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.

The manual translation of many, frequently rather complex SPSS scripts often presents itself as a tedious and error-prone task, and represents a rather large obstacle for many analysts and companies to migrate to a modern, open source data management and analysis tool like R. With translate2R this hurdle will be diminished substantially.

Find at https://service.eoda.de/translater/?lang=en

Pine Biotech, Inc., a US-based startup working with the Tauber Bioinformatics Research Center is offering a full curriculum online preparing students without any technical background for real-life challenges with large scale biomedical data. Workshops on processing, analysis and biomedical interpretation of Next Generation Sequencing data cover important up-to-date algorithms and machine learning approaches. The most important thing is that there are virtually no pre-requisites such as coding, biostatistics or advanced medical skills. If you know what gene is and how the genes are expressed, you are ready to take the courses or join our workshops. Learn more: https://edu.t-bio.info/workshops/

To install the stable version: pip3 install --user tinycov

To install the development version:

git clone https://github.com/cmdoret/tinycov.git
cd tinycov
pip install .

Address of the bookmark: https://github.com/cmdoret/tinycov

Then I thaught it migh be a problem of graphical display and I opened the terminal out of curiosity, and I found out that there is no ~/Desktop folder at all. What happened? What do I need to do?

Then I google the problem and found this is a very common problem after updates. To fix this problem, follow these steps:

You need to edit the ~/.config/user-dirs.dirs file, and make sure the contents of the file are like the following:

XDG_DESKTOP_DIR="$HOME/Desktop"
XDG_DOWNLOAD_DIR="$HOME/Downloads"
XDG_TEMPLATES_DIR="$HOME/"
XDG_PUBLICSHARE_DIR="$HOME/Share"
XDG_DOCUMENTS_DIR="$HOME/Documents"
XDG_MUSIC_DIR="$HOME/Music"
XDG_PICTURES_DIR="$HOME/Pictures"
XDG_VIDEOS_DIR="$HOME/Videos"

Then restart nautilus:

killall nautilus

or

nautilus -q

Then, open nautilus via Unity menu (press the Super key) or using the run command (Alt+F2)

python -V

at the terminal. For purposes of this example we'll assume you have 3.1 installed. You'll next need to execute the following commands:

sudo apt-get install python2.6 idle-python2.6
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.1 1
sudo update-alternatives --install /usr/bin/python python /usr/bin/python2.6 10
sudo update-alternatives --config python

This last command will allow you to choose which version of python to use by default. If you have done everything above correctly, python2.6 should already be set as the default. If it is not, choose it to be the default. From now on, running python should start version 2.6.

sudo update-alternatives --remove-all python
sudo ln -s python3.1 /usr/bin/python

https://www.forbes.com/sites/jasonevangelho/2018/07/23/5-reasons-you-should-switch-from-windows-to-linux-right-now/#70c74923777b

The following table shows SLURM commands on the SOE cluster.

For more information, run man on the commands above. See some examples below.

1. Info about the partitions and nodes
List all the partitions available to you and the nodes therein:

sinfo

Nodes in state idle can accept new jobs.

Show a partition configuratuin, for example, SOE_main

scontrol show partition=SOE_main

Show current info about a specific node:

scontrol show node=<nodename>

You can also specify a group of nodes in the command above. For example, if your MPI job is running across soenode05,06,35,36, you can execute the command below to get the info on the nodes you are interested in:

scontrol show node=soenode[05-06,35-36]

An informative parameter in the output to look at would be CPULoad. It allows you to see how your application utilizes the CPUs on the running nodes.

2. Submit scripts
The header in a submit script specifies job name, partition (queue), time limit, memory allocation, number of nodes, number of cores, and files to collect standard output and error at run time, for example

#!/bin/bash

#SBATCH --job-name=OMP_run     # job name, "OMP_run"
#SBATCH --partition=SOE_main   # partition (queue)
#SBATCH -t 0-2:00              # time limit: (D-HH:MM) 
#SBATCH --mem=32000            # memory per node in MB 
#SBATCH --nodes=1              # number of nodes
#SBATCH --ntasks-per-node=16   # number of cores
#SBATCH --output=slurm.out     # file to collect standard output
#SBATCH --error=slurm.err      # file to collect standard errors

If the time limit is not specified in the submit script, SLURM will assign the default run time, 3 days. This means the job will be terminated by SLURM in 72 hrs. The maximum allowed run time is two weeks, 14-0:00.
If the memory limit is not requested, SLURM will assign the default 16 GB. The maximum allowed memory per node is 128 GB. To see how much RAM per node your job is using, you can run commands sacct or sstat to query MaxRSS for the job on the node - see examples below.
Depending on a type of application you need to run, the submit script may contain commands to create a temporary space on a computational node - see the discussion about using the file systems on the cluster.
Then it sets the environment specific to the application and starts the application on one or multiple nodes - see sbatch sample scripts in directory /usr/local/Samples on soemaster1.hpc.rutgers.edu.
You can submit your job to the cluster with sbatch command:

sbatch myscript.sh

3. Query job information
List all currently submitted jobs in running and pending states for a user:

squeue -u <username>

Command squeue can be run with format options to expose specific information, for example, when pending job #706 is scheduled to start running:

squeue -j 706 --format="%S"

START_TIME
2015-04-30T09:54:32

More info can be shown by placing additional format options, for example:

squeue -j 706 --format="%i %P %j %u %T %l %C %S"

JOBID PARTITION   NAME    USER STATE   TIMELIMIT  CPUS START_TIME
706   SOE_main  Par_job_3 mike PENDING 3-00:00:00 64   2015-04-30T09:54:32

To see when all the jobs, pending in the queue, are scheduled to start:

squeue --start 

List all running and completed jobs for a user

sqlog -u <username>

or

sqlog -j <JobID>

The following appreviations are used for the job states:

       CA   CANCELLED      Job was cancelled.

       CD   COMPLETED      Job completed normally.

       CG   COMPLETING     Job is in the process of completing.

       F    FAILED         Job termined abnormally.

       NF   NODE_FAIL      Job terminated due to node failure.

       PD   PENDING        Job is pending allocation.

       R    RUNNING        Job currently has an allocation.

       S    SUSPENDED      Job is suspended.

       TO   TIMEOUT        Job terminated upon reaching its time limit.

You can specify the fields you would like to see in the output of sqlog:

sqlog --format=list

The command below, for example, provides Job ID, user name, exit state, start date-time, and end date-time for job #2831:

sqlog -j 2831 --format=jid,user,state,start,end

List status info for a currently running job:

sstat -j <jobid>

A formatted output can be used to gain only a specific info, for example, the maximum resident RAM usage on a node:

sstat --format="JobID,MaxRSS" -j <jobid>

To get statistics on completed jobs by jobID:

sacct --format="JobID,JobName,MaxRSS,Elapsed" -j <jobid>

To view the same information for all jobs of a user:

sacct --format="JobID,JobName,MaxRSS,Elapsed" -u <username>

To print a list of fields that can be specified with the --format option:

sacct --helpformat

For example, to get Job ID, Job name, Exit state, start date-time, and end date-time for job #2831:

sacct -j 2831 --format="JobID,JobName,State,Start,End"

Another useful command to gain information about a running job is scontrol:

scontrol show job=<jobid>

4. Cancel a job
To cancel one job:

scancel <jobid>

To cancel one job and delete the TMP directory created by the submit script on a node:

sdel <jobid>

To cancel all the jobs for a user:

scancel -u <username>

To cancel one or more jobs by name:

scancel --name <myJobName>