BOL

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...

sourceforge.net - Contiguity preserving transposition and sequencing (CPT-seq) is an entirely in vitro means of generating libraries comprised of 9216 indexed pools, each of which contains thousands of sparsely sequenced long fragments ranging from 5 kilobases to...

harvest.readthedocs.io - Harvest is a suite of core-genome alignment and visualization tools for quickly analyzing thousands of intraspecific microbial genomes, including variant calls, recombination detection, and phylogenetic trees. Tools Parsnp - Core-genome...

ftp.ncbi.nih.gov - Now a days there are a lots of genomics databases available around the world. This bookmark is created to provide all links in one place ... ftp://ftp.ncbi.nih.gov/genomes/ https://hgdownload.soe.ucsc.edu/downloads.html

github.com - MitoHiFi v3.2 is a python pipeline distributed under MIT License ! MitoHiFi was first developed to assemble the mitogenomes for a wide range of species in the Darwin Tree of Life Project...

github.com - MECAT is an ultra-fast Mapping, Error Correction and de novo Assembly Tools for single molecula sequencing (SMRT) reads. MECAT employs novel alignment and error correction algorithms that are much more efficient than the state of art of aligners and...

If we only had Illumina reads, we could also assemble these using the tool Spades. You can try this here, or try it later on your own data. Get data We will use the same Illumina data as we used above: illumina_R1.fastq.gz: the Illumina...

The genome assemblers generally take a file of short sequence reads and a file of quality-value as the input. Since the quality-value file for the high throughput short reads is usually highly memory-intensive, only a few assemblers, best suited for...

instructions successfully build Bandage on a fresh installation of Ubuntu 14.04

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