Copy Number Variation (CNV) is a type of structural variation in which sections of the genome are duplicated or deleted. Associated with being causal mutations for disease phenotypes, it is important in clinical and research settings to identify CNV events in samples or datasets. Golden Helix’s VarSeq-CNV (VS-CNV) is a calling algorithm that uses one testing paradigm to provide a true simplification of a clinical workflow. VarSeq incorporates the ability to accurately call and annotate CNVs and evaluate germline and somatic mutations according to the Association for Molecular Pathology (AMP) guidelines. Golden Helix CancerKB is an AMP workflow feature that streamlines the analysis time and report generation.

Attendees will learn the following: setting up the VS-CNV caller using BAM files from whole exome data; filtering down to high quality; understanding high confidence CNV events; annotating CNVs using publicly curated catalogs and databases; adding clinically relevant CNVs to the VSClinical AMP workflow; and utilizing Golden Helix CancerKB to obtain expert-curated interpretations. This class will demonstrate updated software features, provide insights into best practice workflows, and show participants how to implement the software into a pipeline solution.

https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/varseq-copy-number-variation-vs-cnv-caller-and-golden-helix-cancerkb-amp-workflow-0

This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. 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: define R data frames; characterize how to inspect data frames; list the major methods for describing the content and structure of data frames; illustrate how to index and subset a data frame; describe how to use comparison operators on a data frame; discuss R factors; describe how to convert R factors; describe how to rename factors; discuss options for dealing with missing data in R; describe how to save data in R.
Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class.
View Details and Register: introduction-r-data-types

Speakers: Nikhita Amod Gogate and Daniel Lyman, George Washington University

Data on biomarkers are being collected for a wide range of cancers and stored in data sets around the world. Staying abreast of these discoveries can be difficult. OncoMX was designed to give researchers a single source for managing discoveries in cancer mutation and expression biomarkers. A collaborative project between The George Washington University, NASA’s Jet Propulsion Laboratory, the Swiss Institute of Bioinformatics, and the University of Delaware, OncoMX offers researchers a centralized, unified, and integrated web resource for tracking and comparing the latest findings on biomarkers to improve cancer detection, prevention, and treatment. This training will demonstrate how to use OncoMX to explore cancer biomarker data, and how to mine existing literature using specifically designed tools and pathways.

For questions, contact Daoud Meerzaman.

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.

https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/snp-and-variation-suite-svs-genome-wide-association-studies-gwas-0

Abstract: Machine learning (ML) has emerged as an essential tool for building models which can be used to predict clinical outcomes for age-related diseases. A significant challenge of ML is knowing which algorithms and parameter settings are appropriate for a given data set and the hidden patterns to be discovered. Automated ML or AutoML has emerged to take the guesswork out of selecting an ML method by letting the computer optimize the method and parameter selection. This makes ML more accessible to non-experts. We introduce here the tree-based pipeline optimization tool (TPOT) for automated discovery of ML pipelines. We applied TPOT to predicting coronary artery disease (CAD) phenotypes using 73 nuclear magnetic resonance-derived lipoprotein and metabolite profiles and 27 demographic and clinical features in the Angiography and Genes Study (ANGES) with a sample size of 925 subjects. We show that TPOT outperforms a standard grid search approach for predicting CAD outcomes and identifies pipelines unlikely to be selected by human experts. The TPOT software is programmed in Python and freely available as open-source from Github (https://github.com/EpistasisLab/tpot).

Speaker: Jason Moore, Ph.D., Director of the Penn Institute for Biomedical Informatics, Philadelphia, PA

Register here with NIH Webex Events: https://nih.webex.com/nih/onstage/g.php?MTID=e7b2a0e8c2dd5ddf486316a551fe555d4

The training will overview the current status of pathway tools, with focus on software available to NIH community. It will discuss the biological interoperation of mutation and expression data in the context of pathways, pathway databases, and popular web-based pathway tools. The pathway software GSEA, g: Profiler, and PATHVIEW will be used to demonstrate how to run the pathway analysis of expression data against GO (Gene Ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes), and MSigDB (Molecular Signatures Database).

https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/pathway-analysis-3

This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them.

Feel free to install the IGV browser in advance from here – https://software.broadinstitute.org/software/igv/download (the UCSV browser is web browser based)

https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/genome-browser-2

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. During Part 2, participants will learn about sharing and archiving data. Audience: Researchers, fellows, post-docs, and trainees. This single registration signs you up for both classes, Part 1 and Part 2.

https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/data-management-and-sharing-two-part-course-0

For the Advanced Training, topics covered depend upon survey results: find key hubs using over-connectivity analysis; using Microarray repository for gene comparisons against public data; building networks with MetaCore; constructing your own pathway maps; performing toxicogenomic analysis in MetaCore; analyzing and building networks with miRNA and mRNA data; analyzing multi-omics data (RNA-seq, proteomics, metabolomics, etc).

https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/metacore-advanced-session-1

NCI Cancer Research Data Commons (CRDC) provides access to large-scale datasets such as The Cancer Genome Atlas (TCGA) that span multiple data types, including genomics, proteomics, imaging, and clinical data. The CRDC offers bioinformatics tools directly in the cloud, thus eliminating the need to download and store large-scale datasets. The cloud also offers greater computational capacity to manage big data analysis, further accelerating research and promoting new discoveries. The training session will provide an overview of NCI CRDC and highlight several research studies to show the utility of the CRDC and its resources. In addition, there will be hands-on training on one of the following platforms: the NCI Cloud Resource platform Seven Bridges’ Cancer Genomics Cloud (SB-CGC), Broad Institute’s FireCloud, or the Institute for Systems Biology’s Cancer Genomics Cloud (ISB-CGC) for prospective users.

https://www-nihlibrary-nih-gov.ezproxy.nihlibrary.nih.gov/training/nci-cancer-research-data-commons-cloud-computing-0