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

No. CBSH/MBGE/356

Subject: Advertisement for the award of Junior Research Fellowship.

Applications are invited for award of one Junior Research Fellowship on a consolidated fellowship of Rs. 12,000/- pm in the project “Bioinformatics Sub-DIC ”, under the Coordinatorship Dr. Anil Kumar. The fellowship is purely temporary and may continue till the duration of the project or maximum three years which ever is earlier. The appointment shall be given on six monthly review basis.

ESSENTIAL QUALIFICATION

M.Sc. Bioinformatics having research experience on In silico experimentation.

Candidates possessing the above qualifications may submit their application on
plain paper in the following format to the undersigned latest 18 April, 2014 the interviews will be held on 19 April, 2014 at 11.00 AM in the office of the undersigned. No separate letter for interview will be issued or any TA/DA will be paid for attending the interview.

Advertisement: http://www.gbpuat.ac.in/01042014_18april14_Advertisement%20for%20JRF%20Position,%20BI.pdf

Address of the bookmark: https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html

Applications are invited from qualified individuals for a JRF/SRF position (sponsored by DBT/DST) at Prof. Jagan Pongubala’s laboratory, University of Hyderabad. Dr. Pongubala’s laboratory is investigating the molecular pathways that control the development of innate and adaptive immune cell types utilizing a combination of genetic, molecular and computational approaches.

JRF/SRF

Masters degree in Bioinformatics (M.Sc./M.Tech.)

Rs. 12,000+HRA
Rs. 16,000+HRA

Initial appointment is for one year and subjected to renewal up to 2 years

Candidates selected for the above position would have a choice to work on computational biology or experimental biology. Candidates interested to work on computational biology are expected to perform high-throughput sequencing (NGS) data analysis and should have a strong background in Bioinformatics & Computational Biology, good programming skills particularly Perl, Python, R and work experience in Linux environment.

Candidates interested to work on experimental biology should have work experience in techniques that are routinely used in molecular biology and mammalian cell culture. A basic knowledge of bioinformatics is also desired.

Applicants for the above positions should have a Masters degree (M.Tech/M.Sc) with an aggregate marks greater than 70% or a 7.5 CGPA. Candidates having JRF-fellowship through CSIR/UGC/ICMR/DBT will be encouraged to enroll into Ph.D. program. The interested candidates having excellent organizational skills and the ability to work in a team environment with an aspiration to learn new techniques and explore new scientific areas are requested to generate their resume using the link https://cvmkr.com/CV/new#0 and forward to pongubalajagan@gmail.com

Review of applications will begin immediately and continue until the position is filled. Eligible candidates will be called for an interview. No TA/ DA will be paid for attending the interview or at the time of joining the post. Applicants should note that the appointment is purely temporary and subjected to renewal up to three years and there is no Right to Claim for any regular appointment with the University.

Corresponding address: Jagan Pongubala, Ph.D.
Department of Animal Sciences
School of Life Sciences, Room:S44
University of Hyderabad
Gachibowli, Hyderabad 500046

Advertisement: https://www.uohyd.ac.in/images/recruitment/jrf-srf_130414.pdf

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

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

Bose Institute, Kolkata, invites applications from Indian Citizens for ONE (01) temporary position of Junior Research Fellow in the DBT sponsored project entitled, “Centre of Excellance (CoE) in Bioinformatics at Bose Institute”, running under Prof. Pinakpani Chakrabarti, Project Co-ordinatior, Bioinformatics Centre. The project is tenable upto 31.03.2017, but duration of the fellowship is one year only. The JRF will work with one of the faculty members of the center based on his / her motivation in any specific area on Bioinformatics.

Essential Qualification: 1st class M.Sc. / M.Tech degree in any stream of Chemical/ Biological Sciences with CSIR-UGC-NET-JRF / ICMR-JRF / DBT-JRF or CSIR-UGCNET- LS / GATE qualification.

Desirable qualification:

(i) Specialized knowledge in Organic / Physical chemistry.
(ii) Any exposure to research involving the small molecules (like drug) and / or protein structure determination or prediction.
(iii) Basic knowledge in computer programming, e.g. using FORTRAN, C, shell, perl etc.
(iv) Hands-on-experience on any of the following software : CHARMM/AMBER/NAMD/GROMACS,Gaussian/Gamess, Haddock/Autodock, Schrodinger etc. (or any other software serving similar purposes in molecular modeling)

Fellowship :

(i) Rs. 16,000/- p.m., plus admissible HRA & Medical Benefit for M.Sc. with CSIRUGC NET-JRF/ICMR-JRF/DBT-JRF or M.Tech. with CSIR-UGC NETJRF/
ICMR-JRF/DBT-JRF/CSIR-UGC NET-LS/GATE
(ii) Rs. 12,000/- p.m., plus admissible HRA & Medical Benefit for M.Sc. with CSIRUGC NET-LS/GATE

Age : Below 28 years as on the day on which the application is made (relaxable in case of SC/ST/OBC/WOMEN candidates only as per rule).

Interested and eligible candidates should apply on plain paper duly signed by them clearly mentioning the area of interest in research, possession of any desirable qualification (s) as mentioned above and quoting Advertisement No. on the envelop as well as application with complete Bio-data giving e-mail ID, Phone No. and details of qualification i.e. examination passed, year, division, percentage of marks, from Secondary onwards with attested copies of testimonials, addressed to the Registrar, Bose Institute, P-1/12, CIT Scheme VII-M, Kankurgachi, Kolkata-700054 on or before April 25, 2014.

The shortlisted candidates will be called for an interview. Applicants are advised to check our website for future updates.

Advertisement: www.boseinst.ernet.in/ADVT/14/p_2.pdf

https://www.bioconductor.org/packages/devel/bioc/vignettes/maftools/inst/doc/maftools.html

Address of the bookmark: https://github.com/PoisonAlien/maftools

The fate of a cell is ultimately the product of the regulation of its genes. Gene regulation is a coordinated process acting at multiple levels of which transcription and translation are the most prominent. The Valen group is dedicated to the fundamental question of how transcription and translation is integrated to obtain the desired protein abundance. The recent development of high-throughput next generation sequencing techniques to monitor both active translation and transcription has made it possible to study this connection at the genome scale.

This project aims to elucidate the links between regulation of translation and transcription. The applicant will analyze next generation sequencing data and model gene regulation on a genome-wide level to identify the features that affect the translational output of transcripts. The work will be done in close collaboration with experimental scientists who will test the predictions of the computational models.

Additional information on the position can be obtained by contacting Eivind Valen (eivind.valen@ii.uib.no).

The research fellow must take part in the University’s approved PhD program leading to the degree within a time limit of 3 years. Application for admission to the PhD program, including a project plan outline for the training module, will be worked out in collaboration with the research group in question.

In total, the fellowship period is 4 years, 25 % of this will be allocated to teaching and/or administrative duties. The fellowship period may be reduced if the successful applicant has held previous employment as a research fellow or similar.

http://www.jobbnorge.no/en/available-jobs/job/102235/research-fellow-phd-candidate-in-computational-biology-2-positions

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 forward reads
• illumina_R2.fastq.gz: the Illumina reverse reads

Assemble

Run Spades:

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o spades_assembly_all_illumina

• -1 is input file of forward reads
• -2 is input file of reverse reads
• --careful minimizes mismatches and short indels
• --cov-cutoff auto computes the coverage threshold (rather than the default setting, “off”)
• -o is the output directory

Results

Move into the output directory and look at the contigs:

infoseq contigs.fasta

1. Make friends with local bioinformatics groups
2. Talk to your computing group
3. Obtain clear expectations
4. Rewrite your job description
5. Papers
6. Attend bioinformatics meetings
7. Try first, ask later

Address of the bookmark: http://biomickwatson.wordpress.com/2013/04/23/a-guide-for-the-lonely-bioinformatician/