Course Catalog

PubMed comprises tens of millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites. This class introduces advanced PubMed features that allow for focused searches. Participants will learn how to personalize PubMed using My NCBI, use internal PubMed vocabulary lists (MeSH) to create more efficient search strategies, truncation use, and searching with Boolean operators. Additionally, skills in applying search filters, creating subject alerts, and exporting search results to bibliographic management software such as EndNote will be discussed.

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.

This class focuses on structuring spreadsheet data so that it is ready for data analysis, or for importing into R or Python. By the end of this class, students should be able to: describe best practices for data entry, and formatting data in spreadsheets; list common formatting mistakes; describe different approaches for handling dates in spreadsheets; identify tips for quality control and data manipulation in spreadsheets; exporting data from spreadsheets.

Publication bias refers to the phenomenon where studies with statistically significant results are much more likely to be published in peer-refereed journals than studies that report a nonsignificant conclusion. Bias develops when results are pooled from published studies alone, leading to an overestimation of the effectiveness of the intervention. In this presentation we define publication bias, how it affects the results of clinical research literature reviews, how it can be detected and minimized, and how it can be prevented.

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.

Participants will learn how to use the point-and-click interface in Partek Flow for RNA-Seq analysis to go from raw data to experimental results. By completing this session, attendees will acquire a working knowledge of the tools available to NIH researchers for the start to finish RNA-Seq data analysis.

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.