Відео

This quick tutorial shows you how to use the fantastic block editing feature in VSCode to, for example, apply the same command to many samples while only typing it once!

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applied-computational-genomics.​). This lecture introduces the concepts and considerations related to Monte Carlo experiments for testing whether overlaps between two genome interval datasets differ from expectation.

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applied-computational-genomics.​). This lecture introduces VIM, the utility of the bash_profile, and new UNIX commands (ps, kill, top).

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applied-computational-genomics). This lecture introduces the concepts of annotating new genome sequences using molecular and computational techniques.

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applied-computational-genomics). This lecture introduces awk, one of the most powerful UNIX commands for manipulating and transforming data. We close with an introduction to bioawk from Heng Li, which extends awk to genomics file formats.

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applied-computational-genomics). This lecture introduces the concepts of DNA mapping and its complexities with hash-based mapping strategies. It also introduces the Smith-Waterman alignment algorithm to discern an optimal sequence alignment given a scoring scheme.

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applie...​). This lecture introduces discusses sources of Poisson and non-Poisson technical variation in RNA-seq that complicate biological interpretation.

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applie...​). This lecture introduces Poisson processes and its relevance to counting applications (e.g., RNA-seq) in biology.

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applie...​). This lecture digs deeper into R data frames, precision v. accuracy, and a basic introduction to RNA-seq. Level is beginner.

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applie...​). This lecture introduces R (RStudio) through basic data analysis concepts and introductory commands. Level is beginner.

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applie...). This lecture continues with UNIX by introducing the use of `cat`, `more`, `less`, `grep`, and `cut`.

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applie...). An introductory lecture to the history of computational biology and quick intro to UNIX.

SLLOBS was fortunate to have a guest lectures from Susan Holmes, Professor of Statistics at Stanford University (statweb.stanford.edu/~susan/). This lecture provides a wonderful introduction to Expectation Maximization and how it can be applied to biological questions.

From Aaron Quinlan's course on Applied Computational Genomics at the University of Utah (github.com/quinlan-lab/applie...). This lecture introduces the the concepts of supervised machine learning through a conceptual demonstration of how handwriting recognition tools work. This covers the application of Bayes's Rule and Naive Bayes Classifiers.

Applied Computational Genomics - 15 - Monte Carlo analyses with bedtools

Applied Computational Genomics - 14 - Real-world analyses with bedtools

Applied Computational Genomics - 13 - An introduction to bedtools.

Applied Computational Genomics - 12 - Genome Annotation Formats

Applied Computational Genomics - 11 - What is Structural Variation and how do we detect it?

Applied Computational Genomics - 10 - VCF Annotation

Applied Computational Genomics - 09 - Variant Calling Format (VCF) and Hardy-Weinberg Equilibrium.

Applied Computational Genomics - 08 - Detecting Genetic Variation Part 2.

Applied Computational Genomics - 07 - Detecting Genetic Variation Part 1.

Applied Computational Genomics - 06 - SAM, BAM, CRAM format.

Applied Computational Genomics - 05 - Sequence Mapping and Alignment: Methods and Considerations.

Applied Computational Genomics - 04 - DNA Sequencing Technologies, Sequencing Error, & FASTQ format.

Applied Computational Genomics - 03- Genetic Variation

Applied Computational Genomics - 02- Pattern Matching in the Human Genome

Applied Computational Genomics - 01- The Human Genome