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...
Clinical Development Services Agency (CDSA) is an extramural unit of Translational Health Science and Technology Institute (THSTI), Department of Biotechnology, Ministry of Science & Technology, Government of India. CDSA has a national mandate...
microscope.readthedocs.org - Microscope Platform user documentation.
The MicroScope platform is available at this URL:
https://www.genoscope.cns.fr/agc/microscope
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...
www.bioinformatics.babraham.ac.uk - Understanding Following table and graphs
Duplication level
kmer profile
per base GC content
per base N content
per base quality
per base sequence content
per sequence GC content
per sequence quality
sequence length distribution
More at...
http://docs.bpipe.org/ - Bpipe provides a platform for running big bioinformatics jobs that consist of a series of processing stages - known as 'pipelines'.
January 20th, 2016 - New! Bpipe 0.9.9 released!
Download latest, all
Documentation
Mailing List (Google...
github.com - GroopM is a metagenomic binning toolset. It leverages spatio-temoraldynamics (differential coverage) to accurately (and almost automatically)extract population genomes from multi-sample metagenomic datasets.
GroopM is largely parameter-free. Use:...
github.com - This project contains scripts and tutorials on how to assemble individual microbial genomes from metagenomes, as described in:
Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes
Mads...
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...