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 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 | Your Score |
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Lecture 1: Getting started
How is bioinformatics practiced. Computer setup. |
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Lecture 2: How do I use the command line?
Unix command line use. Find help on commands. |
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Lecture 3: How to access published data from the command line
Reproducibility. Data repositories. Entrez Direct |
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Lecture 4: How to visualize biological data
Learn to use the IGV genome browser |
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Lecture 5: Sequence Ontology
What do words mean. How is biology encoded into data |
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Lecture 6: Gene Ontology and Functional Analysis
How to make sense of your data |
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Lecture 7: Sequencing Instruments, computing coverages
How do sequencing instruments work. How to compute coverages. |
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Lecture 8: Automating the download of published sequence data
Obtain data deposited in the Short Read Archive. |
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Lecture 9: Quality control of sequencing data
How to evaluate and improve the sequencing data quality |
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Lecture 10: Writing and Refactoring Scripts
How to write reusable data analysis scripts |
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Lecture 11: Reproducibility and Bioinformatics Recipes
What is reproducibility and its most common fallacies |
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Lecture 12: Sequence Alignment
What sequence alignment are and how to control the algorithms. |
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Lecture 13: Using BLAST
How to use BLAST to search for similarities |
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Lecture 14: Short Read Aligners
How to use short read aligners such as bwa and bowtie |
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Lecture 15: Sequence Alignment Maps: The SAM/BAM format.
Learn how to interpret the information stored in BAM files |
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Lecture 16: Large scale genomic variation
How to recognize large scale variation from alignment data |
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Lecture 17: Small scale variation calling
How to detect small scale variation in genomic data |
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Lecture 18: RNA-Seq Analysis
learn to perform RNA-Seq data analyses |