BOL: Related items

The Role of lncRNA in Bioinformatics: Unlocking the Secrets of the Genome

LEGE — Sat, 07 Dec 2024 02:09:47 -0600

In the intricate dance of molecular biology, long non-coding RNAs (lncRNAs) have emerged as key players, capturing the interest of researchers worldwide. These RNA molecules, once dismissed as "junk," have proven to be vital in the regulation of gene expression, cellular processes, and the progression of diseases. The intersection of lncRNA studies and bioinformatics is transforming our understanding of these enigmatic molecules, offering profound insights into their structure, function, and therapeutic potential.

What Are lncRNAs?

lncRNAs are RNA transcripts longer than 200 nucleotides that do not code for proteins. Despite their non-coding nature, they play diverse roles in gene regulation, including chromatin remodeling, transcriptional control, and post-transcriptional processing. Unlike messenger RNAs (mRNAs), lncRNAs often function as scaffolds, decoys, or guides in cellular machinery, influencing biological processes such as cell differentiation, immune response, and even cancer metastasis.

Challenges in lncRNA Research

Identifying and understanding lncRNAs pose unique challenges:

High Sequence Variability: Unlike protein-coding genes, lncRNAs exhibit low sequence conservation across species, making functional predictions difficult.
Low Expression Levels: lncRNAs are often expressed at low levels, complicating their detection in transcriptomic data.
Diverse Functions: The multifunctional nature of lncRNAs requires advanced computational tools to decipher their roles in complex networks.

Bioinformatics: A Crucial Ally in lncRNA Research

Bioinformatics bridges the gap between raw biological data and meaningful insights, making it indispensable in lncRNA research. Here’s how:

1. Identification and Annotation

High-throughput sequencing technologies like RNA-seq generate vast amounts of data. Bioinformatics tools such as StringTie, Cufflinks, and HISAT2 help assemble and annotate lncRNAs from this data. Additionally, databases like NONCODE, LNCipedia, and Ensembl provide curated repositories of lncRNA sequences and annotations.

2. Functional Prediction

Bioinformatics algorithms predict the potential functions of lncRNAs by analyzing their interactions with DNA, RNA, and proteins. Tools like LncRNA2Function and RIblast utilize sequence motifs and secondary structure predictions to hypothesize about the roles of specific lncRNAs.

3. Network Construction

lncRNAs often act as regulatory hubs. Bioinformatics platforms such as Cytoscape enable the visualization of lncRNA-mediated networks, elucidating their roles in pathways like cell cycle regulation and apoptosis.

4. Epigenetic Studies

lncRNAs are known to interact with chromatin-modifying complexes, influencing gene expression epigenetically. Tools like ChIP-seq and ATAC-seq, combined with computational pipelines, identify these interactions and map them to the genome.

5. Clinical Applications

Bioinformatics aids in the discovery of lncRNA biomarkers for diseases like cancer and neurodegenerative disorders. Machine learning models analyze differential expression profiles, helping prioritize lncRNAs with therapeutic potential.

Case Study: lncRNAs in Cancer Research

lncRNAs such as HOTAIR and MALAT1 have been implicated in cancer progression. Bioinformatics analyses have revealed their roles in promoting metastasis and altering the tumor microenvironment. For example, transcriptome analysis in cancer patients identifies lncRNA expression signatures, enabling precision medicine approaches.

Future Directions

The fusion of bioinformatics with experimental biology is unlocking the secrets of lncRNAs. Advances in artificial intelligence, single-cell sequencing, and structural modeling promise to overcome current limitations. Here are some promising directions:

Integrative Analysis: Combining multi-omics data to understand the interplay of lncRNAs with other biomolecules.
CRISPR Screens: Leveraging bioinformatics to design CRISPR-based functional screens for lncRNAs.
Therapeutic Development: Using bioinformatics to design lncRNA-based therapeutics, including antisense oligonucleotides and RNA interference tools.

Conclusion

lncRNAs are the hidden gems of the genome, and bioinformatics is the key to unearthing their full potential. As research progresses, lncRNAs could pave the way for novel diagnostics, targeted therapies, and personalized medicine, revolutionizing our approach to complex diseases.

The journey into the world of lncRNAs is only beginning, and bioinformatics will continue to play a pivotal role in decoding these molecular mysteries. Whether you’re a researcher, clinician, or bioinformatics enthusiast, the study of lncRNAs offers a fascinating frontier of discovery.

Cancer's origins revealed

Jitendra Narayan — Thu, 15 Aug 2013 13:06:56 -0500

Researchers have provided the first comprehensive compendium of mutational processes that drive tumour development. Together, these mutational processes explain most mutations found in 30 of the most common cancer types. This new understanding of cancer development could help to treat and prevent a wide-range of cancers.

More at >> http://www.sanger.ac.uk/about/press/2013/130814.html

Mycology Research Resources for Bioinformaticians: Unlocking the Fungal Kingdom

Neel — Fri, 13 Dec 2024 11:21:45 -0600

Mycology, the study of fungi, is a field that bridges ecology, medicine, and biotechnology. With advancements in bioinformatics, researchers now have unprecedented opportunities to explore the fungal kingdom at molecular, genetic, and ecological levels. From understanding pathogenic fungi to harnessing fungal enzymes for industrial applications, the potential is vast.

To fully leverage these opportunities, bioinformaticians require specialized tools and databases. This blog highlights essential resources for mycology research, focusing on databases, tools, and platforms tailored for fungal biology.

1. Fungal Databases

1.1. MycoCosm

Website: MycoCosm
Developed by the DOE Joint Genome Institute, MycoCosm is a comprehensive portal for fungal genomics. It offers genomic and transcriptomic data for a wide range of fungi, including saprobes, pathogens, and symbionts.

Key Features: Genome browsers, comparative genomics tools, and functional annotations.
Best For: Large-scale studies on fungal evolution and ecology.

1.2. FungiDB

Website: FungiDB
FungiDB is an integrated genomic resource for fungal pathogens and non-pathogens. It provides access to genome sequences, transcriptomic data, and functional annotations.

Key Features: Advanced search options, BLAST, and pathway analysis tools.
Best For: Studying fungal pathogenesis and host-pathogen interactions.

1.3. Index Fungorum

Website: Index Fungorum
This nomenclatural database provides information on the scientific names of fungi. It’s an essential resource for taxonomists and researchers focused on fungal biodiversity.

Key Features: Taxonomic hierarchy and synonymy tracking.
Best For: Identifying and classifying fungal species.

1.4. UNITE

Website: UNITE
UNITE is a specialized database for fungal ITS (Internal Transcribed Spacer) sequences, often used in fungal identification and phylogenetics.

Key Features: Curated reference datasets and community annotations.
Best For: Environmental mycology and microbial ecology studies.

2. Analytical Tools

2.1. Funannotate

Repository: GitHub - Funannotate
Funannotate is a genome annotation tool designed for fungi. It supports tasks like gene prediction, functional annotation, and orthology analysis.

Best For: Annotating newly sequenced fungal genomes.

2.2. BUSCO (Benchmarking Universal Single-Copy Orthologs)

Website: BUSCO
BUSCO evaluates genome assembly and annotation completeness using orthologs. It includes a fungal-specific dataset.

Best For: Assessing the quality of fungal genome assemblies.

2.3. Pathogen-Host Interactions Database (PHI-base)

Website: PHI-base
PHI-base is a manually curated resource containing information on pathogen-host interactions, including fungal pathogens.

Best For: Exploring virulence factors and host-pathogen relationships.

3. Visualization Platforms

3.1. Cytoscape

Website: Cytoscape
A powerful tool for visualizing molecular interaction networks, Cytoscape can be used to study protein-protein interactions, gene networks, and metabolic pathways in fungi.

Best For: Network biology and functional genomics.

3.2. iTOL (Interactive Tree of Life)

Website: iTOL
iTOL is an interactive tool for visualizing phylogenetic trees.

Best For: Displaying fungal phylogenies and comparing evolutionary relationships.

4. Community Resources

4.1. Mycological Society of America (MSA)

Website: MSA
The MSA promotes fungal research and provides access to resources, conferences, and publications.

Best For: Networking with fungal researchers and accessing recent studies.

4.2. OpenFungi

Website: OpenFungi
OpenFungi is an open-source initiative providing fungal genomic and transcriptomic datasets for research and education.

Best For: Sharing and accessing public fungal datasets.

5. Genomics Workflows

5.1. Galaxy

Website: Galaxy Project
Galaxy offers a web-based platform for reproducible bioinformatics workflows, including tools for fungal genome and transcriptome analysis.

Best For: User-friendly analysis pipelines without requiring coding skills.

5.2. Snakemake

Repository: Snakemake
A flexible pipeline management tool that supports fungal data processing and analysis.

Best For: Custom workflows for large-scale fungal datasets.

Conclusion

Fungal research is a rapidly growing field with vast implications for medicine, agriculture, and industry. For bioinformaticians, the availability of specialized resources—databases, tools, and community platforms—opens doors to innovative discoveries. Whether you are investigating fungal genomics, studying host-pathogen interactions, or exploring fungal biodiversity, the resources outlined above will empower your research journey.

Dive into these resources and help unravel the mysteries of the fungal kingdom!

Computer Theory & Genetics: George Chao at TEDxUMNSalon

Thu, 15 Aug 2013 22:08:10 -0500

George Chao is an undergraduate senior studying Genetics and Computer Science at the University of Minnesota. Having started genetics research as soon as he entered the university, he has worked in labs spanning multiple disciplines as well as in Japan. Some of these researches include developmental genetics in Drosophila, computational techniques for analyzing protein interactions, and helping with the development of algorithms to analyze motion capture data of patients with neck pain. During this time, George steadily developed a fascination with the field of bioinformatics, the study of using computational techniques to learn from genetic data. He would like to go into a career of research into the application of bioinformatics in various fields. ---- The individuals involved with TEDxUMN have a passion for bringing together the great thinkers at the University of Minnesota and giving them the opportunity to share their ideas worth spreading and to discuss our shared future. We provide these great people the opportunity to share these ideas on a global stage and with an incredibly diverse audience. We believe in the power of ideas to change attitudes, lives and ultimately the world. Check out TEDxUMN at http://www.TEDxUMN.com/ In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)

Cracking the Code: A Guide to Bioinformatics Job Hunting

Abhi — Mon, 23 Dec 2024 19:36:41 -0600

Entering the world of bioinformatics is an exciting journey, filled with opportunities to combine biology, data science, and technology to address some of the most pressing scientific challenges. However, securing a position in this competitive field can be daunting, especially for newcomers. Here’s a guide to help you navigate the job-hunting process and land your dream role in bioinformatics.

1. Understand the Landscape

Before diving into applications, take the time to understand the bioinformatics job market. Common roles include:

Bioinformatics Analyst/Scientist: Focused on data analysis and interpretation.
Computational Biologist: Combines computational techniques with biological research.
Data Scientist in Genomics: Applies machine learning and statistical models to genomic data.
Software Developer in Bioinformatics: Designs and develops tools and pipelines for biological research.

Familiarize yourself with the key industries hiring bioinformaticians, such as academia, biotech, pharmaceuticals, healthcare, and agriculture.

2. Build a Strong Foundation

Bioinformatics demands a diverse skill set. Ensure you have a solid foundation in the following areas:

Programming Skills: Proficiency in Python, R, or Perl is often required. Familiarity with tools like Bash scripting and version control systems (e.g., Git) is a plus.
Statistics and Data Analysis: Knowledge of statistical methods, machine learning, and data visualization is crucial.
Biological Knowledge: Understanding genomics, transcriptomics, and proteomics will help you communicate effectively with biologists.
Specialized Tools and Databases: Be comfortable using tools like BLAST, Bowtie, and databases like NCBI and Ensembl.

3. Create a Winning Resume and Portfolio

Highlight your technical skills, biological knowledge, and relevant experience. Tips for a standout application:

Tailor your resume to each job, emphasizing skills mentioned in the job description.
Showcase your experience with real-world datasets by linking to your GitHub profile or online portfolio.
Include details of any publications, presentations, or significant projects.

4. Network Actively

Networking is often the key to discovering opportunities. Here’s how to build connections:

Attend Conferences and Workshops: Events like ISMB or specialized bioinformatics workshops are great for meeting professionals.
Engage Online: Join LinkedIn groups, participate in bioinformatics forums, and follow relevant hashtags on Twitter.
Leverage Alumni Networks: Connect with alumni from your university who are working in the field.

5. Gain Relevant Experience

Experience is a major factor for hiring managers. Ways to enhance your profile include:

Internships: Seek out internships in research labs or biotech companies.
Collaborations: Volunteer to work on projects with professors or peers.
Open Source Contributions: Participate in bioinformatics software development on platforms like GitHub.

6. Prepare for Interviews

Bioinformatics interviews often combine technical and behavioral questions. Prepare by:

Reviewing Key Concepts: Refresh your knowledge of algorithms, sequence analysis, and statistical methods.
Practicing Coding: Be ready to solve coding challenges or discuss code snippets.
Understanding the Organization: Research their recent projects, publications, or products.
Preparing Questions: Demonstrate interest by asking about their tools, workflows, or team structure.

7. Stay Resilient and Persistent

Job hunting can be a long process, but persistence pays off. Tips to keep moving forward:

Keep improving your skills by taking online courses or certifications.
Stay updated with advancements in bioinformatics by following journals and blogs.
Apply to multiple positions and don’t get discouraged by rejections. Each application is a learning experience.

Closing Thoughts

Landing a bioinformatics job requires a mix of technical expertise, networking, and resilience. By understanding the market, showcasing your skills effectively, and continuously learning, you’ll be well on your way to a rewarding career in this dynamic field. Remember, the key to cracking the code is perseverance—stay curious, stay determined, and success will follow.

4 positions in high throughput computational metagenomics and systems biology of natural products

Tue, 20 Aug 2013 08:42:29 -0500

The Research and Innovation Centre at the Fondazione Edmund Mach (CRI-FEM) is a major international research institution with strong and expanding research interests in Fruit Genomics, Quality Health and Nutrition of Agricultural Products, Agro-ecosystems Sustainability, Biodiversity and Molecular Ecology.

CRI-FEM hosts GMPF, an International PhD Program in Genomics and Molecular Physiology of Fruit Crops and Fox-Lab, an international initiative in forest and wood research.
4 positions in high throughput computational metagenomics and systems biology of natural products - deadline September 30th, 2013

To support interdisciplinary research, CRI-FEM has established the Computational Biology Centre (CBC).

The mission of CBC is to develop systems-level integrative approaches connecting genotype to phenotype with a special focus on genome-wide analyses and next generation sequencing technologies.

CRI-FEM is seeking to attract 4 high calibre scientists in the areas of high throughput computational metagenomics and systems biology of natural products.

Here below the list of the 4 positions:

http://www.fmach.it/eng/Servizi-Generali/Lavora-con-noi/Annunci-lavoro-e-borse-di-studio/Details-of-the-5-positions-in-high-throughput-computational-metagenomics-and-systems-biology-of-natural-products-deadline-September-30th-2013/Post-doc-in-Metagenomics-screening-and-characterization-of-bioactive-microbial-compounds-130_CRI_MSC

http://www.fmach.it/eng/Servizi-Generali/Lavora-con-noi/Annunci-lavoro-e-borse-di-studio/Details-of-the-5-positions-in-high-throughput-computational-metagenomics-and-systems-biology-of-natural-products-deadline-September-30th-2013/Post-doc-in-Modeling-transcriptional-control-programs-at-a-genome-wide-scale-131_CRI_TCP

http://www.fmach.it/eng/Servizi-Generali/Lavora-con-noi/Annunci-lavoro-e-borse-di-studio/Details-of-the-5-positions-in-high-throughput-computational-metagenomics-and-systems-biology-of-natural-products-deadline-September-30th-2013/Technologist-in-Purification-of-plant-bioactive-molecules-from-complex-matrixes-132_CRI_PBM

http://www.fmach.it/eng/Servizi-Generali/Lavora-con-noi/Annunci-lavoro-e-borse-di-studio/Details-of-the-5-positions-in-high-throughput-computational-metagenomics-and-systems-biology-of-natural-products-deadline-September-30th-2013/Researcher-in-Methods-for-algorithmic-and-integrative-genomics-for-metagenomics-134_CRI_AIG

For more information on the CBC or informal inquiries on the advertised positions please contact Dr Duccio Cavalieri (e-mail duccio.cavalieri@fmach.it).

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 !

Dynamic Programming Alignment

Thu, 22 Aug 2013 09:38:28 -0500

lecture 9, Chem. C100, Spring 2013, UCLA

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.

PhD position in biochemistry towards bioinformatics at the Department of Biochemistry and Biophysics.

Tue, 03 Sep 2013 06:09:03 -0500

PhD position in biochemistry towards bioinformatics at the Department of Biochemistry and Biophysics. Reference number: SU FV-2293-13. Deadline for application: September 10, 2013.

Project title: Functional Inference from Domain Architecture and Orthology

Requirements

To be accepted as a PhD student, credits corresponding to four years of full-time studies at the undergraduate level are required, including credits corresponding to at least two years of fulltime studies in chemistry, life sciences or physics, depending on the program. The credits should include courses at the advanced level (second cycle) corresponding to one year and of these one semester should be a degree thesis. In order to facilitate the evaluation of merits and suitability for the PhD studies the curriculum vitae (CV) should contain information about the extent and focus of the academic studies. The quantity (as part of an academic year) and the quality mark of courses in chemistry and physics are of particular interest. The title, number of credits and the length in full-time months of undergraduate thesis and project work, should be specified.

Information

More information about the project can be provided by the project leader. General information about the PhD training program may be requested from the director of graduate studies, Stefan Nordlund, stefan@dbb.su.se or from Lena Mäler, Head of Department (prefekt), lenam@dbb.su.se

Further information on the web:

The Department of Biochemistry and Biophysics: www.dbb.su.se

Stockholm University: www.su.se/english

Faculty of Science: www.science.su.se/english

The handbook for postgraduate students: www.doktorandhandboken.nu/english

Application The application should contain a personal letter (a letter of intent explaining why you are interested in the specific project, why you are interested in studying for a PhD, what you hope to accomplish during your PhD studies, and what skills you can bring to this project), a curriculum vitae, a list of two persons who may act as referees (with telephone numbers and e-mail addresses), copies of degree certificates and transcripts of academic records, and a copy of your undergraduate thesis and articles, if any.

In order to apply for this position, please use the Stockholm University web-based application form (where it is possible to select language):

To the application form for this position.

Welcome with your application no later than September 10, 2013.

Project leader: Erik Sonnhammer, Erik.Sonnhammer@sbc.su.se,
www.sonnhammer.sbc.su.se

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