Current projects in the Morota lab include developing kernel-based whole-genome prediction and kernel-based genome-wide association models, polygenic modeling of binary traits, reexamining the results from quantitative genetics analysis in light of functional annotation, and extending kernel methods (such as GBLUP and RKHS) specifically tailored for diverse types of emerging omics data.

In addition, candidates will be expected to leverage opportunities to interact with faculty in animal genetics and biometrics at the UNL in the areas of bioinformatics, breeding, functional genomics, quantitative genetics, and molecular genetics.

Candidates should have a B.S. or M.S. degree in quantitative disciplines with strong background and interest in statistical computing.
The starting date is Fall 2015.
For more information about research in the Morota lab at the UNL, visit: http://www.morotalab.org

A letter of interest in the position, C.V., and contact information for
three references should be emailed to Gota Morota at .
Review of applications will begin immediately, and continue until the
positions are filled. Informal inquiries are also welcome.

Also, please see: http://animalscience.unl.edu/anscprospectivegraduatestudents

Key Findings:

1. Chromosomal Fusion and Its Molecular Signature:

   • The fusion site is located at 2q13–2q14.1 and is characterized by degenerate telomeric sequences appearing interstitially, indicating the historical head-to-head joining of ancestral chromosomes.
   • Despite being a signature of a past fusion event, these telomeric repeats are no longer functional and have undergone sequence degradation over time.

2. Extensive Duplications in the Surrounding Genomic Region:

   • The study identifies large-scale segmental duplications flanking the fusion site, with several of these regions duplicated and scattered across multiple chromosomes.
   • These duplications are predominantly located in subtelomeric and pericentromeric regions, suggesting their role in genomic instability and chromosomal evolution.

3. Paralogous Regions and Their Evolutionary Relationships:

   • A 168-kilobase (kb) segment near the fusion site has 98%–99% sequence identity with three regions on chromosome 9 (9pter, 9p11.2, and 9q13).
   • Another 67-kb region distal to the fusion site shows a high degree of homology to sequences in chromosome 22qter.
   • Additionally, a 100-kb segment exhibits 96% sequence identity with a region in chromosome 2q11.2.

4. Comparative Genomics and Evolutionary Implications:

   • By comparing the duplicated sequences and their arrangement in primates, the researchers traced the order of duplication events leading to their present distribution.
   • The presence of specific repetitive elements within these duplicated segments serves as evolutionary markers that help infer their historical rearrangements.
   • Some of these duplicated regions are associated with chromosomal inversion breakpoints, potentially contributing to evolutionary changes in primates.
   • Recurrent structural rearrangements in these regions have been linked to human chromosomal disorders.

Conclusions and Implications:

• The findings provide valuable insights into the structural evolution of human chromosome 2, which played a crucial role in human speciation.
• Understanding these segmental duplications and their evolutionary trajectories sheds light on genomic instability, which may contribute to human genetic diseases.
• The study highlights how large-scale chromosomal rearrangements, such as fusion and duplication, have influenced the evolutionary divergence of humans from other primates.

This research advances our understanding of human genome evolution and offers a foundation for studying the effects of structural variants in genetic disorders.

Essential qualifications: First class M. Sc. in any branch of life sciences and qualified CSIR-UGC NET/GATE Examination.

Desirable qualifications: Practical experience in biochemical and biophysical studies of proteins

Emoluments: as per DBT norms

The project is tenable for two years, initially for one year.

Age: Below 28 years (relaxable in the case of SC/ST/OBC/women candidates)

Candidates are requested to bring two sets of complete applications on plain paper furnishing bio-data and copies of attested certificates along with originals (for verification) on the date of interview.

No TA/DA is admissible for candidates appearing at the interview.

Dr. Rajat Banerjee
Assistant Professor
Department of Biotechnology and
Dr. B. C. Guha Centre for Genetic Engineering and Biotechnology
University College of Science
35, Ballygunge Circular Road
Kolkata 700019

Advertisement: www.caluniv.ac.in/news/jrf_biotech_2.pdf

You might learn a lot about that house—the rooms, the doors, the furniture—but you would miss the bigger story: the streets, the neighborhoods, and all the ways the city changes from one place to another.

Something similar happens when scientists study bacterial genomes one at a time.

Over the past decade, researchers have collected thousands of bacterial genomes. These genomes contain an enormous amount of information about how bacteria survive, adapt, and evolve. But comparing thousands of individual genomes can quickly become a computational maze.

That is where PANORAMA enters the story.

Developed by Jérôme Arnoux and colleagues, PANORAMA is a computational tool designed to explore bacterial pangenomes—the complete collection of genetic possibilities found across a species or group of related organisms. Instead of looking at every genome as an isolated object, the approach represents their shared and variable genetic features as a graph.

Think of this graph as a giant subway map.

Some stations appear on almost every route. These represent genes that are highly conserved. Other stations appear only on certain routes, representing genes that some bacteria possess while others do not. By looking at the entire network, scientists can begin to see not just *what genes exist*, but how they are organized and how biological systems are distributed across populations.

The researchers put PANORAMA to the test using 941 genomes of Pseudomonas aeruginosa, a bacterium important in human health. They used the tool to investigate biological systems, including bacterial defense mechanisms against viruses known as bacteriophages. They then expanded the analysis to more than 6,000 genomes from four Enterobacteriaceae species.

The result is more than a faster way to process data.

PANORAMA allows researchers to ask a bigger question: What can an entire microbial species do, genetically speaking?

By comparing pangenomes, the researchers could identify systems shared between species as well as distinctive features. They could also find recurring genomic locations where genetic material is inserted—clues that may reveal common evolutionary processes.

And this is perhaps the most exciting part of the story.

Every bacterial genome is like a page in a huge evolutionary book. Until recently, reading thousands of those pages together was difficult. PANORAMA provides a way to turn those pages into a map, allowing scientists to see patterns that might disappear when each genome is studied separately.

The study, published in PLOS Computational Biology in July 2026, presents PANORAMA as a foundation for large-scale comparative pangenomics. The software and accompanying analysis resources are openly available, giving other researchers the opportunity to explore microbial diversity themselves.

So the story is not really about one bacterium or one genome.

It is about changing the way we look at life.

Instead of asking, “What is inside this genome?”, scientists can increasingly ask, “What is the full genetic landscape of this species—and how did it become this way?”

And sometimes, when you stop looking at one house and finally see the whole city, the most interesting discoveries are hiding in the streets between them.

Read more at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013856

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

A recent study by Webster and Chapman introduces Buscogeny (https://github.com/Jwebster89/Buscogeny), an open-source bioinformatics pipeline designed to make this process easier. The tool uses BUSCO (Benchmarking Universal Single-Copy Orthologs), which identifies conserved genes that are expected to occur as single copies in particular groups of organisms. These genes act like evolutionary landmarks, allowing researchers to compare genomes and investigate their relationships.

Buscogeny brings several steps of phylogenomic analysis into one workflow. It identifies BUSCO genes, extracts and aligns their sequences, removes unreliable regions, combines information from multiple genes, and uses the resulting data to construct a phylogenetic tree. It also provides quality-control information, helping researchers identify genomes that may be too incomplete for reliable analysis.

The authors demonstrate the usefulness of Buscogeny with bacterial and fungal genomes, including Alternaria. This is particularly valuable because fungal genomes can vary greatly in quality and annotation. Ultimately, Buscogeny helps transform massive amounts of genomic data into an understandable evolutionary story. Each DNA sequence becomes a clue, each conserved gene becomes a landmark, and together these clues reveal a picture of how life is connected.

More at https://link.springer.com/article/10.1007/s10123-025-00752-6?

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

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.

Is this available, or still under trial mode? 

https://www.nanoporetech.com/

https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system

Our main advantages over other similar tools are:

• GeneAnalytics is very simple and intuitive to use.
• GeneAnalytics is based on our proprietary databases – GeneCards, MalaCards, PathCards and LifeMap Discovery, each of them integrates information from a very large number of resources.
• GeneAnalytics supplies links for extensive background information on each of the matched results.

I invite you to try it out for free at geneanalytics.genecards.org, and would be happy to hear your comments and thoughts on how we can improve.

Yours,

Shani Ben-Ari Fuchs

LifeMap Sciences Team