Address of the bookmark: https://www.nature.com/articles/srep20802

Here are few companies:

https://mynucleus.com/

https://myome.com/

https://nebula.org/whole-genome-sequencing-dna-test/

The software is implemented in Python 3 and runs in both Python 2.7 and 3.4– on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast_core/ under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/.

Address of the bookmark: https://dfast.nig.ac.jp/

Address of the bookmark: http://www.h-invitational.jp/g-compass/

mScaffolder scaffolds a genome using an existing high quality genome as the reference. It aligns the two genomes using nucmer utility from MUMmer and then orders and orients the contigs of the candidate genome guided by their alignments to the reference genome. Please send your questions and comments to mchakrab@uci.edu.

Citation https://www.nature.com/articles/s41588-017-0010-y

Address of the bookmark: https://github.com/mahulchak/mscaffolder

• Step 1. Run mugsy to get multiple sequence alignment results.
• Step 2 & 3. Extraction of the Coordinates of Core Blocks, Construction of Synteny Blocks and Generating Signed Permutations.
• Step 4. Generate pairwise genome rearrangement scenarios and find repeats at the breakpoints of each rearrangement events.

https://github.com/DanwangJessica/GRSR

Address of the bookmark: https://github.com/DanwangJessica/GRSR

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ

To create single-genome plot by aligning genome data along all chromosomes of a single genome, go to the Single-genome plot menu.

To cretae two-genome plot for comparison of data across two genomes, go to the Two-genome plot menu.

For the detail format of input data, check the Input data format submenu of the Help menu.

shinyChromosome is deployed at http://150.109.59.144:3838/shinyChromosome/, http://shinyChromosome.ncpgr.cn, and https://yimingyu.shinyapps.io/shinyChromosome for online use. The source code and manual of shinyChromosome are freely available at https://github.com/venyao/shinyChromosome.

https://yimingyu.shinyapps.io/shinychromosome/

https://www.sciencedirect.com/science/article/pii/S1672022919301883

Address of the bookmark: https://yimingyu.shinyapps.io/shinychromosome/

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

Genome Workbench can display sequence data in many ways, including graphical sequence views, various alignment views, phylogenetic tree views, and tabular views of data. It can also align your private data to data in public databases, display your data in the context of public data, and retrieve BLAST results.

Genome Workbench is built on the NCBI C++ ToolKit and uses cross-platform APIs for graphics. It runs on your local machine, and is available for Windows 2000/XP, Linux, MacOS X, and various flavors of Unix.

NCBI Genome Workbench is an integrated application for viewing and analyzing sequence data. Genome Workbench was developed entirely in-house at NCBI and makes use of the NCBI C++ ToolKit. The C++ ToolKit provides a convenient and flexible cross-platform API for managing system internals, database connections, network sockets, and the NCBI data model. In addition, the C++ ToolKit provides the Object Manager, which abstracts handling of sequences and sequence-related objects.

 New Features in Genome Workbench 2.7.15

• Multiple Alignment View: implemented adaptive feature display when zooming in
• Active Objects Inspector replaces Selection Inspector. New View should offer an improved selection context examination. See Using Active Objects Inspector tutorial for more details.
• Binary packages for Linux OpenSUSE 13.1 are now available

Bug Fixes and Improvements in Genome Workbench 2.7.15

• Fixed major issue with OpenGL overlay/scrolling. Could cause crashes or view scrolling irregularities
• Multiple Pane View: fixed crash on loading BLAST results
• Graphical Sequence View: fixed crash on zooming in and out, related to SNP track
• Graphical Sequence View: fixed Go To Position dialog to give better diagnostics in case of a user error
• Graphical Sequence View: PDF export fixed rendering of Markers with commas in the name
• Text View / Flat File: fixed Mac OS rendering issues
• Text View / Flat File: performance optimization, extended capabilities of real-time rendering of molecules to tens of thousands
• File Import: optimization improvement to speed up load of files containing multiple project items
• File Import: remapping stage now shows accession.version and description of molecules, instead of plain GI numbers
• Mac OS: improved tooltips for toolbar buttons
• Phylogenetic Tree Builder Tool: improved diagnostics of errors
• Multiple Alignment View: optimizations to avoid main GUI freezes
• Open Dialog: removed duplicate elements in table of genomes (load Genome)
• PDF export: fixed issue with XREF table errors
• Tree View: fixed issues with showing Force Layout progress on Mac OS
• Tree View: PDF export fixed issues for showing labels of collapsed nodes
• Tree View: added an option to stop layout
• Tree View: broadcasting mechanism fixed not to accumulate selected nodes

Reference:

NCBI news

http://www.ncbi.nlm.nih.gov/tools/gbench/