training Category: Bioinformatics Classes
  • Choosing a quality journal to publish in can be a daunting task. In this class, participants will become familiar with the available resources and tools to assist in targeting quality journals, as well as ways to recognize questionable or predatory journals. Additional topics include tips to avoid a predatory conference, evaluating invitations to serve as a peer review or editorial board member, preprint servers, and what to do if you published with a questionable journal will be covered.

    Training Category: Writing and Editing Classes
  • 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.

    Training Category: Bioinformatics Classes
  • QIAGEN’s CLC Biomedical Genomics Workbench enables researchers to analyze NGS data without the use of command line. In this workshop, we will cover RNA-Seq and variant calling as applicable to human and other organisms. We will explore workflows within the Microbial Genomics Module, including tools for pathogen typing and metagenomics (16S and whole genome).

    Registrants will receive an email with information and instructions to install CLC Genomics Workbench before the class. Contact your systems office or TS Bioinformatics <TS-Bioinformatics@qiagen.com> for assistance with downloading the software. If you register the day before the class, you may not have time to download and properly install CLC. If you do not have the software installed, this training will be demo only.

    Training Category: Bioinformatics Classes
  • This class will give you an in-depth look at tools for evidence-based clinical information.  Explore our available resources and learn search tips for DynaMed (clinical decision support tool), Access Medicine (decision making tool), Cochrane (evidence to inform healthcare decisions), EBMcal (clinical criteria sets and decision tree analysis tools), and PubMed shortcuts to evidence based clinical information. Learning Objectives: become familiar with the NIH Library’s clinical resource tools and know how to use them; and do better, more efficient searching using clinical resource tools.

  • Leveraging Network Biology to Drive Drug Discovery is the first of a two-part series exploring the Computational Biology Methods for Drug Discovery (CBDD), a consortium of pharmaceutical companies focused on implementing state-of-the-art approaches for network and pathway analysis of OMICs datasets to help accelerate innovation. This first class of the series provides an overview of the impact and benefits of the CBDD consortium and how pharmaceutical companies are leveraging network and pathway algorithms to understand disease mechanisms and drive drug discovery. Participants will learn how systems biology tools that combine data with prior knowledge uncover insights from WGS, GWAS, RNA-Seq, and single cell data; how algorithms developed in the consortium can help advance cutting-edge research; and which algorithms are optimal depending on research goal(s). Register separately for Part 2 of this series, Bioinformatics Use Cases.

    Training Category: Bioinformatics Classes
  • Bioinformatics Use Cases is the second of a two-part series exploring the Computational Biology Methods for Drug Discovery (CBDD), a consortium of pharmaceutical companies focused on implementing state-of-the-art approaches for network and pathway analysis of OMICs datasets to help accelerate innovation. The second class of the series provides further discussion on the CBDD package in technical detail. Participants will learn how to repurpose and use existing drug information for a new indication; reconstruct functional mechanisms for disease; identify biomarkers to advance drug development; discuss common challenges with peers to uncover solutions; develop more accurate algorithms in a shorter timeframe; and make quicker and more informed R&D decisions.

    Training Category: Bioinformatics Classes
  • This class will help authors navigate the legal and ethical issues surrounding copyright and plagiarism, identify and avoid potential copyright infringement issues, and ensure the integrity of their work as a component of their publishing process. This is part of NIH LIbrary's writing and publishing class series that supports writing, publishing, and scholarly communication.

     

    Training Category: Writing and Editing Classes
  • This one-hour training will provide detailed information on how to create charts in MS Excel, including reviewing and selecting chart types, layouts, and styles. The training will also cover changing colors and format options, as well as how to make changes to titles and labels. This is an introductory class for those who need to quickly learn basic Excel chart features and for those who are interested in a refresher.  

    No previous experience with Excel is required, but basic familiarity with Microsoft Office is helpful. 

    Training Category: Data Services Classes
  • The NIH Library's Data Services program provides classes on a variety of topics related to managing, analyzing, and visualizing data.

  • 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.

    Training Category: Data Services Classes
  • 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.

    Training Category: Data Services Classes
  • 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.

    Training Category: Data Services Classes
  • 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.

    Training Category: Data Services Classes
  • 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.

    Training Category: Data Services Classes
  • Over 80% of technical information in patents are not published in scientific/ medical literature. This one-hour class will review the importance of patent literature in fostering innovation and the formulation of research questions. Derwent Innovations Index (DII) covers worldwide chemical, electronic, electrical, and mechanical engineering patents from 1963 to present, with expanded titles, rewritten abstracts, and additional indexing. The instructor will review DII’s advantages and demonstrate its use in harnessing patent data to research drugs and medical devices in development and as part of a state-of-the-art background review; and support research into breakthrough discoveries and innovative applications of existing knowledge, from initial discovery to patenting decisions. This resource is available to NIH staff via the NIH Library’s Web of Science platform.  

  • Small molecules tend to interact with many, often unrelated, targets, some of which may lead to adverse effects. Early identification of off-target interactions is essential to help reduce attrition rates related to toxicology and safety issues through the use of OFF-X.

     

    Training Category: Bioinformatics Classes
  • This session focuses on defining the scope of your review by applying techniques to formulate a workable research question. The class introduces various frameworks used for developing a research question, and presents the requirements and steps involved in conducting the literature search for the systematic review. Useful resources are introduced throughout the session.

  • 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.

    Training Category: Bioinformatics Classes
  • Writing compelling and concise research papers requires many skills including a familiarity with ethical writing. This class provides an overview of the major issues surrounding the topic of publication ethics. Participants will form a foundation of best practices for professional writing by understanding ethical issues and ways to effectively avoid them.

    Training Category: Writing and Editing Classes