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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.

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.

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Lecture 2: How to use the command line

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

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Lecture 3: Data analysis at the command line

How to process biological data from the command line.

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Lecture 4: What do the words mean?

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

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Lecture 5: Statistics Survival Guide

Terminology and definition for the most commonly used statistical terms.

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Lecture 6: How to interpret a list of genes?

Functional enrichment, functional over-representation.

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Lecture 7: Biological data formats

Learn how information is represented in biology

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Lecture 8: Automating data access

How to automate data access

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Lecture 9: Sequence format FASTA and FASTQ

Understand sequence representation data formats

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Lecture 10: Quality control of sequencing data

How to evaluate and improve sequencing data quality

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Lecture 11: How to write Unix data analysis scripts

Learn to automate data analysis with reusable scripts

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

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

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Lecture 13: Sequencing concepts

How do sequencers work. Sequencing coverage. How much data do we need.

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Lecture 14: Sequence alignments

Introduction to sequence alignments, alignment scoring, local, global and semi-global alignments

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Lecture 15: BLAST, Basic Local Alignment Search Tool

Learn to use BLAST at the command line, build BLAST databases, learn to customize BLAST

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Lecture 16: Short Read Alignment

Learn to perform high throughput sequencing data alignment.

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Lecture 17: Sequence Alignment Maps (SAM/BAM)

What information does a SAM file contain.

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Lecture 18: Visualizing and interpreting BAM files

Understand how to visually evaluate high throughput sequencing data alignments.

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Lecture 19: Large Scale Genomic Variation

How to do insertions, deletions, copy number variations appear in high throughput sequencing data

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Lecture 20: Working with BAM files.

How to filter and process BAM files from command line.

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Lecture 21: Variant (SNP) calling from short reads

How to call SNPs and short variations from sequencing reads

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Lec 22: RNA-Seq data analysis

Quantifying gene expression via sequencing

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