www.homolog.us - Useful bioinformatics tutorial, such as
De Bruijn Graphs for NGS AssemblyAlgorithms for PacBio ReadsSoftware and Hardware Concepts for BioinformaticsFinding us in Homolog.us (Search Algorithms)NGS Genome and RNAseq Assembly - a Hands on...
This tutorial is intended to introduce users quickly to the basics of R, focusing on a few common tasks that biologists need to perform some basic analysis: load a table, plot some graphs, and perform some basic statistics. More...
homer.salk.edu - This tutorial covers topics independently of HOMER, and represents knowledge which is important to know before diving head first into more advanced analysis tools such as HOMER.
Setting up your computing environment
Retrieving and storing...
engr.case.edu - In this report we provide an overview of known techniques for discovery of patterns of biological sequences (DNA and proteins). We also provide biological motivation, and methods of biological verification of such patterns. Finally we list publicly...
github.com - This is not so much an instructional manual, but rather notes, tables, and examples for Python syntax. It was created by the author as an additional resource during training, meant to be distributed as a physical notebook. Participants (who favor...
homes.sice.indiana.edu - Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models...
omega.omicsbio.org - Omega found overlaps between reads using a prefix/suffix hash table. The overlap graph of reads was simplified by removing transitive edges and trimming short branches. Unitigs were generated based on minimum cost flow analysis of the overlap graph...
github.com - Miniasm is a very fast OLC-based de novo assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by minimap) as input and outputs an assembly graph in the GFA format. Different from mainstream...