At present, some people have posted articles about the analysis process of WGD. I searched for the keyword "wgd pipeline" and found the following:

GenoDup: https:// github.com/MaoYafei/GenoDup-Pipeline
https://peerj.com/articles/6303/
WGDdetector: https:// github.com/yongzhiyang2 012/WGDdetector
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2670-3
wgd: https:// github.com/arzwa/wgd
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1142-2#Sec1
https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-017-0399-x
GeNoGAP https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1142-2
https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-017-0399-x
https://github.com/dfguan/purge_dups
https://www.biorxiv.org/content/10.1101/2020.01.24.917997v1

This article introduces the usage of wgd.

Wgd cannot be installed directly with bioconda at present, so it is a little troublesome to install, because it depends on a lot of software. wgd depends on the following software

BLAST
MCL
MUSCLE/MAFFT/PRANK
PAML
PhyML/FastTree
i-ADHoRe

But the good news is that most of the software it depends on can be installed with bioconda

conda create -n wgd python=3.5 blast mcl muscle mafft prank paml fasttree cmake libpng mpi=1.0=mpich
conda activate wgd

Here mpi=1.0=mpich is selected, because i-adhore depends on mpich. If openmpi is installed, an error will appear while loading shared libraries: libmpi_cxx.so.40: cannot open shared object file: No such file or directory

After that, the installation is much simpler

git clone https://github.com/arzwa/wgd.git
cd wgd
pip install .
pip install git+https://github.com/arzwa/wgd.git
For i-ADHoRe, you need to register at http:// bioinformatics.psb.ugent.be /webtools/i-adhore/licensing/Agree to the license to download i-ADHoRe-3.0

Since my miniconda3 installed ~/opt/, the installation path is so~/opt/miniconda3/envs/wgd/

tar -zxvf i-adhore-3.0.01.tar.gz
cd i-adhore-3.0.01
mkdir -p build && cd build
cmake .. -DCMAKE_INSTALL_PREFIX=~/opt/miniconda3/envs/wgd/
make -j 4
make insatall

Take the sugarcane genome Saccharum spontaneum L as an example. The genome is 8-ploid with 32 chromosomes (2n = 4x8 = 32)

Download the tutorial for CDS and GFF annotation files

mkdir -p wgd_tutorial && cd wgd_tutorial
wget http://www.life.illinois.edu/ming/downloads/Spontaneum_genome/Sspon.v20190103.cds.fasta.gz
wget http://www.life.illinois.edu/ming/downloads/Spontaneum_genome/Sspon.v20190103.gff3.gz
gunzip *.gz

First conda activate wgdstart our analysis environment, and then start the analysis

Step 1 : Use to wgd mclidentify homologous genes in the genome

wgd mcl -n 20 --cds --mcl -s Sspon.v20190103.cds.fasta -o Sspon_cds.out

Step 2 : Use to wgd ksdbuild Ks distribution

wgd ksd --n_threads 80 Sspon_cds.out/Sspon.v20190103.cds.fasta.blast.tsv.mcl Sspon.v20190103.cds.fasta

Step 3 : If the quality of the genome is good, then wgd syncollinearity analysis can be used . It can help us find the collinearity block in the genome and the corresponding anchor point

wgd syn --feature gene --gene_attribute ID \
-ks wgd_ksd/Sspon.v20190103.cds.fasta.ks.tsv \
Sspon.v20190103.gff3 Sspon_cds.out/Sspon.v20190103.cds.fasta.blast.tsv.mcl

 For more reading - There are 9 sub-modules in WGD

• kde: KDE fitting to the Ks distribution
• ksd: Ks distribution construction
• mcl: BLASP comparison of All-vs-ALl + MCL classification analysis.
• mix: Hybrid modeling of Ks distribution.
• pre: preprocess the CDS file
• syn: Call I-ADHoRe 3.0 to use GFF files for collinearity analysis
• viz: draw histogram and density plot
• wf1: Ks standard analysis procedure of the whole genome paranome (paranome), call mcl, ksd and syn
• wf2: Ks standard analysis procedure of one-vs-one homologous gene (ortholog), call wcl and kSD

Address of the bookmark: https://github.com/GMOD/jb2profile

Address of the bookmark: https://ics.hutton.ac.uk/strudel/

You can find the detail of reconstructed data at http://bioinfo.konkuk.ac.kr/DESCHRAMBLER/

Address of the bookmark: https://github.com/jkimlab/DESCHRAMBLER

Further information about fragScaff, including source code, is available at:https://sourceforge.net/projects/fragscaff/files.

Manuscript describing fragScaff was published as: Adey A, Kitzman JO, Burton JN, Daza R, Kumar A, Christiansen L, Ronaghi M, Amini S, L Gunderson K, Steemers FJ, Shendure J#. In vitro, long-range sequence information for de novo genome assembly via transposase contiguity. Genome Research 2014 Dec;24(12):2041-9. doi: 10.1101/gr.178319.114. PubMed PMID: 25327137.

Address of the bookmark: https://sourceforge.net/projects/fragscaff/files/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4783527/

Awards of $5 million to four grantees have been made in fiscal year 2013 under the Genomic Sequencing and Newborn Screening Disorders research program. The program will be funded at $25 million over five years, as funds are made available.

"Hundreds of US babies will be pioneers in genomic medicine through a US$25-million programme to sequence their genomes soon after they are born."

Source:

http://blogs.nature.com/news/2013/09/scientists-to-sequence-hundreds-of-newborns-genomes.html

http://www.genome.gov/27554919

https://gold.jgi.doe.gov/

Address of the bookmark: https://gold.jgi.doe.gov/

Address of the bookmark: https://github.com/iansealy/coursera-assembly

Availability and implementation: Source code and binaries are freely available at https://github.com/rrwick/Bandage. Bandage is implemented in C++ and supported on Linux, OS X and Windows. A full feature list and screenshots are available at http://rrwick.github.io/Bandage.

Address of the bookmark: http://rrwick.github.io/Bandage/