Rajiv Gandhi Centre for Biotechnology (RGCB) invites applications for the following three faculty scientist positions:

Scientist E-II or F in Bioinformatics & Computational Biology

SCIENTIST E-II OR F IN COMPUTATIONAL BIOLOGY & BIOINFORMATICS

Highly motivated and innovative individual who will pursue basic research, solve biological problems with emphasis on computational and quantitative experimental methods and build active bridges to translational research. The scientist will also provide computational biology support to ongoing research programs in disease biology, provide assistance to analyze complex data sets generated by RGCB scientists and collaborators inclusive of including high dimensional “omics” data and next generation sequencing data, such as whole genome, exome, RNA-seq and ChIP-seq as well as provide leadership for high quality training for junior scientists and regular teaching programs of the institute. Areas of research of interest to RGCB include but are not limited to computational, systems, or quantitative biology with applications to cell biology, developmental biology, metabolism, genomics, proteomics, biophysics, biological information systems, network pharmacology, drug design and cancer research. The scientist’s responsibilities include efforts for the integration of DNA variant annotation with statistical genetic analysis methods including linkage, imputation and association methods, adopting novel and innovative methodologies to analyze, integrate and interpret high dimensional data sets, provision of annotation to robust genetics and genomics findings using several data sources and methods, data management of exploratory clinical and R&D studies in partnership with other lines of genetic data generated from internal and external studies, delivery and documentation of genomic information to support genetic studies, ensuring high-quality genetic and genomic data is incorporated into exploratory- clinical research programs, developing tools that make maximum use of multiple data sources to support annotation of DNA variation and contributes to systems biology initiatives within RGCB

More at http://rgcb.res.in/scientist-positions/

Application Form http://rgcb.res.in/wp-content/uploads/2015/11/APPLICATION-FORMAT-FOR-SCIENTISTS.docx

SRF Job vacancies in National Bureau of Animal Genetic Resources (NBAGR) on contractual basis

No. of Posts : 03

Qualifications : Master degree in any one of discipline/ Subjects Biotechnology/Bioinformatics/Life Sciences/ Computer Applications with 4 years/5 years of Bachelor’s degree. Candidates having post graduate degree in above mentioned subjects with 3 years Bachelor’s degree should be NET qualified.

Note : i. The Ph.D. Degree holders are eligible for the posts. ii. The Master’s degree holders with the integrated degrees like B.Tech. & M. Tech. (5 years) are eligible. iii. Graduates with three years Bachelor’s degree, who have acquired professional Master’s degree (3 years) like M.Sc. (Ag.), Masters in Computer Applications, M. Tech., etc., are eligible.

Desirable : The candidates should have zeal to work in R environment and NGS data analysis

Emolument : Rs. 28,000/-

Age Limit : 35 year for Men and 40 years for Women in case of SRF on the date of Interview.
How to apply

Walk-in-Interview is proposed to be held at National Bureau of Animal Genetic Resources, Karnal (Haryana)-132 001 at 10:30 AM on 02.12.2015. Interested candidates may attend Walk-in-Interview at 10:30 AM on 02.12.2015 alongwith original certificates and typed bio-data with one set of attested copies of each their certificates with passport size photograph.

Center of Innovative and Applied Bioprocessing (CIAB) invites applications to recruit on vacant posts of Scientist, Associate Plant Manager/ Plant Engineer, Informatics Officer. Applications against these Government Jobs can be submitted on or before 7th January 2016.
CIAB Vacancy 2015 Details

9. Research & Development – Informatics Officer
Qualification: M. Tech or M.E. in computer science or information technology or bioinformatics with 4 years of R&D experience (after Master’s Degree) in an R&D organization with work profile of large data assembly, analysis.
Age Limit: 45 years
How to Apply : Duly filled-in applications in prescribed application format along with copies of required documents should be reach to: Chief Exec

More at http://catchenlab.life.illinois.edu/stacks/

Address of the bookmark: http://catchenlab.life.illinois.edu/stacks/

Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license

More at http://scikit-learn.org/stable/index.html

Address of the bookmark: http://scikit-learn.org/stable/auto_examples/index.html

More at http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html

Address of the bookmark: http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html

We present methods for the identification of protein-coding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10% of previously annotated genes) and refining the gene structure of hundreds of genes. We present novel methods for the systematic de novo identification of regulatory motifs. The methods do not rely on previous knowledge of gene function and in that way differ from the current literature on computational motif discovery. Based on the genome-wide conservation patterns of known motifs, we developed three conservation criteria that we used to discover novel motifs. We used an enumeration approach to select strongly conserved motif cores, which we extended and collapsed into a small number of candidate regulatory motifs. These include most previously known regulatory motifs as well as several noteworthy novel motifs. The majority of discovered motifs are enriched in functionally related genes, allowing us to infer a candidate function for novel motifs.

Our results demonstrate the power of comparative genomics to further our understanding of any species. Our methods are validated by the extensive experimental knowledge in yeast, and will be invaluable in the study of complex genomes like that of human.

Address of the bookmark: http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf

The research covers diverse areas of evolutionary biology, and molecular evolution in particular. It combines theoretical and empirical approaches, and particularly evolutionary inference from genome sequence data.

Links @ http://www.gen.cam.ac.uk/research/welch/GroupPage/Home.html