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

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.

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

Chettinad Academy of Research and Education (CARE) invites applications from eligible and translational research-oriented candidates to the posts of Professor/Associate Professor/ Assistant Professor Computational Biology, Bioinformatics, and Pharmaceutical Chemistry.

Emoluments: As per UGC norms (Adequate Compensation for Postdoctoral/Teaching experience)

Candidates fulfilling the eligibility criteria as per the UGC norms can send their full CV with copies of certificates and reference letters to the following address by post or by e-mail on or before 31st May 2014

The Registrar,
Chettinad Academy of Research and Education,
Chettinad Health City
Kelambakkam, Chennai 603 103
Tamil Nadu
T +91 (0)44 4741 1000
F +91 (0)44 4741 1011
Email: jobs @chettinadhealthcity.com

Advertisement: http://182.73.176.163/chc/ads2014.pdf

• Whole-genome sequencing
• Whole-exome sequencing
• RNA-seq (speical mode available)
• ChIP-seq

Address of the bookmark: http://qualimap.bioinfo.cipf.es/

http://www.eva.mpg.de/genetics/bioinformatics/overview.html?Fsize=0%2C%20%40%2F%27

Kelso Group
Department of Evolutionary Genetics
Max Planck Institute for Evolutionary Anthropology
Deutscher Platz 6
04103 Leipzig
Germany
Phone: +49 341 3550 500

Job:
http://www.eva.mpg.de/genetics/bioinformatics/jobs.html?Fsize=0%2C%2B%40

R script that makes a plotly interactive and/or static (png/pdf) dot plot.

Shiny app available for testing

Address of the bookmark: https://github.com/tpoorten/dotPlotly

Tasks:  239 total,   1 running,  238 sleeping,   0 stopped,   0 zombie

This line tells you how many processes are running. If you are using laptops machines it’s not so interesting because you really are the only one using this machine.

Cpu(s):  0.0%us,  0.0%sy,  0.0%ni,100.0%id,  0.0%wa,  0.0%hi,  0.0%si,  0.0%st

This line contains the CPU load. The first two numbers are how busy the system is doing computation (“us” stands for “user”) and how busy the system is doing system-y things like accessing disks or network (“sy” stands for “system”). We’ll talk more about this later.

Mem:   49457320k total,    3492174k used,  14535596k free,    1435148k buffers

This should be easy to understand – how much memory you’re using!

Swap:   539356k total,   28332k used,   836562k free,    29862014k cached

Swap is just on-disk memory that can be used to “swap” out programs from main memory. Again, we’ll talk about this later.:

PID USER      PR  NI  VIRT  RES  SHR S %CPU %MEM    TIME+  COMMAND
  1 root      39   19  0  0  0 S  0.0  0.0   246:57.22 kipmi0
  2 root      RT   0     0    0    0 S  0.0  0.0   0:00.00 migration/0

And... finally! What’s actually running! The two most important numbers are the %CPU and %MEM towards the right, as well as the COMMAND. This tells you how compute- and memory-intensive your program is. Right now, nothing’s running so the numbers aren’t very interesting, but just wait until we run something...

More recently, we've been enthused about using k-mer based similarity measures and computing and searching k-mer-based sketch search databases for all the things.

But I haven't spent too much talking about using k-mers for taxonomy, although that has become an ahem area of interest recently, if you read into our papers a bit.

In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!

Address of the bookmark: http://ivory.idyll.org/blog/2017-something-about-kmers.html