Part of the reason R has become so popular is the vast array of packages available at the cran and bioconductor repositories. In the last few years, the number of packages has grown exponentially!
This is a short post giving steps on how to...
www.r2d3.us - In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.
More at http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
wiki.fysik.dtu.dk - SLURM workload manager software, a free open-source workload manager designed specifically to satisfy the demanding needs of high performance computing.
This page is a HOWTO guide for setting up a SLURM installation, currently focused on a CentOS 7...
astrobiomike.github.io - This site aims to be a useful resource for bioinformatics beginners. Feel free to jump right in with the section most relevant to you, and if you're not sure, then the place to start is definitely Unix
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...
github.com - This pipeline performs the following steps:
Assembly of nanopore reads using Canu.
Polish canu contigs using racon (optional).
Map a paired-end Illumina dataset onto the contigs obtained in the previous steps...
github.com - dnaPipeTE (for de-novo assembly & annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs...
github.com - Integration of the Ra assembler - a de novo DNA assembler for third generation sequencing data developed on Faculty of Electrical Engineering and Computing (FER), Ruder Boskovic Institute (RBI) and Genome Institute of Singapore (GIS).
Ra is in...
atifrahman.github.io - SWALO (scaffolding with assembly likelihood optimization) is a method for scaffolding based on likelihood of genome assemblies computed using generative models for sequencing.
Please email your questions, comments, suggestions, and bug reports to...