BOL

github.com - KAT is a suite of tools that analyse jellyfish hashes or sequence files (fasta or fastq) using kmer counts. The following tools are currently available in KAT: hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in...

FYI, I've found it useful to use MUMmer to extract the specific changes that Racon makes, so I can evaluate them individually: minimap -t 24 assembly.fasta long_reads.fastq.gz | racon -t 24 long_reads.fastq.gz - assembly.fasta...

github.com - pyGenomeTracks aims to produce high-quality genome browser tracks that are highly customizable. Currently, it is possible to plot: bigwig bed (many options) bedgraph links (represented as arcs) Hi-C matrices (if HiCExplorer is...

github.com - The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by Oxford Nanopore flow cells. Computational methods used by the Shasta assembler include: Using...

www.cs.utoronto.ca - With the relative ease and low cost of current generation sequencing technologies has led to a dramatic increase in the number of sequenced genomes for species across the tree of life. This increasing volume of data requires tools that can quickly...

github.com - MeDuSa (Multi-Draft based Scaffolder), an algorithm for genome scaffolding. MeDuSa exploits information obtained from a set of (draft or closed) genomes from related organisms to determine the correct order and orientation of the contigs. MeDuSa...

www.nature.com - GMOL was developed based upon our multi-scale approach that allows a user to scale between six separate levels within the genome. With GMOL, a user can choose any unit at any scale and scale it up or down to visualize its structure and retrieve...

shendurelab.github.io - LACHESIS is method that exploits contact probability map data (e.g. from Hi-C) for chromosome-scale de novo genome assembly. Further information about LACHESIS, including source code, documentation and a user's guide are available...

COMPARATIVE MICROBIAL GENOMICS ANALYSIS WORKSHOP  @ cbs.dtu.dk Free Bioinformatics workbench https://www.mn.uio.no/ifi/english/research/networks/clsi/earlier_seminars/2012/tammivesth_osloseminarfinal.pdf

For a beginner this can be is the hardest part, it is also the most important to get right. It is possible to create a vector by typing data directly into R using the combine function ‘c’ x same as x creates the vector x...