http://www.extension.org/pages/67926/upcoming-webinar:-selective-sequencing-through-combinatorial-pooling#.UggsVuHyPqU

Appointment Adv.No.2

Applications in plain paper are invited from Indian citizens for one/two position each of Research Associate, Traineeship and Studentship for BIF facility, Division of Life Sciences, IASST.

Applications with complete Bio-data containing contact address, e-mail and phone number, two recent passport size photographs and attested copies of mark sheets, certificates etc., should be sent to the Registrar, IASST, Paschim Boragaon, Garchuk, Guwahati – 781035, Assam, so as to reach on or before 5/05/2014.

A. Research Associate:

Number of vacancies: 1 (One)

Qualifications:

PhD in Bioinformatics or allied disciplines with knowledge of Bioinformatics. The candidates who have submitted PhD thesis may also apply.

In case, candidates having PhD are not found, candidates having MSc in Bioinformatics or allied disciplines with sound knowledge of Bioinformatics will be preferred.

Remuneration: Candidate having PhD will get a consolidated remuneration of Rs. 22,000/- +HRA per month. MSc having NET/GATE/SLET qualified candidate will get a remuneration of Rs. 16,000/= and HRA and candidate with only MSc will get a remuneration of Rs.14,000/- and HRA.

Tenure:

The post is initially for one year and may be extended depending on the performance till the tenure of the project.

B. Traineeship:

Number of vacancies: 2 (Two)

Qualifications:

Candidate with a postgraduate degree in Bioinformatics/Biotechnology/Life sciences from a recognised University

Remuneration: Rs. 5000/month for 6 months

C. Studentship:

Number of vacancies: 2 (Two)

Qualifications:

Candidate pursuing M.Sc in bioinformatics in a recognised University.

Remuneration: Rs. 5000/month for 6 months

Advertisement:

http://iasst.gov.in/pdf/recruitment/advt%20no_2_24042014.pdf

More at http://faculty.iisertvm.ac.in/~nishantkt/index.html

We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM). Using CRM, the algorithm assigns a confidence score based on “unusual” sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set.

When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain.

Availability: The method is publicly available athttp://research.haifa.ac.il/~ssagi/software/nearHGT.zip

Address of the bookmark: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004408

Address of the bookmark: http://pasapipeline.github.io/

Please enter a list of Gene Ontology IDs below, each on its own line. The GO IDs may be followed by p-values or another quantity which describes the GO term in a way meaningful to you. 

Address of the bookmark: http://revigo.irb.hr/

https://www.readcube.com/articles/10.3390%2Fv9040072

Address of the bookmark: https://pubmed.ncbi.nlm.nih.gov/28387730/

A recent study published in PLOS Computational Biology introduces REvolutionH-tl 2.0 (https://pypi.org/project/revolutionhtl/), a computational tool designed to help solve this mystery. The software analyzes protein sequences and identifies evolutionary relationships between genes. It can help researchers detect important events such as gene duplication, gene loss, and speciation, allowing them to reconstruct how genes evolved across different organisms.

What makes the tool especially interesting is its focus on both speed and interpretation. The researchers compared REvolutionH-tl 2.0 with several existing computational tools and reported that it achieved competitive accuracy while requiring less computational time in their tests. The tool also includes visualization features, helping researchers turn complicated genetic data into evolutionary stories that are easier to explore.

Genes are more than simple sequences of DNA; they are records of life’s long history. Each surviving gene carries traces of the changes that shaped it. By bringing together computational analysis and visualization, REvolutionH-tl 2.0 acts like a detective, helping scientists uncover these hidden stories. Ultimately, tools like this can improve our understanding of how genomes evolved and how genes acquired the roles they perform today.

More at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013017

It efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.

GeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck

Address of the bookmark: http://lce.biohpc.swmed.edu/geneck/