Reference Number IIT/SRIC/R/DIC/2014/314, DATED 28thNovember, 2014

Temporary Position(s)

i) Junior/ Senior Project Officer (1)
ii) Junior Project Assistant (2)

Consolidated Compensation

i) Rs.16,000/- to Rs.18,000/- p.m. (depending on qualification & experience)
ii) Rs.8,000/- to Rs.10,000/- p.m. (depending on qualification & experience)

Coordinator / PI Dr. Sudip K. Ghosh, Dept of Biotechnology.

Department / Centre / School Dept of Biotechnology

Qualifications & Experience

(i) M. Sc in any branch of Life Sciences with experience in Molecular Biology/Genetics/Biochemistry preferably a Post Graduate diploma in Bioinformatics or two years working experience in bioinformatics (Minimum 60% marks starting from matriculation examination).

(ii) B. Sc. /B.A. /B. Com/Diploma in Management or Computer Science. Knowledge in computer software will be preferred (minimum 50% marks starting from matriculation examination).

More Information

Interested eligible persons may apply on plain paper, giving full bio-data along with attested copies of testimonials to the undersigned on or before 11thDecember, 2014.

Sponsor DBT, NEW DELHI

Last Date 11 Dec 2014

Application Fee Demand Draft for Rs.50/- (Not for female candidates) drawn in favour of IIT Kharagpur payable at Kharagpur

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

http://rosalind.info/problems/list-view/

Address of the bookmark: http://rosalind.info/problems/list-view/

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/

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

Originally design to map the location of objects in a 10 km map IrishGrid includes:

• native support of the Irish Grid System (see http://www.osi.ie/)
• optimize for speed (there's as less as possible data to conversion)
• customized color functions

https://code.google.com/p/irishgrid/downloads/detail?name=irishgrid.pl

Address of the bookmark: https://code.google.com/p/irishgrid/

Source:http://www.nejm.org/doi/full/10.1056/NEJMp1114866

Another video on precision medicine:

http://www.youtube.com/watch?v=Pi8W0yOXnzE

Precision Medicine basically intergrates bioinformatics, genomics , genetics, molecular biology and nanotechnology to deliver precise cure/diagnotics to a specific patient.

Examples:

• The drug imatinib (Gleevec) designed to inhibit an altered enzyme produced by a fused version of two genes found in chronic myelogenous leukemia.
• The breast cancer drug trastuzumab (Herceptin) works only for women whose tumors have a particular genetic profile called HER-2 positive.

E.g. source :

http://www.bionews-tx.com/news/2013/08/15/how-the-impact-of-cancer-genomics-on-precision-medicine-is-revolutionizing-cancer-treatment/

Address of the bookmark: http://www.cbsnews.com/video/watch/?id=50149783n

1. Remuneration : Rs. 12000/- per month (consolidated)

2. Qualification : M.Sc./M.Tech. Bioinformatics (preference will be given to the candidates with BINC / NET / GATE / Bioinformatics DBT-Traineeship / Studentship and teaching experience)

3. Place of work : Department of Bioinformatics, SVIMS, Tirupati

4. No. of Position : One

Terms and conditions:

1. Candidates are required to submit the Biodata, attested copies of relevant certificates in support of their age, educational qualification, experience etc., before the interview committee, SVIMS University, Tirupati.
2. No TA / DA will be paid for attending the interview.
3. Interim enquiries will not be entertained.
4. The maximum age limit for Teaching Assistant is 34 years as on 8th August, 2015.
5. The tenure of will be further extended subject to performance of the incumbent.

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" the microbes exist in very low concentrations, of around 1,000 microbes in every tea spoon full of rock, compared with billions or trillions of bacteria that would typically be found in the same amount of soil at Earth's surface."

Reference:

http://www.bbc.co.uk/news/science-environment-23855436