Adv. No. URDIP/ 6/2014

Opportunity for young Bioinformatics Professionals to make a career in the area of Intellectual Property CSIR has set up a Unit for Research and Development of Information Products (CSIR-URDIP) at Pune to work in the area of scientific informatics. One of the major focus areas of research work at CSIR-URDIP is PATENT INFORMATICS. With the increasing applications of Bioinformatics in the areas of life sciences industry such as Agriculture and Health Care (Diagnostics and Drugs), the output of research in these area is being protected by different forms of Intellectual Property rights. Realizing the importance of IP in the Bioinformatics field, Department of Biotechnology (DBT) has sanctioned a project on “Development, Facilitation and Harvesting of Bioinformatics related Intellectual Property” at CSIR-URDIP.

The project will involve application of Patent Informatics tools and techniques to Bioinformatics (including creation of patent landscapes, preparation of techno-legal reports of patentability, freedom to operate studies) to help protect IPRs and develop and conduct training programmes on IPRs related to Bioinformatics.

CSIR-URDIP invites applications from young Bioinformatics professionals to work on this emerging area which offers challenging opportunities and attractive career possibilities in future.

Position I: Research Associate

No of Positions: One

Consolidated amount Payable: Rs. 22,000/- per month + 20% HRA= Rs.26,400

Qualification: PhD in Bioinformatics. In exceptional cases, candidature of M. Tech. candidates with First class in Bioinformatics with three years of relevant work experience will also be considered.

Age Limit: 35 years. The age should not exceed the limit indicated as on a closing date of receipt of completed application form.

Upper age limit is relaxable for 5 years for SC/ST, OBC, Physically handicapped and female candidates as per CSIR/Government of India rules.

Position II: Junior Research Fellow

No of Positions: one

Consolidated amount Payable: Rs. 16,000/- + 20% HRA = 19,200

Qualification: M.Sc / BE or equivalent in Bioinformatics with minimum of 55% marks in aggregate Job requirement: Scientific literature and patent search, analysis and Report Writing

Preference: Preference will be given to candidates with knowledge of patents and or 1-2 years of experience + Knowledge of Computers (MS Excel + Word Processing)

Age Limit: 28 years. The age should not exceed the limit indicated as on a closing date of receipt of completed application form.

For details please visit our website (www.urdip.res.in/careers) for further details and apply online by 30th September, 2014.

Advertisement: http://www.urdip.res.in/download/Advt6_2014.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

Address of the bookmark: https://arvados.org/

Address of the bookmark: https://cmdcolin.github.io/awesome-genome-visualization/?latest=true&selected=%23BRIG&tag=Comparative

a) M.Sc/or equivalent in biological sciences/related areas [Position Code: PA_JRF_PF_a]
b) B.E/B.Tech/ M.Sc in biotechnology/bioinformatics/computer science/Chemistry/Physics or MCA [Position Code: PA_JRF_PF_b]
c) M.Sc/or equivalent in wildlife sciences/ecology/environmental sciences or MBBS/BVSc/MVSc. [Position Code: PA_JRF_PF_c]

(Candidates with result awaited are NOT eligible to apply)

Upper Age limit 28years

Rs.12000 / Rs.16000 (as sanctioned by the funding agency)

2. Post Doctoral Fellow/Research Associate in multiple research areas [PDF_RA]

Ph.D. (submitted/awarded) in any branch of biological Sciences. Candidates with Ph.D. in other sciences are also encouraged to apply.

Experience in molecular biology, biochemistry, structural biology, cell biology, infectious disease, conservation genetics, veterinary science, reproductive biology, and molecular diagnostics is desired but not mandatory.

[Position Code: PDF_RA]

UpperAge limit 35years

Rs. 22000- 26000 (as sanctioned by the funding agency)

3. Post Doctoral Scientist Fellow [PDSF]

Ph.D in any of the following areas: bioinformatics, next generation sequencing, high throughput data analysis, proteomics, bio-statistics, computer science, information technology, computer hardware and networking/clustering, parallel processing.
[Position Code: PDSF]

Upper Age limit 40 years

Rs. 40000 consolidated (as sanctioned by the funding agency)

Download Application: Last date for apply online: 09th Oct 2014

Advertisement: www.ccmb.res.in//index.php?view=notifications&mid=0&id=71&nid=38

Apply online http://www.ccmb.res.in/positions/temp_notif/online_form.html

More at http://www.ccmb.res.in//index.php?view=notifications&mid=0&id=71&nid=38

Variant calling and/or genotyping

• DiscoSNP++ and discoSnpRAD: Reference-free small variant discovery (SNPs and indels)
• MindTheGap: Detection and assembly of large insertion variants
• TakeABreak: reference-free inversion discovery tool
• SVJedi: Structural Variant genotyper with long read data
• SVJedi-graph: Structural Variant genotyper with long read data using a variation graph

Sequence assembly

• MinYS: reference-guided genome assembly in metagenomics data
• MTG-link: local assembly tool for linked-read data
• Minia: De novo short read assembler
• de-novo pipeline: de-novo assembly pipeline (error correction / contigs / scaffolding) for genomes and meta-genomes
• Mapsembler2: Targeted assembly (not maintained)

Managing k-mers & indexation

• findere: simple strategy for speeding up queries and for reducing false positive calls from any Approximate Membership Query data structure.
  • fimpera extends findere adding the abundance information.
• kmtricks: modular tool suite for counting kmers, and constructing Bloom filters or kmer matrices, for large collections of sequencing data.
• kmindex is a tool for indexing and querying sequencing samples. It is built on top of kmtricks.
• back to sequences: Find sequences (reads, unitigs, genes) related to a set of kmers in large datasets, in a matter of seconds.
• Backpack Quotient Filter: k-mer indexing data structure with abundance
• short read connector: Detect similar reads from potentially large read set
• DSK: Count K-mer in sequences

Pangenome graph manipulation

• Pancat: Pangenome Comparison and Analysis Toolkit
• GFAGraphs: a Python library to handle pangenome graph files in GFA format.

Comparative metagenomics with k-mers

• Simka and SimkaMin: Comparative metagenomics for large-scale datasets
• Comparead & Commet: comparison of metagenomic datasets

Species and bacterial strains identification

• ORI: software using long nanopore reads to identify bacteria present in a sample at the strain level
• StrainFLAIR: STRAIN-level proFiLing using vArIation gRaph

General-purpose sequencing data manipulation

• GASSST: long read mapper
• Leon: short read compressor (now included in GATB-core)
• Bloocoo: short read corrector
• BCALM: Construct compacted de Bruijn graphs (unitigs)

 Protein Structure

• A_Purva: Contact Map Overlap solver
• MD-Jeep: Distance Geometry solver
• CSA: Comparative Structural Alignment

Workflow

• SLICEE: parallel execution of bioinformatics workflows

Comparative Genomics

• CASSIS: detection of rearrangement breakpoints
• PLAST: intensive bank-to-bank sequence comparison
• DRJBreakpointFinder: detection and precise localization of excision sites in proviral segments

ArrayGen specializes in Genomics data analysis and research, as we believe in the level of precision, predictability, benchmark-ability, and data analysis capability of genomics data over other forms of biological data. ArrayGen constantly strives to develop new solutions, and plug the existing gaps in the technological advancement of the field.

More http://www.arraygen.com/

Research topic: Protein function annotation, multiple probabilistic models, domain architecture, machine learning, combinatorial optimization, computer grid.

This project is run on the Laboratoire de Biologie Computationnelle et Quantitative UMR7238 CNRS-UPMC – Analytical Genomics team, headed by A.Carbone. It is co-advised with Pierre-Henri Wuillemin, Laboratoire d’Informatique de Paris 6 – Equipe DECISION.

The postdoc will be payed under a contract of Ingénieur de Recherche lasting 3.5 years and it is available from September 1st, 2014.

Group Web Page: http://www.lcqb.upmc.fr/AnalGenom/home.html

Ref. E-Mail: Alessandra Carbone alessandra.carbone@lip6.fr

About JNIAS
Jawaharlal Nehru Institute of Advanced Studies (JNIAS), Hyderabad has been established by Dr. D. Swaminadhan Research Foundation (DSRF), Hyderabad as a Research and Educational Institution with a view to contribute in developing advanced technologies and build „core competence‟ in specific areas. The activities of JNIAS involves: Education, Research Training and Innovations in the fields of Sciences, Technologies, Humanities and Social Sciences. It aims to blossom into an Advanced Institute of education and research with a reservoir of expertise and experience in the relevant fields and the necessary capability to harness multi-disciplinary research and studies. JNIAS has been recognized as an Advanced Research Institute by Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad and Jawaharlal Nehru Technological University Anantapur (JNTUA), for offering Ph.D., P.G M.Phil, P.G Diploma and Training Programmes in Sciences and Engineering & Technology.

Jawaharlal Nehru Architecture and Fine Arts University (JNAFAU) Hyderabad also recognized JNIAS for offering UG, PG degree in Architecture.

Projects & Facilities

JNIAS offers wide range of projects:

Biotechnology area:

Molecular Biology
Microbiology
Nanotechnology
Bioinformatics (Schrodinger Software)
In Silico studies & Drug Designing
Sequence analysis
Protein structure function studies

Registration
Tuition Fees: Interested students need to pay the following tuition fees:
1. Six Month’s Project: Rs. 20,000/-
2. Four Month’s Project: Rs. 15,000/-
3. Three Month’s Project: Rs. 10,000/-
4. One Month - Hands on Training : Rs. 8,000/-

For enquires call:
91-7893203414 (Biotechnology), 91-9949582263 (Environmental Sciences) 91-8977369305 (Computer Science)

Interested student may download the application from the website (www.jnias.in) and send the hard copy of the completed application forms and Curriculum Vitae along with the Demand Draft drawn on any nationalized Banks in favor of “The Registrar, JNIAS, Secunderabad”. Application forms can be sent through email to academicprojects@jnias.in

Address
Jawaharlal Nehru Institute of Advanced Studies (JNIAS)
6th Floor, Buddha Bhavan, M.G Road,
Secunderabad - 500 003
Andhra Pradesh, India
Tele/Fax: 040- 27541551; 27541553
Mobile: 08885541554
Web site: www.jnias.in

Brochure : https://drive.google.com/file/d/0B3zPwhgA-u-nU0dyMFd2OWcxNUpSTWNYc0xDSGs5UDI4UDNB/view?usp=sharing

• Store alignments and variant calls for one genome or a million.
• Process genomic data in batch by running principal component analysis or Hardy-Weinberg equilibrium, in minutes or hours, by using parallel computing frameworks like MapReduce.
• Explore data by slicing alignments and variants by genomic range across one or multiple samples -- for your own algorithms or for visualization; or interactively process entire cohorts to find transition/transversion ratios, allelic frequency, genome-wide association and more using BigQuery.
• Share genomic data with your research group, collaborators, the broader community, or the public. You decide.

Google Genomics is implementing the API defined by the Global Alliance for Genomics and Health for visualization, analysis and more. Compliant software can access Google Genomics, local servers, or any other implementation.

Address of the bookmark: https://cloud.google.com/genomics/