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

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

A recent study published in Genome Biology (https://link.springer.com/article/10.1186/s13059-026-04280-y) addressed this challenge by introducing dicast (https://github.com/burgshrimps/dicast), a machine-learning approach for detecting structural variants in short-read sequencing data. Structural variants, such as large deletions, insertions, duplications, and inversions, can have significant biological and clinical implications, yet they are challenging to identify with short-read technologies. Because different detection methods frequently yield divergent results, researchers commonly employ consensus calling, considering a variant valid only if multiple tools detect it. While this approach reduces false positives, it relies on the potentially flawed assumption that the majority is always correct.

The researchers explored the impact of evaluating the supporting evidence for each variant, rather than simply tallying the number of algorithms that identified it. To establish a ground truth, they analyzed nine genomes using multiple sequencing technologies and 15 detection methods, initially identifying approximately 35 million potential variants. Through extensive filtering, evidence integration, and manual review of over 11,500 variants, they developed a robust benchmark comprising more than 236,000 structural variants. The findings underscored the complexity of the problem: short-read methods detected fewer than half of deletions and less than 10 percent of insertions, with performance declining markedly in repetitive genomic regions. In contrast, long-read technologies demonstrated superior detection capabilities. However, replacing the substantial volume of existing short-read data in clinical and research settings is not immediately feasible. Consequently, the researchers questioned whether short-read data might harbor more information than conventional analytical pipelines currently extract.

This line of inquiry led to the development of dicast. Rather than merely confirming agreement among multiple tools, dicast identifies patterns in sequencing data, including split and clipped reads, discordant read pairs, alignment characteristics, and the surrounding genomic context. An XGBoost machine-learning model evaluates which combinations of these signals are indicative of genuine structural variants. Thus, the approach shifts from tallying algorithmic consensus to interpreting the underlying evidence.

The researchers subsequently conducted a targeted evaluation by examining structural variants detected by only a single short-read tool, which are typically missed by consensus-based approaches. dicast successfully recovered approximately 81% of these single-caller deletions, insertions, and duplications. The signals for these variants were present in the data, but conventional filtering methods failed to integrate them effectively.

The utility of dicast was further demonstrated in cohorts with rare diseases, including congenital limb malformations, atrial fibrillation, and neuromuscular disorders. In one instance, dicast achieved a deletion recall rate of approximately 0.96, compared to 0.74 using consensus calling. The median number of variants requiring manual review was 29 per sample. Among 31 experimentally validated variants that standard filters would have missed, dicast identified 12, whereas consensus calling detected only one.

Overall, dicast identified approximately 20 percent more potential disease-causing deletions than consensus-based methods. While a 20 percent increase may appear modest, in clinical genomics such improvements can have significant practical implications. Missing a deletion may leave a case unresolved, whereas detecting a structural variant can provide critical diagnostic insights.

The study does not claim that machine learning has rendered short-read sequencing superior to long-read approaches. Instead, the results underscore the effectiveness of long-read sequencing for structural variant detection. However, dicast highlights a more nuanced perspective: substantial biological information may still be recoverable from the extensive short-read datasets already available.

The principal lesson extends beyond the detection of structural variants. For many years, bioinformatics pipelines have relied on threshold-based criteria, such as minimum coverage, quality scores, or support from multiple tools. While these rules are useful, biological phenomena do not always conform to rigid checklists; multiple weak signals, when considered collectively, can provide compelling evidence.

This perspective prompts consideration of the solitary variant: one algorithm identifies it, while several others do not. Traditional consensus techniques might have dismissed it, yet machine learning approaches evaluate the available evidence to determine whether the variant is plausible.

Occasionally, the most significant variant within a genome is the one that is almost universally overlooked.

Read more at https://link.springer.com/article/10.1186/s13059-026-04280-y

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

minimap -t 24 assembly.fasta long_reads.fastq.gz | racon -t 24 long_reads.fastq.gz - assembly.fasta racon_assembly.fasta
nucmer -p nucmer assembly.fasta racon_assembly.fasta
show-snps -C -T -r nucmer.delta

This reports Racon's changes in a table. You can exclude indels with the -I option in show-snps. 

This process (Racon -> MUMmer -> SNP table) solves the problem I originally raised in this issue. So as far as I'm concerned, you can close this issue (or keep it open if you still want to implement some kind of variant table).

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

We accomplish this by first calculating genotype likelihoods for every site. For a given site in a diploid genome, there are 10 possible genotypes (AA, AC, AG, AT, CC, CG, CT, GG, GT, TT). Referee takes as input the genotype likelihoods calculated for all 10 genotypes given the called reference base at each position.

Referee is a program to calculate a quality score for every position in a genome assembly. This allows for easy filtering of low quality sites for any downstream analysis.

https://github.com/gwct/referee

Address of the bookmark: https://gwct.github.io/referee/#

A swift, versatile phylogenomic and high-throughput sequencing simulator https://jackalope.lucasnell.com

Address of the bookmark: https://github.com/lucasnell/jackalope

More at https://kevlar.readthedocs.io/en/latest/

https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf

Address of the bookmark: https://github.com/kevlar-dev/kevlar