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/

 UPARSE >
OTU clustering for 16S and other marker genes. Highly accurate OTU sequences and improved diversity measures. UCHIME >
Chimeric sequence detection. PILER >
De novo genome repeat finder. PILER-CR >
Detection of CRISPR repeats in bacterial genomes. QSCORE >
Compare two multiple alignments for benchmarking. PALS >
Whole-genome alignment. PREFAB >
Protein Reference Alignment Database. MSA benchmark collection >
Selected multiple alignment benchmarks in a standardized FASTA format.

Address of the bookmark: http://drive5.com/software.html

TMAP is a fast and accurate alignment software for short and long nucleotide sequences produced by next-generation sequencing technologies.

• The latest TMAP is unsupported. To use a supported version, please see the TMAP version associated with a Torrent Suite release below.

• Get the latest source code:

  git clone git://github.com/iontorrent/TMAP.git
   cd TMAP
   git submodule init
   git submodule update

https://github.com/iontorrent/TS/tree/master/Analysis/TMAP

Address of the bookmark: https://github.com/iontorrent/TS/tree/master/Analysis/TMAP

Study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. Comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.

Following tools are useful for mis sense muation detection ... 

PolyPhen‐2 (PP2)
“Predicts possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations”

MutationTaster‐2 (MT2)
“Evaluation of the disease‐causing potential of DNA sequence alterations”

MutationAssessor (MASS)
“Predicts the functional impact of amino acid substitutions in proteins, such as mutations discovered in cancer or missense polymorphisms”

LRT
“Identify a subset of deleterious mutations that disrupt highly conserved amino acids within protein‐coding sequences, which are likely to be unconditionally deleterious”

SIFT
“Predicts whether an amino acid substitution affects protein function”

GERP++
“Identifies constrained elements in multiple alignments by quantifying substitution deficits. These deficits represent substitutions that would have occurred if the element were neutral DNA, but did not occur because the element has been under functional constraint. We refer to these deficits as “rejected substitutions.” Rejected substitutions are a natural measure of constraint that reflects the strength of past purifying selection on the element”

phyloP
“Compute conservation or acceleration P values based on an alignment and a model of neutral evolution”

FatHMM unweighted (FatHMM‐U)
Predicts “functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants”

FatHMM weighted (FatHMM‐W)
Predicts “functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants” and its weighting scheme attributes higher tolerance scores to SNVs in proteins, related proteins, or domains that already include a high fraction of pathogenic variantsh

Combined Annotation Dependent Depletion (CADD)
“CADD is a tool for scoring the deleteriousness of single‐nucleotide variants as well as insertion/deletions variants in the human genome”

3D-Dock Suite

Global rigid search: FFTShape complementarity and electrostatics

Re-scoring and clustering. Refinement of interface side-chains

3D-Garden

Global rigid search in ensamble

Shape complementarity and Lennard–Jones potential

Side chain and backbone dihedral refinement

DOT

Global rigid search: FFTShape complementarity, electrostatics and VDWNone

Escher NG

Global rigid searchShape complementarity, hydrogen bonds and electrostatic

Integrated in VEGA

GRAMM 

Global rigid search: FFT. smooth protein surface representation for soft docking

Shape complementarity and Lennard-Jones potential

Clustering of conformations

GRAMM-X 

Global rigid search: FFT. smooth protein surface representation for soft docking

Shape complementarity and Lennard-Jones potentialminimization and re-scoring with multiple filters

HEX

Global rigid search: Fourier correlation of spherical harmonics

Shape complementarity

HADDOCK

Global rigid searchElectrostatic ,VDW and desolvation energy termsMD simulated annealing refinement . Filtering based on external data. 

ICM

Global rigid search: Monte CarloEmpirical scoring function

Clustering and selection of conformations. Refinement of interface side-chains and re-scoring

MolFit 

Global rigid search: FFTShape complementarity

Clustering of good solutions, filtering using a priori information and small, local rigid rotations around selected conformations

PatchDock

Global rigid searchShape complementarity and atomic desolvation energy

Clustering of conformations

PyDock

Global rigid search:FFTShape complementarity

rescoring by binding electrostatics and desolvation energy

RosettaDock

Local rigid search: Monte Carlo with low and high resolution structure representation levels

Different scoring parameters for the different resolutions 

ZDOCK

Global rigid search: FFTShape complementarity, desolvation energy, and electrostatics.

Energy minimization and re-scoringFree for academics

Point to note:

The proper treatment of flexibility in protein–protein docking is still an active field of research. You first should analyzed your proteins in order to define their conformational space and then choose the most suitable method for your docking problem.

Download 

http://arthropods.eugenes.org/EvidentialGene/trassembly.html

https://sourceforge.net/p/evidentialgene/blog/

Address of the bookmark: http://arthropods.eugenes.org/EvidentialGene/trassembly.html

Address of the bookmark: http://www.phytools.org/Cordoba2017/ex/2/Intro-to-phylogenies.html

2. Parallel sequencing lives, or what makes large sequencing projects successful https://academic.oup.com/gigascience/article/6/11/gix100/4557140?login=false

3. Common-sense approaches to sharing tabular data alongside publication https://www.sciencedirect.com/science/article/pii/S2666389921002300

4. A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker https://psyarxiv.com/8xzqy/

5. Practical Computational Reproducibility in the Life Sciences https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30140-6

6. A video by Dr.Keith A. Baggerly from MD Anderson [The Importance of Reproducible Research in High-Throughput Biology](https://www.youtube.com/watch?v=7gYIs7uYbMo) highly recommended.

7. Ten Simple Rules for Reproducible Computational Research http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285)

8. Good Enough Practices in Scientific Computing http://arxiv.org/abs/1609.00037 

9. Best Practices for Scientific Computing https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745

10. A Quick Guide to Organizing Computational Biology Projects http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.100042  A must read for computational biologists!

11. Reproducibility of computational workflows is automated using continuous analysis https://www.nature.com/articles/nbt.3780

12. Five selfish reasons to work reproducibly https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0850-7

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.