simNGS takes fasta format sequences and a file describing the covariance of noise between bases and cycles observed in an actual run of the machine, randomly generates noisy intensities representing the signals for the sequence at each cycle and calculates likelihoods for all possible base calls.

Address of the bookmark: https://www.ebi.ac.uk/goldman-srv/simNGS/

Address of the bookmark: https://github.com/yjx1217/LRSDAY

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.

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Genobuntu is a software package containing more than 70 software and packages oriented towards NGS. In its current version, Genobuntu supports pre assembly tools, genome assemblers as well as post assembly tools.

Commonly used biological software and example script files for different assembly pipelines have also been provided, where the example script files can be updated to suit one’s experimental needs. Genobuntu attempts to reduce the amount of time and energy needed to build software workstations and it can also act as a good teaching source for a class room setting.

Therefore, Genobuntu offers a well-tailored environment for both novices and experts working in the field of genome assembly.

Address of the bookmark: https://sourceforge.net/projects/genobuntu/

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

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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

Ref. No. GU/Estt/T/308(VI)/2014/6451-61

Applications are invited from the Indian citizens for five (5) teaching posts of Assistant Professor (Contractual) under various departments of Gauhati University. Details of the advertisement, other terms and conditions and the application forms are available in the University website www.gauhati.ac.in

Asstt. Professor (Contractual)

2. M.Sc. Microbiology Course in Botany

3 1.M.Sc. Microbiology/M.Sc. Botany (Specialization in Microbiology)/M.Sc. Biochemistry (1 post). (Preference will be given to candidates having experience in Biochemistry).

2.M.Sc. Microbiology/M.Sc. Botany (Specialization in Microbiology)/M.Sc. Biotechnology(1 post). (Preference will be given to candidates having experience in Bioinformatics).

3.M.Sc. Microbiology/M.Sc. Botany (Specialization in Microbiology)/M.Sc. Biotechnology(1 post). (Preference will be given to candidates having experience in Microbial Genetics).

As per UGC norms

Pay Band & Academic Grade Pay : (Consolidated pay) : Rs. 21,600/- per month

Application Form : Prescribe application form may download from the G.U. website www.gauhati.ac.in

Last date of receipt of filled-in application is 08.01.2015.

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Dan Kortschak, Bioinformatics Group, School of Molecular and Biomedical Science ,The University of Adelaide

Address of the bookmark: http://talks.biogo.googlecode.com/git/illumination/illumination.pdf

Study Subject(s): PhD position is award in the field of Bioinformatics/Computer Science.
Course Level: Position is available for pursuing PhD degree level at the University of Macau.
Scholarship Provider: University of Macau
Scholarship can be taken at: China

Eligibility: The ideal candidate would be a master degree holder in Bioinformatics or related disciplines with knowledge in Medical sciences or Life sciences (with GPA of at least 3.0 on a 4-point scale or equivalent) . Knowledge in programming (C and C++) and Linux scripting are necessary; experience in molecular docking, molecular dynamics simulations or molecular modeling is an advantage. The candidate should be fluent in spoken and written English; preference will be given to applicants with good publication records in relevant areas.

Scholarship Open for International Students: Researchers from China can apply for this PhD position.

Scholarship Description:

The Computational Biology and Bioinformatics Group at the University of Macau is looking for a motivated PhD student in Bioinformatics or Computer Science to work on a research project focusing on the flexible receptor protein-ligand docking algorithms for computer-aided drug design. The candidate will be working as part of a team in developing novel metaheuristic algorithms and scoring functions for large-scale, highly flexible protein-ligand docking problems.

Number of award(s): There is only one PhD position available.

Duration of award(s): The duration of this PhD position is 2-3 years.

What does it cover? Remuneration paid to candidate is MOP 11000-14000/month (~USD 1375-1750/month).

Selection Criteria: Not Known

Notification: Not Known

How to Apply: Send your current CV, your academic transcripts, a letter of motivation and research interests, two letters of recommendations from academic faculty to Dr. Shirley Siu at shirleysiu[at]umac.mo before March 2015.

Scholarship Application Deadline: The applications should be submitted before March 2015.