BOL: Related items

Professor/Senior Lecturer of Comparative Genomics @ University of Glasgow

Fri, 06 Dec 2024 05:16:09 -0600

University of Glasgow
College of Medical, Veterinary and Life Sciences
School of Biodiversity, One Health and Veterinary Medicine

Professor/Senior Lecturer of Comparative Genomics
Vacancy Ref: 153610
Salary: Professor, Grade 10 will be within the Professorial range and
subject to negotiation
Senior Lecturer, Grade 9, 57,696 - 64,914 per annum

The School of Biodiversity, One Health and Veterinary Medicine has an
exciting opportunity to appoint a Professor/Senior Lecturer in Comparative
Genomics. You will make a substantial and positive contribution to the
strategic direction of the School/College through leading and contributing
to research of international standard, high quality teaching at both
undergraduate and postgraduate level, securing research funding, and
providing academic leadership and management within the School/College.

Applications are invited from candidates of international standing with
an appropriate record of academic achievement in comparative genomics
and associated omics technologies. We are looking for a candidate who
will complement our existing strengths in clinical veterinary medicine,
evolutionary biology, and animal physiology, with a demonstrable interest
in using domestic mammals among their study systems. We are particularly
interested in applications from candidates with a track record of
studying health related traits and their underlying genomic basis in
companion animals. Traits of specific interest include those related
to metabolism, ageing, and disease (e.g. cancer, autoimmune diseases,
neuromuscular disorders).

The School of Biodiversity, One Health and Veterinary Medicine is home to
researchers studying organismal biology and animal health across a diverse
range of systems, approaches and disciplines with existing strengths
in infectious disease, physiology, ageing, veterinary epidemiology, and
evolution among others. You will be based on the University of Glasgow's
Garscube campus, where the majority of veterinary teaching and research
infrastructure is located. This includes the Small Animal Hospital (a
recent 15M investment) and our Veterinary Diagnostic Services, offering
excellent opportunities for collaborative research at the clinical and
translational interface, especially with respect to companion animals.

We welcome applications from candidates with a Scottish Credit and
Qualification Framework level 12 (PhD) in animal biology, genomics and
health or related discipline with an extensive and established reputation
in research and significant teaching experience within the subject area.

This post is full time and open ended.

Visit our website for further information on The University of
Glasgow's, School of Biodiversity, One Health & Veterinary Medicine,
https://www.gla.ac.uk/schools/bohvm/

Informal Enquiries should be directed to Professor Roman Biek,
Roman.Biek@glasgow.ac.uk

Apply online at:
https://my.corehr.com/pls/uogrecruit/erq_jobspec_version_4.jobspec?p_id=153610

Welch Lab

Mon, 15 Jul 2013 18:21:13 -0500

They are based in the Department of Genetics at the University of Cambridge.

The research covers diverse areas of evolutionary biology, and molecular evolution in particular. It combines theoretical and empirical approaches, and particularly evolutionary inference from genome sequence data.

Links @ http://www.gen.cam.ac.uk/research/welch/GroupPage/Home.html

Exploring RNA Sequence Analysis: Tools for Every Bioinformatician

Neel — Fri, 13 Dec 2024 04:03:04 -0600

RNA sequence analysis has become an essential part of modern biological research. From RNA-seq pipelines to specialized tools for specific RNA types, here's a comprehensive guide to tools you can use to make sense of RNA data.

1. RNA-Seq Analysis Pipelines

RNA-seq is one of the most popular techniques for studying RNA. These tools streamline processing raw sequence data:

FASTQC: For quality control of raw RNA-seq reads.
Trimmomatic: For trimming and filtering RNA-seq reads.
HISAT2/STAR: High-performance aligners for RNA-seq reads.
FeatureCounts: For quantifying gene expression.
DESeq2/EdgeR: For differential expression analysis.

2. Transcriptome Assembly and Annotation

For analyzing transcriptomes from non-model organisms or assembling novel transcripts:

Trinity: For de novo transcriptome assembly.
StringTie: For transcript assembly and quantification from RNA-seq alignments.
TransDecoder: To predict coding regions within assembled transcripts.
TAU: Tools for annotating non-coding and coding RNAs.

3. Exploring Non-Coding RNA (ncRNA)

Non-coding RNAs play critical regulatory roles. Dedicated tools for studying them include:

Infernal: For identifying ncRNA sequences based on covariance models.
Rfam: Database and tools for ncRNA families.
miRDeep: For identifying microRNAs in RNA-seq datasets.

4. RNA Structure and Motif Analysis

Structural biology of RNA helps in understanding its function:

RNAfold (ViennaRNA): Predicts secondary structures from RNA sequences.
RNAstructure: Tools for RNA secondary structure prediction and analysis.
MEME Suite: For identifying motifs in RNA sequences.
IntaRNA: For RNA-RNA interaction prediction.

5. RNA Editing and Modifications

Epitranscriptomics is a growing field focusing on RNA modifications:

REDItools: For RNA editing analysis.
m6Aboost: For identifying m6A modifications in RNA.

6. Long-Read RNA Sequencing Analysis

Long-read technologies like Nanopore and PacBio are transforming RNA research:

FLAIR: For isoform-level analysis of long-read RNA-seq data.
NanoMod: For detecting modifications in RNA from Nanopore sequencing.

7. RNA-Protein Interactions

To study RNA-protein interactions and complexes:

RBPmap: For identifying RNA-binding protein motifs.
PARalyzer: For analyzing PAR-CLIP data.

8. Functional Enrichment Analysis

Understanding biological functions and pathways from RNA-seq data:

getENRICH: A tool designed for pathway enrichment analysis of non-model organisms (hypergeometric P-value calculation with FDR correction).
ClusterProfiler: For GO and KEGG pathway enrichment analysis.

9. Visualization and Data Sharing

Presenting and sharing RNA sequence analysis results effectively:

IGV: Genome browser for visualizing RNA-seq alignments.
Circos: Circular visualization of RNA-seq data.
DashBio: A Python library for creating bioinformatics visualizations.

Conclusion

The bioinformatics landscape for RNA sequence analysis is vast, with tools catering to specific needs. Whether you’re studying coding RNAs, non-coding RNAs, or exploring RNA-protein interactions, the right tools can transform your data into biological insights.

List of popular bioinformatics software/tools

Jitendra Narayan — Tue, 16 Jul 2013 14:30:30 -0500

In current genome era, our day to day work is to handle the huge geneome sequences, expression data, several other datasets. This link provide a comprehensive list of commonly used sofware/tools.

Address of the bookmark: http://samtools.sourceforge.net/swlist.shtml

Life as a Bioinformatician – Expectation vs. Reality

Abhi — Mon, 23 Dec 2024 19:32:36 -0600

You enter the world of bioinformatics envisioning a sleek, high-tech career, surrounded by cutting-edge algorithms, advanced computational tools, and groundbreaking discoveries. You imagine a seamless integration of biology and data science, where every day you decode the mysteries of life at a molecular level. Your days will be spent analyzing elegant datasets, publishing in top-tier journals, and making significant contributions to human health and the environment. To top it off, you picture yourself working in a comfortable, quiet environment, with plenty of time to perfect your skills and learn new ones.

While the expectations are not entirely off base, the reality of life as a bioinformatician is a mix of exciting discoveries, troubleshooting, and, let’s admit it, a fair amount of frustration. Here’s what it’s really like:

1. Expectation: Seamlessly Working with Perfect Datasets

Reality: You often receive messy, incomplete, or poorly annotated datasets. Hours are spent cleaning, normalizing, and validating data before you even begin your analysis. "Garbage in, garbage out" is a constant reminder in your workflow. Tools designed to handle these problems exist, but they require significant customization, which adds another layer of complexity.

2. Expectation: Effortless Multidisciplinary Integration

Reality: Bridging biology and computational science is far from straightforward. You need to be proficient in both domains while keeping up with advancements in genomics, machine learning, and statistics. Additionally, collaborating with biologists who might not be fluent in computational jargon requires patience and effective communication skills.

3. Expectation: Rapid, Groundbreaking Results

Reality: Analysis often involves waiting—waiting for scripts to run, pipelines to complete, or software to install. Bioinformatics projects are iterative; you analyze, debug, and refine repeatedly. A single project might take months to complete due to unforeseen challenges, like computational bottlenecks or the need for additional experiments.

4. Expectation: Beautiful Visualizations with a Click

Reality: While tools like R, Python, and specialized software can create stunning plots, generating a publication-ready visualization requires significant effort. You’ll spend hours tweaking axes, labels, and color palettes, ensuring clarity and accuracy.

5. Expectation: All Work, No Bugs

Reality: Debugging is an integral part of the job. Whether it’s a misconfigured server, a script throwing unexpected errors, or a pipeline breaking due to an update, you’ll develop a knack for problem-solving under pressure.

6. Expectation: Ample Time for Skill Development

Reality: Bioinformatics moves fast. Juggling ongoing projects, tight deadlines, and the constant stream of new tools and algorithms leaves little time for leisurely learning. Staying updated requires proactive effort—evenings, weekends, or dedicated study breaks.

7. Expectation: Publishing Papers Regularly

Reality: Publishing in bioinformatics is a marathon, not a sprint. Your analysis needs to be thorough, reproducible, and supported by strong biological insights. Reviewers often demand additional experiments or clarifications, stretching the timeline even further.

8. Expectation: A Clear Career Path

Reality: Bioinformatics offers diverse career paths, from academia and industry to healthcare and government. However, the choice can be daunting, with each path requiring unique skill sets and presenting different challenges. Navigating these options takes time, research, and sometimes trial and error.

Finding Joy in the Chaos

Despite these challenges, being a bioinformatician is immensely rewarding. You are at the forefront of science, enabling discoveries that impact medicine, agriculture, and the environment. The thrill of uncovering insights hidden in complex datasets and the satisfaction of solving biological puzzles make the hard work worthwhile.

Advice for Aspiring Bioinformaticians

Embrace Learning: The field is ever-evolving. Stay curious and adaptable.
Develop Communication Skills: Bridging the gap between biology and computation is as much about explaining your methods as it is about applying them.
Find a Community: Collaborate with peers, join forums, and attend conferences to stay inspired and updated.
Celebrate Small Wins: Every cleaned dataset, successful script, or informative plot is a step forward.

Bioinformatics is a blend of science, technology, and artistry. While the reality might not match the polished expectations, the journey is nothing short of exhilarating. If you’re ready to embrace the chaos and keep learning, the field of bioinformatics will never cease to amaze you.

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 !

Life of BI !!!

Jitendra Narayan — Thu, 22 Aug 2013 16:13:36 -0500

Hmm .. Don't worry you read it right .. this is not pi but bi ... "life of Bioinformatician(BI)".

Disclaimer: This cartoon is solely designed to create humour and fun, not to offend any PI, supervisor or student.

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.

UK Genome Science Meeting, sept 2nd-4th, 2013

Poonam Mahapatra — Mon, 12 Aug 2013 12:03:21 -0500

Following the success of the last three years' UK Next Gen Sequencing meetings at Nottingham, the newly named UK Genome Science meeting aims to bring together experts from around the world to meet and discuss the current and future state of all aspects and applications of Next Generation Sequencing.

More at >> http://www.nottingham.ac.uk/deepseq/events.aspx