Following are the weblink for different available browsers:

http://www.ensembl.org/index.html

http://ensemblgenomes.org/

http://genome.ucsc.edu/

http://www.ncbi.nlm.nih.gov/genome

http://www.ebi.ac.uk/genomes/

http://flybase.org/

http://cmr.jcvi.org/tigr-scripts/CMR/CmrHomePage.cgi

http://www.sanger.ac.uk/resources/databases/

Our research centres on the use of genetics/genomics and phenotypic studies to address complex questions in the ecology, epidemiology and evolution of microbes. Our most recent interest focuses upon comparative genome analysis to describe the core and flexible genome of pathogenic bacteria (Campylobacter, Acinetobacter, Escherichia coli, Helicobacter, Staphylococcus and Streptococcus suis) and how this is related to population genetic structuring, the maintenance of species, and the evolution of host/niche adaptation and virulence.

More at https://sheppardlab.com/research/

Main research topics:
- Comparative and Population Genomics of Plant Symbionts
- Parasite Genome Evolution
- Experimental Evolution of Microbial Symbionts and Parasites
- Phylogenomics of Early Branching Fungi

More at http://corradilab.weebly.com/

ArrayGen specializes in Genomics data analysis and research, as we believe in the level of precision, predictability, benchmark-ability, and data analysis capability of genomics data over other forms of biological data. ArrayGen constantly strives to develop new solutions, and plug the existing gaps in the technological advancement of the field.

More http://www.arraygen.com/

Fungi are of central importance for the global carbon cycle because of
their role in the degredation of complex organic matter such as plant
material. Fungi also represent one of the last frontiers of
biodiversity, as their taxonomic diversity and metabolic potential
remain poorly understood. This is particularly true for those fungi that
are abundant in freshwaters.

\"MycoLink\" (Linking aquatic mycodiversity to ecosystem function) is an interdisciplinary project integrating the expertise of 4 Leibniz Institutes: IGB, ZALF, DSMZ, the Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), the Leibniz Centre for Agricultural Landscape Research (ZALF), and the Leibniz-Institute of Zoo- and Wildlife Research in Berlin (IZW). We are seeking to recruit outstanding young scientists to establish an innovative research program, and currently invite applications for:

PostDoc will focus on global biodiversity and evolutionary genomics of freshwater fungi, using second- and third-generation sequencing and bioinformatics to analyse natural populations and experimental cultures. For further information, contact Michael T. Monaghan (monaghan@igb-berlin.de) (http://monaghanlab.org).

PostDoc will focus on the ecological and functional role of aquatic fungi by combining state-of-the-art biochemical analyses with modeling in experimental and natural ecosystems. For further information, contact Hans-Peter Grossart & Katrin Premke (hgrossart@igb-berlin.de; premke@igb-berlin.de)

Applicants must hold a PhD in a relevant field. Positions are available for up to three years. Salary is according to the German TvD. Positions will be based at IGB Berlin, IGB Neuglobsow, and at the Berlin Centre for Genomics in Biodiversity Research. The institutes of the Leibniz Association strive to increase the proportion of female scientists. Therefore, female candidates are specifically encouraged to apply. Disabled applicants with identical technical and personal qualification will be preferentially selected.

Please submit a curriculum vitae (including publication list), a brief statement of motivation and research interests, and the names and contact information of two referees. Please send all documents as a single pdf file to monaghan@igb-berlin.de.

Review of the applications will start on 21 February 2014 and continue until the positions are filled. Interviews for shortlisted applicants will take place in March.

Biodiversity, Ecology, and Genomics of Aquatic Fungi
Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany

Deadline for applications : unknown.

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.

We work in a highly interdisciplinary environment at the interface of Computer Science and Biology. Since its inception, our lab has eagerly engaged in collaborative research partnerships with biological and experimental collaborators, facilitated by our affiliation with the Broad Institute and the Computational and Systems Biology initiative (CSBi) at MIT, our participation in the Epigenome Roadmap, ENCODE, and modENCODE consortia, and by several other ongoing collaborations at MIT, Harvard, and the Harvard Medical School affiliated hospitals.

http://compbio.mit.edu/

Current research interests include next-generation DNA sequencing, structural variation, genome rearrangements in cancer and evolution, and network analysis of somatic mutations in cancer. Earlier research included topics in comparative genomics, multiple sequence alignment, and motif finding.

More athttp://compbio.cs.brown.edu/

Ability to detect, quantify, and visualize complex and de-novo splicing variations from RNASeq.

MAJIQ’s accuracy compares favorably to other algorithms.

MAJIQ 2 is *way* faster, more memory and I/O efficient

New visualization (VOILA 2.0) Ability to analyze hundreds and thousands of samples Why so negative? (Support for a confident negative set)

Finally, a major reason we are excited about MAJIQ 2.0 is that it sets the code base for many new exciting algorithmic and visualization improvements, with application to new research questions so stay tuned!

More at https://biociphers.wordpress.com/2019/04/01/majiq-2-is-out/

Address of the bookmark: https://majiq.biociphers.org/