https://evirusbioinfc.notion.site/18e21bc49827484b8a2f84463cb40b8d?v=92e7eb6703be4720abf17a901bc9a947

Address of the bookmark: https://evirusbioinfc.notion.site/18e21bc49827484b8a2f84463cb40b8d?v=92e7eb6703be4720abf17a901bc9a947

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.

However, nanopore sequencing is an emerging and rapidly developing technology. It is not clear what will be most informative. We hope that IONiseR will provide a framework for visualisation of metrics that we haven’t thought of, and welcome feedback at mike.smith@embl.de.

If you’re not interested in the quality assement of the raw or event level data, and want to jump straight to the getting FASTQ format files from fast5 files you can go straight to the final section of this document.

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/IONiseR/inst/doc/IONiseR.html

more at https://ccb.jhu.edu/software/hisat2/index.shtml

Address of the bookmark: https://github.com/infphilo/hisat2

It installs in any computer even if you don't have administrator rights!

Address of the bookmark: http://www.dnabaser.com/download/DNA-Counter/index.html

During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.

ENCODE’s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.

The Encyclopedia paper of ENCODE 3, published in Nature, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate cis-regulatory elements (cCREs).

More at https://www.nature.com/immersive/d42859-020-00027-2/index.html

This also includes:

• A2Amapper: ATAC, Assembly to Assembly Comparision tool:
  • Comparative mapping between two genome assemblies (same species), or between two different genomes (cross species).

• Sim4db:
  • Spliced alignment of cDNA and genomic sequences, from the same (sim4) or related (sim4cc) species. Optimized for high-throughput batched alignment.

• LEAFF:
  • LEAFF (ahem, Let's Extract Anything From Fasta) is a utility program for working with multi-fasta files. In addition to providing random access to the base level, it includes several analysis functions.

• Meryl:
  • An out-of-core k-mer counter. The amount of sequence that can be processed for any size k depends only on the amount of free disk space.

Address of the bookmark: https://help.rc.ufl.edu/doc/Kmer

1. SPAdes: An assembler specifically designed for single-cell and multi-cell bacterial genomes, as well as small eukaryotic genomes.

2. ABySS: A parallelized assembler for large genomes that uses de Bruijn graphs.

3. Velvet: Another de Bruijn graph-based assembler optimized for short-read sequencing data.

4. SOAPdenovo: A de Bruijn graph-based assembler designed for short reads, widely used for assembling large and complex genomes.

5. MaSuRCA: A hybrid assembler that combines data from multiple sequencing technologies, such as Illumina and PacBio.

6. Canu: A long-read assembler optimized for PacBio and Oxford Nanopore sequencing data.

7. Flye: A long-read assembler suitable for bacterial and small eukaryotic genomes.

8. SMARTdenovo: An assembler designed for long reads, particularly suited for PacBio data.

9. SPAdes Long Read (SPAdesLR): An extension of SPAdes for long-read data, such as those from PacBio or Nanopore.

10. Minia: An assembler optimized for low memory consumption, suitable for small and medium-sized genomes.

11. Unicycler: A hybrid assembler that combines short and long reads for circular bacterial genome assembly.

12. wtdbg2: A de Bruijn graph assembler for long reads, efficient for very large genomes.

13. Shasta: A long-read assembler that uses the Overlap-Layout-Consensus approach, suitable for PacBio and Nanopore data.

14. Sparc: An assembler designed to handle noisy long reads from Nanopore sequencing.

15. CANA: An assembler for metagenomic data, particularly for complex and diverse microbial communities.

16. Ra Assembler: A metagenome assembler for long reads, designed for highly complex metagenomic samples.

Please note that the field of bioinformatics is constantly evolving, and new assembly tools may have emerged since my last update. Additionally, the performance of these tools can vary depending on the characteristics of the sequencing data and the genome being assembled. When selecting an assembly tool, consider the specific requirements of your project, the available data types, and the computational resources at your disposal. Always refer to the respective tool's documentation and publications for the most up-to-date information and recommendations.

Nearly 20% of the such journals have a flashy impact factor and quick publication time, which are quick give-aways. Interestingly, under contact address, some journal websites do not even provide any address to contact. All of this has led to the emergence of a new and dark market of deceptive publishers that exploit the concept of open access and provide channels for “scientific journal” publication with little or no peer review. For a fee, they will publish almost anything – even if the study was fatally flawed. And these journals provide a forum that can be used as a channel to publish fraudulent “advocacy research.” You can find list of certain such publishers at "Beall's List" http://beallslist.weebly.com/

Keeping all these in mind, Government of India (GOI) decided to approved certain bioinformatics and computational biology journals for your research publication.

Following are the list of GOI validated and peer reviewed bioinformatics and computational biology journals:

NOTE:Each journal details are in following order Tittle\nSource\nSubject.
Point to remember: The list of journals are NOT sorted in any ascending or descending order.

If I missed any other GOI validated bioinformatics journal, then please report me in comment section.

Open Bioinformatics Journal
Scopus
Computer Science; Engineering; Medicine

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
WoS
BIOLOGY & BIOCHEMISTRY

Advances and Applications in Bioinformatics and Chemistry
Scopus
Biochemistry, Genetics and Molecular Biology Chemistry; Computer Science

Advances in Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology; Computer Science; Engineering

Applied Bioinformatics
Scopus
Agricultural and Biological Sciences; Computer Science

BIOINFORMATICS
WoS & Scopus
COMPUTER SCIENCE

Bioinformatics and Biology Insights
Scopus
Biochemistry, Genetics and Molecular Biology; Computer Science; Mathematics

BMC BIOINFORMATICS
WoS & Scopus
COMPUTER SCIENCE

BRIEFINGS IN BIOINFORMATICS
WoS & Scopus
COMPUTER SCIENCE

Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference
Scopus
Medicine

Current Bioinformatics
WoS & Scopus
COMPUTER SCIENCE

Current Protocols in Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology

JOURNAL OF COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS
ICI
BIOLOGICAL SCIENCE

Journal of integrative bioinformatics
Scopus
Medicine

Journal of Proteomics and Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology; Computer Science

Mathematical Biology and Bioinformatics
Scopus
Engineering; Mathematics

Trends in Bioinfprmatics
Scopus
Computer Science

Eurasip Journal on Bioinformatics and Systems Biology
Scopus
General; Computer Science; Mathematics; Medicine

Evolutionary Bioinformatics
WoS & Scopus
COMPUTER SCIENCE

Genomics, Proteomics and Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology;Mathematics

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology;Mathematics

IEEE-ACM Transactions on Computational Biology and Bioinformatics
WoS
COMPUTER SCIENCE

International Journal of Bioinformatics Research and Application
Scopus
Biochemistry, Genetics and Molecular Biology; Medicine, Health

International Journal o f Data M ining and Bioinformatics
WoS & Scopus
COMPUTER SCIENCE

IPSJ Transactions on Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology;Computer Science

Journal of Bioinformatics and Computational Biology
WoS & Scopus
COMPUTER SCIENCE

Journal of Clinical Bioinformatics
Scopus
Medicine

PLoS Computational Biology
WoS & Scopus
BIOLOGY & BIOCHEMISTRY

Reviews in Computational Chemistry
WoS & Scopus
CHEMISTRY

RSC Theoretical and Computational Chemistry Series
Scopus
Chemistry; Computer Science

Annual Reports in Computational Chemistry
Scopus
Chemistry; Mathematics

Computational and Structural Biotechnology Journal
Scopus
Biochemistry, Genetics and Molecular Biology; Computer Science

Computational and Theoretical Chemistry
WoS & Scopus
CHEMISTRY

COMPUTATIONAL BIOLOGY AND CHEMISTRY
WoS & Scopus
COMPUTER SCIENCE

COMPUTATIONAL CHEMISTRY
WoS
CHEMISTRY

Journal of Theoretical and Computational Chemistry
Scopus
Chemistry; Computer Science

Theoretical and Computational Chemistry
Scopus
Chemistry

Wiley Interdisciplinary Reviews: Computational Molecular Science
Scopus
Biochemistry, Genetics and Molecular Biology;Chemistry; Computer Science; Materials Science; Mathematics

Wiley Interdisciplinary Reviews- Computational Molecular Science
WoS
CHEMISTRY

Interdisciplinary sciences, computational life sciences
Scopus
Medicine

Interdisciplinary Sciences-Computational Life Science
WoS
Biology and Biochemistry

International Journal of Computational Biology and Drug Design
Scopus
Computer Science; Pharmacology, Toxicology and Pharmaceutics

The Department of Informatics is a vacant position as a researcher in computer science, related to Computational Biology Unit (CBU), for 3 years.

The position is part of CBU Service Group and will focus on bioinformatic analysis project and especially the analysis of high-throughput data, including NGS (sequencing), and proteomics data.

The successful candidate will be part of the Norwegian bioinformatics platform's national helpdesk within the project ELIXIR.NO

Applicants must hold a PhD in a relevant subject such as computer science, mathematics, molecular biology and also possess expertise and experience in bioinformatics statistics and analysis of data from high-throughput molecular experiment.

Basic programming or scripting skills are required. Experience in Python, R, Perl, Linux-based operating systems and moreover knowledge of databases and web programming will be a strength for applicants.

We expect enthusiasm and independence and moreover the ability to work in an interdisciplinary team environment.

Good knowledge of English is required.

Salaries start at level 57 (code 1109/LR 24.1) by appointment. Further promotion occurs after
service seniority in the position (at grade 57-65). Of particularly highly qualified applicants may be considered a higher salary.

Further information about the position is available from the chair of the CBU,
Professor Inge Jonassen, e-mail: Inge.Jonassen @ ii.uib.no

The successful applicant must comply with the guidelines that apply at any given time the position.

State employment shall as far as possible reflect the diversity of the population. It is therefore an objective to achieve a balanced age and sex composition and the recruitment of persons with immigrant backgrounds. Persons with immigrant background are requested to apply for the position.

Women are particularly encouraged to apply. If the experts find that several applicants have approximately equivalent qualifications, the rules on equal in the Personnel Regulations for Academic Positions will be applied.

University of Bergen applies the principles of public openness when recruiting staff to scientific positions.

Information about the applicant may be made public even though the applicant has requested not to be named in the list of applicants. If the request does not host admitted to the result, the applicant shall be notified of this.

Send application, CV, certificates, diplomas, undergraduate work and a list of publications (list of publications) online by clicking on https://www.jobbnorge.no/jobbsoknet/login.aspx?returnurl=/jobbsoknet/jobapplication.aspx?jobid=95196

You need to upload certified translations into English or a Scandinavian language of appendices, such as diplomas and transcripts.

Applications sent by email to individuals at the institute will not be considered.

Deadline: 9 August 2013