Bachelor/ BS Bioinformatics at
1. Al-khair University, Bhimber
2. Government College University, Faisalabad
3. University Of Agriculture, Faisalabad
4. Comsats Institute Of Information Technology [isb], Islamabad
5. International Islamic University, Islamabad
6. Quaid-e-azam University, Islamabad
7. Khushal Khan Khattak University, Karak
8. Virtual University Of Pakistan, Lahore
9. Virtual University Of Pakistan, Lahore
10. Hazara University, Mansehra
11. Shaheed Benazir Bhutto Women University, Peshawar
12. Comsats Institute Of Information Technology, Sahiwal
13. Capital University Of Science And Technology, Islamabad
14. Foundation University, Islamabad
15. Baqai Medical University/hospital, Karachi
16. Institute Of Business And Technology(main Campus), Karachi
17. Sir Syed University Of Engineering & Technology, Karachi
18. Forman Christian College, Lahore
19. Qarshi University (lhr), Lahore
20. The Superior University, Lahore
21. University Of Management And Technology, Lahore
22. Federal Institute Of Health Sciences, Lahore
23. Shaheed Benazir Bhutto Women University Peshawar, Sub Campus, Swabi
24. Government Postgraduate College ( Mandian), Abbottabad
25. Federal Institute Of Health Sciences, Multan
26. Fedral Institute Of Health Sciences, Muzaffarabad
27. The Limit Institution Of Health Sciences, Sahiwal

Master/ MS Bioinformatics cources at
1. Government College University, Faisalabad
2. Comsats Institute Of Information Technology [isb], Islamabad
3. International Islamic University, Islamabad
4. National University Of Science & Technology, Islamabad
5. Quaid-e-azam University, Islamabad
6. University Of Sindh, Jamshoro
7. Virtual University Of Pakistan, Lahore
8. Hazara University, Mansehra
9. Shaheed Benazir Bhutto Women University, Peshawar
10. Capital University Of Science And Technology, Islamabad
11. Cecos University Of Information Tech. & Emerging Sciences, Peshawar

The real bioinformatics scope lies if there are research labs which work in this field. One has to take account of that. If so then try to get information of those labs and visit them to get a hang of the work they pursue.

There is a huge buzz of precision medicine in light of genomics all around the world. One should also try to see how genomics infrastructure is built up or standing in Pakistan. If research labs having collaboration with hospitals employ genomics then one must also visit such labs. This will bring new avenues in healthcare advances. Not only it opens up the wealth of knowledge one can make out of genomics study but will also advance the critical thinking of therapies.

So I would encourage to target research labs working in the fields and also get information of hospitals employing genomics, this will give you an overall understanding of the fields demand in your country.

The Laboratory of Statistics and Computational tools for Bioinformatics (BioinfoLab) is hosted at the Istituto per le Applicazioni del Calcolo "Mauro Picone" - CNR . The laboratory has been officially opened in 2012 with the support of Programma Operativo Nazionale "Ricerca e Competitività" 2007-2013 (PON "R&C"), and it incorporates several expertise and research activities started since 2007, and supported by several CNR projects. Main interest of BioinfoLab is to develop novel statistical methods and computational tools for the analysis of high dimensional data arising from "Multi-omics" applications. In particular, current activities involve the analysis of ChIP-seq and RNA-seq experiments.

More at http://bioinfo.na.iac.cnr.it/BioinfoLab/index.html

For help on the bigBed and bigWig applications see:
http://genome.ucsc.edu/goldenPath/help/bigBed.html
http://genome.ucsc.edu/goldenPath/help/bigWig.html

View the file 'FOOTER' to see the usage statement for each of the applications.

Address of the bookmark: http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/

The most common newbie questions are:

Should I try to use these free open source programs?  Why are we not trying GUI software for computational analysis? Should I use commercial bioinformatics programs/software?”

1. Let’s be open

We generally think free and cheap are useless. But this concept is not applicable when we discuss open source software. Mostly, the bioinformatics software is developed by highly competitive biological programmers who believe in open sharing of knowledge. They come under Open Bioinformatics Foundation or O|B|F which is a non-profit, volunteer run organization focused on supporting open source programming in bioinformatics. The best part about open source tools/software is that they’re free to download the source code and read exactly what the program does. If you are so inclined, you can view all of the parts of the program and see the logical flow of the pipeline. In addition, open source makes an excellent learning tool for any beginning bioinformatician. Moreover, you can modify existing open source programs to deal with cutting-edge problems or to customize your pipeline. Apart from your computational and analysis work, most of the reviewer also prefers the open source based results so that they can validate the results if validation required.

2. Code headache

As a bioinformatician you are supposed to know the basics of programming languages, and if you are not good at it, then please learn it as soon as possible because you are not a bio-analyst but biological programmers. The open source programs usually lack dedicated service and support teams (often because they were the product of an overworked doc/postdoc!) so you are responsible for troubleshooting your own errors most of the time. We commonly receive the HELP email to support and assist to setup the pipeline; you can also find this kind of request on any QA forum. I personally believe this coding horror brings the biggest downside of open-source programs; where you need some programming skills in order to implement the program in your pipeline. But, if you are not able to fix the pipeline and modify the open source code according to your requirements them you should re-think on your bioinformatician name tag!!!

3. Dive into the codes

Some of the biologist turn bioinformatician says “if you can do the same thing with commercial software then why to get migraine with weird codes”, well this statement looks to me that guys are keen to learn swimming but still don’t like to get wet. If you are still using paid software and doing your work by customer support and clicking some of the well-designed GUI button then perhaps you are not interested in learning and trying new and challenging bioinformatics works. You are missing the basic flavour of bioinformatics. Let’s dive into the coding world, I am sure your will enjoy it. I recommend your to swim freely in code’s sea, and enjoy the journey; do not merely watch it from the outside.  

4. Paid does not mean better

The bioinformatics company which are specializes in bioinformatics solutions develop well designed/packed, user friendly software by using a large number of specialised scientist, programmers and support staff. They also provide good services to accomplice your biological analysis work. This means that if you hit a ‘snag’ with your data, help is likely only a phone call away! These companies price their products competitively against the cost of a dedicated bioinformatician. You may be able to afford the program, but not the additional staff! Additionally, most of the functionality that you need in your analysis is already coded into the program. Need to plot a graph? Just click this button right here. It is that easy. But, as a bioinformatician this is not generally well encouraged approach in biological analysis work, because the software is not available to everyone and your data can’t be validated. Moreover, there is very less chances that anyone will repeat your work or love to do similar kind of research (because not all the labs in the world are rich like yours).

5. Take a caution

In biological analysis work, in which you deal GB/TB of data are having maximum chances of getting errors, so please be careful and always cross check your data before coming to any conclusion. Even an error in two line code can alter your entire analysis and display weird results. Some of the scientist blindly believes on commercial software, which is entirely wrong. Using proprietary tools does not absolve you of the need to actually read and research the type of analysis that you are doing. This is particularly true in the case of genome assembly and annotation.

At the end, I would like to tell only one think that open source solutions allows you to do more cutting edge analysis than the commercial tools. So let’s go for it.

Disclaimer:

This is my personal view. I have nothing to do with any company or open source community. The views expressed on these pages are mine alone and not those of my current/past employers. I do reserve the right to remove comments left by spammers or off-topic comments.

More at https://boards.greenhouse.io/twistbioscience/jobs/3135495?gh_src=9ecc0b941us

What is Bacterial Comparative Genomics?

Comparative genomics involves the systematic comparison of genomes across different bacterial species or strains. This approach allows scientists to:

• Identify conserved and unique genes.

• Explore genetic determinants of pathogenicity.

• Understand bacterial evolution and phylogenetics.

• Investigate horizontal gene transfer and its role in antibiotic resistance.

Bioinformatics is central to these analyses, enabling the processing and interpretation of large-scale genomic data.

Key Steps in Bacterial Comparative Genomics

1. Genome Sequencing and Assembly: The process begins with obtaining high-quality bacterial genome sequences. Advances in next-generation sequencing (NGS) technologies have made it faster and more affordable to sequence bacterial genomes. Tools such as SPAdes and Velvet are commonly used for genome assembly.

2. Genome Annotation: Annotating a genome involves identifying genes, regulatory elements, and other genomic features. Automated tools like Prokka and RAST provide functional annotations, allowing researchers to predict the roles of genes and proteins.

3. Genome Alignment: Aligning genomes is crucial for identifying conserved regions, single-nucleotide polymorphisms (SNPs), and structural variations. Tools like Mauve and progressiveMauve are commonly employed for whole-genome alignments.

4. Comparative Analyses:

   • Core and Pan-genome Analysis: The core genome consists of genes shared across all strains of a species, while the pan-genome includes all genes found in any strain. Software like Roary and BPGA can perform core and pan-genome analyses.

   • Phylogenetic Analysis: Comparative genomics often involves reconstructing evolutionary relationships. Tools such as MEGA and IQ-TREE facilitate phylogenetic tree construction based on genomic data.

   • Functional Enrichment Analysis: To understand the biological significance of unique or shared genes, functional enrichment analysis using databases like GO (Gene Ontology) and KEGG is essential.

Here are some additional bioinformatics tools that can aid bacterial comparative genomics:

• OrthoFinder: For accurate ortholog identification across multiple genomes.

• PanOCT: Specifically designed for pan-genome clustering and annotation.

• FASTANI: A tool for calculating Average Nucleotide Identity (ANI) for microbial genome comparisons.

• CIRCOS: For visually comparing genomic data through circular genome plots.

• Galaxy Platform: A user-friendly web-based platform offering numerous genomic analysis tools.

• BLAST: Essential for sequence alignment and similarity searches.

• PhyloSift: Focused on phylogenetic analysis of microbial genomes using marker genes.

These tools, in combination with the methods discussed, provide a robust framework for conducting comprehensive comparative genomic studies.

Applications of Bacterial Comparative Genomics

1. Understanding Pathogenicity: Comparative genomics helps identify virulence factors that distinguish pathogenic strains from non-pathogenic relatives. For instance, comparing genomes of Escherichia coli strains has revealed key genetic determinants of pathogenicity in enterohemorrhagic strains.

2. Antibiotic Resistance Research: The spread of antibiotic resistance genes through horizontal gene transfer is a major global concern. Comparative analyses can trace the origins and dissemination of resistance genes, aiding in the development of countermeasures.

3. Microbial Ecology and Evolution: By studying genomic variations, researchers can understand how bacteria adapt to different environments. This is particularly relevant for extremophiles and symbiotic bacteria.

4. Vaccine Development: Identifying conserved antigens across pathogenic strains is critical for vaccine design. Comparative genomics has been instrumental in developing vaccines against pathogens like Neisseria meningitidis.

5. Biotechnology Applications: Comparative studies can uncover unique metabolic pathways in bacteria, paving the way for applications in bioremediation, synthetic biology, and industrial microbiology.

Challenges in Bacterial Comparative Genomics

While the field has made significant strides, several challenges remain:

• Data Overload: The rapid growth of sequencing data requires robust computational infrastructure and efficient algorithms.

• Genome Plasticity: High rates of horizontal gene transfer and genome rearrangements in bacteria complicate comparative analyses.

• Annotation Accuracy: Automated annotation tools are not infallible, and manual curation is often needed for high-confidence results.

• Interpreting Non-Coding Regions: Understanding the functional significance of non-coding genomic regions remains a challenge.

Future Directions

The integration of bacterial comparative genomics with other ‘omics’ approaches—such as transcriptomics, proteomics, and metabolomics—promises a more comprehensive understanding of bacterial biology. Additionally, advancements in machine learning and artificial intelligence are likely to further enhance bioinformatics analyses, enabling the prediction of complex phenotypes from genomic data.

Conclusion

Bacterial comparative genomics, driven by bioinformatics, continues to unravel the complexities of bacterial life. From combating antibiotic resistance to uncovering the secrets of microbial evolution, this interdisciplinary field holds immense potential for addressing pressing challenges in microbiology and beyond. As technology advances, so too will our ability to harness the power of comparative genomics for scientific and societal benefit.

In an age where pathogens continue to evolve and cross boundaries, understanding what makes them virulent—that is, capable of causing disease—has become a critical focus in modern microbiology and genomics. Virulence prediction bridges computational biology, genomics, and machine learning to forecast the pathogenic potential of microbes before they strike.

What Is Virulence?

Virulence refers to the degree of damage a pathogen can inflict on its host. It is determined by a combination of genetic factors—called virulence factors (VFs)—that allow the organism to attach, invade, evade, and harm the host. These include genes coding for toxins, secretion systems, adhesins, and enzymes that disrupt host defenses.

Understanding virulence factors not only helps in deciphering the mechanisms of infection but also provides early warning signs for emerging threats.

Why Predict Virulence?

Traditional virulence studies relied heavily on experimental infection models, which, although accurate, are time-consuming, expensive, and ethically constrained.
Today, the availability of whole-genome sequences and large-scale pathogen databases has paved the way for in silico virulence prediction—a computational approach that can screen thousands of genomes within hours.

This approach enables researchers to:

• Rapidly identify potential high-risk strains.

• Prioritize pathogens for containment, surveillance, or further study.

• Guide vaccine development and drug target discovery.

• Support One Health frameworks, linking animal, human, and environmental health data.

How Is Virulence Predicted?

Virulence prediction combines bioinformatics pipelines with machine learning and comparative genomics. The process generally involves:

1. Genome Annotation: Identifying genes and coding sequences in microbial genomes.

2. Feature Extraction: Comparing sequences with curated databases like VFDB (Virulence Factor Database), PATRIC, or Victors.

3. Pattern Recognition: Using algorithms (e.g., Random Forest, SVM, or deep learning models) to classify genes or strains as virulent or non-virulent based on sequence patterns, motifs, and protein domains.

4. Scoring and Visualization: Assigning a virulence score or confidence level and visualizing it through heatmaps or genome maps.

Tools and Resources for Virulence Prediction

A number of tools and databases make virulence prediction accessible to the scientific community:

• VFanalyzer – For identifying virulence genes based on VFDB.

• PathoFact – Predicts virulence, antimicrobial resistance (AMR), and toxin genes from metagenomic data.

• Pangenome-based models – Identify virulence-associated gene clusters across strains.

• Machine learning models – Use features like GC content, codon usage bias, or protein domains to predict pathogenicity.

Emerging tools now integrate multi-omic data—including transcriptomics, proteomics, and metabolomics—to understand virulence in a systems biology framework.

Applications in the Real World

Virulence prediction has major implications across public health and research sectors:

• Epidemic preparedness: Early identification of virulent strains in outbreak samples.

• AMR surveillance: Linking virulence profiles with antibiotic resistance determinants.

• Environmental monitoring: Predicting pathogenic potential of soil or waterborne microbes.

• Clinical diagnostics: Supporting personalized treatment through pathogen profiling.

For instance, integrating virulence prediction pipelines into national surveillance networks could enable faster risk assessment and response to infectious outbreaks.

The Road Ahead

As machine learning and genomics advance, virulence prediction will evolve from simple gene-based detection to dynamic, context-aware models that account for host–pathogen interactions, environmental signals, and evolutionary adaptation.

Future tools may predict not just if a strain is virulent, but under what conditions it expresses that virulence—bridging the gap between genotype and phenotype.

In Summary

Virulence prediction is redefining how we understand and anticipate infectious diseases. By coupling genomic insights with computational intelligence, researchers can identify potential threats earlier, design smarter interventions, and ultimately, strengthen our preparedness against emerging pathogens.

High Throughput Sequencing Analysis
Comparative Genomics
Identification and Annotation of Non-coding RNAs
Bioinformatic Analysis and System Biology of Viruses
Coevolution of Proteins and RNAs
Algorithmic Bioinformatics
Phylogenetic Analysis

http://www.rna.uni-jena.de/index.php

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

Bii.a-star.edu.sg

Bio research and development. Has course information and research information.

Isb-sib.ch

SIB operates the ExPASy proteomics server and the Swiss node of EMBnet. Teaching activities include a series of post-graduate courses given at the Universities of Geneva and Lausanne, as well as at the EPFL, and a Masters Degree in bioinformatics. Major research areas include the development of integrated databases and software resources in the field of proteomics.

Bioinformatics.ca

Provides information about bioinformatics in Canada. Workshops, certification and resources.

Chickscope.beckman.uiuc.edu

Students raise chicken embryos in the classroom and obtain magnetic resonance images through the Internet.

Bcb.iastate.edu

Graduate program at Iowa State University offering Undergraduate Major (BCBio) and the PhD program (BCB).

Bu.edu/bioinformatics/

Interdisciplinary PhD and Masters Programs that include an internship in the local industry companies. In conjunction with the NE masters program.

Bioinformatics.ubc.ca

A computational biology research centre covering many areas of genomics, proteomics, computer science and statistics. Research, training, news and events, resources and support, director's message, faculty and personnel.

Openhelix.com

Provides onsite training on specific bioinformatics databases and tools. Also offers bioinformatic software testing and research consulting services.

Igb.uci.edu

Specializing in making publicly available software and database services for computational biology.

Bioinformatics.pe.kr

Maintained by Dr. Seyeon Weon, Korea providing information on courses, a database archive, software archive and online resources.

Groups.yahoo.com/group/bimatics/

Bioinformatics group for students interested and/or working in the bioinformatics/computationalbiology fields. Offers opportunities to exchanging information and sharing ideas.

Ncbi.nlm.nih.gov/books/NBK22183/

Information about several medically important genes and related diseases. Illustrates the use of bioinformatics in their study.

Bioinfo.mbb.yale.edu/mbb452a/2003/

Bioinformatics course at Yale University. All course slides are available online.

Cs.iastate.edu/~honavar/comp-bio-courses.html

Listing of computational molecular biology course pages that have extensive online course materials.

Bioinf.manchester.ac.uk/dbbrowser/bioactivity/prefacefrm.html

A web-based tutorial associated with "Introduction to bioinformatics" published by Addison Wesley Longman.

Northeastern.edu/bioinformatics/

From the Biology department and in cooperation with Boston University. Emphasis on the ability to integrate knowledge from biological, computational, and mathematical disciplines.

Biocomp.unibo.it/lsbioinfo/

A two year, international master's programme in bioinformatics at the Universita di Bologna, Italy.

Cs.helsinki.fi/bioinformatiikka/mbi/programme.html

A two year Masters Degree Programme in Bioinformatics (MBI) offered by the University of Helsinki and Helsinki University of Technology, Finland.

Ornl.gov/sci/techresources/Human_Genome/education/education.shtml

A resource for introductory information on the Human Genome Project.

His.se/bioinformatics

A one-year, international master's programme in bioinformatics at the University of Skovde, Sweden.

Members.tripod.com/C.elegans/

Resources in biochemical, molecular, cellular, system, and organism biology, including over 25,000 indexed links, accumulated since 2000, from topic menus or from search interface.

Bioinformatics.org/faq/#contents

Summary of basics of bioinformatics for the intelligent newcomer.

Jiscmail.ac.uk/archives/bioinformatics.html

Forum featuring various aspects, events and developments in the bioinformatics field.

Biinoida.blogspot.com

Blog focusing on bioinformatics, biotechnology, pharma regulatory affairs, IPR and clinical trials.

Colorbasepair.com/bioinformatics_courses_tutorials.html

A list of on-line course materials and tutorials for bioinformatics and computational biology.

Geospiza.com/education/

Instructional materials for teaching bioinformatics. These include animated tutorials on topicssuch as BLAST, finding mutations in a protein, and graphing with MS-Excel.

Bioinformatics.fi

An international, two-year Master's programme jointly managed by the University of Tampere and the University of Turku, Finland.

Perlsource.net

Provides online courses in Perl programming for bioinformatic tools.