Description:

sequanadenovo 0.10.0

This is is the denovo pipeline from the Sequana projet

Overview:
a de-novo assembly pipeline for short-read sequencing data

Input:
A set of FastQ files

Output:
Fasta, VCF, HTML report

Status:
production

Citation:
Cokelaer et al, (2017), ‘Sequana’: a Set of Snakemake NGS pipelines, Journal of Open Source Software, 2(16), 352, JOSS DOI doi:10.21105/joss.00352



Installation
sequana_denovo is based on Python3, just install the package as follows:
pip install sequana --upgrade
You will need third-party software such as fastqc. Please see below for details.


Usage
The following command will scan all files ending in .fastq.gz found in the local
directory, create a directory called denovo/ where a snakemake pipeline is
stored. Depending on the number of files and their sizes, the
process may be long:
::

sequana_denovo –help
sequana_denovo –input-directory DATAPATH

This creates a directory with the pipeline and configuration file. You will then need
to execute the pipeline:
cd denovo
sh denovo.sh # for a local run
This launch a snakemake pipeline. If you are familiar with snakemake, you can
retrieve the pipeline itself and its configuration files and then execute the pipeline yourself with specific parameters:
snakemake -s denovo.smk -c config.yaml --cores 4 --stats stats.txt
Or use sequanix interface.


Requirements
This pipelines requires the following executable(s):

spades
busco
bwa
khmer : there is not executable called kmher but a set of executables (.e.g .normalize-by-median.py)
freebayes
picard
prokka
quast
spades
sambamba
samtools




Details
Snakemake de-novo assembly pipeline dedicates to small genome like bacteria.
It is based on SPAdes.
The assembler corrects reads and then assemble them using different size of kmer.
If the correct option is set, SPAdes corrects mismatches and short INDELs in
the contigs using BWA.
The sequencing depth can be normalised with khmer.
Digital normalisation converts the existing high coverage regions into a Gaussian
distributions centered around a lower sequencing depth. To put it another way,
genome regions covered at 200x will be covered at 20x after normalisation. Thus,
some reads from high coverage regions are discarded to reduce the quantity of data.
Although the coverage is drastically reduce, the assembly will be as good or better
than assembling the unnormalised data. Furthermore, SPAdes with normalised data
is notably speeder and cost less memory than without digital normalisation.
Above all, khmer does this in fixed, low memory and without any reference
sequence needed.
The pipeline assess the assembly with several tools and approach. The first one
is Quast, a tools for genome assemblies
evaluation and comparison. It provides a HTML report with useful metrics like
N50, number of mismatch and so on. Furthermore, it creates a viewer of contigs
called Icarus.
The second approach is to characterise coverage with sequana coverage and
to detect mismatchs and short INDELs with
Freebayes.
The last approach but not the least is BUSCO, that
provides quantitative measures for the assessment of genome assembly based on
expectations of gene content from near-universal single-copy orthologs selected
from OrthoDB.


Version
Description



0.10.0

use click / include multiqc apptainer



0.9.0

Major refactoring to include apptainers, use wrappers



0.8.5

add multiqc and use newest version of sequana



0.8.4

update pipeline to use new pipetools features



0.8.3

fix requirements (spades -> spades.py)



0.8.2

fix readtag, update config to account for new coverage setup



0.8.1


0.8.0
First release.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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