At the Repository for Tomato Genomics Resources, we are working on Tomato Functional Genomics, using TILLING, Insertional Mutagenesis, proteomics, metabolomics approaches to study fruit ripening in tomato. The current aims of the group include using reverse and forward genetics strategies to isolate tomato mutants delayed in ripening, having high lycopene and folate content in tomato fruits and analysis of light and hormonal signal transduction pathways. For recent publications of the group see (Plant Physiol 161: 2085–2101, Plant Physiol 156: 1424-1438; Molecular Plant 3: 854-869; Plant Methods 6: 3; Plant Methods 5:18; Plant Signaling and Behavior 5:11.).

Currently we have one position available in the projects awarded to Prof. R.P. Sharma funded by Dept of Biotechnology. The qualification for this Position is as follows:

Research Assistant: Applicants should have experience in networking using R language and should be able to develop networks using the transcriptome, proteome and metabolite data sets. M.Tech. in Bioinformatics is required. The selected candidate would be paid Rs. 13,000/-pm- consolidated.

Candidates interested in above positions should send a one page statement clearly explaining how their skills are relevant to the position. The candidates should also enclose detailed CV and the name/email id for three referees. The candidates can send their application by email at rameshwar.sharma@uohyd.ac.in and y.sreelakshmi@uohyd.ac.in on or before December 10th, 2013. The position is purely temporary in nature. Shortlisted candidates would be called for interview. No TA/DA would be provided for attending the interview. We also have openings for CSIR-NET JRF candidates for pursuing PhD in above research areas.

Interested candidates with CSIR-NET JRF can send their CV to the above email
addresses.

Advertisement:

http://www.uohyd.ac.in/images/recruitment/tomanet_positions_221113.pdf

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

Highlights of our research include:

Optimization of microarray design, probe signal interpretation
Advanced models and tools for expression profiling
State-of-the-art applications and integrated analyses

Lab page @ http://bioinf.boku.ac.at/

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

Postdoctoral position is available for three years to work on development of Bioinformatics resources for the analysis and visualization of ribosome profiling data. Ribosome profiling (ribo-seq) is a technology that allows mapping positions of the ribosomes on the whole transcriptome level with a nucleotide precision. The technology allows obtaining high resolution digital snapshots of gene expression in cells. The position is available starting on the 1st of October, 2013.

Candidate is expected to have Ph.D. in Bioinformatics or Computational Biology. Candidates with the degree in non-Biological disciplines such as Computer Science, Statistics, Applied Mathematics, Physics or Electrical Engineering will also be considered.

The position is available at LAPTI (http://lapti.ucc.ie) that is located in the Western Gate Building (http://www.stwarchitects.com/project-information.php?c=1&p=09993) at University College Cork. Western Gate Building Research Complex hosts several UCC departments and provides ideal environment for interdisciplinary research. Cork (sometimes referenced as “Venice of Ireland”) is the second most populous city in the Republic. It has friendly cosmopolitan atmosphere and vibrant culture. A number of American industrial giants such as Apple , EMC and Pfizer have chosen Cork as a home for their European headquarters.

The details of the application process are given at http://lapti.ucc.ie/jobs.html. To ensure prompt processing of your application use the subject line: ‘Postdoc computational’. All applications received prior to August the 1st are guaranteed equal consideration. However, applications at the later dates will also be considered until the position is filled.

For more info visit http://lapti.ucc.ie

• 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- TEST CUM INTERVIEW

Walk- in- Test cum Interview (based on test) for the selection of Research Associate (Bioinformatics) & Bioinformatic Trainees under the scheme ‘Distributed Information Sub Centre- DISC’ will be held at this Institute as per details indicated below.

Research Associate

Date of Interview : 21 -01-2014 at 10.00 A.M

Qualifications : a) Essential: Doctorate degree in Bioinformatics or Biotechnology/Life Sciences/Biochemistry with expertise in Bioinformatics as evidenced by publications.

OR

Three years research experience after MVSc/MPharm/ME/MTech with Bioinformatics Specialization.

b Desirable: Experience in handling NGS data Programming skills in Python/Bioperl

Emoluments : Rs:22000/- per month + HRA (higher pay upto Rs.24000/- can be paid depending on the qualifications and experience.

Upper age limit : 40 years for Men & 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)

Duration of Project : Till the closure of the project.

General Terms and conditions

1. The above positions are purely on temporary basis and is co-terminus with the closure of the project. There is no provision of re-employment after termination of project. The selected candidate will not have any right for claiming pay scale or absorption against any regular post being vacant on a later date at this Institute.
2 . No TA/DA will be paid for attending the Interview.
3. Canvassing in any form will lead to cancellation of candidate.
4. The decision of Director, IISR would be final and binding in all aspects.
5. Candidates will not be permitted to enter the Examination Hall after 10.00 A.M.
6. Candidates who secure the minimum marks prescribed by the Institute in written test only will be eligible for calling for the interview. The number of candidates to be called for the interview will be decided by the Director of the Institute.
7 Those who do not possess original Degree/PG certificate or Provisional certificate will not be allowed to attend the Test/Interview.

Note: All relevant certificates (in original) and bio data
No objection certificate in case he/she is employed elsewhere and experience certificate in original (if any) need to be produced at the time of interview.
Location of IISR Kozhikode Main Campus - Pallithazham bus stop between Moozhikkal East and Chelavoor on the NH 212 ”Kozhikode - Kollegal” Road.

Advertisement: www.spices.res.in/pdf/DISC-Website.pdf

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