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 provide an overview of single-cell sequencing, especially in single cell RNA-seq. The workshop will also highlight tips and potential approaches to 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).

9:30 a.m.  |   Welcome and Opening Remarks
Keith Cogdill, Director, NIH Library

9:35 a.m.  |   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. This session 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.

10:40 a.m.  |   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.

12:00 p.m.  |   Break (1 hour)

1:00 p.m.  |  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.

2:00 p.m.  |  Single Cell Analysis in Partek Flow
Uchenna Emechebe, Genomic Application Scientist, 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.

3:00 p.m.  |   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.

Biosketches are used more and more in NIH internal reports. Participants will learn how to create a Biosketch with efficiency. Participants will also learn the appropriate components needed to put their best foot forward.

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.

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.

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.

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.