BOL: Related items

A Bioinformatician’s Lament

LEGE — Thu, 29 May 2025 01:33:31 -0500

"I have a presentation tomorrow," they say,

With hopeful eyes, like it’s all child's play.
As if results bloom overnight, full-grown—
Not wrangled from chaos, and error-prone.

Oh brave soul, sit, let’s walk through the tale,
Of pipelines broken and servers that fail.
The journey starts: “The data? It’s there—
Just fetch it from S3, easy, I swear.”

Now I summon awscli with dread,
Reset my keys, credentials fed.
Configure regions, IAM roles too—
All this, and still no peek at the view.

Next up, the tool: “It’s open source!”
On GitHub, rotting, no sign of remorse.
Python 2.7, some GCC trick—
The install alone might make you sick.

Finally, progress! The pipeline runs…
Till RAM collapses and error stuns.
Oh, and the metadata? A crime,
Merged cells, font soup, out of time.

Sample IDs—what a cryptic game:
Sample_1, S1, sample-1... the same?
Controls mislabeled, cases flipped,
No wonder my sanity's starting to slip.

Then QC plots, PCA joy—
Wait, that’s a tumor labeled as a boy?
Clusters cross, and axes lie,
And I still don’t know which sample’s "guy."

But the clock ticks on, and it’s half-past doom,
They want the final UMAP soon.
With pastel colors, labeled clear—
"Can we move that legend to right here?"

Tweak by tweak, I adjust each frame,
Resize Panel B, annotate a name.
Export the plot—it starts to gleam…
Then my laptop crashes. I scream.

This is the grind, the long-haul game,
Where science hides behind code and flame.
No “Export to Nature” button to press,
Just toil and logic and hope for success.

So next time you whisper that fated line—
“I have a talk, can you make it shine?”
Know: bioinformatics is craft, not a click,
It’s science with scars, not just a quick fix.

To all who debug at 3AM light,
Who ghostwrite figures through sleepless night—
You are the backbone, silent and true,
First-author-worthy, if only they knew.

"कल मेरी प्रेज़ेंटेशन है," वो कहते हैं,

आशा भरी आँखों से, जैसे सब सहज है।
जैसे परिणाम रातोंरात प्रकट हो जाएं—
ना कि डेटा की भूलभुलैया से उखाड़े जाएं।

आओ बैठो, एक किस्सा सुनाता हूँ,
जहाँ पाइपलाइन टूटती है, और सर्वर भी थक जाते हैं।
कहानी शुरू होती है: “डेटा तो है—
बस S3 बकेट में, एकदम पास में कहीं।”

अब awscli बुलाता हूँ डरते हुए,
कुंजी सेट करूँ, क्रेडेंशियल जोड़ूं, रीजन भरूँ।
इतनी मशक्कत, फिर भी डेटा नहीं मिला,
बस सेटअप में ही पूरा दिन चला।

फिर आता है टूल: “ओपन-सोर्स है!”
GitHub पर है, 2019 से सूखा पड़ा है।
Python 2.7 चाहिए, एक पुराना कम्पाइलर,
और साथ में थोड़ी सी दुआ की ताकत।

आख़िरकार टूल चला, खुशी सी हुई,
लेकिन रन करते ही, मेमोरी ने हार मानी।
और मेटाडेटा? एक एक्सेल की आफ़त,
मर्ज़ किए हुए सेल, बस और क्या चाहिए काफ़ियत?

सैंपल आईडी? बस भगवान ही जाने—
Sample_1, sample-1, S1, और control1—
ये सब एक ही सैंपल हैं क्या?
पता तब चलता है जब पूछो दो-तीन बार।

काउंट मैट्रिक्स तैयार, अब R या Python की बारी,
QC करो, PCA प्लॉट—पर कुछ गड़बड़ भारी।
ट्यूमर और नॉर्मल का अदला-बदली खेल,
बार-बार, वही पुरानी झमेल।

आख़िर में आया मॉडलिंग का समय,
स्टैट्स, प्लॉट्स, डिफरेंशियल एक्सप्रेशन का श्रम।
लेकिन घड़ी में 5 बज चुके हैं जनाब,
और 8 बजे तक UMAP चाहिए, साफ़-सुथरा जबाब।

तो मैं कोड लिखता हूँ रात भर बैठ कर,
कलर पैलेट, जीन लेबल, लीजेंड बाहर रख कर।
फ़ॉन्ट, पैनल, एक्सिस सब सुधार,
एक्सपोर्ट करता हूँ... और लैपटॉप कहता है—"अब नहीं यार!"

इसीलिए बायोइन्फॉर्मेटिक्स में लगता है समय,
ये “बस सीरत चलाओ” या “वोल्कैनो प्लॉट बनाओ” नहीं है।
ये है सिस्टम एडमिन का काम, डेटा की सफ़ाई,
QC, डिबगिंग, और सांइस की सच्ची लड़ाई।

तो कुछ सीखें इस व्यथा से आप भी आज:
24 घंटे पहले चमत्कार मत माँगिए।
अच्छे फ़िगर साफ़ डेटा से बनते हैं।
बायोइन्फॉर्मेटिक्स जादू नहीं, विज्ञान है।
समय से बात कीजिए, प्रक्रिया का सम्मान कीजिए।

और उन सभी बायोइन्फॉर्मेटिशियनों को सलाम,
जो दूसरों की प्रेज़ेंटेशन के लिए रातों में जागते हैं—
तुम हो फ़िगर्स के भूत लेखक,
तुम हो बिना नाम के सह-लेखक।
तुम पहले लेखक बनने के हक़दार हो—
और एक लंबी नींद के भी।

Note: Written with the help of AI/LLM Tools !

Courses to Get You Started with Bioinformatics

LEGE — Tue, 30 Sep 2025 13:07:19 -0500

Bioinformatics is now at the heart of modern biology and medicine. From decoding genomes and predicting antimicrobial resistance, to developing personalized medicine and advancing evolutionary research, computational skills are no longer optional — they are essential.

Yet, for many students, biologists, and even computer scientists, the question is: “Where do I begin?” With so many platforms, books, and tutorials available, it’s easy to feel overwhelmed.

To make it easier, I’ve compiled 10 excellent resources — ranging from beginner-friendly introductions to advanced computational genomics courses. Many of these are freely available, created by pioneers in the field, and widely used in classrooms and research labs worldwide.

Whether you are a complete beginner or looking to strengthen your foundations, these courses will help you build the skills needed to analyze biological data, design workflows, and think computationally about complex biological systems.

1. HarvardX Data Analysis for Genomics by Rafael Irizarry

From the almighty Rafa, this set of online courses (via edX/HarvardX) is a classic starting point for genomic data science and bioinformatics.

2. Applied Computational Genomics – Aaron Quinlan

Aaron Quinlan (creator of bedtools and many other tools) has made his course materials open. A practical, tool-driven genomics introduction.

3. Bioinformatics Algorithms (Coursera + Companion Book)

Find the highly visual video classes on Coursera, backed by the popular Bioinformatics Algorithms book.

4. The Biostar Handbook

Not a course per se, but a hands-on manual by Istvan (founder of Biostars.org) that’s even used in classes at Penn State.

5. Introduction to Bioinformatics and Computational Biology (by Shirley Liu)

A comprehensive introduction from Shirley Liu’s lab (Harvard DFCI). Covers both theory and computational practice.

6. Data Carpentry: Genomics Workshops

Community-driven training workshops that focus on practical, reproducible research. I was honored to serve as curriculum committee chair here.

7. Computational Genomics: Applied Comparative Genomics

From the Schatz Lab — applied comparative genomics with real-world data.

8. Introduction to Computational Biology (Mike Love, creator of DESeq2)

This course bridges statistics, biology, and computation — a solid primer for anyone entering computational biology.

9. MIT Computational Biology (6.047 / 6.878 / HST.507) by Manolis Kellis

Covers genomes, networks, evolution, and health. A deep-dive from MIT’s OpenCourseWare archive.

10. An Introduction to Applied Bioinformatics

An interactive textbook with Python code, designed for practical applied bioinformatics learning.

Great Place to Study Bioinformatics in Europe

Rahul Agarwal — Sun, 18 Aug 2013 18:41:34 -0500

Study bioinformatics is like being jack of all trade so it is important you choose where it will be good backup of computer science, and natural science infrastructure and faculty. Especially having a good teachers in computer science is indispensible.

Few places in Europe where good (and @some places no tution fees) master bioinformatics courses and recommended are:

KU Leuven, Belgium

http://onderwijsaanbod.kuleuven.be/opleidingen/e/CQ_50269018.htm

ETH, Zurich

http://www.cbb.ethz.ch/

University of Copenhagen, Denmark

http://studies.ku.dk/masters/bioinformatics/

University of Helsinki, Finland

http://www.cs.helsinki.fi/en/mbi/

Stockholm University, Sweden

http://www.sbc.su.se/masters/

Universities in Netherlands

http://www.nbic.nl/education/msc-programmes/

TUM , Munich Germany

http://www.mastersportal.eu/studies/865/bioinformatics.html

University of Bergen, Norway

http://www.uib.no/en/studieprogram/MAMN-INF/BI/plan

Goethe-University in Frankfurt am Main

http://www.uni-frankfurt.de/58444439/mabi

Other links:

http://www.masterstudies.com/Masters-Degree/Bioinformatics/Europe/

https://studyinsweden.se/programmes/?query=bioinformatics&period=ht-2016&level=ma&subject=natural-science&university=#search

SocBiN Bioinformatics 2014

Tue, 03 Sep 2013 18:50:20 -0500

14th annual conference in Bioinformatics

Date : June 10-13

Organizers: The Society for Bioinformatics in Northern European countries (SocBiN) and the Norwegian Bioinformatics Platform / ELIXIR.NO

Venue: Department of Informatics, University of Oslo, Norway

Topics:
Tools and technologies for integrative bioinformatics
Metagenomics
Comparative genomics and phylogeny
Post-ENCODE bioinformatics
Gene regulation
Cancer genomes
Marine genomics

Free math books

Manisha Mishra — Thu, 12 Dec 2013 19:38:34 -0600

Bioinformatics require some match skills, therefore I decided to provide this wonderful math eBooks links to the BOL community.

Please add ur links/bookmarks in comment section.

Address of the bookmark: http://physicsdatabase.com/free-math-books/

Bioinformatics infrastructure speed up Indian agriculture

Rahul Agarwal — Tue, 07 Jan 2014 12:44:44 -0600

"Realizing the paradigm shift it can bring about, the government is focusing on increased bioinformatics intervention in agri-sciences. Currently under process, the national grid on bioinformatics is expected make much better sense out of huge genomic" -

http://www.biospectrumindia.com/biospecindia/features/203849/supercomputing-indian-agriculture-fast-track-mode/page/1

Bioinformatics software for biologists in the genomics era

Poonam Mahapatra — Sun, 22 Dec 2013 17:31:05 -0600

The genome sequencing revolution is approaching a landmark figure of 1000 completely sequenced genomes. Coupled with fast-declining, per-base sequencing costs, this influx of DNA sequence data has encouraged laboratory scientists to engage large datasets in comparative sequence analyses for making evolutionary, functional and translational inferences. However, the majority of the scientists at the forefront of experimental research are not bioinformaticians, so a gap exists between the user-friendly software needed and the scripting/programming infrastructure often employed for the analysis of large numbers of genes, long genomic segments and groups of sequences. We see an urgent need for the expansion of the fundamental paradigms under which biologist-friendly software tools are designed and developed to fulfill the needs of biologists to analyze large datasets by using sophisticated computational methods. We argue that the design principles need to be sensitive to the reality that comparatively small teams of biologists have historically developed some of the most popular biological software packages in molecular evolutionary analysis. Furthermore, biological intuitiveness and investigator empowerment need to take precedence over the current supposition that biologists should re-tool and become programmers when analyzing genome scale datasets.

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/23/14/1713.full

International Conference on Bioinformatics Models, Methods and Algorithms

Rahul Agarwal — Sun, 05 Oct 2014 11:42:52 -0500

The purpose of the International Conference on Bioinformatics Models, Methods and Algorithms is to bring together researchers and practitioners interested in the application of computational systems and information technologies to the field of molecular biology, including for example the use of statistics and algorithms to understanding biological processes and systems, with a focus on new developments in genome bioinformatics and computational biology. Areas of interest for this community include sequence analysis, biostatistics, image analysis, scientific data management and data mining, machine learning, pattern recognition, computational evolutionary biology, computational genomics and other related fields.

Position Paper Submission Extension: October 9, 2014
Regular Paper Authors Notification: November 3, 2014
Position Paper Authors Notification: November 6, 2014
Regular and Position Paper Camera Ready and Registration: November 17, 2014

Address of the bookmark: http://www.bioinformatics.biostec.org/

List of bioinformatics packages for NGS analysis !

Rahul Nayak — Sat, 20 Mar 2021 00:28:51 -0500

Package suites gather software packages and installation tools for specific languages or platforms. We have some for bioinformatics software.

Bioconductor – A plethora of tools for analysis and comprehension of high-throughput genomic data, including 1500+ software packages. [ paper-2004 | web ]
Biopython – Freely available tools for biological computing in Python, with included cookbook, packaging and thorough documentation. Part of the Open Bioinformatics Foundation. Contains the very useful Entrez package for API access to the NCBI databases. [ paper-2009 | web ]
Bioconda – A channel for the conda package manager specializing in bioinformatics software. Includes a repository with 3000+ ready-to-install (with conda install) bioinformatics packages. [ paper-2018 | web ]
BioJulia – Bioinformatics and computational biology infastructure for the Julia programming language. [ web ]
Rust-Bio – Rust implementations of algorithms and data structures useful for bioinformatics. [ paper-2016 ]
SeqAn – The modern C++ library for sequence analysis.