Advt. No. 3 (8)/BP/13

A walk-in-interview will be held in the Biotech Park Office at Sector G, Jankipuram, Kursi Road, Lucknow (U.P.) August 27, 2013 at 11.00 a.m. for the following posts of DBT sponsored project tenable at Biotech Park. Interested candidates fulfilling the requisite qualifications, experience and age as given below, may appear on the date of interview, before the Selection Committee. The candidate will have to join immediately.

INTERVIEW ON August 27, 2013 at 11.00 A.M.

2. SENIOR PROGRAMMER (ONE POST)

a) Educational Qualification M.Sc. Bioinformatics with minimum 60% marks with two years of relevant experience or B.Tech. Bioinformatics or Biotechnology with minimum 60% marks with two years experience in Bioinformatics.

b) Job Requirement Development of databases in multi user environment and application softwares, maintenance of website, Drug designing and QSAR study etc.

c) Desirable Knowledge of Bioinformatics tools, Windows, Linux, C++, JAVA / JAVA Script, Visual Basic, CGI, DBMS/RDBMS and HTML. Experience in various domains of bioinformatics such as structure based drug designing, Newtonian dynamics and OSAR studies.

d) Age Below 35 years (as on the date of interview)

e) Emoluments Rs. 12,000/- per month fixed.

Appointment will be made initially for one year extendable on satisfactory performance till the duration of the project.

3. SENIOR RESEARCH FELLOW: (ONE POST)

a) Educational Qualification M.Sc. in Biotechnology/Botany with minimum 60% marks and knowledge of handling database & database searching.

b) Essential Qualification Expertise in windows, Microsoft excel.

c) Desirable Good knowledge of statistical software packages like SPSS.

d) Age Below 35 years ( as on the date of interview)

e) Job Requirement: Management of database & website in multi user environment, computation of biological field data and generation of reports.

f) Emoluments

18000+ HRA for Net/GATE qualified
14000+ HRA for others

The appointment will be made till the duration of project.

Note: All the candidates should report for interview on or before 10.45 A.M.

General Conditions

The aforesaid positions are purely temporary and do not give the incumbent any right whatsoever for appointment on regular basis.
More Advertisement: http://www.biotechpark.org.in/html/jobs%20in%20Biotech%20Park/Job_2013_04.htm

This is where KiNext comes into the picture. Introduced in a 2024 study published in BMC Bioinformatics, KiNext is a computational workflow designed to identify and classify protein kinases from predicted protein sequences. Instead of relying on a single search method, it brings together several approaches, including Hidden Markov Models, sequence alignment, phylogenetic analysis, and structural comparison.

The search begins with a simple question: does a protein contain the characteristics of a kinase? Protein kinases can change considerably during evolution, but important regions of their sequences often retain recognizable patterns. KiNext uses Hidden Markov Models, or HMMs, to detect these patterns. An HMM does not require a protein to be an exact match to a known kinase. Instead, it looks for a statistical sequence signature associated with kinase proteins, making it possible to detect more distant candidates.

Once potential kinases are identified, KiNext takes the analysis further. It distinguishes conventional eukaryotic protein kinases from atypical protein kinases and then attempts to classify them into different kinase groups and families. This distinction is important because simply identifying a protein as a kinase does not tell the complete story. Different kinase families can have very different evolutionary histories and biological functions.

The next stage brings evolution into the picture. KiNext can align kinase sequences and construct phylogenetic trees, allowing researchers to examine how newly identified proteins are related to previously characterized kinases. When sequence evidence alone is difficult to interpret, structural information can provide another clue. The workflow can incorporate AlphaFold-predicted structures and Foldseek-based structural comparisons to investigate whether an unusual protein resembles known kinase structures.

The researchers tested KiNext using two very different organisms: the Pacific oyster, Crassostrea gigas, and the green alga Ostreococcus tauri. In C. gigas, KiNext recovered previously reported kinases while identifying additional candidates. Structural analysis provided further evidence for many of the newly detected proteins. In O. tauri, the workflow similarly recovered most previously reported kinases and identified additional candidates while refining some of their classifications.

What makes KiNext particularly interesting is not just its ability to find kinases, but how the entire analysis is organized. The workflow uses Nextflow, allowing the different computational steps to be connected into a reproducible pipeline. Containers can also help manage software dependencies, making it easier to run the workflow across different computing environments.

This reproducibility becomes increasingly important as the number of available genomes continues to grow. A researcher studying one organism may be able to perform an analysis manually, but repeating the same process across hundreds or thousands of genomes quickly becomes impractical. A standardized workflow provides a way to perform the analysis consistently while keeping track of how the results were generated.

At its core, KiNext demonstrates a broader change taking place in modern genomics. Sequencing a genome provides an enormous amount of information, but the real scientific challenge begins afterward: understanding what all those sequences mean. Protein kinases are only one part of this larger puzzle, yet they are particularly important because they act as molecular switches throughout the cell.

By combining sequence profiles, evolutionary analysis, and structural evidence within a reproducible computational framework, KiNext provides researchers with a systematic way to uncover these molecular switches. Its real value lies not only in finding more kinases, but in making the process scalable, repeatable, and easier to apply to new genomes.

As genome sequencing continues to expand across the tree of life, tools such as KiNext can help turn enormous collections of protein sequences into meaningful biological stories—one kinase at a time.

Read more about it @

https://link.springer.com/article/10.1186/s12859-024-05953-w

Address of the bookmark: http://www.genengnews.com/keywordsandtools/print/3/32115/

Computational and systems biology: studying the interplay between network topology and biological function, disease, and evolution in molecular (e.g., protein-protein interaction) networks.

Computational chemistry: protein folding; computational drug discovery and design.

Synthetic biology.

More at http://www.cse.nd.edu/~tmilenko/index.html

• The market size of the Indian Bioinformatics Industry , FY’2007-FY’2013
• Market segmentation of India bioinformatics industry by application by sectors, FY’2007-FY’2013
• Market Segmentation of India bioinformatics industry by products and services,FY’2007-FY’2013
• Market Segmentation of India bioinformatics industry by applications of bioinformatics ,FY’2007-FY’2013
• India bioinformatics industry trends and developments
• Government regulations and initiatives of India bioinformatics industry
• Major bioinformatics research institutes in India
• Market Share of leading players in bioinformatics industry in India,FY’2013
• Company profiles of major players in India bioinformatics industry
• Future outlook and projections on the basis of revenue in India bioinformatics market, FY’2014-FY’2018

                                                                                                         (Source: Ken Research)

Address of the bookmark: http://www.kenresearch.com/healthcare/biotechnology/india-bioinformatics-industry-research-report/392-91.html

genomic scale studies
endogenous mechanisms of mutations, germ line and somatic
computational aspects of immunology in cancer
signalling networks
three-dimensional organization of information in the nucleus
gene silencing
metastatic cross-talk
kinase signaling
personalized medicine
detection of biomarkers in cancer
historical DNA variation

From : http://www.ous-research.no/hovig/

Group address:
Eivind Hovig, The Norwegian Radium Hospital, Montebello, 0310 Oslo,Norway
Email: ehovig@radium.uio.no

WALK-IN-INTERVIEW FOR JUNIOR RESEARCH FELLOWSHIP

Project title: Structural and Functional Evolution of Bacterial ADP-ribosylation Superfamily–A Special Emphasis for Engineering Immunotoxins from Binary toxin A Funding Agency: Life Science Research Board, Defence Research and Development Organization, New Delhi

Tenure of the project: Three years or till the end of the project period.

Position: Junior Research Fellow (1 no.)

Essential qualification: First class in M.Sc. in Genomics/Biotechnology/ Microbiology/ Biochemistry/Life Sciences

Desirable qualification: Experience in an area relevant (Molecular Microbiology, Protein engineering and Structural Bioinformatics) to the project.

Fellowship: Rs. 16, 000 per month plus HRA as per University rule.

Upper age limit: 28 years

Date of interview: 16-09-2013

Venue of interview: Department of Bioinformatics, Bharathidasan University, Tiruchirappalli -620 024, Tamil Nadu

The above post is purely temporary and will be terminated with three month notice. The Terms and the condition of the appointment shall be governed according to DRDO, Govt. of India. The eligible candidates will bring their original certificates and documents at the time of interview. No TA/DA will be paid for attending the interview.

Dr. P. CHELLAPANDI
Principal Investigator,
Department of Bioinformatics,
Bharathidasan University,
Tiruchirappalli -620 024, Tamil Nadu

Advertisement: http://www.bdu.ac.in/tender_list.php

High Throughput Sequencing Analysis
Comparative Genomics
Identification and Annotation of Non-coding RNAs
Bioinformatic Analysis and System Biology of Viruses
Coevolution of Proteins and RNAs
Algorithmic Bioinformatics
Phylogenetic Analysis

http://www.rna.uni-jena.de/index.php