Personal Genome Variation: SVs
Human Genome Annotation: Processing Next-Gen Sequencing Data
Comparative Genomics: Pseudogenes as Molecular Fossils
Protein Structure and Function: Macromolecular Motions
Analysis of Diverse Networks
Genomics at the Forefront of Data Science

Lab page: http://www.gersteinlab.org/

Applications are invited for admission to the Ph.D. programme in the Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta for the year 2014 from eligible candidates who would be placed under the departmental teachers or affiliated research supervisors for the pursuance of their Ph.D. programme.

Candidates are requested to download the Ph.D. admission test application form from the University website and apply in the prescribed proforma by paying Rs. 100/- through a challan available through different University Cash counters. The challan is to be duly forwarded through the Head, Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta.

The completed application form with a copy of the paid challan is to be submitted to the office of the Department by April 16, 2014.

Syllabus for the Test: The questions for the admission test and interview will be based on topics in the following areas:

Mathematical methods, Molecular and Cellular Biophysics, Molecular and Cell Biology, Biochemistry, Genetics, Plant Biology, Developmental biology, Neurobiology, Biotechnology and Bioinformatics.

However, the interview will be primarily based on the research emphasis of the candidate. Candidates must clearly indicate the program in which they want to apply.

Date of Admission test : April 22, 2014 (Tuesday)

Date of publication of selection list for the interview : April 22, 2014(Tuesday)

Date of Interview : April 23, 2014 (Wednesday)

Number of vacancies for the Ph.D. programme : 12

Reservation policy will be followed as per rules.

Candidates with valid NET/GATE/M.Phil. or equivalent qualifications are not required to appear at the admission test but would need to qualify in the interview.

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http://www.caluniv.ac.in/admission%20notice/PHD_BIO_PHYSICS.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

Welcome to phylobabble.org, a discussion forum for phylogenetic theory and applications. The primary goal of this forum is to discuss best practice and new developments in phylogenetics. Although we do have a Troubleshooting category for getting feedback on analyses, this is not a help site for running phylogenetics programs.

A great place to chat about phylogenetics for researchers and the broader community of students and science-interested citizens. 

Address of the bookmark: http://phylobabble.org/

Address of the bookmark: https://github.com/broadinstitute/VariantBam

Computational methods used by the Shasta assembler include:

• Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
• Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

More at https://chanzuckerberg.github.io/shasta/index.html

Address of the bookmark: https://github.com/chanzuckerberg/shasta

Algorithms scripts @ https://github.com/jschendel/bioinformatics-algorithms-coursera

Address of the bookmark: https://github.com/jorvis/biocode

https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

Address of the bookmark: https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

WALK IN INTERVIEW

A walk-in Interview for the following position tenable at the Bioinformatics Infrastructure Facility (BIF), Department of Biotechnology, Alagappa University will be held at the Department of Biotechnology, Alagappa University, Karaikudi 630 003 on 15.05.2014 (Thursday) at 01:00 PM. This national facility is funded by the Department of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi. The main objectives of the Centre involve teaching and research activities in bioinformatics/biotechnology.

RA (One Post):

Salary : Rs. 11000 p.m. plus admissible HRA

Qualification: M.Sc., in Bioinformatics/Biotechnology/Biophysics/Biochemistry/ Life Sciences

Interested candidates are encouraged to send their Curriculum Vitae by email to “sk_pandian@rediffmail.com” in advance. On the day of interview, the candidates must produce original certificates in proof of their educational qualification and experience and a recommendation letter from the Head of the Department/Institution where last studied/worked. Candidates who have already passed the required Degree alone are eligible to appear for interview. No TA&DA will be given for attending the interview.

Advertisement: http://www.alagappabiotech.org/Walk%20in%20interview.pdf