The fate of a cell is ultimately the product of the regulation of its genes. Gene regulation is a coordinated process acting at multiple levels of which transcription and translation are the most prominent. The Valen group is dedicated to the fundamental question of how transcription and translation is integrated to obtain the desired protein abundance. The recent development of high-throughput next generation sequencing techniques to monitor both active translation and transcription has made it possible to study this connection at the genome scale.

This project aims to elucidate the links between regulation of translation and transcription. The applicant will analyze next generation sequencing data and model gene regulation on a genome-wide level to identify the features that affect the translational output of transcripts. The work will be done in close collaboration with experimental scientists who will test the predictions of the computational models.

Additional information on the position can be obtained by contacting Eivind Valen (eivind.valen@ii.uib.no).

The research fellow must take part in the University’s approved PhD program leading to the degree within a time limit of 3 years. Application for admission to the PhD program, including a project plan outline for the training module, will be worked out in collaboration with the research group in question.

In total, the fellowship period is 4 years, 25 % of this will be allocated to teaching and/or administrative duties. The fellowship period may be reduced if the successful applicant has held previous employment as a research fellow or similar.

http://www.jobbnorge.no/en/available-jobs/job/102235/research-fellow-phd-candidate-in-computational-biology-2-positions

Features

• Circular and linear views of genomes

• Capable of drawing genomes up to 10 Mbp with 1000's of features and 100's contigs

• Smooth zooming down to the sequence level

• Easily generate features and plots directly form the sequence (e.g. ORFs, GC-content and GC-Skew)

• Save high resolution PNG maps up to 8000x8000px

• Fully documented API for interacting with CGView.js maps

Address of the bookmark: https://js.cgview.ca/

1. Make friends with local bioinformatics groups
2. Talk to your computing group
3. Obtain clear expectations
4. Rewrite your job description
5. Papers
6. Attend bioinformatics meetings
7. Try first, ask later

Address of the bookmark: http://biomickwatson.wordpress.com/2013/04/23/a-guide-for-the-lonely-bioinformatician/

Understanding Genome Screening

Genome screening involves analyzing an individual's DNA to identify genetic variations that may influence health and disease susceptibility. This can range from simple single-gene tests to comprehensive whole-genome sequencing. By peering into our genetic blueprint, we can uncover risks for conditions like cancer, diabetes, cardiovascular diseases, and even rare genetic disorders.

The process is straightforward: a saliva or blood sample is collected, and advanced sequencing technologies decipher the genetic code. The results provide a personalized health map, guiding lifestyle modifications, preventive measures, or medical interventions.

A Shift from Reactive to Proactive Healthcare

Traditional healthcare often focuses on treating diseases after they manifest. Genome screening flips this model on its head, enabling a shift toward prevention and early intervention. For instance:

• Cancer Risk Management: Individuals with BRCA1 or BRCA2 gene mutations can opt for enhanced screening programs or preventive surgeries to mitigate their risk of breast and ovarian cancers.

• Cardiovascular Health: Genetic predispositions to conditions like familial hypercholesterolemia can prompt early cholesterol monitoring and lifestyle adjustments.

• Rare Diseases: Identifying carriers of genetic disorders can aid in family planning and reduce the incidence of inherited conditions.

The Ethical and Practical Concerns

While genome screening offers incredible promise, it is not without challenges:

1. Accuracy and Interpretation: Genetic predisposition does not guarantee disease. Misinterpretation of results can lead to unnecessary anxiety or unwarranted medical interventions.

2. Privacy and Data Security: Genetic data is highly sensitive. Ensuring robust data protection measures is crucial to prevent misuse.

3. Accessibility and Equity: High costs and limited availability may restrict access to genome screening, exacerbating health disparities.

Balancing Science and Pseudoscience

The comparison of genome screening to horoscopes isn’t entirely unfounded. Both offer predictive insights, but the scientific foundation of genome screening distinguishes it from astrology. Unlike the alignment of celestial bodies, genetic predictions are based on rigorous data and evidence. However, the probabilistic nature of genetic predispositions underscores the importance of interpreting results in conjunction with clinical and lifestyle factors.

The Road Ahead

As genome screening becomes more affordable and integrated into routine healthcare, its potential to transform lives is immense. Policymakers, healthcare providers, and genetic counselors must collaborate to ensure ethical implementation, public awareness, and equitable access.

Imagine a future where your genetic "horoscope" is a trusted guide, not just a prediction. Early genome screening could help chart a healthier path for generations, making it a cornerstone of personalized medicine. After all, our genes might just hold the key to unlocking a future of better health and well-being.

The Job detail

Advancing the SNP-discovery and polymorphism assessment work across several germplasm panels representing global genetic diversity
Population genetic and genomic analyses, testing the hypothesis related to adaptation in multiple geographic regions
Develop SNP assays from large scale GBS and other re-sequencing data for several target traits utilizing available phenotyping data
Combined analyses of genotypic and phenotypic data for discovery of marker-trait associations, and conducting GWAS
Processing, analyzing, and archiving large-scale genomic data sets, assessing data quality, conducting analyses, interpreting findings, and communicating findings to others including preparation of reports, presentations, posters and journal articles
Providing support to MSc and PhD students on topic related to its major core of research
Any other work assigned by the supervisor

The Person:

PhD in bioinformatics, genetics, computational biology preferably with 1 to 2 years of experience;
familiar with standard bioinformatics tools and scripting languages and emerging and evolving software platforms relevant to bioinformatics and computational biology;
ability to create new analytical pipelines; experience with handling large data sets;
ability to program in at least two of the following: C++, PERL, Python, R, Java.
will use next-generation sequencing technologies to generate marker data for genetic mapping and transcriptome data for expression QTL mapping, and will be responsible for data generation as well as data analysis.

Period and Remuneration: The assignment is for a period of two years, and can be extended for another year depending on performance. ICRISAT pays a very attractive all inclusive lump sum assignment fee payable in Indian Rupees.

How to Apply: Please send your application by email to icrisatjobs@cgiar.org, stating the job title (Special project Scientist-Sorghum Genomics) clearly in the subject column, addressed to the Director, Human Resources and Operations, ICRISAT, Patancheru, Andhra Pradesh 502 324, India, latest by 10 June 2014. The application should include an up-to-date Curriculum Vitae, a short statement of competencies and experience for the position, and the names and addresses (including phone/e-mail) of three referees. Only short-listed candidates will be contacted.

More at: http://www.icrisat.org/careers/Special-Project-Scientist-Sorghum-Genomics.htm

We have developed panHiTE, a comprehensive and accurate pipeline for TE detection in large-scale population genomes. It has been successfully applied to hundreds of plant population genomes, demonstrating its effectiveness and scalability.

For detailed instructions, please refer to the panHiTE tutorial.

Address of the bookmark: https://github.com/CSU-KangHu/HiTE

Webpage : https://www.scilifelab.se/about-us/
Opportunity: https://www.scilifelab.se/about-us/career/

“It’s like a subway map,” she said, “but for DNA.”

That idea is exactly what Sequence Tube Map brings to life.

Instead of showing a genome as one long, confusing sequence, it displays different genomic paths like routes on a metro map. Where sequences are shared, the paths travel together. Where genetic variation occurs, they branch into different routes and may join again later.

This makes complex genome graphs much easier to explore.

Maya could now see how different haplotypes moved through the same genomic region and where they took different paths. She could even view sequencing reads mapped onto the graph, helping her understand what was happening in regions with genetic variation.

What once looked like a tangled network now looked like a journey.

Sequence Tube Map turns the complexity of graph genomes into a visual story—making it easier for scientists to see how DNA can take different routes.

Read more at  https://github.com/vgteam/sequencetubemap 

and

https://academic.oup.com/bioinformatics/article/35/24/5318/5542397

Demo at https://vgteam.github.io/sequenceTubeMap/

Walk- in- Test cum Interview (based on test) for the selection of Research Associate

under the scheme “Distributed Information Sub Centre –DISC” & Research Assistant under scheme “Phytophthora, Fusarium and Ralstonia diseases of Horticultural and Field Crops” will be held at this Institute as per details indicated below.

WALK -IN- TEST CUM INTERVIEW

Name of the post : Research Associate

Date of Interview : 21-05-2014 at 10.00 AM

No. of posts : One

Qualifications : a)Essential

Ph.D Degree in Bioinformatics OR : Masters degree in Bioinformatics with a minimum of
60% marks or equivalent OGPA with at least two years research experience as evidenced from fellowship/ associateship/training/published papers etc.

b)Desirable: Experience in NGS data analysis.

Emoluments : Rs. 23,000/- per month + HRA (Masters Degree Holders)

Rs. 24,000/- per month + HRA (Ph.D Degree Holders)

Upper age limit : 40 years for Men & 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)

Duration of Project : Till 31-03-2017.

Title of Assigment : Research Assistant (on contract basis)

No. of vacancy : One

Qualification : Essential : Post Graduation in Bioinformatics and Minimum one year experience in NGS data analysis

Desirable : Experience in Perl/Python/R

Remuneration : Rs. 20,000/- per month (consolidated)

Scope of work :

1. Analysis of different file formats and their conversions.

2. Assessing the quality of data and filtering of raw reads.
3. Assembling the raw reads-de novo as well as reference mapping.
4. Compression of aligned reads using Jam tools
5. RNA-seq. Analysis
6. Differential expression testing involving Normalization, Statistical testing, heat map generation & hierarchical clustering
7. Annotating the assembled genome and geneet testing and their validation
8. Metabolic pathway analysis
9. Comparative genomics
10. Setting up of genome browsers.

Period of Assigment : Initially for six months.

Date & Venue of Interview : 21-05-2014 at IISR, Kozhikode at 10.00 AM

More at http://www.spices.res.in/pdf/disc-advtmnt.pdf

Instead of asking only whether two genes occur together across genomes, CORGIAS asks a deeper question: did these genes actually evolve together, repeatedly gaining or losing their presence in concert, or are they merely travelling together because of their inherited history? The framework introduces two complementary approaches, Ancestral State Adjustment (ASA) and the Simultaneous EVolution test (SEV), which incorporate evolutionary information into the search for gene associations. This distinction becomes increasingly important as genome sequencing continues to uncover enormous numbers of microbial genomes, many from organisms that have never been cultured and whose genes remain functionally mysterious. In this growing genomic landscape, simply finding a gene is no longer enough—we need clues about what that gene might be doing. CORGIAS approaches this problem almost like a genomic detective: rather than treating every association as evidence, it reconstructs the evolutionary story behind the association and asks whether the evidence survives that history. By doing so, it offers a way to separate coincidence from biological connection and potentially uncover functional relationships hidden within the vast microbial genomic landscape. The broader message is beautifully simple: genes do not evolve in isolation, and understanding what they do may require us to look beyond where they are today and reconstruct how they arrived there. Sometimes, the most important clue in a genome is not the gene itself, but the evolutionary journey it has taken.

More at https://academic.oup.com/nargab/article/7/4/lqaf182/8377910?