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

Additional Information:

If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).

Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).

Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.

Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.

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

More at https://genohub.com/ngs-instrument-guide/

Address of the bookmark: https://genohub.com/ngs-instrument-guide/

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

• BigDataScript – A cross-system scripting language for working with big data pipelines in computer systems of different sizes and capabilities. [ paper-2014 | web ]
• Bpipe – A small language for defining pipeline stages and linking them together to make pipelines. [ web ]
• Common Workflow Language – a specification for describing analysis workflows and tools that are portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. [ web ]
• Cromwell – A Workflow Management System geared towards scientific workflows. [ web ]
• Galaxy – a popular open-source, web-based platform for data intensive biomedical research. Has several features, from data analysis to workflow management to visualization tools. [ paper-2018 | web ]
• Nextflow (recommended) – A fluent DSL modelled around the UNIX pipe concept, that simplifies writing parallel and scalable pipelines in a portable manner. [ paper-2018 | web ]
• Ruffus – Computation Pipeline library for python widely used in science and bioinformatics. [ paper-2010 | web ]
• SeqWare – Hadoop Oozie-based workflow system focused on genomics data analysis in cloud environments. [ paper-2010 | web ]
• Snakemake – A workflow management system in Python that aims to reduce the complexity of creating workflows by providing a fast and comfortable execution environment. [ paper-2018 | web ]
• Workflow Descriptor Language – Workflow standard developed by the Broad. [ web ]

• Whole-genome sequencing
• Whole-exome sequencing
• RNA-seq (speical mode available)
• ChIP-seq

Address of the bookmark: http://qualimap.bioinfo.cipf.es/

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/

Computational methods used by the Shasta assembler include:

• Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
• Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

More at https://chanzuckerberg.github.io/shasta/index.html

Address of the bookmark: https://github.com/chanzuckerberg/shasta