BOL

New SPAdes 3.15.0.0. announced by the SPADes team

ucdavis-bioinformatics-training.github.io - Our team offers custom bioinformatics services to academic and private organizations. We have a strong academic background with a focus on cutting edge, open source software. We replicate standard analysis pipelines (best practices) when...

github.com - Just import the assembly, bam and ALE scores. You can convert the .ale file to a set of .wig files with ale2wiggle.py and IGV can read those directly.  Depending on your genome size you may want to convert the .wig files to the BigWig format.

metagraph.ethz.ch - The MetaGraph framework is designed to work with a wide range of input data sets, indexing from a few samples up to the contents of entire archives with hundreds of thousands of records. The indexing workflow always follows the same principle,...

christophergandrud.github.io - Mike Bostock’s D3.js is great for creating interactive network graphs with JavaScript. The d3Network package makes it easy to create these network graphs from R. The main idea is that you should able to take...

https://r-graphics.org/ - R is powerful tool for data analysis, visualization, and machine learning. And it costs $0 to use! Here are six FREE books you can use to learn R...

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...

github.com - ALPACA requires Celera Assembler 8.3 or later. It is recommended to build Celera Assembler from source. (Why? The pre-built binaries CA_8.3rc1 and CA8.3rc2 will work for any large data set.  Detail paper...