R is a functional based language, the inputs to a function, including options, are in brackets. Note that all dat and options are separated by a comma
Function(data, options)
Even quit is a function
q()
So is...
tldp.org - This tutorial assumes no previous knowledge of scripting or programming, yet progresses rapidly toward an intermediate/advanced level of instruction . . . all the while sneaking in little nuggets of UNIX® wisdom and lore. It serves as a...
https://gatb.inria.fr/ - The Genome Analysis Toolbox with de-Bruijn graph (GATB) provides a set of highly efficient algorithms to analyse NGS data sets. These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge...
biobits.org - SAMtools: Primer / Tutorial by Ethan Cerami, Ph.D.keywords: samtools, next-gen, next-generation, sequencing, bowtie, sam, bam, primer, tutorial, how-to, introductionRevisions 1.0: May 30, 2013: First public release on...
sfg.stanford.edu - This website and accompaning documents are intended as a tool to help researchers dealing with non-model organisms acquire and process transcriptomic high-throughput sequencing data without having to learn extensive bioinformatics skills. It covers...
scilifelab.github.io - SciLifeLab is a national center for molecular biosciences with focus on health and environmental research.
Courses
Old courses (2012-2014)
Metagenomics Workshop
2015 November - Uppsala2016 November - Uppsala2017 November - Uppsala
Introduction...
github.com - ComplexHeatmap (Gu, Eils, and Schlesner (2016)) is an R Programming Language (R Core Team (2020)) package that is currently listed in the Bioconductor package repository.
install and load required packages...
bioinformatics.uconn.edu - This section explains some of the commonly used file formats in bioinformatics. The information provided here is basic and designed to help users to distinguish the difference between different formats. Please refer user manual or other information...
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...