Extent of employment: 30 Hours per Week
Duration of employment: 1st of October 2015 to 30th of September 2019
Gross monthly salary and pay grade in terms of collective agreement for university staff (payable 14 times per year): B1, € 1.997,20

Responsibilities
The successful candidate (f/m) will pursue a Ph.D. project related to the interpretation of plant genome and transcriptome sequencing data from next-generation sequencing (NGS) platforms. In particular, the candidate will characterize the unexplored genome of quinoa, a crop plant of long-standing tradition in Latin America. We collaborate with research partners in Austria and abroad, and the candidate’s project will be of central importance in the context of this research network.

Required skills and qualifications
We are looking for a graduate student (f/m) with a Master’s degree in bioinformatics or in a related field, solid programming skills (e.g. developing sequence analysis tools), experience with the analysis of NGS data sets, understanding of lab methods and knowledge of genomics/transcriptomics. The group has successfully performed several projects using NGS technology. We have recently published the reference genome sequence of sugar beet (Dohm et al., Nature, 2014), a crop plant closely related to quinoa (same family, but different genus). Not yet published is a quinoa genome assembly that we have generated, and which will serve as the starting point of the candidate’s project. We are a multidisciplinary team and offer work in a lively and friendly atmosphere, and state-of-the-art computing infrastructure. We are looking forward to expanding our team by a dedicated and strongly motivated person with a distinct interest in the challenges of plant genomics.

Applications can be submitted until: 16th of August 2015

University of Natural Resources and Life Sciences Vienna seeks to increase the number of its female faculty and staff members. Therefore qualified women are strongly encouraged to apply. In case of equal qualification, female candidates will be given preference unless reasons specific to an individual male candidate tilt the balance in his favour.

Please send your job application (incl. letter of motivation, CV, summary of Master’s thesis and contact details for two referees) to Personnel department, University of Natural Resources and Life Sciences, 1190 Vienna, Peter-Jordan-Straße 70; E-Mail: kerstin.buchmueller@boku.ac.at. (Reference code: 59)

We regret that we cannot reimburse applicants travel and lodging expenses incurred as part of the selection and hiring process.

www.boku.ac.at

The Shenzhen company says the low cost will be possible with an “extreme” DNA sequencing system it plans to offer that is capable of decoding the genomes of 100,000 people a year.

Ref: https://www.technologyreview.com/s/615289/china-bgi-100-dollar-genome/

• Updated human gene set (GENCODE 21)
• Updated rat gene set including manual annotation from HAVANA
• New species: Vervet-African green monkey
• Imported Transcript Support Levels (TSLs) from UCSC for human and mouse
• Imported APPRIS flag for human and mouse
• Updated Amazon molly gene set

Find more at http://www.ensembl.info/blog/2014/10/02/ensembl-77-has-been-released/

Address of the bookmark: https://github.com/AppliedBioinformatics/RefKA

Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.

Address of the bookmark: https://pypi.python.org/pypi/Orange-Bioinformatics/2.5.34

More at https://www.biorxiv.org/content/10.1101/2020.03.15.992941v1.full

Address of the bookmark: https://github.com/marbl/merqury

Qualified and interested candidates can send their curriculum vitae by e-mail to hr@drils.org on or before 27th October 2014 mention in the subject line of the mail the following code: AMPK-Biology.

Selected candidates will be called for a personal interview to Dr. Reddy’s Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad. The selected candidate is expected to report within two weeks from the date of selection to start work on the project.

Junior Research Fellowship (Molecular Modeling/Biology) for two years and Senior Research fellowship for one year

Junior Research Fellowship: Rs. 15,600/- (consolidated) per month for first two years.
Senior Research Fellowship: Rs. 18,200/-(consolidated) per month for the 3rd year.

Duration: The duration of the fellowship is for three years. However, the performance of the candidate will be reviewed after the completion of every year and the fellowship will be renewed only upon satisfactory performance.

Responsibilities:

1) Literature search.
2) Design, plan and execute experiments under the supervision of the scientist.
3) Provide scientific support to the scientist in his/her research activities.
4) Book keeping and maintenance of stocks and consumables.

Essential Qualifications:

Required: M.Sc. in Microbiology/Biotechnology/Bioinformatics or any other related branch of basic Sciences from a recognized university/institute with a consistent academic record of minimum 60% aggregate in all qualifying examinations. The candidate should be NET qualified for lectureship. The candidate should be motivated to work with dedication.

Desirable: expertise/experience in both Molecular Modeling and Molecular Biology.

Experience: 0-2 years in the areas of Molecular Modeling and/or Molecular Biology and cell biology and Biochemistry.

Preferable: Relevant research experience as evident from thesis/dissertation/project work.

Advertisement: http://www.ilsresearch.org/userfiles/Junior%20REsearch%20Fellowship%20-%20AMPK(Biology).pdf

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

Responsibilities

Lead teams of scientists in structuring, prototyping, and executing large-scale bioinformatic and other analysis.
Develop novel bioinformatics, statistical, data processing, pathway, data mining and other algorithms to identify biological signals and their clinical correlates in broad kinds of individual and population data.
Develop novel platform-level analytical tools for sequence-based assays (assembly, annotation, variant calling and interpretation, phasing, genome structure, etc.), expression assays (RNAseq and microarray), proteomics, and metabolomics.
Develop statistical models that robustly correlate complex laboratory-derived information with phenotypic and clinical information.
Create scientifically rigorous visualizations, communications, and presentations of results.

Reference @ https://www.google.com/about/careers/search#!t=jo&jid=62095001

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