Syllabus

Instructor

Course details

  • Monday, Wednesday
  • January 25 – May 7, 2021
  • 9:00am – 10:20sm
  • Online

Contacting me

E-mail is preferred. I will try to respond to all course-related e-mails within 1 business day.

Course Overview

Instructor(s): Mikhail Dozmorov
Duration: 15 Weeks
Dates: Monday/Wednesday class: January 25 to May 7, 2021. No classes on March 24 (reading day, also February 23)
Time: 9:00am - 10:20am
Location: online
Office Hours: online, by appointment

Course Description

This is a graduate-level course in cancer bioinformatics. This course aims to introduce the core principles of Cancer Bioinformatics, Computational Genomics, and Biostatistics.

Genomics and the use of next-generation sequencing are at the forefront of biomedical research. Sequencing market is constantly evolving, stimulating the development of new analytical approaches and software tools. To effectively analyze and interpret genomic sequencing data, it is crucial to (1) understand the technologies that produce the data and (2) develop strong computational skills that will be flexible in this dynamic environment.

This is a blended course that combines in-class learning (lectures, labs) with self-directed activities. The course will introduce high-throughput genomic assays including DNA sequencing and genome variation analysis, transcriptome profiling with RNA-seq and miRNA-seq, metagenomics, epigenomic analysis including ChIP-seq and methylation assays, single-cell sequencing, and chromatin conformation capture technologies.

Prerequisites

  • No formal course requirements, but basic knowledge of the following will help
    • Basic statistics knowledge: descriptive statistics, estimators, (linear) modeling (e.g., BIOS 544 or 554 courses)
    • Basic programming skills in R, familiarity with command line (e.g., BIOS 524 course)
  • Hardware
    • A laptop, Mac or Linux OSs are recommended
  • Textbook
    • All articles, reading, course material, and references will be posted on the course website
    • No formal textbook requirement

Topics

  • Biology overview
  • Unix/Linux overview
  • Genomic technologies & DNA sequencing
  • Intro to R data analysis in RStudio
  • Genomic data/resources
  • R/Bioconductor overview
  • DNA sequencing mapping and alignment
  • Genetic variation
  • RNA-seq
    • Experimental Design, Quality Control
    • Normalization, Batch effect
    • Differential expression, Multiple hypothesis testing
    • Supervised and Unsupervised learning
      • Clustering, dimensionality reduction
    • Gene Ontology, Pathway and Functional enrichment
    • Alternative splicing analysis
  • Single-cell sequencing
  • Chromatin Immunoprecipitation (ChIP)-, DNAse-, ATAC-seq
  • Epigenomics
    • Methylation
  • miRNAs
  • Metagenomic
  • Hi-C

Learning Objectives

  • Understand the core principles, strengths, and limitations of high-throughput technologies
  • Learn statistical models and approaches used in sequencing data analysis
  • Gain practical experience in high-throughput Exploratory Data Analysis, visualization, and quality control using Unix and R environment
  • Critically evaluate and apply statistical methods and tools for sequencing data analysis
  • Interpret biological findings provided by different sequencing technologies, and be able to integrate different layers of -omics data

At the conclusion of the course, students will be able to collect, analyze and interpret genomics data using the R programming environment.

Attendance

Attendance is not checked, but students are responsible for all announcements made in class.

Homework

Homework will consist of problem sets for the material covered. Files should be named, e.g., HW01_LASTNAME.Rmd. The homework due date is one week after the lecture. Late homework will NOT be accepted unless permission was given by the instructor. Assignments that are not well organized/documented will receive no credit. Working in small groups is allowed. However, you must do the final writing-up of solutions entirely by yourself. Any two assignments that are word-for-word exactly the same or highly similar in the coding style will receive zero credit. There will be no extra credit projects available.

Final project

  • A final take-home project

Grading Policy

  • Each homework is graded on the scale 0-10, 10 points being the best
  • Total homework grade possible - 100 points
    • Missed deadline, within 24h - minus 3 points
    • Missed deadline, within 48h - minus 6 points
  • Final Project is graded on the scale 0-100, 100 points being the best

Homework and final project grades will be averaged.

Standard A-F grading system will be applied:

  • A: 90-100
  • B: 80-89
  • C: 70-79
  • D: 60-69
  • F: 0-59

Diversity and inclusivity

A primary goal of this course is to be inclusive and of value to the largest number of contributors, with the most varied and diverse backgrounds possible. All participants in this course are encouraged to help to provide a friendly, safe and welcoming environment for all, regardless of age, gender, gender identity or expression, culture, ethnicity, language, national origin, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, and technical ability.

Policies and resources

Students should visit http://go.vcu.edu/syllabus and review all syllabus statement information. The full university syllabus statement includes information on safety, registration, the VCU Honor Code, student conduct, withdrawal, and more.

VCU Honor System

Observe the VCU Honor Pledge in any class- and homework activities

Student Code of Conduct

Guidelines for a productive return to VCU

The Office of Graduate Education

Keep on Teaching and Keep on Learning

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