Southwest-University

Biomedical Genomics July 8-19, 2019 Instructor: Weigang Qiu, Ph.D.
Professor, Department of Biological Sciences, City University of New York, Hunter College & Graduate Center
Adjunct Faculty, Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weil Cornell Medical College Office: B402 Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA Email: weigang@genectr.hunter.cuny.edu Lab Website: http://diverge.hunter.cuny.edu/labwiki/

Host: Shunqin Zhu (祝顺琴), Ph.D.
Associate Professor, School of Life Science, South West University

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Figure 1. Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)

Course Overview

Welcome to BioMedical Genomics, a computer workshop for advanced undergraduates and graduate students. A genome is the total genetic content of an organism. Driven by breakthroughs such as the decoding of the first human genome and next-generation DNA -sequencing technologies, biomedical sciences are undergoing a rapid and irreversible transformation into a highly data-intensive field.

Genome information is revolutionizing virtually all aspects of life sciences including basic research, medicine, and agriculture. Meanwhile, use of genomic data requires life scientists to be familiar with concepts and skills in biology, computer science, as well as data analysis.

This workshop is designed to introduce computational analysis of genomic data through hands-on computational exercises, using published studies.

The pre-requisites of the course are college-level courses in molecular biology, cell biology, and genetics. Introductory courses in computer programming and statistics are preferred but not strictly required.

Learning goals

By the end of this course successful students will be able to:

• Describe next-generation sequencing (NGS) technologies & contrast it with traditional Sanger sequencing
• Explain applications of NGS technology including pathogen genomics, cancer genomics, human genomic variation, transcriptomics, meta-genomics, epi-genomics, and microbiome.
• Visualize and explore genomics data using RStudio
• Replicate key results using a raw data set produced by a primary research paper

Web Links

• Install R base: https://cloud.r-project.org
• Install R Studio (Desktop version): http://www.rstudio.com/download
• Download: R datasets
• A reference book: R for Data Science (Wickharm & Grolemund)

Quizzes and Exams

Student performance will be evaluated by attendance, three (4) quizzes and a final report:

• Attendance: 50 pts
• Assignments: 5 x 10 = 50 pts
• Quizzes: 2 x 25 pts = 50 pts
• Mid-term: 50 pts
• Final presentation: 50 pts

Total: 250 pts

Course Schedule

Date & Hour	Tutorials	Assignment	Quiz & Exam
July 8 (Mon), 8:40-12:10	Introduction; R Tutorial I; File:R-part-1-small.pdf Lecture slides	Assignment #1 (create a WORD document including scripts & graphs (i.e., compile your work into a lab report, due tomorrow) Install R/R studio and the "tidyverse" package on your own computer Recreate Script 1 & Mini-Practical Show help page for function "seq" Download dataset Create a new folder (e.g., Desktop/rtutor) Create a sub-folder (e.g., Desktop/rtutor/data/) Download from http://www.r4all.org/the-book/datasets Save to the sub-folder Unzip the file
July 9 (Tu), 8:40-12:10	R Tutorials II && III, File:R-part-2.pdf Lecture slides	Assignment #2 The following is a portion of the dataset of Mycobacterium growth (kindly shared by Aswad from Dr Xie's lab). It shows OD (optical density) values. Transform this table ("wide" format) into the "tall/tidy" format (use paper & pen, no need to use R studio or any computer program): Hour Control Gene Control.with.Arg Gene.with.Arg 0 0.06 0.022 0.031 0.01 4 0.087 0.102 0.082 0.081 8 0.113 0.185 0.086 0.135 In R studio, read the dataset from the file "FlowerColourVisits.csv" and save it into an object named as "flower" Show head, tail, dimension of the data frame "flower" Show data summary with "summary" & "glimpse" commands. Which column is a categorical data type? Select the column named "colour" Select rows from the 3rd to the 20th Select the 3rd, 10th, and 20th rows Select only the rows that have the colour of "red" (hint use colour=="red" Create a new column, named "logVisit", that is log(1+number.of.visit) Sort the "flower" data by the column "number.of.visit" Perform the following data transformation using the chaining operator (i.e., "%>%"): Select rows from the 3rd to the 20th, then filter by colour of "red", and then show head Obtain the mean number of visit for each colour as a group (Hint: use "group_by" & "summarise")
July 10 (Wed), 8:40-12:10	R Tutorial IV File:R-part-3.pdf Lecture slides	Assignment #3 Task Graph Use the "iris" dataset to make the plot shown at right (Hint: load data with data(iris)) Use the "flower" dataset (see Assignment #2 on how to load data) to make the plot shown at right	Quiz I
July 11 (Thur), 8:40-12:10	Intro to NGS; R Tutorial V	Assignment #4 List pros & cons of Sanger vs NGS Compare accuracy, read length, and error rate between Illumina and PacBio Describe sequence information captured with each of the following file formats: FASTA, FASTQ, SAM, VCF Run t-test
July 12 - 15 (Fri, Sat, Sun, Mon)	(Weekend Break)
July 16 (Tu), 8:00-12:10	Case Study 1. Fish microbiome		Quiz II
July 17 (Wed), 8:00-12:10	Case Study 2. Transcriptome	Assignment #5
July 18 (Thur), 8:00-12:10	Case Study 3. Lyme Disease
July 19 (Fri), 8:00-12:10		Presentations

Papers & Datasets

Omics Application	Paper link	Data set	NGS Technology
Microbiome	Rimoldi_etal_2018_PlosOne	S1 Dataset	16S rDNA amplicon sequencing
Transcriptome	Wang_etal_2015_Science	Tables S2 & S4	RNA-Seq
Transcriptome & Regulome	Nava_etal_2019_BMCGenomics	Tables S2 & S3	RNA-Seq & CHIP-Seq
Proteome	Qiu_etal_2017_NPJ	(to be posted)	SILAC
Population genomics (Lyme)	Di_etal_2018_JCM	Data & R codes	Amplicon sequencing (antigen locus)
Population genomics/GWAS (Human)	Simonti_etal_2016_Science	Table S2	whole-genome sequencing (WGS); 1000 Genome Project (IGSR)
TB surveillance	Brow_etal_2015	Sequence Archives	Whole-genome sequencing (WGS)
Example	Example	Example	Example
Example	Example	Example	Example
Example	Example	Example	Example