Analyze your Genome! (edX)

You will learn current best-practice workflows for Genome Assembly, Variant Calling, Trio Analysis, and Differential Expression Analysis as well as the types of biological problems that motivate them.

What you'll learn

- How to reconstruct a genome by stringing together its tiny fragments using popular genome assembly tools to sequence a pathogen

- How to find mutations in your genome and how to test whether they are linked to genetic diseases.

- Investigate the genetic makeup of a "trio" (mother, father, and child) to figure out whether the child inherited the disease from the parents or unluckily developed the genetic disease through de novo mutations

- How to analyze gene expressions

- How to investigate what genes are turned on or off in various tissues in order to figure out which genes are important for each cell type and which genes are over-expressed in cancer and other diseases.

Course Syllabus

Week 1: Assembling Genomes

Given raw whole genome sequence data, you will learn how to perform genome assembly, assess the quality of the assembled genome, perform annotation and gene prediction, and perform basic comparative genomics

Week 2: Searching for Disease-Causing Mutations

Given raw whole genome sequence data as well as raw whole exome sequence data, you will learn how to perform variant calling and will dive into comparing and contrasting the pros and cons of each sequencing method

Week 3: Will Modifications of Embryos Treat Genetic Diseases?

Given raw whole genome sequence data from parents and a child, you will learn how to perform variant calling on each and how to perform “trio analysis” (e.g. finding which parent was the source of each of the child’s SNVs, searching for rare de novo mutations, and finding compound heterozygous traits)

Week 4: Analyzing Gene Expression

Given raw RNA-Seq data from different samples, you will learn how to align the reads, count the number of transcripts of each gene in either sample, and perform pairwise differential expression across the samples to determine which genes underwent the most significant expression changes