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!
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Documentation
Mailing List (Google...
darkhorse.ucsd.edu - DarkHorse is a bioinformatic method for rapid, automated identification and ranking of phylogenetically atypical proteins on a genome-wide basis. It works by selecting potential ortholog matches from a reference database of amino acid...
drive5.com - USEARCH >Extreme high-throughput sequence analysis. Orders of magnitude faster than BLAST. MUSCLE >Multiple sequence alignment. Faster and more accurate than CLUSTALW.
UPARSE >OTU clustering for 16S and other marker genes....
This book is a manifestation of my desire to teach researchers in biology a bit more about statistics than an ordinary introductory course covers and to introduce the utilization of R as a tool for analyzing their data. My goal is to reach those...
github.com - Simple ideogram plotting and annotation in R.
Basic usage:
Rscript Ideoplot.R --heatmap hm.bed --annotate annotations.bed --out ideogram.pdf -or- Rscript Ideoplot.R --annotate annotations.bed
Options
--ideobed, i A bed file of reference...
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...
www.ploscollections.org - PLOS present collection of Education articles: “Translational Bioinformatics”. This collection is presented as an online “book” which could serve as a reference tool for a graduate level introductory course, marking a...
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...