github.com - Genome U-Plot for producing clear and intuitive graphs that allows researchers to generate novel insights and hypotheses by visualizing SVs such as deletions, amplifications, and chromoanagenesis events. The main features of the Genome U-Plot are...
github.com - GRSR is a Tool for Deriving Genome Rearrangement Scenarios for Multiple Uni-chromosomal Genomes. This tool will do the following steps:
Step 1. Run mugsy to get multiple sequence alignment results.
Step 2 & 3. Extraction of the Coordinates...
wiki.bits.vib.be - compare two BWA mapping methods with the online hg18-mapped data
We first operate a rapid inspection of the different BAM files using samtools flagstat. Illumina provided chr21 read mapping obtained with their GA IIx deep...
github.com - Miniasm is a very fast OLC-based de novo assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by minimap) as input and outputs an assembly graph in the GFA format. Different from mainstream...
http://assemblytics.com/ - Download and install MUMmer
Align your assembly to a reference genome using nucmer (from MUMmer package)
$ nucmer -maxmatch -l 100 -c 500 REFERENCE.fa ASSEMBLY.fa -prefix OUT
Consult the MUMmer manual if you encounter problems
Optional: Gzip...
sepsis-omics.github.io - This is a tutorial for a workshop on long-read (PacBio) genome assembly.
It demonstrates how to use long PacBio sequencing reads to assemble a bacterial genome, and includes additional steps for circularising, trimming, finding plasmids, and...
github.com - SKESA is a DeBruijn graph-based de-novo assembler designed for assembling reads of microbial genomes sequenced using Illumina. Comparison with SPAdes and MegaHit shows that SKESA produces assemblies that have high sequence quality and contiguity,...
cab.spbu.ru - QUAST-LG is an extension of QUAST intended for evaluating large-scale genome assemblies (up to mammalian-size).
QUAST-LG is included in the QUAST package starting from version 5.0.0 (download the latest release). Run QUAST as...
To decide which strategy should be our “preferred” genome assembly approach based on data rather than my gut-feeling about the “best assembly” I decided to do some testing with a known “true” reference E Coli K12 MG1655