(INDIAN COUNCIL OF MEDICAL RESEARCH)

P.O BOX 101,
Dr. M. Miyazaki Marg,
Tajganj, Agra - 282001

Applications are invited for a walk-in interview to be held in the Seminar Hall of the on 15th November, 2013, 9:30 am for temporary positions of JRF, Lab Technician and Field attendant in a ICMR funded project entitled "Elucidating the strain differentiation and transmission dynamics of M. leprae through simple sequence repeats ISSR-PCR marker"

1. JRF (one Post)

Essential qualification: Candidates with M.Sc/IVI.Tech or equivalent degree in any life science related subjects with UGC-CSIR/ICMR/DBT-Net qualified

Desirable qualification: Experience in Molecular Biology/Computational Biology will be preferred.

Age. Maximum 28 years as on 11.11.2013. Age relaxation as per GOI rules.

Emoluments: Rs. 6,000 + 20% HRA per Month

2. Lab Technician (One Post)

Essential Qualification: 12th with DMLT/B.SCA4.SC in Life sciences

Desirable qualification: Experience in Molecular Biology/Computational Biology will be preferred.

Age: Maximum 30 years as on 11.11.2013. Age relaxation as per GOI rules.

Emoluments: Rs13,760/ Per Month

3. Field Attendant (One Post)

Essential Qualification: 10th Pass

Desirable Qualification: Experience in field work

Age: Maximum 28 years as on 11.11.2013. Age relaxation as per GOI rules.

Emoluments: Rsl2,040l Per Month

Terms: posts are purely temporary. Appointment will be initially made for a period of one (01) year and may be extended further based on the performance of the candidate up to completion of the project.

Application & Selection procedure: candidates have to appear in the walk-in-interview in person along with an application/CV on plain paper giving details of at educational qualificationq experience and submit photocopies of relevant documents at the time of interview. Selection will be based on the performance of the candidate in the interview' Candidates will not be sent any interview call letter separately. No TA/DA will be paid to the candidate for appearing in the interview. selection is not possible without appearing in the interview. All candidates must report by 9:00am on the date of interview. Advance copy of CV may be sent to m.sarathipartha@gmail.com

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1. Big Data and Bioinformatics

The exponential growth in biological data, driven by advancements in sequencing technologies and high-throughput experiments, has made bioinformatics an indispensable tool. By 2030, we anticipate:

• Petabyte-Scale Data Management: Enhanced storage solutions and cloud computing platforms will allow researchers to handle the vast amounts of data generated from omics studies, including genomics, transcriptomics, and proteomics.

• AI and Machine Learning Integration: Sophisticated algorithms will uncover patterns and relationships in large datasets, enabling predictions about gene function, disease susceptibility, and therapeutic outcomes.

2. Personalized Medicine and Genomics

Bioinformatics will play a pivotal role in tailoring healthcare to individual patients. Key developments include:

• Whole-Genome Sequencing in Clinics: The decreasing cost of sequencing will make it routine in medical diagnostics, enabling personalized treatment plans based on an individual’s genetic makeup.

• Drug Repurposing and Development: Computational tools will identify potential new uses for existing drugs, accelerating the development of targeted therapies.

3. Advancing Computational Tools

The future will see the development of more user-friendly and powerful bioinformatics tools:

• Graph-Based Approaches: Enhanced algorithms for analyzing complex biological networks, such as protein-protein interaction maps.

• Visualization Tools: Intuitive software for visualizing multi-dimensional data, enabling researchers to interpret findings more effectively.

4. Synthetic Biology and Systems Biology

Bioinformatics will continue to drive progress in synthetic and systems biology by:

• Gene Circuit Design: Leveraging computational models to design and simulate synthetic biological systems.

• Understanding Cellular Pathways: Integrating multi-omics data to model cellular processes with unprecedented accuracy.

5. Bioinformatics in Agriculture and Environmental Science

Beyond healthcare, bioinformatics will revolutionize agriculture and environmental conservation:

• Crop Improvement: Genomic studies will help develop high-yield, disease-resistant, and climate-resilient crops.

• Microbial Ecology: Metagenomics will enhance our understanding of microbial communities, aiding in bioremediation and ecosystem management.

6. Democratization of Bioinformatics

Open-source software and accessible education will broaden participation in bioinformatics research:

• Community-Driven Projects: Collaborative platforms like GitHub will continue to foster innovation in tool development.

• Education and Training: Online courses and workshops will bridge skill gaps, enabling researchers from diverse backgrounds to contribute.

Challenges and Ethical Considerations

While the future is bright, challenges remain. Data privacy and ethical concerns surrounding genetic information require careful navigation. Furthermore, addressing the digital divide is critical to ensuring equitable access to bioinformatics resources globally.

Conclusion

The future of bioinformatics is boundless, with opportunities to revolutionize our understanding of life and improve human health. As technologies evolve and collaborations flourish, bioinformatics will undoubtedly remain at the forefront of scientific discovery, unlocking the secrets of life one dataset at a time.

Deadline for applications : January 15, 2014.

Description :

We seek a talented and motivated post-doc to develop computational methods for inferring the molecular basis of genetic diseases by integration of personal omics data. Research topics include: identifying causal mutations of rare disease patients by meta-analysis; inferring disease-causing molecular pathways from genotype, human phenotypes, and omics profile of patient-derived cell lines; and causal inference from longitudinal omics studies of patients. The developed methods will be applied to analyze data from our medical collaborators.

Candidates must either hold a PhD in computational biology or bioinformatics, or hold a PhD in physics, statistics, or applied mathematics with practical experience with high-dimensional data analysis. Experience in quantitative genetics is a plus. Applicants must have a proven publication record and an interest for translational research.

The Gagneur lab is a young, lively and multidisciplinary group with a research focus on systems genetics and gene regulation. It is located at the Gene Center of the LMU (University of Munich), an interdisciplinary institution whose 16 independent research groups investigate the regulation of gene expression at all levels - from the underlying molecular mechanisms to the biological system. The institute is located on the biomedical research campus Munich-Grosshadern, offering a dynamic, interactive and internationally oriented research environment. The dynamism of Munich and the proximity of the Alps provide an excellent quality of life.

The salary is according to the TV-L (German academic salary scale).
Applications including a cover letter, CV, and references must be sent by January 15th 2014 to Julien Gagneur (gagneur@genzentrum.lmu.de)

About the lab: http://www.gagneur.genzentrum.lmu.de

1️⃣ Data Analysis for the Life Sciences
A fantastic starting point to learn statistics, R programming, and exploratory data analysis in the context of biology. The best part? It’s available free online from HarvardX.

2️⃣ Practical Computing for Biologists
The very first book I picked up when I started learning computational biology. It’s beginner-friendly and focuses on essential computing skills every biologist needs.

3️⃣ A Primer for Computational Biology
An open-access, hands-on introduction to computational biology concepts and coding techniques. Perfect if you want to learn through real examples.

4️⃣ Computational Genomics with R
For those who already know R and want to dive deeper into genome-scale data analysis, from sequence alignment to gene expression.

5️⃣ The Biologist’s Guide to Computing
Bridges the gap between biological problems and computational thinking, making it easier for life scientists to approach programming and data analysis.

6️⃣ Bioinformatics Data Skills
A must-read to sharpen your bioinformatics toolkit — from command-line skills to reproducible research workflows. Ideal once you’ve covered the basics.

7️⃣ Bioinformatics Workbook
A practical tutorial series to help scientists design bioinformatics projects, analyze data, and understand best practices.

8️⃣ Modern Statistics for Modern Biology
An essential guide to modern statistical methods applied to biology, blending theory with hands-on examples in R.

9️⃣ Algorithms on Strings, Trees, and Sequences by Dan Gusfield
A classic reference for anyone wanting to understand the algorithms behind sequence alignment, genome assembly, and biological data structures.

Disclaimer: This cartoon is solely designed to create humour and fun, not to offend any computer experts.

People using computers were often seen
as helpers, not leaders—
useful, but not essential.

Sometimes, the criticism was direct.
Sometimes subtle.
But the message was the same—
this work doesn’t really count.

Then biology changed.
The questions became bigger,
and experiments alone
were no longer enough.

Organizing knowledge by hand worked once.
Now it needs computers—
to handle scale, speed, and complexity.

Some patterns are simply invisible
if you look at one sample.
You need many—
and the right tools to understand them.

So we started building maps—
of genomes, cells, and systems.
Not perfect,
but extremely useful.

Ideas also had to become clearer.
It’s no longer enough to say something sounds right—
you have to measure it.

The divide between “types” of biologists
never really made sense.
We are solving the same problems—
just in different ways.

Progress didn’t wait for agreement.
It moved forward with data,
with code,
and with careful analysis.

What matters now is simple:
• Biology depends on computation
• Coding is an important skill
• Statistics helps us think clearly
• And the people building these tools
are shaping the future of science

Problem :

The CG island is a stretch of DNA (usually longer than 200 bases) in which the frequency of the CG sequence is higher than other regions. It is also called the CpG island, where "p" simply indicates that "C" and "G" are connected by a phosphodiester bond.

CpG islands are often located around the promoters of housekeeping genes (which are essential for general cell functions) or other genes frequently expressed in a cell. At these locations, the CG sequence is not methylated. By contrast, the CG sequences in inactive genes are usually methylated to suppress their expression. The methylated cytosine may be converted to thymine by accidental deamination. Unlike the cytosine to uracil mutation which is efficiently repaired, the cytosine to thymine mutation can be corrected only by the mismatch repair which is very inefficient. Hence, over evolutionary time scales, the methylated CG sequence will be converted to the TG sequence.

Find step wise explanationand implementation steps at http://dna.cs.byu.edu/bio465/Labs/hmm.shtml

Source code with explanation http://www.tannerhelland.com/1187/hidden-markov-models-viterbi-algorithm-cpg-islands-in-vb6/

Fore detail understanding of HMM read this excellent tutorial http://www.cs.ubc.ca/~murphyk/Software/HMM/labman2.pdf

Viterbi Algo at http://en.wikipedia.org/wiki/Viterbi_path

For firther reading Wiki page http://en.wikipedia.org/wiki/Hidden_Markov_model

On CpG island paper and for indepth understanding http://www.biomedcentral.com/1471-2164/12/S2/S10

If you are more interested in exploring Markov Chain Exploration and understand it with graphical version please visit http://www.planet-source-code.com/vb/scripts/ShowCode.asp?txtCodeId=75049&lngWId=1

Reference:

1.http://www.planet-source-code.com

2. http://www.tannerhelland.com

More at https://sites.temple.edu/liberles/

the study of the sequence-structure relationship
(application -> structure prediction)
the study of the structure-function relationship
(application -> function prediction)

Therefore, anything related to the configuration (sequence) and conformation (structure) in atomic systems of proteins and nucleic acids, and the interaction of these with other elements (function) is of our major interest.

Lab page @ http://melolab.org/mbl/

Our Research: Our lab conduct research at the cutting edge of computational biology and bioinformatics. We aim to understand human genome variation and evolution across different genomic regions by developing interpretable and efficient methods in comparative pan-genomics, leveraging machine learning methods and statistical analysis.

More at https://cgrlab.github.io/