BOL: Related items

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

seqloc 0.6

Gudiya Pal — Sun, 28 Dec 2014 12:51:29 -0600

The Bio.SeqLoc modules in seqloc are designed to represent positions and locations (ranges of positions) on sequences, particularly nucleotide sequences. My original motivation for writing these packages was handing the locations of genes in eukaryotic genomes.

Handle sequence locations for bioinformatics http://www.ingolia-lab.org/seqloc-tutorial.html

Address of the bookmark: http://www.stackage.org/snapshot/nightly-2014-12-28/package/seqloc-0.6

Linux operating system aimed at scientists

Pranjali Yadav — Mon, 19 Jan 2015 08:30:49 -0600

The Bio-Linux operating system is based on Ubuntu 14.04 LTS (Trusty Tahr), and the previous version was using Ubuntu 12.04 LTS. The developers only use LTS releases and that means that upgrades for this distro don't come along all that often.

This Linux distribution is aimed at scientists and it comes with more than 250 bioinformatics packages, 50 graphical applications and several hundred command line tools. And this is just skimming the surface of what the OS can do. Users have access to even more apps from the official repositories.

Bio-Linux is using an Ubuntu LTS version as its base

The fact that it uses Ubuntu LTS versions for the base is a good thing because it means its users won't have to worry about the support. Ubuntu 14.04 LTS is supported until 2019, so people who are using Bio-Linux shouldn't have a problem.

"An updated Bio-Linux 8 version is now on the website in ISO and OVA versions. As usual, there is no need to download this version if you are an existing user. All updates to existing packages will be applied to your system through the update manager and new packages are all available via apt-get or Synaptic," reads the announcement.

The changelog also states that a problem that was preventing the desktop to not start on VirtualBox has been fixed, the QIIME and Bowtie-Bio tools have been upgraded, the pandaseq paired end assembler has been added, and the beginners tutorial specific to Bio-Linux 8 has been improved.

Check out the official announcement for a complete list of changes and updates. You can download Bio-Linux 8.0.5 right now from Softpedia and give it a spin. It has the Unity desktop and now it runs very well in virtual environments.

Reference @ http://news.softpedia.com/news/Bioinformatics-Distro-Bio-Linux-8-0-5-Now-Available-for-Download-469867.shtml

R for Microsoft Excel

Jitendra Narayan — Wed, 18 Feb 2015 00:43:27 -0600

If you currently use a spreadsheet like Microsoft Excel for data analysis, you might be interested in taking a look at this tutorial on how to transition from Excel to R by Tony Ojeda. The tutorial explains how to use R functions in place of Excel formulas, including tools like =AVERAGE and =VLOOKUP. For the most part, it uses modern R packages to keep the R code clear and concise.

You'll likely still be using Excel as a data source, though, so you'll also want to check out this guide to importing data from Excel to R from MilanoR.

Reference http://www.r-bloggers.com/an-r-tutorial-for-microsoft-excel-users/

A guide for complete R beginners :- Installing R packages

Archana Malhotra — Tue, 24 Feb 2015 20:23:34 -0600

Part of the reason R has become so popular is the vast array of packages available at the cran and bioconductor repositories. In the last few years, the number of packages has grown exponentially!

This is a short post giving steps on how to actually install R packages. Let’s suppose you want to install the ggplot2 package. Well nothing could be easier. We just fire up an R shell and type:
> install.packages("ggplot2")

In theory the package should just install, however:

if you are using Linux and don’t have root access, this command won’t work.
you will be asked to select your local mirror, i.e. which server should you use to download the package.

Installing packages without root access

First, you need to designate a directory where you will store the downloaded packages. On my machine, I use the directory /data/Rpackages/ After creating a package directory, to install a package we use the command:
> install.packages("ggplot2", lib="/data/Rpackages/") > library(ggplot2, lib.loc="/data/Rpackages/")

It’s a bit of a pain having to type /data/Rpackages/ all the time. To avoid this burden, we create a file .Renviron in our home area, and add the line R_LIBS=/data/Rpackages/ to it. This means that whenever you start R, the directory /data/Rpackages/ is added to the list of places to look for R packages and so:

> install.packages("ggplot2") > library(ggplot2)

just works!

Setting the repository

Every time you install a R package, you are asked which repository R should use. To set the repository and avoid having to specify this at every package install, simply:

create a file .Rprofile in your home area.
Add the following piece of code to it:

cat(".Rprofile: Setting UK repositoryn") r = getOption("repos") # hard code the UK repo for CRAN r["CRAN"] = "http://cran.uk.r-project.org" options(repos = r) rm(r)

I found this tip in a stackoverflow answer .

BINC Sample Question Paper !!!

Jitendra Narayan — Thu, 16 Apr 2015 09:12:39 -0500

BINC sample question paper for round ONE.

BINC examination 2015 !!!

Jitendra Narayan — Fri, 17 Apr 2015 03:34:28 -0500

BioInformatics National Certification (BINC) Examination 2015 organized by Department of Biotechnology, Government of India, New Delhi Pondicherry University, Puducherry