sourceforge.net - Metassembler combines multiple whole genome de novo assemblies into a combined consensus assembly using the best segments of the individual assemblies.
Genome assembly projects typically run multiple algorithms in an attempt to find the single best...
github.com - NovoGraph: building whole genome graphs from long-read-based de novo assemblies
An algorithmically novel approach to construct a genome graph representation of long-read-based de novo sequence assemblies. We then provide a proof of...
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 - 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 - EAGLER is a scaffolding tool for long reads. The scaffolder takes as input a draft genome created by any NGS assembler and a set of long reads. The long reads are used to extend the contigs present in the NGS draft and possibly join overlapping...
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