Why Remote Session Management is a Must-Have

In bioinformatics, tasks like genome assembly, RNA-seq analyses, and phylogenetic computations often take hours or days. A dropped SSH connection can result in:

• Lost Progress: Restarting a job from scratch wastes valuable time.
• Workflow Interruptions: Disruptions can derail your focus and productivity.
• Corrupted Data: Interrupted processes may lead to incomplete or corrupted outputs.

By integrating screen, tmux, or mosh into your workflow, you can avoid these setbacks and ensure a seamless experience.

Screen: The Classic Workhorse

Screen is a terminal multiplexer that comes pre-installed on most Linux systems. It allows you to manage multiple terminal sessions and reconnect to them even after being disconnected.

Getting Started with Screen:

1. Start a Session:
   screen
2. Detach from a Session:
   Press Ctrl+A, then D.
3. Reattach to a Session:
   screen -r

Pro Tip: Enhance your screen experience with a customized .screenrc configuration file. Download one here: Get .screenrc.

Tmux: A Modern Alternative

Tmux takes everything great about screen and adds modern features, including better key bindings and intuitive session management. It\u2019s perfect for bioinformaticians who want more control over their workflow.

Getting Started with Tmux:

1. Start a Session:
   tmux
2. Detach from a Session:
   Press Ctrl+B, then D.
3. Reattach to a Session:
   tmux attach

Customize Your Tmux Experience:
Use a .tmux.conf file to personalize your setup. Grab one here: Download .tmux.conf.

Mosh: The Mobile Shell for Unreliable Connections

SSH works well for stable networks, but it struggles in areas with spotty connectivity. Enter Mosh, the Mobile Shell. Designed for intermittent networks, Mosh keeps your session alive even when the connection drops temporarily.

Why Mosh is a Game-Changer:

• No lag over high-latency networks.
• Automatically reconnects when the network is restored.
• Ideal for working on the go, from cafes to trains.

Getting Started with Mosh:

1. Install Mosh:
   sudo apt install mosh # For Debian/Ubuntu
2. Connect to a Server:
   mosh username@server

Learn more at mosh.org.

Why This Matters for Bioinformatics

Every bioinformatician knows the value of time and data integrity. Tools like screen, tmux, and mosh provide a lifeline when running long analyses, enabling you to:

• Safeguard your work against disconnections.
• Easily manage multiple workflows in parallel.
• Stay productive, even in challenging environments.

Quickstart Cheat Sheet

• Screen:

  screen # Start a session Ctrl+A, D # Detach screen -r # Reattach

• Tmux:

  tmux # Start a session Ctrl+B, D # Detach tmux attach # Reattach

• Mosh:

  mosh username@server

Final Thoughts

As a bioinformatician, your time is too valuable to spend restarting analyses due to technical hiccups. With screen, tmux, and mosh in your toolkit, you can work smarter, protect your progress, and stay productive no matter where you are. Start using these tools today and transform the way you work with remote systems.

Let me know how these tools work for you, and don\u2019t forget to follow for more bioinformatics tips!

Bioinformatics is a rapidly growing field at the intersection of biology, computer science, mathematics, and statistics. As data volumes increase, as well as the diversity of data types (genomics, proteomics, metabolomics, imaging, single‑cell data, etc.), the need for robust computational methods, rigorous models, and reproducible tools has never been greater.

A key decision for researchers is: Where should I publish my work? The choice of journal impacts visibility, peer recognition, and long‑term influence of your research. Below I provide a guide to leading journals in bioinformatics, criteria for selecting the journal that best fits your work, and why these considerations matter.

Leading Journals in Bioinformatics

Criteria to Use When Choosing a Journal

• Scope & Audience
• Impact & Visibility
• Review Time & Speed
• Open Access
• Cost / APCs
• Reputation vs Practical Fit
• Reproducibility, Data & Code Sharing Policies
• Indexing & Reach
• Quality of the field
• Accelerating discovery
• Fair access
• Credibility & trust
• Read recent papers in the journal
• Tailor the manuscript
• Check the author guidelines
• Have backup journals ready
• More emphasis on machine learning / AI
• Single‑cell, spatial omics, multimodal data
• Cloud workflows, reproducible pipelines
• Preprints / open peer review
• Alternative metrics (software use, downloads, community adoption)

Selecting where to publish in bioinformatics isn’t just about prestige; it’s about reaching the right audience, ensuring your work is usable, and contributing to the field responsibly.

Reference: http://www.unitedformedicalresearch.com/wp-content/uploads/2013/06/The-Impact-of-Genomics-on-the-US-Economy.pdf

Main mission:

Develop and implement strategies for whole-genome sequencing of non-model
species
Generate high-quality de novo genome assemblies using short- and long-read
sequencing technologies
Perform genome annotation and structural/functional characterization
Conduct comparative genomic analyses across related species or populations
Design and implement genome-wide association studies (GWAS) to identify
loci associated with phenotypic or adaptive traits
Integrate genomic, phenotypic, and environmental datasets
Contribute to the development of reproducible bioinformatics pipelines

Activités (Activities):

Lead the genomic component of the research project
High-molecular-weight DNA extraction optimization
Long-read genome assembly (PacBio HiFi / ONT)
Genome polishing and quality assessment (BUSCO, QUAST)
Structural and functional annotation
Variant discovery (SNPs, indels, SVs)
Population genomic analyses (FST, demographic inference)
Mixed-model GWAS accounting for structure
Workflow development (Snakemake/Nextflow)
HPC-based pipeline implementation
Publish results in peer-reviewed journals
Present findings at international conferences
Collaborate with experimental and computational team members
Contribute to project development
Mentor graduate students when appropriate

More at https://evol.mcmaster.ca/brian/evoldir/PostDocs//MontpellierU.ComparativeGenomics

1. Learn and get updated

Some of the bad biological programmers only learn new technical or non-technical things when it’s absolutely necessary. The good biological programmers learn new technical skills proactively. But great biological programmers not only learn new technical skills on their own but also learn non-technical skills, and have an open mind to sources of knowledge that others may shut out.

In other concrete term, the bad biological programmer learn Perl's regular expression when they started a project on comparative genomics; the good biological programmer learned it a year before because it looked interesting; and the great biological programmer also read about the BioPerl packages, genomics, DNA string, genomic theories, or some similar course of study so that they could understand the results and explain it biologically.

2. Not a merely coder!!!

I often encountered with biological programmer who call themself a hard-core computer programmer and avoid biology. I can almost guarantee that if you are one of them then you are not doing research but merely writing "dry" codes.

According to my supervisor most of the computational biologist, don't know what they are doing biologically. Even they struggle to explain their own programs output and results. Therefore, It is highly advisable to learn basic of biology which can assist you to explain the result and understand your discovery. Always remember you are a researcher not a coder.

3. Be Social with biologist

The computational biologist spends most of the time in from of computers, writing codes. They always think their job is to produce working codes, not technical research perfections. But, they are completely wrong. You should not forget that apart from your computational skills you also need some biologist, other than your supervisor, to explain and make you understand the complex biological mechanism.

I highly recommend your to interact with biotech researchers and learn how do they explain their one graph (which they generally produce after one year of work) biologically. Remember, the origin of your research project is complex biological phenomenon, which is more complex than that of your limited programming rules.

4. Do not search, research for answers

Researching for answers means more than typing several keywords into a search engine or posting a question at Stack Overflow or the BioStars forums. I have entered problems into search engines that generate no results, and every question I posted on Stack Overflow or the BioStars forums never got anything resembling an answer, yet I solved the issues and moved on. I’m not a magician — I just know how to find answers or discover root causes.

Many problems are situational, and if you depend on search engines and forums, you can waste a lot of time going down a rabbit hole and possibly never getting a solution. Learn to perform root cause analysis, learn enough about the underlying system to look for other clues and solutions, and learn to take a long distance view of an issue before deep diving into it.

5. Love and defend your research

You cannot rise to the top in this research profession without loving your work. There are some very good “it’s just a job” biological programmers (I’ve been one at times), but if that is your outlook, you won’t be willing to do whatever it takes to succeed. This idea gets a lot of folks in a huff, because they feel it is a personal insult. “I’m a good programmer, but I have other priorities and can’t make work my life.” I understand completely; I have other priorities too. As much as I hate to say it, when I am passionate about my work, I am willing (though not eager) to abandon my other priorities to finish the job. It is not an insult to say that if you aren’t willing to pull out all the stops you can’t be the best, it is a fact.

You must be passionate about more than programming — you must also be excited about your research, the tools and technology you are using, and so on. I have seen very good and even great biological programmers operating at mediocre levels because something was not a good fit, such as they hated the project or were using a technology they disliked. Therefore, like your research project and get excited about your discoveries. You have not only to discover but also defend your finding with scientific words.

Thanks to all of you for reading.

http://elements.eaglegenomics.com/

MEME Suite software development; gene expression; mathematical modelling; gene regulation and transcription

Specialization:
Pattern recognition and modelling in computational biology

Link @ http://www.imb.uq.edu.au/tim-bailey

http://www.isb-sib.ch/education/training-courses.html

Canadian bioinformatics also organises various bioinformatics and sequencing courses:

http://bioinformatics.ca/workshops

In addition to above two, EMBI Europe, EMBO Europe, Cold Spring Harbour USA, Wellcome Trust UK and NOVA Europe also organise bioinformatics and sequencing courses annually:

http://www.embl.de/training/events/index.php?p_outstation=ALL

http://www.embo.org/funding-awards/courses-workshops

http://meetings.cshl.edu/courses.html

http://www.wellcome.ac.uk/Education-resources/Courses-and-conferences/Advanced-Courses-and-Scientific-Conferences/Advanced-Courses/index.htm

http://www.nova-university.org/pagetop.cfm?MenySidorTop_id=2&open=7

Link @ http://www.newcastle.edu.au/research-and-innovation/centre/cibm/about-us

Uni Computing (formerly Uni BCCS) is a department of Uni Research, affiliated with the University of Bergen.

5 groups in this lab works on computational resources, methods, algorithms, and software.

Following two bioinformatics groups are:

The Computational Biology Unit (CBU) provides education and research in bioinformatics focused on functional genomics.

The Computational Ecology Unit (CEU) is basically deal with population fluctuations, behavioural patterns and the ways life cycles emerge.