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Applied Bioinformatics (2020)

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The purpose of this course is to introduce students to the various applications of high-throughput sequencing including RNA-Seq, SNP calling, de-novo assembly and others.

The course material will concentrate on presenting data analysis scenarios for each of these domains of applications and will introduce students to a wide variety of existing tools and techniques.

The course will run between Aug 24- Dec 13, 2020

Note: The tests were intended to be quite difficult, substantially more challenging than the typical homework assignments. Each question was designed to probe a deeper and more profound understanding of the subjects.

We recommend forming small study groups to discuss each question. You may learn quite a bit from hearing how others think. Answers may be submitted an unlimited number of times but successive submissions must be at least one hour apart.

Some questions/answers may end up being a little subjective with different valid interpretations, we are working on identifying and reformulating these.

Lecture Your Score
Lecture 1: Getting started with Bioinformatics

How is bioinformatics practiced? Computer setup.

Lecture 2: How to use the command line

How to use Unix? Why is the command line so useful in bioinformatics.

Lecture 3: Data analysis at the command line

How to process biological data from the command line.

Lecture 4: What do the words mean?

How to make sense of terminology. Sequence and gene ontologies.

Lecture 5: Statistics Survival Guide

Terminology and definition for the most commonly used statistical terms.

Lecture 6: How to interpret a list of genes?

Functional enrichment, functional over-representation.

Lecture 7: Biological data formats

Learn how information is represented in biology

Lecture 8: Automating data access

How to automate data access

Lecture 9: Sequence format FASTA and FASTQ

Understand sequence representation data formats

Lecture 10: Quality control of sequencing data

How to evaluate and improve sequencing data quality

Lecture 11: How to write Unix data analysis scripts

Learn to automate data analysis with reusable scripts

Lecture 12: How to get better at writing data analysis scripts

Learn how to read code, how to use recipes to get started

Lecture 13


Lecture 14