1. Session 1: WELCOME AND INTRODUCTIONS
2017
Session 1: WELCOME AND INTRODUCTIONS

2. Instructors and Teaching Assistants
Main Instructors:
James C. Fleet, PhD (Nutrition Science)
Wanqing Liu, PhD (Medicinal Chemistry and Molecular Pharmacology)
Pete Pascuzzi, PhD (Libraries)
Min Zhang, PhD (Statistics)
Teaching Assistants: (Statistics)
Chen Chen
Min Ren
Kirsen Sullivan
Will Eagan
Harley Schawadron
Fleet 2017

3. Introductions Who are you? Where are you from?
What is your research interest?
Why are you interested in “big data”?
Fleet 2017

4. Workshop Overview Fleet and Pascuzzi Unit 1: Microarray
Unit 2: Next Generation
Sequencing
Liu and Zhang
Unit 3: Biomarker Discovery
Unit 4: Genetic Variation
Technical Goals:
Analysis pipelines
Statistical issues
Visualization
Functional annotation
Databases
Project management
Computation and programming
Fleet 2017

5. Course Materials http://www.stat.purdue.edu/bigtap/index.html

6. Guest Lecturers Doug Crabill (Purdue University)
Bruce Craig (Purdue University)
Xiang Zhang (University of Louisville)
Sean Davis (National Cancer Institute)
Dan Raftery (University of Washington)
Yonglan Zheng (University of Chicago)
Nancy Cox (Vanderbilt University)
Nadia Atallah (Purdue University)
Fleet 2017

7. Session 2: Working with the Purdue Computer Infrastructure
Doug Crabill
Department of Statistics
Purdue University

8. Sites to Understand Computing
UNIX operating system
Learn UNIX
Linux operating system
R coding
Fleet 2017

9. Session 3: Data Repositories and Pre-processed Data Sites
James C. Fleet, PhD
Distinguished Professor
Department of Nutrition Science

10. Data Archives Web link Description
NIH Data Sharing Repositories
Trans-NIH BioMedical Informatics Coordinating Committee (BMIC) sites
Gene Expression Omnibus (GEO)
NCBI; transcriptome and ChIP-seq datasets
Array Express
EMBL-EBI repository to archive functional genomics data
European Nucleotide Archive (ENA)
Comprehensive record of worlds nucleotide sequencing information
The Cancer Genome Atlas (TCGA)
Multi "omic" phenotype characterization of tumors
Proteomics IDEntifications (PRIDE)
European proteomics datasets
Metabolomics Workbench
metabolomic datasets
Fleet 2017

11. Genotype-Tissue Expression project (Gtex)
Data Archives
Web link
Description
Oncomine
715 microarray datasets from 19 cancers
Gene Expression across Normal and Tumor tissue (GENT)
gene expression patterns in human cancer from Affy Chips ( cell lines)
cBioPrortal
TCGA cancer genomics
Genotype-Tissue Expression project (Gtex)
human, multi-tissue gene expression and gene variation for eQTL
Immunological Genome Project (Immgen)
transcriptome data from cultured mouse immune cells
Human Brain Transcriptome
transcriptome and associated metadata for developing and adult human brain.
NHLBI Kidney Transcriptome database
Segment-specific expression in rat kidney
Kidney Systems Biology Project
Multi-omic database from rat and mouse studies
Saccharomyces Genome Database
Integrated biological information on budding yeast
miRBase
published miRNA sequences, annotation. Expression dataset links available
Fleet 2017

12. Fleet 2017

13. Training GEO Datasets Unit 1 and 2
GSE15947: Time course of 1,25(OH)2 D treated RWPE1 cells (Unit 1)
GSE80182: A TGFb-PRMT5-MEP50 Axis regulates cancer cell invasion through histone H3 and H4 arginine methylation coupled to transcriptional activation and repression. (Unit 2)
GSE #: Accession number for an original, submitter supplied record that summarizes a study
GDS #: GSE data that is reassembled by GEO staff into a curated data set
GSM #: Accession number for a specific sample within a dataset
GPL #: The platform used to generate a dataset
SRX #: Accession number for a sample generated by NGS that is deposited in the Sequence Read Archive (SRA)
Fleet 2017

14. Assignment 1 (Individual)
Search GEO for datasets that relate to your research
Select one dataset
Identify important information about your dataset
Description/design
GSE and GDS #
Sample information
Platform
Analyze your dataset using
GEO2R
Tools in the dataset browser