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 from Aug 22rd, 2022 to December 11, 2022. Lectures will appear every week.
Note: Lectures have tests associated with them. These tests were intended to be substantially more challenging than the typical homework assignments! Each question was designed to probe a more profound understanding of the subjects.
I recommend forming small study groups to discuss each test. 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.
| Lecture | Your Score |
|---|---|
|
Lecture 1: Getting started with Bioinformatics
How is bioinformatics practiced? Computer setup. |
Test |
|
Lecture 2: How to use the command line
How to use Unix? Why is the command line so useful in bioinformatics. |
Test |
|
Lecture 3: Data analysis at the command line
How to process biological data from the command line. |
Test |
|
Lecture 4: What do the words mean?
How to make sense of terminology. Sequence and gene ontologies. |
Test |
|
Lecture 5: Statistics Survival Guide
Terminology and definition for the most commonly used statistical terms. |
Test |
|
Lecture 6: How to interpret a list of genes?
Functional enrichment, functional over-representation. |
Test |
|
Lecture 7: Biological data formats
Learn how information is represented in biology |
Test |
|
Lecture 8: Automating data access
How to automate data access, download genome wide information, download data by accession number |
Test |
|
Lecture 9: Sequence format FASTA and FASTQ
Understand sequence representation data formats |
Test |
|
Lecture 10: Quality control of sequencing data
How to evaluate and improve sequencing data quality |
Test |
|
Lecture 11: How to write Unix data analysis scripts
Learn to automate data analysis with reusable scripts |
Test |
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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 |
Test |
|
Lecture 13: Sequencing concepts
How do sequencers work. Sequencing coverage. How much data do we need. |
Test |
|
Lecture 14: Sequence alignments
Introduction to sequence alignments, alignment scoring, local, global and semi-global alignments |
Test |
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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 |
Test |
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Lecture 16: Short Read Alignments
Learn to perform high throughput sequencing data alignment. |
Test |
|
Lecture 17: Sequence Alignment Maps (SAM/BAM)
What information does a SAM file contain. |
Test |
|
Lecture 18: Visualizing and interpreting BAM files
Understand how to visually evaluate high throughput sequencing data alignments. |
Test |
|
Lecture 19: Visualizing Genomic Variation
How to do insertions, deletions, copy number variations appear in high throughput sequencing data |
Test |
|
Lecture 20: Working with BAM files.
How to filter and process BAM files from command line. |
Test |
|
Lecture 21: Variant (SNP) calling from short reads
How to call SNPs and short variations from sequencing reads |
Test |
|
Lec 22: RNA-Seq data analysis
Quantifying gene expression via sequencing |