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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 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.

    Training Category: Bioinformatics Classes
  • Understanding the biology in next generation sequencing data requires a comprehensive and integrated approach and must be supplemented with highly-curated data content from multiple sources. Genomatix, a computational biology company with a 22-year history of developing tools and data content for understanding the molecular mechanisms of eukaryotic gene expression, provides an easy-to-use bioinformatics software platform for the detailed analysis of next generation sequencing data. This lecture and hands-on training workshop series will introduce students to two complete, automated workflows for the independent analysis of RNA-Seq and ChIP-Seq data using the Genomatix platform.

    Students will additionally have the option of taking a follow-on course and will learn about the advanced applications of the Genomatix platform for the meta-analysis and integration of RNA-Seq and ChIP-Seq data, including the identification of transcription factor binding sites, gene regulatory cassettes and genome-wide analysis of genes sharing a common transcription factor binding site pattern.

    This course is broken down into morning and afternoon sessions. NOTE: This is the first course in a two-part series. Morning registration and attendance is required to attend the afternoon session.

    The morning session (9:00 am-12:00 pm) will focus on RNA-Seq and will cover the topics of: project creation and management on the Genomatix Genome Analyzer (GGA); uploading FASTQ files to the GGA and the generation/visualization of sequence statistics; mapping of RNA-Seq reads to a reference genome and the generation/visualization of mapping statistics; principle component analysis; differential expression analysis using DESEq2; pathway and network analysis of differentially-expressed genes.

    The afternoon session (1:30 pm-4:30 pm) will focus on ChIP-Seq and will cover the topics of: peak calling algorithms (MACS, SICER, NGSAnalyzer); peak finding; read classification; peak classification; sequence extraction of called peaks; Transcription Factor Binding Site (TFBS) overrepresentation; annotation of called peaks: next neighbor analysis.

    Training Category: Bioinformatics Classes
  • Base SAS includes a programming language that manages data, and includes procedures for data analysis, reporting, and managing SAS files. This class is for those who need to learn data manipulation techniques using the SAS DATA step and procedures to access, transform, and summarize data. Participants should have some experience with SAS and be able to write DATA step code to subset rows and columns, compute new columns, and process data conditionally; sort tables using the SORT procedure; and apply SAS formats. This intermediate course is two full days and is limited to the first fifteen registrants.

    Registrants must attend a SAS class facilitated through the NIH Library for at least half of the class sessions, or they must cancel their registration (or alert Joelle Mornini at Joelle.Mornini@nih.gov) if they cannot attend at least 2 business days prior to the start of the class. If a registrant misses more than half of SAS class sessions and fails to cancel their registration or provide notice of cancellation at least 48 hours in advance, they will be unable to register for SAS classes facilitated through the NIH Library for 12 months.

    Training Category: Data Services Classes
  • Base SAS includes a programming language that manages data, and includes procedures for data analysis, reporting, and managing SAS files. SAS also includes a windowing environment for text editing and file management. This course addresses how to write SAS programs and provides a starting point to learning SAS programming for data science, machine learning, and artificial intelligence. Participants should be able to understand file structures and system commands on their operating systems and be able to access data files on their operating systems. No prior SAS experience is needed. This introductory course is three full days and is limited to the first fifteen registrants.

    Registrants must attend a SAS class facilitated through the NIH Library for at least half of the class sessions, or they must cancel their registration (or alert Joelle Mornini at Joelle.Mornini@nih.gov) if they cannot attend at least 2 business days prior to the start of the class. If a registrant misses more than half of SAS class sessions and fails to cancel their registration or provide notice of cancellation at least 48 hours in advance, they will be unable to register for SAS classes facilitated through the NIH Library for 12 months.

    Training Category: Data Services Classes
  • Scientific posters are communication tools that draw people in to read the research information being displayed. In this class, participants will learn about the form and function of design through hands-on and demonstrations of different technologies that are available to create scientific posters that engage readers. This class is a mix of demos and hands-on, with a focus on the form and function of design. It will provide an introduction to, as well as, hands-on experience in developing scientific posters.

    Training Category: Writing and Editing Classes
  • This class will provide a comprehensive look into the most critical steps of the review process – establishing inclusion and exclusion criteria and screening and collecting data from the relevant studies. Participants will receive best practices for performing these steps to facilitate an effective review.

  • One of the biggest challenges for researchers is keeping up on all the literature in their field of study. There are several types of alerts which can be created to keep up to date on various advances. This session will provide a short overview of some of the various library products, web resources, and alert services available, demonstrating how to build a better search and create email alerts from those searches.

  • Do you want to write a review, but not sure what type of review would be best for your research question or topic? In today’s research environment, everyone seems to be doing a review of some type, especially systematic reviews.  However, not all literature reviews are systematic reviews, nor need to be. There are many other types of evidence-based reviews. This session will explore the various literature review types, the associated methodologies of each, and how to select the best option for you. Class objectives: understand the different types of literature reviews; learn how to identify the best review type for you; and identify at least two review types and their differences.

  • Participants will learn about data loading, quality control, statistical analysis as well as biological contextualization of miRNA microarray data.

    Training Category: Bioinformatics Classes
  • Participants will learn how to identify cell populations and detect differentially expressed genes in a simple Single Cell RNA-Seq experiment with the point-and-click interface in Partek Flow. By completing this session, attendees will acquire a working knowledge of the tools available to NIH researchers for Single Cell RNA-Seq data analysis.

    Training Category: Bioinformatics Classes
  • Single nucleotide polymorphism (SNP) & Variation Suite (SVS) is an analytic tool created to empower researchers to perform complex analyses and visualizations on genomic and phenotypic data. Genome-Wide Association Studies (GWAS) continues to be an effective method for identifying disease susceptible genes in humans and other organisms. Attendees will learn how SVS can be used to perform GWAS and genomic prediction, how to analyze high-quality SNPs by performing the association test, how to use quality control metrics, and how to use genomic prediction with K-Fold to estimate which genotypes best predict a desired phenotype.

    Training Category: Bioinformatics Classes
  • This is a two-day intensive introduction to modern computational techniques for data management, analysis, and visualization with an emphasis on the programming language R. The course assumes no prior programming knowledge. By the end of the workshop, participants will be able to efficiently organize and clean data, manipulate data frames, estimate and work with statistical models, produce a variety of publication-quality plots, and compose dynamic documents that integrate writing, code, and code output. More information and the full curriculum is available at the Software Carpentry website. Attendees should bring a laptop and will be provided a list of software to install in advance.

    Training Category: Data Services Classes
  • During the first part of the session attendees will learn how to use Partek Flow to perform special transcriptions by overlaying cell location information and histological images on single cell RNA-Seq data using 10X genomics data. During the second part of the session, attendees will learn how to run trajectory analysis and calculate pseudo-time.

    Training Category: Bioinformatics Classes
  • In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class to assist researchers in writing a manuscript. Participants will become familiar with statistical issues that can arise as well as recommendations to prevent them. At the end of the class, participants should have a good sense of what to do and not to do when writing the statistical sections of a manuscript. Most of the examples will be related to clinical research; however, anyone can benefit from the tips shared. Plenty of time will be devoted for questions, and references will be provided for more in-depth self-study.

    This class complements, How to Write a Research Paper Parts 1 & 2, as it specifically covers key points in writing the statistical portion of a manuscript – particularly for clinical research. Attendance to How to Write a Research Paper is not required to benefit from this class.

  • What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study?

    This is Part 1 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 1 will address the frequentist approach, and will cover the concepts of hypothesis testing, confidence intervals, Type I and Type II errors, statistical power, and p-values. Technical details will be kept to an absolute minimum.

    Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 2 of this class series.

  • What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study?

    This is Part 2 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 2 will address the Bayesian approach, and will cover the concepts of Bayes’ Theorem, prior and posterior distributions, and Bayes factor. Technical details will be kept to an absolute minimum.

    Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 1 of this class series.

  • 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.

    Training Category: Bioinformatics Classes
  • This session will focus on NGS data, multi-omics analysis of array, and sequence data. Participants will work with text files in GeneSpring for NGS gene expression workflow and .vcf files for variant analysis. 

    Training Category: Bioinformatics Classes
  • Tables, charts, and figures are often used to describe and share complex scientific information. However, it can be difficult to determine when it's appropriate to use these visual tools and how to design them effectively. During this session, participants will learn the best practices for creating tables, charts, and figures and how to customize them for specific journal requirements. An overview of design tools and resources will also be provided.

  • The Cancer Genomics Cloud (CGC), powered by Seven Bridges, is an NCI-funded resource that provides a unified platform for cancer data analysis by co-localizing three components within the cloud: 1) large cancer datasets from TCGA, CPTAC and several others; 2) more than 500 bioinformatics tools and best-practice workflows for analyzing multi-omics data; and 3) the computational capabilities to do large-scale analyses. This hands-on session of CGC allows the participants to browse, query, and filter datasets of interest and bring their own data for collaborative analysis. The CGC also provides the flexibility to use private tools and the ability to complete reproducible and interactive data analyses (e.g., RStudio, Jupyter notebook). Currently, data analysis is supported in both Google and Amazon cloud environments. Altogether, the CGC is a network of findable, accessible, interoperable, and reusable (FAIR) datasets, workflows, and services which make cancer data analysis faster and more easily available for all.

    Training Category: Bioinformatics Classes