BOL: Related items

4273π: Bioinformatics education on low cost ARM hardware

Jitendra Narayan — Mon, 02 Sep 2013 07:02:43 -0500

Are you teaching bioinformatics at universities and found it complicated by typical computer classroom settings. As well as running software locally and online, students should gain experience of systems administration. Hmm don't worry there is one new OS for the rescue. 4273π, an operating system image for Raspberry Pi based on Raspbian Linux. It provides an attractive, general-purpose computing environment, within which the course 4273π Bioinformatics for Biologists is embedded.

Though far slower than current desktop and laptop computers, the Raspberry Pi is notably faster than the Cray 1 supercomputer, a marvel of computer speed in its day. The Raspberry Pi approach includes all the benefits of the laptop approach, above, but at lower cost. In addition, the Raspberry Pi is a new and exciting computer system, which in itself can add interest to the course.

As the Raspbian operating system, Raspberry Pi firmware and hardware and 4273π Bioinformatics for Biologists teaching material develop, further releases of 4273π will be made available. It is anticipated that there will be a minimum of two releases per year during the next four years.

4273π is a means to teach bioinformatics, including systems administration tasks, to undergraduates at low cost.

Descriptive paper @ http://www.biomedcentral.com/1471-2105/14/243

Image source: BMC Bioinformatics

Tech and Bioinformatics roles at Basepaws

Wed, 18 Aug 2021 23:34:25 -0500

Basepaws is an LA-based pet genomics company, quickly growing and focused on feline and canine at-home genetic and biome tests, along with many other projects and products in the works. Thank you for taking a look!

Bioinformatics : https://www.linkedin.com/jobs/view/2681785372/

Engineer: https://www.linkedin.com/jobs/view/2681796993/

Huber Lab

Mon, 09 Sep 2013 21:57:03 -0500

The Huber group develops computational and statistical methods to design and analyse novel experimental approaches in genetics and cell biology.

Future projects and goals

Large-scale systematic maps of gene-gene and gene-environment interactions by automated phenotyping, using image analysis, machine learning, sparse model building and causal inference.
DNA-, RNA- and ChIP-Seq and their applications to gene expression regulation: statistical and computational foundations.
Cancer genomics, genomes as biomarkers, cancer phylogeny.
Image analysis for systems biology: measuring the dynamics of cell cycle and of cell migration of individual cells under normal conditions and many different perturbations (RNAi, drugs).

More @ http://www.embl.de/research/units/genome_biology/huber/index.html

Bioinformatics tools for genome assembly !

BioStar — Mon, 24 Jul 2023 07:04:26 -0500

There are numerous genome assembly tools available, each with its strengths and weaknesses. Here is a list of some widely used genome assembly tools as of my last update in September 2021:

SPAdes: An assembler specifically designed for single-cell and multi-cell bacterial genomes, as well as small eukaryotic genomes.
ABySS: A parallelized assembler for large genomes that uses de Bruijn graphs.
Velvet: Another de Bruijn graph-based assembler optimized for short-read sequencing data.
SOAPdenovo: A de Bruijn graph-based assembler designed for short reads, widely used for assembling large and complex genomes.
MaSuRCA: A hybrid assembler that combines data from multiple sequencing technologies, such as Illumina and PacBio.
Canu: A long-read assembler optimized for PacBio and Oxford Nanopore sequencing data.
Flye: A long-read assembler suitable for bacterial and small eukaryotic genomes.
SMARTdenovo: An assembler designed for long reads, particularly suited for PacBio data.
SPAdes Long Read (SPAdesLR): An extension of SPAdes for long-read data, such as those from PacBio or Nanopore.
Minia: An assembler optimized for low memory consumption, suitable for small and medium-sized genomes.
Unicycler: A hybrid assembler that combines short and long reads for circular bacterial genome assembly.
wtdbg2: A de Bruijn graph assembler for long reads, efficient for very large genomes.
Shasta: A long-read assembler that uses the Overlap-Layout-Consensus approach, suitable for PacBio and Nanopore data.
Sparc: An assembler designed to handle noisy long reads from Nanopore sequencing.
CANA: An assembler for metagenomic data, particularly for complex and diverse microbial communities.
Ra Assembler: A metagenome assembler for long reads, designed for highly complex metagenomic samples.

Please note that the field of bioinformatics is constantly evolving, and new assembly tools may have emerged since my last update. Additionally, the performance of these tools can vary depending on the characteristics of the sequencing data and the genome being assembled. When selecting an assembly tool, consider the specific requirements of your project, the available data types, and the computational resources at your disposal. Always refer to the respective tool's documentation and publications for the most up-to-date information and recommendations.

Asst. PROF IN BIOINFORMATICS at JAIPUR NATIONAL UNIVERSITY

Thu, 12 Sep 2013 07:18:02 -0500

JAIPUR NATIONAL UNIVERSITY, SCHOOL OF LIFE SCIENCES (SIILAS CAMPUS) URGENTLY REQUIRES

Asst. PROF IN BIOINFORMATICS.

QUALIFICATION: AS PER UGC

DESIRABLE: 1 YEAR EXPERIENCE IN ACADEMICS

CONTACT immediately

Prof D.S.Bhatia
Director
9351288070

Last date within 7 days of the publication.

Find more @ http://jnujaipur.ac.in/downloads/AdvtDec2012.jpg

Bioinformatics: The Bridge Between Curiosity and Discovery

BioStar — Mon, 24 Nov 2025 05:16:49 -0600

In the sprawling universe of modern science, bioinformatics stands as one of the most transformative and empowering fields of our time. It is where biology meets computation, where data becomes meaning, and where curiosity becomes discovery. If you’ve stepped into this world—or are considering it—here’s your reminder: you’re part of a revolution.

Why Bioinformatics Matters More Than Ever

Every day, our world generates massive amounts of biological data—from genome sequences to microbiome profiles to real-time pathogen surveillance. Hidden within these datasets are the answers to some of the greatest challenges humanity faces: emerging diseases, antimicrobial resistance, environmental stress, genetic disorders, sustainable agriculture, and more.

Bioinformatics isn’t just a skill.
It’s the language of the future of biology.

By mastering it, you give yourself the power to:

Decode genomes and understand life at its most fundamental level

Identify patterns no microscope could ever reveal

Predict disease outbreaks before they occur

Accelerate drug discovery with computational precision

Contribute to open-source tools that empower scientists worldwide

You don’t just follow science—you drive it.

Every Expert Was Once a Beginner

Many newcomers feel intimidated. Command-line interfaces. R scripts. Python packages. Next-generation sequencing data. Complex machine learning models.

But here’s the truth: every bioinformatician started exactly where you are now—curious, unsure, but excited.

No one writes perfect code on day one.

No one understands genomics pipelines immediately.

What makes you a bioinformatician is not perfection, but perseverance.

When your script throws a cryptic error…
When your data refuses to format…
When your pipeline runs for 6 hours only to crash…

Remember: this is part of the journey.
Every error teaches you. Every retry strengthens you. Every breakthrough energizes you.

Bioinformatics Is Not Just a Career—It’s a Mindset

It’s the mindset of:

Problem-solving.

Continuous learning.

Turning chaos into clarity.

Seeing what others can’t.

Bioinformaticians are detectives of biological complexity. You sit at the intersection of innovation, using tools that can shape public health, medicine, agriculture, and ecology. Few fields give you such direct impact on the world.

Your Contribution Matters

As you work on your script, pipeline, genome, or model, remember:

Somewhere, your analysis might contribute to:

A new therapy

A faster diagnostic test

A better understanding of a pathogen

A more resilient crop

An open-source dataset that helps thousands

A discovery that rewrites textbooks

Your code may be small, but its ripple effect is powerful.

The Future Is Bioinformatics—And You Are Part of It

The world is shifting. Wet labs are integrating AI. Hospitals rely on genomic insights. Farmers use gene-level predictions. Governments monitor disease in real time. Students launch pipelines that become global tools.

This is a golden era—and you are not late.
You are exactly where you need to be.

Keep Pushing. Keep Learning. Keep Discovering.

Bioinformatics is a journey filled with challenges, but also with unmatched rewards.

So the next time you feel stuck, frustrated, or overwhelmed, remember:
You’re building the science of tomorrow.

Be proud. Stay curious. Keep going.
Your work matters more than you think.

Research Associate@ School of Computational and Integrative Sciences under Jawaharlal Nehru University

Fri, 22 Nov 2013 19:06:44 -0600

School of Computational and Integrative Sciences under Jawaharlal Nehru University, New Delhi invited applications for filling up 4 posts of Research Associates (RA) and Junior Research Fellow (JRF) (2 posts each) purely on temporary basis, liable to be terminated at any time without prior notice or ceased/withdrawn by the funding agency. The vacancies are for a Department of Biotechnology, Government of India funded project entitled "Computational Core for Plant Metabolomics" (Project ID: 632) being administered by Prof Indira Ghosh. Interested candidates should send their applications till 13 December 2013.
Important Dates
Last Date for receipt of applications: 13 December 2013
Vacancy Details
Total Vacancies: 4 posts
Type of recruitment: Temporary
Sl. No.: 01
Name of the Post: Research Associate
No of Posts: 1 post
Remuneration: Rs. 23000 + 30%
Qualifications: PhD in Bioinformatics / computational biology / Biophysics / Physical Chemistry / Computer Science. Computational experience, proven by paper published, is a necessary qualification.
Sl. No.: 02
Name of the Post: Research Associate
No of Posts: 1 post
Remuneration: Rs. 23000 + 30%
Qualifications: PhD in Computational Biology / Bioinformatics & related subjects. Computational experience, proven by paper published, is a necessary qualification.
Sl. No.: 03
Name of the Post: Junior Research Fellow
No of Posts: 1 post
Remuneration: Rs. 12000 + 30%
Qualifications: M. Sc. / B. Tech. preferably in Computational Biology /Bioinformatics and related fields with experience in Website designing & maintenance of Database.
Sl. No.: 04
Name of the Post: Junior Research Fellow
No of Posts: 1 post
Remuneration: Rs. 12000 + 30%
Qualifications: M. Sc. / MCA / B. Tech. preferably in Computational Biology / Computer science with experience in Programming in Java / Python, C++ etc & designing of Database.
Selection Procedure: Selection will be done on the basis of candidates’ performance in the Interview.
Candidates short-listed / selected for Interview will be informed through email only.
How to Apply: Interested eligible candidates should send their applications, in the prescribed format, along with their current CV by post to “Prof Indira Ghosh, Project Investigator, Hall#6, School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110 067” so as to reach the concerned authority by 13 December 2013.
Name of the post applied for’ must be superscripted on the envelope containing the application.
NOTE: For the post of Research Associates, only those candidates who have submitted thesis are eligible to apply. However, salary will be provided as per DBT / DST guidelines (i.e. candidates who have qualified NET /BET / BINC will have higher pay scale).
Candidates interested to register for PhD may not apply for JRF.
More @ http://www.jnu.ac.in/Career/currentjobs.htm

Synima: Synteny Imaging tool

Jit — Sun, 10 Dec 2017 17:03:48 -0600

Synteny Imaging tool (Synima) written in Perl, which uses the graphical features of R. Synima takes orthologues computed from reciprocal best BLAST hits or OrthoMCL, and DAGchainer, and outputs an overview of genome-wide synteny in PDF. Each of these programs are included with the Synima package, and a pipeline for their use. Synima has a range of graphical parameters including size, colours, order, and labels, which are specified in a config file generated by the first run of Synima – and can be subsequently edited. Synima runs quickly on a command line to generate informative and publication quality figures. Synima is open source and freely available from https://github.com/rhysf/Synima under the MIT License.

Address of the bookmark: https://github.com/rhysf/Synima

Human Genome Meeting 2014, Geneva, Switzerland

Fri, 20 Sep 2013 12:36:44 -0500

The spectacular advances of the last few years resulted in the rapid analysis of the genome sequence of each individual. The biomedical world is now faced with the enormous challenges of assigning pathogenicity to each genomic variant, the functional analysis of the genome of each individual, and the accurate and detailed phenotypic characterization. Advances in these challenges are likely to fundamentally change the medical practice in a global scale.

This 2014 HUGO Meeting in Geneva will be a Forum for discussions on innovative approaches, and proposals to tackle the anticipated challenges.

Time : 27 April 2014 - 30 April 2014

For enquiries, please email hugo2014@mci-group.com or visit www.hugo-international.org

More at http://www.hgm2014-geneva.org/

Mercator: Multiple Whole-Genome Orthology Map Construction

Jit — Tue, 19 May 2020 16:46:22 -0500

Whole-genome homology maps attempt to identify the evolutionary relationships between and within multiple genomes. The term "syntenic" is often used to describe regions of multiple genomes that are believed to have evolved from the same region in an ancestral genome. However, it has been pointed out that this use of the term is incorrect (Passarge et al. 1999) and thus we will use the terms "homologous", "orthologous", and "paralogous" instead. Ideally, given K genomes, we would like to identify all orthologous genomic regions as well as paralogous regions within each genome and hypothetical ancestral genome. Maps listing these relationships are extremely valuable to researchers performing comparative analyses of genomic sequence. Here we present our initial work in the form a program called Mercator that constructs orthology maps between multiple whole genomes.

Address of the bookmark: https://www.biostat.wisc.edu/~cdewey/mercator/