BOL: Related items

Computational Biology Summer Research Programme

Mon, 20 Jan 2020 23:38:44 -0600

IMSc has a limited programme for highly motivated bachelors and masters students interested in research in the areas of Theoretical Physics, Mathematics, Theoretical Computer Science and Computational Biology to visit the Institute over their summer vacation. In addition, IMSc also accepts students through the summer program organized by the joint Indian Academies of Science.
General Structure
This is a limited programme, depending on the availability of infrastructure and faculty advisors. We typically select about 25 students across disciplines although this number varies a bit from year to year. These visits typically span 6-8 weeks during the summer (May-July). There is also a provision for a 4-6 month visit, typically during January-April or August-November for extended project work.

Qualifications
Students currently in their pre-final or final year of BSc/BE/BTech or first year MSc/ME/MTech or equivalent with a good academic record are encouraged to apply through IMSc's formal application process.

To apply through the summer program jointly organized by the Academies of Science, please check the Indian Academy of Sciences for their application process: http://web-japps.ias.ac.in:8080/fellowship2018/index.html.
Stipend
Selected students will be paid 2nd class round trip train fare plus Rs.200 per diem. Accommodation will be provided in the hostel during summer, subject to availability. Since our ability to provide accomodation is often limited, we suggest that students also explore alternative possibilities for stay in Chennai. Accommodation will not be provided for longer visits.

Application Process
To apply for our summer programs please follow the instructions for the respective fields:
Theoretical Physics
Mathematics
Theoretical Computer Science
Computational Biology

Other information
If you have more questions about our application procedures, about your eligibility or simply about life and work at IMSc, do write to any of the faculty members listed on our home page.

Bioinformatics Scientist/Research Software Engineer at University of Dundee Dundee, United Kingdom

Wed, 26 Aug 2020 10:31:25 -0500

We are recruiting for an exceptional individual to join us as a computational scientist, bioinformatician, or (research) software engineer with an interest in interactive data analysis platforms for biology and medicine within our Jalview (www.jalview.org) research software engineering team.

More at https://www.jobs.dundee.ac.uk/fe/tpl_uod01.asp?s=4A515F4E5A565B1A&jobid=104342,2382988671&key=147934117&c=99413415238921&pagestamp=sesxbbuyifokdsfygf

Last date: 30th August 2020

Informal enquiries about this position may be made to Prof. Geoff Barton (gjbarton@dundee.ac.uk) or Dr Jim Procter (jprocter@dundee.ac.uk). To find out more about Jalview research software engineering team please visit www.jalview.org and www.compbio.dundee.ac.uk

Bioinformatics Research Scientist @ Oklahoma State University (OSU)

Tue, 17 Aug 2021 13:24:39 -0500

This position is an early career research scientist in the area of Bioinformatics to support research projects involving faculty and staff, at Oklahoma State University (OSU). This is a highly technical position that requires a strong research background in biomedical or life sciences, including a high level of expertise with bioinformatics algorithms, databases, and analyses with a focus on next-generation sequence data. Although most of the projects will deal directly with the analysis of DNA and RNA sequence data the individual should be well versed in other types of data sources as well (i.e., microarrays) and handling of large datasets (using data analytics, machine learning, and deep learning techniques).

More at https://okstate.csod.com/ats/careersite/JobDetails.aspx?site=8&id=9874

PhD Hunt: Your Gateway to Nordic Academic Opportunities

LEGE — Thu, 02 Jan 2025 19:55:22 -0600

Embarking on a PhD journey is a transformative step in academia. To ease this transition, we brings you a curated list of top resources and institutions across Denmark, Sweden, Norway, and Finland. These links will guide you through finding opportunities and navigating the academic landscape in the Nordic region.

PhD Opportunities in Denmark Denmark boasts a robust academic infrastructure with world-class universities. Here are some essential resources:

Study in Denmark: https://studyindenmark.dk

Aarhus University: https://phd.au.dk

Euraxess Denmark: https://euraxess.dk

Technical University of Denmark (DTU): https://dtu.dk

University of Copenhagen: https://phd.ku.dk

Copenhagen Business School: https://cbs.dk

Jobindex: https://jobindex.dk

Roskilde University: https://ruc.dk

University of Southern Denmark: https://sdu.dk

Academic Positions Denmark: https://academicpositions.dk

PhD Opportunities in Sweden Sweden is renowned for its innovation-driven academic culture. Here’s where you can find opportunities:

FindAPhD Sweden: https://findaphd.com/phds/sweden

Euraxess Sweden: https://euraxess.se

Academic Positions Sweden: https://academicpositions.se

KTH Royal Institute of Technology: https://kth.se

Lund University: https://lu.se

Uppsala University: https://uu.se

Chalmers University of Technology: https://chalmers.se

Linköping University: https://liu.se

Stockholm University: https://su.se

Swedish University of Agricultural Sciences (SLU): https://slu.se

Study in Sweden: https://studyinsweden.se

Malmö University: https://mau.se

PhD Opportunities in Norway Norway offers unique research opportunities, complemented by its stunning natural landscapes:

JobbNorge: https://jobbnorge.no

Euraxess Norway: https://euraxess.no

University of Oslo: https://uio.no

Norwegian University of Science and Technology (NTNU): https://ntnu.edu

Norwegian Business School (BI): https://bi.edu

Norwegian School of Economics: https://nhh.no

Norwegian University of Life Sciences (NMBU): https://nmbu.no

Norwegian School of Sport Sciences: https://nih.no

University of Bergen: https://uib.no

Nord University: https://nord.no

UiT The Arctic University of Norway: https://uit.no

PhD Opportunities in Finland Finland’s education system emphasizes research excellence and innovation. Explore these resources:

FindAPhD Finland: https://findaphd.com/phds/finland

Euraxess Finland: https://euraxess.fi

University of Helsinki: https://helsinki.fi

Aalto University: https://aalto.fi

University of Turku: https://utu.fi

Tampere University: https://tuni.fi

University of Eastern Finland: https://uef.fi

University of Jyväskylä: https://jyu.fi

Åbo Akademi University: https://abo.fi

Hanken School of Economics: https://hanken.fi

LUT University: https://lut.fi

Conclusion The Nordic countries offer exceptional opportunities for PhD aspirants. From top-ranked universities to specialized research institutions, the possibilities are endless. Bookmark PhD Hut as your starting point, and let these resources guide you to your academic aspirations.

AI Uncovers Hidden Secrets in Bacterial DNA, Opening New Frontiers in Genomic Research

Jitendra Narayan — Fri, 25 Sep 2026 22:38:42 -0500

Scientists are now using artificial intelligence in order to examine sections of bacterial DNA that have not been looked at before. This method is showing potential RNA interactions and previously unknown genetic systems which could alter our understanding of microbes. A new study presents Minerva, a genome language model, demonstrating that AI can assist researchers in identifying biological patterns that traditional techniques might fail to detect.

The study, which was made available as a preprint on bioRxiv on 23 September 2026, focuses on the non-coding sections of DNA that are still largely unknown. Although these areas do not produce proteins, they can contain important signals and instructions which have an effect on cell function. The research presents a novel approach to investigating how bacteria handle and control their genetic information.

AI maps previously unexplored genomic regions

The team developed Minerva in order to identify possible interactions between different regions in microbial genomes; rather than depending on similarities with known sequences, Minerva predicts these relationships directly from the DNA by using patterns learned by a genome language model.

On 150 bacterial genomes, Minerva identified a large number of interactions that were not included in the existing annotations. The researchers stated that 84.3 per cent of the predicted intergenic base-pairing interactions were not present in the current annotations, which demonstrates that AI can be of help in generating new ideas in biology.

Unusual RNA structures and viral genetic systems identified

The study also examined a bacterial RNA family known as TwoAYGGAY in Pseudomonas; the model anticipated longer RNA structures and identified associations with repeated DNA sequences, thus providing new insights into how these non-coding elements are organised and how they have evolved.

In a separate section of the study, the researchers examined reverse transcriptase systems associated with bacteriophages, which are viruses that infect bacteria. They identified RNA arrays that maintain their structure but have different sequences and were linked to Unknown Group 27 reverse transcriptases. The findings indicate that these RNAs could function as templates for the production of complementary DNA that is capable of forming hairpin shapes.

The researchers also observed that Minerva was able to detect patterns associated with protein-coding areas, even though it had not been trained to do so. This indicates that genome language models may pick up on biological signals that go beyond what they were intended to identify.

Implications for future genomic research

The fact that artificial intelligence is becoming increasingly important in the field of microbial genomics is shown by the fact that models such as Minerva are able to predict interactions and identify patterns in areas which have not been extensively studied, thus helping researchers to decide what to study next and enabling them to gain a better understanding of biological systems that are still not well understood.

Yet the predictions do not reveal the exact function of each element identified. In order to verify which of the predicted interactions actually take place in living cells and the way in which they affect the microbes, experiments will be necessary. Although the study has undergone peer review, it does nonetheless offer a promising illustration of how machine learning can complement traditional genomics and assist scientists in moving from the identification of known genes to the exploration of the complex relationships that shape microbial life.

More at https://www.biorxiv.org/content/10.64898/2026.09.22.753630v2.full.pdf

INRAE organises open competitions to recruit research scientists on permanent positions.

Sun, 02 Feb 2020 23:08:36 -0600

Each year, INRAE organises open competitions to recruit research scientists on permanent positions. The recruitment campaign is generally aimed at researchers who have recently obtained their PhD. Candidates are recruited on the basis of their scientific competence which they will put to the service of INRAE's major research axes by responding to a research topic. Candidates must have published articles on the results of their PhD.

Campaign calendar:

- Opening date for applications: January 30, 2020
- Deadline for applications: March 5, 2020
- Pre-selections: April-May 2020
- Final selections: May-June 2020
- Starting date for appointments: from September 2020

More at https://jobs.inrae.fr/en/open-competitions/open-competions-research-scientists-crcn

Common Bioinformatics Interview Questions !

Jit — Sat, 23 Jan 2021 06:07:50 -0600

The possibility of an interview for a bioinformatics position in the life sciences may be very disquieting, but the same concerns emerge time and again in my experience. So, it is exceedingly worthwhile to plan for future bioinformatics interview questions. Doing this will really give you the advantage in obtaining the position.

The following 5 questions are those that I have heard many times during the job-search process. There is no reason for not planning responses in such situations.

1. Tell Us About Yourself
This is a very typical opener in interviews. It's a perfect question to ask, and getting something planned will really help you concentrate and ease in the conversation. However, you need to make sure that your response is applicable to the job you're interviewing.
It's probably better to keep your answer professional. Try to include these in the answer as well: where did your love of science and bioinformatics come from? How the heck did you end up in this field? Why programming and scripting ?

2. What is your plan for your bioinformatics career? / How do you look at yourself in five years? / How are your personal objectives to accomplish these goals / What are the plan for your research fundings ?

Your CV/resume has already impressed the selection panel if you have been invited for an interview. The questions from the bioinformatics interview team provide an incentive for you to market yourself and illustrate the work in question with the most appropriate knowledge.

3. What do you understand about the job description/What would your suggested research path be if you were a successful candidate?
Summarize the specifics of the advertised bioinformatics position in your own words. Follow on with some suggestions of how you want to extend your research and create your own projects within the community.

4. Will you work as a group or do you want to work on your own?
This requirement can vary from jobs to job, so when addressing, be alert. A company/research PI may need a bioinformatician that is able to work on a single project autonomously, or they may need a person who can help direct and organize a team. In your response, refer to the job description.

5. What particular methods have you used to date with your experiments?
You might have experience with all the laboratory techniques described in the job description, but stress the ones you highly experienced with. Highlight your professional abilities and stress that you are extremely capable of mastering new techniques with others ...

At the end of the day, remember that you're questioning the jury as well as they're interviewing you. You will ought to think of any questions you would like the interview panel to pose. This indicates that you have done your homework and serious about the position.

All the best for your future job interview.

16sRNA Database Download

LEGE — Wed, 24 Apr 2024 04:33:15 -0500

Downloading 16S rRNA databases can be crucial for various bioinformatics analyses, especially in microbiome research. However, it's important to note that databases can vary based on your specific needs, such as the taxonomic coverage you require or the type of analysis you're performing. Here's a general guideline on how you can obtain 16S rRNA databases:

NCBI (National Center for Biotechnology Information):
- NCBI provides various databases related to genetic information, including 16S rRNA sequences.
- You can access the 16S ribosomal RNA sequences from NCBI's Nucleotide database (https://www.ncbi.nlm.nih.gov/nucleotide/).
- Perform a search using keywords like "16S rRNA" or specific bacterial names to find relevant sequences.
- You can download sequences individually or in batches using the provided tools.
GreenGenes:
- GreenGenes is a widely used 16S rRNA gene sequence database.
- You can access it at http://greengenes.secondgenome.com/.
- GreenGenes provides precompiled databases for various purposes, including classification, alignment, and phylogenetic analysis.
SILVA:
- SILVA (https://www.arb-silva.de/) is another comprehensive database for ribosomal RNA (rRNA) sequences.
- It covers not only 16S rRNA but also other ribosomal RNA sequences.
- SILVA provides precompiled databases for various purposes, including taxonomic classification and alignment.
Ribosomal Database Project (RDP):
- RDP (http://rdp.cme.msu.edu/) is a curated database that offers 16S rRNA sequences.
- It provides tools for sequence analysis and classification.
- You can download sequences and taxonomy information from their website.
QIIME (Quantitative Insights Into Microbial Ecology):
- QIIME (https://qiime2.org/) is a widely used bioinformatics platform for microbiome analysis.
- It provides tools for analyzing microbial communities, including processing 16S rRNA sequences.
- QIIME often includes its own preprocessed 16S rRNA databases that can be used for analysis within the platform.

Before downloading any database, make sure to read the terms of use and citation requirements, as some databases may have specific usage policies. Additionally, consider the compatibility of the database with your analysis pipeline and software tools.

NCBI 16s RNA database location ftp://ftp.ncbi.nih.gov/blast/db/16SMicrobial.tar.gz

ONT assembly and Illumina polishing pipeline

Jit — Thu, 23 Nov 2017 10:13:42 -0600

This pipeline performs the following steps:

Assembly of nanopore reads using Canu.
Polish canu contigs using racon (optional).
Map a paired-end Illumina dataset onto the contigs obtained in the previous steps using BWA mem.
Perform correction of contigs using pilon and the Illumina dataset.

Address of the bookmark: https://github.com/nanoporetech/ont-assembly-polish

dnaPipeTE: de-novo assembly & annotation Pipeline for Transposable Elements

Rahul Nayak — Sat, 02 Dec 2017 18:25:44 -0600

dnaPipeTE (for de-novo assembly & annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (< 1X).

dnaPipeTE is developped by Clément Goubert, Laurent Modolo and the TREEP team of the LBBE: http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en
You can find the original publication in GBE here: https://academic.oup.com/gbe/article/7/4/1192/533768

output examples of quantification and TE landscape (relative age) produced by dnaPipeTE

Address of the bookmark: https://github.com/clemgoub/dnaPipeTE