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Bioinformatics Data Analysis

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This course is offered as BMMB 852: Applied Bioinformatics at Penn State. It is a graduate level course that is also open for advanced undergraduates.

The textbook for the course is:

The course teaches bioinformatics from a data-science perspective. The lectures are designed to familiarize students with data formats and the software tools used to transform, analyze and interpret the data.

The course has launched on January 7th, 2019 and will conclude in April 2019. Lectures will appear in the order that they are presented in class.

Lecture Tests
Lecture 1: Getting started

How is bioinformatics practiced. Computer setup.

Lecture 2: How do I use the command line?

Unix command line use. Find help on commands.

Lecture 3: How to access published data from the command line

Reproducibility. Data repositories. Entrez Direct

Lecture 4: How to visualize biological data

Learn to use the IGV genome browser

Lecture 5: Sequence Ontology

What do words mean. How is biology encoded into data

Lecture 6: Gene Ontology and Functional Analysis

How to make sense of your data

Lecture 7: Sequencing Instruments, computing coverages

How do sequencing instruments work. How to compute coverages.

Lecture 8: Automating the download of published sequence data

Obtain data deposited in the Short Read Archive.

Lecture 9: Quality control of sequencing data

How to evaluate and improve the sequencing data quality

Lecture 10: Writing and Refactoring Scripts

How to write reusable data analysis scripts

Lecture 11: Reproducibility and Bioinformatics Recipes

What is reproducibility and its most common fallacies

Lecture 12: Sequence Alignment

What sequence alignment are and how to control the algorithms.

Lecture 13: Using BLAST

How to use BLAST to search for similarities

Lecture 14: Short Read Aligners

How to use short read aligners such as bwa and bowtie

Lecture 15: Sequence Alignment Maps: The SAM/BAM format.

Learn how to interpret the information stored in BAM files

Lecture 16: Large scale genomic variation

How to recognize large scale variation from alignment data

DRAFT
Lecture 17

coming soon

DRAFT
Lecture 18

coming soon

DRAFT
Lecture 19

coming soon

DRAFT
Lecture 20

coming soon