Scientists face a big challenge with the human genome. They can read its letters, but understanding their roles is much harder. Only about 2 percent of the genome codes for proteins. The rest acts like a huge control panel, deciding when and where genes turn on. With about 9 billion possible single-letter changes, testing them all in a lab just isn’t possible.

So, Google DeepMind asked a new question: what if we could predict what those changes might do?

This question led to the AlphaGenome Atlas (https://deepmind.google.com/science/alphagenome/atlas?), a detailed map of nearly every possible single-letter change in the human genome. Instead of checking each change one by one, researchers can use the Atlas to spot the ones most likely to matter. The AlphaGenome Variant Impact (AVI) score works like a trail marker, pointing scientists toward the changes worth a closer look.

This is where the Atlas gets especially useful. Much of the genome lies outside the protein-coding regions, where DNA acts as a switch or controller for genes. AlphaGenome lets researchers explore these areas and see how small changes could affect gene activity.

An atlas isn't the destination; it's a guide.

The AlphaGenome Atlas doesn’t replace experiments or solve every mystery. Instead, it helps scientists decide where to begin. From billions of possibilities, it turns the vast genetic landscape into something researchers can start to explore.

There are nine billion possibilities, a vast map, and maybe among them therWith nine billion possibilities and a huge map to explore, there may be clues hidden here to some of medicine’s toughest mysteries.

More at https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/alphagenome-atlas.pdf

Bioinformatics supports to researchers
Customized training in Bioinformatics for researchers and faculty members
Support in Installing, implementing and maintaining software on computer.
Create awareness for taking preventive measure against data security
Organize workshops on thrust ares of Bioinformatics
Research Training to students of Biotechnology and Bioinformatics

More at http://bioinfo.bisr.res.in/index.php

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

Project Fellow (Biological Sciences) Pay Scale: Rs. 16,000/- + 30 % HRA per month
Educational Requirements: M.Sc./B.Tech in life sciences/Biological sciences with at least 55 % marks
Experience Requirements: Research experience.
Details will be available at: http://www.igib.res.in/sites/default/files/24July2015.pdf

No of Post: 01
How To Apply: 1. Please fill up the proforma by clicking on the following link HR Online Form. 2. Candidate cannot apply for more than two posts. Last date of receiving application is 12-07-2015. No application would be entertained with “result awaited” status or after due date. List of shortlisted candidates will be put up on CSIR-IGIB website. No TA/DA will be paid to the candidates to attend the interview. The engagement shall be as per guidelines of CSIR/Funding agency. Candidates will have an option to give reply in Hindi. Note: The shortlisted candidates, have to report at 09:00 AM at Mall Road Campus, Delhi – 110007 on the day of interview along with any Photo ID card, (without photo ID card interview will not be conducted). 3 copies of updated signed C. V. (clearly mentioning Date of Birth and Highest Qualification with percentage), Dissertation (if any), PhD thesis (if any) and original certificates/Self attested photocopies for verification.
Detail of Interview: 24 July, 2015 at 10:30 AM
Age Limit: 28 Years

https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbaf208/8257678?login=true

Address of the bookmark: https://github.com/sib-swiss/termal

1. Research Associate – 1 No.

Rs. 36,000/38,000/40,000 per month for I, II and III year + 20% HRA

Essential : Doctoral degree in relevant subject from recognized University/ Institutes

Desirable: Research experience in molecular biology and bioinformatics.

Interested candidates can send their complete CV in plain paper with a passport size photograph, with details of marks secured in all subjects from plus two stage (with proof, full postal address, sex, date of birth, community etc., along with additional qualification or experiences and two address of references whom could be contacted.

DEPARTMENT OF MICROBIOLOGY SCHOOL OF LIFE SCIENCES UNIVERSITY Dr. N. THAJUDDIN Professor & Head Dean, Faculty of Science, Technology & Engineering Tiruchirappalli – 620 024, India, Phone: +91 431 2407082; Mobile +91 098423 79719; E-mail: nthaju2002@yahoo.com

Application should reach the Principal Investigator on or before 5.8.2015 by Speed post/Couriers/Email (nthaju2002@yahoo.com), with subject printed as “Application for Research Associate /Technical Assistant /Lab attendant” in the envelop. Qualifying candidates will be short listed and communicated with date of interview. No TA and DA will be given for attending the interview. Address for Communication Dr.N.Thajuddin Principal Investigator Department of Microbiology Bharathidasan University Tiruchirappalli – 620 024, Tamil Nadu.

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Research positions are open starting 15th August 2015.

Projects are specifically for protein structure analysis. Projects also involve drug binding studies both computationally and experimentally.

CSIR/SPM/INSPIRE/DBT/UGC JRF/post-doc fellowships are essential for applications.

Email curriculum vitae to sb.icgeb@gmail.com by 14 August 2015

Applications with complete bio-data from candidates possessing the required qualifications are invited for the posts of JRF (1) and Project Assistant (1) in DBT, GOI-funded research project “Next Generation Sequencing (NGS)- based de novo assembly of expressed transcripts and genome information of Orchids in North-East India” in DBT’s Twinning programme for NE as per DBT sanction order and norms.

(i) JRF(1 no.):

Qualifications: M.Tech/M.Sc in Life Sciences/ Botany/ Zoology/ Biochemistry/ Biotechnology/ Bioinformatics;

Desirable: Aptitude for Bioinformatics and Computer Programming/ Next generation sequencing data analysis

(ii) Project Assistant (1 no.):

Qualifications: Graduation in Science,

Desirable: Experience of working in a Life Science/Plant Biotechnology lab. and familiarity with computers and field work viz. collection of samples.

The applications through email bicnehu@gmail.com or post must reach the undersigned within 15 days from the date of publication of this advertisement. The advertised posts are purely temporary for the duration of the project and subject to availability of the funds from DBT. The appointment does not confer any entitlement or right over the posts for absorption in the University service. Prof. Pramod Tandon, PI/Mr. Devendra Kumar Biswal (Co-PI) email: bicnehu@gmail.com

Advertisement: www.nehu.ac.in/Advertisements/BIC_AdvtPV_030815.pdf

Jawaharlal Nehru University has been granted funds by the Department of Biotechnology (DBT), Govt. of India to initiate an Inter-School programme in JNU to strengthen training and research called "DBT-Jawaharlal Nehru University, New Delhi-Interdisciplinary Life Science Programme for Advanced Research and education" with the broad project area entitled "From Molecules to Systems: Exploring biological space using chemical and synthetic biology" cutting across Physical, Chemical and Biological Sciences.

Applications are invited for the following purely temporary posts at various levels in the various Group projects for Research and Technical positions. The project is upto March 2017, but the appointments shall be initially for a period of one year, renewable every year depending on the performance of project staff:

Project Sub-Title: Synthetic Genomics: Making sense out of 'junk' DNA

Senior Research Fellow (SRF) - 01 Post

Qualifications: M.Sc in Bioinformatics, with minimum 2 year research experience, specialization in microRNA, molecular modelling, systems and/or Synthetic biology with publication in the relevant area would be desirable.

Investigator: Prof. Pawan Kumar Dhar

Fellowship: Rs. 14000+30% HRA p.m.

Project Sub-Title: "Structure, function, and dynamics of biomolecules and Molecular engineering"

Postdoctoral Fellow (PDF) - 01 Post

Essential Qualifications: PhD in Science(Bioinformatics/Computational Biology, Physics, Chemistry, Mathematics, & Statistics), specialized in the area of Computational Biology/Structural Bioinformatics/ Quantum Chemistry/Molecular Dynamics & Simulation /Systems Biology , reflected by thesis topics and or publication of papers during PhD and/or Post doctoral experience.

Desirable Qualifications: Computational methods and designing experience in the field of Structure based or ligand based drug design, Chemoinformatics, Programming capabilities as required for developing tools in the computational & systems Biology.

Investigator: Prof. Indira Ghosh, SCIS

Salary: Rs. 22, 000 + 30% HRA

(Note: Revised emoluments shall be payable if Educational Qualifications or Eligibility Criteria as per DST OM No. A.20020/11/97-IFD dated 31-03-2010 are met by the research personnel)

The applications on plain paper indicating name, date of birth/age, address, essential / technical / professional qualifications, experience, research work, should reach the Programme Coordinator on or before 27th August 2015 at the following address:

The Envelop should be marked for the Post applied for. Any clarifications regarding projects may be sought from the respective project investigators as mentioned.

Address for correspondence:
Programme Coordinator
DBT-JNU BUILDER programme
School of Biotechnology
Jawaharlal Nehru University
New Delhi 110067

More at http://www.jnu.ac.in/career/currentjobs.htm

The position is open for immediate appointment and available for two years and then extendable for additional one year. The applicant will be appointed as Research Associate based on qualifications as detailed below:

Research Associate:

-Master’s degree with bioinformatics with at least 2 years of research experience in Next Generation sequencing data analysis as evidence from Fellowship/ Associateship / Training / other engagements.

-Familiarity with bioinformatics tools, database development, programming skills

-Minimum 1 publication in any peer reviewed journal

Salary will be as per ICAR rules and guidelines. Application will be shortlisted based on CV, reference letters from mentors and telephonic interview. Candidates will be called for a personal interview at Bangalore before appointment. No travel expense will be provided for attending interview at Bangalore.

Interested candidates may send a Letter of Interest and CV by email to: keshav@ibioinformatics.org before September 29, 2015.