Required Skills: no special skills required for this job post
Required Experience:
Experience in computer aided drug design and or biochemical testing of natural or synthetic compounds is desired
Required Education:
M.Sc. / M.Tech.

Required Job Profile:
Candidate must possess M.Sc. in bioinformatics or computational biology or biotechnology or any branch of life sciences or pharmacology or chemical sciences or M.Tech. in any branch of life sciences with at least fifty five percent marks with NET or GATE.

Desired Job Profile:
Candidate having experience in computer aided drug design and or biochemical testing of natural or synthetic compounds.

How to apply:
Eligible and interested candidates should need to appear for walk-in interview on 21.11.2014 at 1700 hrs at the above mentioned address.

Contact
Pondicherry University
Dr. S. Mohane Coumar, Assistant Professor & Project Investigator, Centre for Bioinformatics, Pondicherry University, Puducherry 605 014
Email:registrar@pondiuni.edu.in
Phone: 0413-2655175

More at http://www.pondiuni.edu.in/sites/default/files/JRF-bioinfor-041114.pdf

More at https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbab022/6146772

Address of the bookmark: https://github.com/huangs001/AlignGraph2

Applications are invited for the AiCADD fellowship (MHRD, Govt. of India) of University of Kerala.

The terms and conditions of the fellowship is given below:

Ø The AiCADD PhD Fellowship scheme will be available for the students registered for full-time research or intending to register and pursue full time research at SIUCEB in frontier areas of bioinformatics, computational biology, systems biology and closely allied areas with focus on Ayur-Informatics.

Ø The fellowships will be widely announced and open to students irrespective of geographical consideration.

Ø Candidates availing of this fellowship shall not be in receipt of any other fellowships concurrently.

Ø Researchers will be selected on the basis of research aptitude test and personal interview.

Ø Each selected student will be eligible for a monthly fellowship of Rs. 10,000/- for the 1st and 2nd year and Rs. 12,000/- for the 3rd year.

Ø Candidates must register for PhD within one year of joining, failing which the fellowship will have to be remitted back.

Ø Candidates receiving the fellowship shall submit bi-annual reports of progress and the continuation of the fellowship will be based on the evaluation of the same.

Ø Candidates are also required to take up academic duties including teaching upto a maximum of 6 hours per week, as directed by AiCADD Principal Investigator.

Interested candidates may please forward their application along with resume on or before 15th November 2014 in the following address. Principal Investigator, AiCADD Centre, Dept. of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram - 695581.

More at https://sites.google.com/site/centreforbioinformatics/announcements

You can try this here, or try it later on your own data.

Get data

We will use the same Illumina data as we used above:

• illumina_R1.fastq.gz: the Illumina forward reads
• illumina_R2.fastq.gz: the Illumina reverse reads

Assemble

Run Spades:

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o spades_assembly_all_illumina

• -1 is input file of forward reads
• -2 is input file of reverse reads
• --careful minimizes mismatches and short indels
• --cov-cutoff auto computes the coverage threshold (rather than the default setting, “off”)
• -o is the output directory

Results

Move into the output directory and look at the contigs:

infoseq contigs.fasta

1. Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome
2. Co-assembly of a large number of metagenomes via merging of individual metagenomes
3. Includes binning and bin checking, for retrieving individual genomes
4. The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.
5. Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.
6. Metatranscriptomic support via mapping of cDNA reads against reference metagenomes

Address of the bookmark: https://github.com/jtamames/SqueezeMeta

Address of the bookmark: https://github.com/Reedwarbler/GAPPadder

Qualification: Highly motivated M.Sc. (Bioinformatics/ Biotechnology/ Life Sciences/ Botany/ Agriculture) students are encouraged to apply. Prior experience in Bioinformatics/Plant tissue culture work is preferable. Preferences would be given to DBT/ CSIR / UGC NET qualified students.

Application Procedure: A detailed CV indicating name, date of birth, address, contact number, e-mail address, educational qualifications, NET qualified or not, research experiences if any, should be e-mailed to This email address is being protected from spambots. You need JavaScript enabled to view it. on or before 24th December 2014.

Important Note: Only short listed candidates will be called for interview at Akbar Bhawan, Chanakyapuri, New Delhi. No TA/DA will be paid for attending the interview. SAU Selection Committee reserves the rights to relax any of the qualifications in case the candidate is found otherwise well qualified. The above- mentioned post is temporary and will be initially offered for a period of one year, which can be extended to one more year.

Advertisement: www.sau.ac.in/recruitment/vacancy.html

Address of the bookmark: http://www.phrap.org/consed/consed.html

Walk in interview for Research Associate, Studentship and Traineeship at BIF

Applications are invited for the Post of Research Associate, Traineeship and Studentship in the DBT sponsored Bioinformatics Infrastructure Facility (BIF) at the Bioinformatics Centre, Department of RDAP, North-Eastern Hil University, Tura Campus, Tura-79402, Meghalaya. The Posts are purely temporary and terminable at any time without prior notice or assigning any reason thereof. The person engaged, shall not be entailed for any claim implicit or explicit for permanent absorption in the University.

Research Associate- 01

Essential Qualification: M.Sc. in Bioinformatics/Biotechnology from a recognized University/ institute.

Desirable: PhD or Pursuing PhD in the relevant subject(s) or equivalent published work in reputed peer reviewed journals or Advance PG diploma in Bioinformatics courses.

Duties: Creation of database, web designing, maintenance of internet, training of students in Bioinformatics, handling and knowledge of Bioinformatics software tools and technique, conducting Bioinformatics based research and other day to day laboratory work, writing report and scientific papers.

Pay:Rs. 2,00/- + Admissible 10% HRA per month

Age: Below 35 years

Traineeship- 02

Students who have completed Masters Degree in Bioinformatics/Biotechnology or any branch of Life Sciences/Agricultural Sciences/Computer Science to cary out a project work in Bioinformatics.

Desirable: Prior Knowledge of programming languages such as C, JAVA, MySQL is preferable.

Stipend: Rs. 800/- p.m. fixed. Purely temporary for a period of six months.

Studentship: 02

Students pursuing postgraduate degree in Bioinformatics/biotechnology/Agricultural Sciences or any branch of Life Science

Desirable: Prior knowledge of bioinformatics/ programming language is preferable.

Stipend: 800/- p.m. fixed. Purely temporary for a period of six months.

Candidates must send the detailed Biodata via mail/post and bring al the relevant documents in original and one set of attested photocopies of the same at the time of interview. No TA/DA will be paid for attending the interview and candidates have to make their own arrangements.

Last date for receiving application by mail or by post: 16.02.2014

Contact Information:
Dr.B.K. Mishra
Cordinator BIF,
RDAP Department, NEHU, Tura Campus
Phone: 91-03651-23107
Fax: 91-03651-23953
E-mail: drbkm1972@yaho.co.in, birendramishra14@gmail.com

Advertisement: http://www.nehu.ac.in/Advertisements/BIF_TuraAdtvPV_171214.pdf

Metagenomic simulators are a way of dealing with this issue; they produce artificial sequencing datasets in which both the composition of the microorganisms and the expected results are known. Researchers can then test a pipeline on such a controlled dataset and see precisely how well it functions. However, as metagenomic studies have become larger, more diverse, and more complex, simply generating artificial reads is no longer sufficient. The simulators now have to replicate community variation, sequencing errors, different sequencing platforms, massive data volumes, and even laboratory-based biases.

MIDASim (2024) was designed with a particular emphasis on microbial community dynamics. Unlike previous tools which regarded each simulated sample as individual, MIDASim is able to efficiently generate realistic multi-sample microbiome datasets, covering the cases where changes over time are being tracked. This feature is particularly useful when investigating how microbial communities vary between people, different experimental setups, or at various times. By overcoming the computational limitations of earlier simulation methods, MIDASim has made it easier to carry out large and complex microbiome simulation studies.

A major challenge is scale. Since modern metagenomic projects can involve millions or even billions of sequencing reads, generating such large synthetic datasets can become a significant computational problem. In order to tackle this issue, Izzy (2025) was developed. As a high-throughput metagenomic read simulator, Izzy is designed with speed in mind while still reproducing the typical patterns of sequencing errors. The people who developed it stated that it can be as much as 60 times faster than earlier tools, which makes large-scale simulations far more practical. Now, researchers can begin to ask not how long a simulation will take but rather what size dataset they want to test.

At the same time, the sequencing technology became more diverse. Researchers were not any longer restricted to using Illumina short reads since Oxford Nanopore and PacBio long-read sequencing also became important in the field of metagenomic research. In order to address these new requirements, MeSS (Metagenomic Sequence Simulator, 2025) was developed. The programme is based on a Snakemake framework and is therefore compatible with the Illumina, Oxford Nanopore and PacBio platforms. MeSS has also been designed to operate efficiently and to use less memory than earlier tools; it provides ready-made templates for the microbiomes of different sites in the human body, thus giving researchers a simple means of beginning to generate realistic simulated communities.

The simulation problem became even more specialized. Suppose that sequencing is not based on uniform sampling of DNA? In the case of targeted sequencing and hybridization-capture experiments, particular sequences are deliberately enriched, and the probability of capturing a target depends on both the probe and the target sequence. A standard model based on uniform sampling might fail to take into account this important feature of real experiments. That is why RAmpSim has been designed specifically to handle simulations for capture-based and targeted sequencing. Instead of depending solely on the assumption of uniform sampling, it includes a thermodynamic nearest-neighbor energy model to represent probe–target interactions and sequence-dependent enrichment. As a result, it is especially suitable for applications such as hybridization capture in metagenomics, where the efficiency of recovering a sequence can depend strongly on how it relates to the capture probes. RAmpSim thus reflects a wider trend towards simulations that aim not only to reproduce sequencing output but also key aspects of the experimental process itself.

MIDASim, Izzy, MeSS and RAmpSim all demonstrate how fast metagenomic simulation is evolving. MIDASim is able to deal with realistic variation within a large number of and changing microbial communities. Izzy overcomes the problem of generating very large datasets. MeSS provides support for a number of sequencing technologies in an efficient and repeatable manner. RAmpSim increases the experimental realism for capture-based sequencing. Although each of these tools addresses a separate issue, they all contribute to advancing the field.

Modern metagenomic simulation therefore aims not only at producing artificial FASTQ files. Researchers nowadays desire simulated datasets that reflect community changes, incorporate the characteristics of the sequencing platform, include real error patterns, take into account computational scale, and account for experimental biases. As metagenomic workflows become more advanced, the simulators have to keep up with this development. The tools are now going beyond that of simple data generators; they produce controlled versions of complex metagenomic experiments and thus provide researchers with something that ordinary sequencing data seldom does: a dataset in which the answer is known before any analysis takes place.