Course Catalog

The class will start with an overview of the new data visualization tool in Partek Flow – Data Viewer, which provides more flexible and easier ways to integrate information collected from the data, that helps biologists discover biological meanings. The instructor will then go through the steps on analyzing and visualizing on CITE-Seq data using the new Data Viewer in Partek Flow. For example: data import, QA/QC, data filter and normalization, clustering analysis, dimension reduction and visualize in 2/3 D, and differential expression. Benefits: Acquire advanced knowledge of the new tools available in Partek Flow to NIH researchers for Multi-omics data analysis.Who should attend: NIH staff interested in learning advanced features and Multi-omics data analysis in Partek Flow.

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.

Web of Science is a database providing access to billions of cited references, dating back to 1900 in the areas of life sciences, social sciences, arts, and humanities. Scopus is a comprehensive abstract and citation database including millions of records from journals, books, and conference proceedings. Leveraging the capability of both powerful research tools to perform bibliometric analysis can provide a wealth of publication information. This class will provide an introduction to methods in assessing individual and organizational publication performance, with an emphasis on citation analysis. Participants will additionally learn different methods to explore how bibliographical data might be analyzed and retrieved using Web of Science and Scopus.

Overview

Advances in technology have reduced the cost of single cell sequencing, opening the doors to many new areas of study including transcriptome, DNA genomics, epigenomics and microbial systems. This workshop, provided by experts from across NIH, will give an overview of single-cell sequencing, especially in single cell RNA-seq, highlight tips and potential related to single-cell RNA sequencing, and introduce the major methods and tools available for single-cell RNA sequencing and analysis (both commercial and open source).

Strategies and Methods in scRNA-seq Data Analysis

Li Jia, Bioinformatician, NIH Library

Single cell sequencing has reopened the definition of a cell’s identity and the ways in which that identity is regulated by the cell’s molecular circuitry. Learn the types of studies that are well suited for single cell sequencing analysis as well as how to design a single cell experiment. The speaker will also walk through the steps of a Single Cell RNA-Sequence (scRNA-seq) processing, common analysis strategies, and state-of-the-art analysis methods using R.

Avoiding Common Pitfalls in Single Cell RNA-Seq Experiments

Michael Kelly, Senior Scientist, Single Cell Analysis Facility, Frederick National Laboratory

As the use of single cell sequencing becomes increasingly common, researchers may have a false sense that the technique is immune to issues that undermine the experiment, only to find limitations at the data analysis stage. The speaker will discuss various examples of potential data issues that can arise such as variability in number of targets datapoints, low gene detection, and technical batch effects. As part of this discussion the speaker will address some strategies for how to avoid them, and what they might look like in the final dataset. The speaker will also discuss some of the approaches used during a typical single cell RNA-Seq analysis workflow to help mitigate effects on your data.

The Applications of Current Single Cell Sequencing

Brian J. Henson, Senior Specialist, Illumina, Inc.

The speaker will provide an overview and demonstration of the current single-cell applications available, including RNA, ATAC, CNV, TCR, Epitope, and spatial gene expression. Several examples from the literature will be highlighted as use cases for the tools. The speaker will conclude with a practical discussion on the utility and capacity of using the single-cell applications on the NovaSeq and the NextSeq 2000 benchtop sequencers.

Single Cell Analysis in Partek Flow

Xiaowen Wang, Senior Technical Support, Partek, Inc.

Demonstration from a Partek scientist who will utilize Single Cell RNA-Seq data within Partek Flow to streamline Multi-omics data analysis. This GUI-based tool helps to overcome common analysis challenges on scRNA-Seq data and has built in data visualization options.

Identifying and Interpreting the Human Liver Cellular Landscape using OmicSoft and IPA

Eric Seiser, Senior Application Scientist, QIAGEN Bioinformatics

The speaker will provide a practical example of how they utilized publicly available scRNA-Seq data in a research study.  Specifically, the speaker processed scRNA-Seq human liver data using the OmicSoft single-cell analysis pipeline to identify numerous discrete cell populations.  Gene signatures from these resident cells were then analyzed in Ingenuity Pathway Analysis to determine both shared and distinct cell biology in the context of pathways, regulation, and functional characteristics.  These results provide insight into hepatic cells as well as the immune microenvironment within the liver.

Learn how to use the Biomedical Translational Research Information System (BTRIS) to streamline the task of gathering and reporting active protocol data. BTRIS staff will demonstrate how to run queries in BTRIS and then guide attendees through running reports for their protocols.

Class Prerequisites:
You must have an established BTRIS user account for access to identified data. These accounts are given to all Principal Investigators or with permission to their designees. If you do not have an account, please complete the BTRIS Access form.

For additional information on these sessions, go to the BTRIS webpage.

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).

Copyright infringement and plagiarism are important considerations when communicating your research to others. In this class, participants will learn how to avoid copyright infringement and plagiarism when writing research papers or making conference presentations. Participants will be introduced to resources that will assist them in determining copyright, obtaining copyright permissions, and alternatives for use of graphics and data when licensing fees are not an option. In addition, an introduction to quick and easy guidelines for citing work that is not your own will help you keep your career on a positive trajectory. 

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.

Note for the webinar version of this class: 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.

This class will take a deep dive into the beginning steps of conducting a systematic review.  At the completion of the class, participants will have a clear understating of the different types of systematic reviews, strategies to formulate a workable systematic review question, how to create a protocol or “roadmap” for the review, and the requirements and steps involved in conducting a literature search for the systematic review. Useful resources are introduced throughout the session.