KisSplice is not a full-length transcriptome assembler. This means that it will output the variable regions of the transcripts, not reconstruct them entirely.

KisSplice comes as a workflow, with several possible post-treatments meant to facilitate the analysis of the results. The choice of the post-treatment depends on the availability of a reference genome/transcriptome and on the need to perform a differential analysis, as summarised in the following table.

Address of the bookmark: http://kissplice.prabi.fr/

Address of the bookmark: http://hibberdlab.com/transrate/index.html

ALF
A Simulation Framework for Genome Evolution
http://www.cbrg.ethz.ch/alf

Bayesian Serial SimCoal
Bayesian Serial SimCoal, (BayeSSC) is a modification of SIMCOAL 1.0, a program written by Laurent Excoffier, John Novembre, and Stefan Schneider.
http://www.stanford.edu/group/hadlylab/ssc/index.html

BEERS
BEERS was designed to benchmark RNA-Seq alignment algorithms and also algorithms that aim to reconstruct different isoforms and alternate splicing from RNA-Seq data
http://cbil.upenn.edu/beers/

BOTTLENECK
Bottleneck is a program for detecting recent effective population size reductions from allele data frequencies
http://www.ensam.inra.fr/urlb/bottleneck/bottleneck.html

BottleSim
BottleSim is a computer simulation program for simulating the process of population bottlenecks
http://chkuo.name/software/bottlesim.html

CASS
Protein Sequence Simulation
http://www.wyomingbioinformatics.org/liberlesgroup/cass/

CDPOP
CDPOP is a landscape genetics tool for simulating the emergence of spatial genetic structure in populations resulting from specified landscape processes governing organism movement behavior.
http://cel.dbs.umt.edu/cdpop

CoalFace
CoalFace is a simulation of the coalescent process with the visual display of gene genealogies.
http://web.up.ac.za/default.asp?ipkcategoryid=3283

CoaSim
CoaSim is a tool for simulating the coalescent process with recombination and geneconversion under various demographic models.
http://users-birc.au.dk/mailund/coasim/index.html

cosi
The cosi package is written in C and is available as a tar file.
http://www.broadinstitute.org/~sfs/cosi/

CS-PSeq-Gen
A program to simulate the evolution of protein sequences under the constraints of the information of a particular reconstructed phylogeny
http://bioserv.rpbs.univ-paris-diderot.fr/software/cs-pseq-gen.html

DAWG
An application designed to simulate the evolution of recombinant DNA sequences in continuous time
http://scit.us/projects/dawg

Easypop
EASYPOP is an individual based model intended to simulate datasets under a very broad range of conditions
http://www.unil.ch/dee/page36926_fr.html

EggLib
EggLib is a C++/Python library and program package for evolutionary genetics and genomics.
http://egglib.sourceforge.net/

EvolSimulator
A simulation test bed for hypotheses of genome evolution
http://acb.qfab.org/acb/evolsim/

EvolveAGene
A realistic coding sequence simulation program that separates mutation from selection and allows the user to set selection conditions
http://bellinghamresearchinstitute.com/software/index.html

fastsimcoal
A continuous-¬‐time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios
http://cmpg.unibe.ch/software/fastsimcoal/

FastSLINK
Simulation of Marker and Phenotype Data in Pedigrees
http://watson.hgen.pitt.edu/

FFPopSim
C++/Python library for population genetics.
http://webdav.tuebingen.mpg.de/ffpopsim/

FLUX SIMULATOR
The Flux Simulator aims at providing a deterministic in silico reproduction of the experimental pipelines for RNA-Seq, employing a minimal set of parameters.
http://flux.sammeth.net/simulator.html

ForSim
ForSim: A Forward Evolutionary Computer Simulation
http://www.anthro.psu.edu/weiss_lab/research.shtml

ForwSim
The program given below is based on the algorithm described in Padhukasahasram et al. 2008 to simulate genetic drift in a standard Wright-Fisher process.
http://badri-populationgeneticsimulators.blogspot.com/

FPG
Forward Population Genetic simulation
http://genfaculty.rutgers.edu/hey/software#fpg

FREGENE
FREGENE is a C++ program that simulates sequence-like data over large genomic regions in large diploid populations.
http://www.ebi.ac.uk/projects/bargen/download/fregen/documentation_html.html

GAMETES
Genetic Architecture Model Emulator for Testing and Evaluating Software: Simulates complex SNP models with pure, strict epistatic interactions with n-loci.
http://sourceforge.net/projects/gametes/?source=navbar

GASP
Genometric Analysis Simulation Program. A software tool for testing and investigating methods in statistical genetics by generating samples of family data based on user specified models.
http://research.nhgri.nih.gov/gasp/

GemSIM
Next generation sequencing read simulator
http://sourceforge.net/projects/gemsim/

GeneArtisan
Simulation of Markers in Case-Control Study Designs
http://www.rannala.org/?page_id=241

GENOME
A rapid coalescent-based whole genome simulator
http://www.sph.umich.edu/csg/liang/genome/

GenomePop2
GenomePop2 is a specialization of the program GenomePop just to manage SNPs under more flexible and useful settings. If you need models with more than 2 alleles please use the GenomePop program version.
http://webs.uvigo.es/acraaj/genomepop2.htm

GenomeSimla
GenomeSIMLA is currently under development- however, we have a beta release that we are asking to be tested
http://chgr.mc.vanderbilt.edu/genomesimla/

GENS2
Simulates interactions among two genetic and one environmental factor and also allows for epistatic interactions.
https://sourceforge.net/projects/gensim/

GWAsimulator
A rapid whole genome simulation program
http://biostat.mc.vanderbilt.edu/wiki/main/gwasimulator

HAP-SAMPLE
An association simulator for candidate regions or genome scans
http://www.hapsample.org/

HAPGEN
A simulator for the simulation of case control datasets at SNP markers
https://mathgen.stats.ox.ac.uk/genetics_software/hapgen/hapgen2.html

HapSim
A simulation tool for generating haplotype data with pre-specified allele frequencies and LD coefficients
http://cran.r-project.org/web/packages/hapsim/index.html

HAPSIMU
A program that simulates heterogeneous populations with various known and controllable structures under the continuous migration model or the discrete model
http://l.web.umkc.edu/liujian/

IBDsim
IBDSim is a computer package for the simulation of genotypic data under general isolation by distance models.
http://raphael.leblois.free.fr/

indel-Seq-Gen
A biological sequence simulation program that simulates highly divergent DNA sequences and protein superfamilies
http://bioinfolab.unl.edu/~cstrope/isg/

Indelible
A powerful and flexible simulator of biological evolution
http://abacus.gene.ucl.ac.uk/software/indelible/

invertFREGENE
InvertFREGENE is a forward-in-time simulator of inversions in population genetic data
http://www.ebi.ac.uk/projects/bargen/

kernalPop
A spatially explicit population genetic simulation engine
http://cran.r-project.org/src/contrib/archive/kernelpop/

MaCS
Markovian Coalescent Simulator
http://www-hsc.usc.edu/~garykche/

Mason
A package for the simulation of nucleotide data.
http://www.seqan.de/projects/mason/

mbs
modifying Hudson's ms software to generate samples of DNA sequences with a biallelic site under selection
http://www.sendou.soken.ac.jp/esb/innan/innanlab/software.html

Mendel's Accountant
Mendel's Accountant (MENDEL) is an advanced numerical simulation program for modeling genetic change over time and was developed collaboratively by Sanford, Baumgardner, Brewer, Gibson and ReMine
http://mendelsaccount.sourceforge.net/

MetaSim
A tool to generate collections of synthetic reads that reflect the diverse taxonomical composition of typical metagenome data sets
http://ab.inf.uni-tuebingen.de/software/metasim/

mlcoalsim
Multilocus Coalescent Simulations
http://code.google.com/p/mlcoalsim-v1/

ms
The purpose of this program is to allow one to investigate the statistical properties of such samples, to evaluate estimators or statistical tests, and generally to aid in the interpretation of polymorphism data sets.
http://home.uchicago.edu/~rhudson1/source/mksamples.html

msHOT
The purpose of this program is to allow one to investigate the statistical properties of such samples, to evaluate estimators or statistical tests, and generally to aid in the interpretation of polymorphism data sets.
http://home.uchicago.edu/~rhudson1/

msms
A coalescent Simlation tool with selection.
http://www.mabs.at/ewing/msms/index.shtml

MySSP
A program for the simulation of DNA sequence evolution across a phylogenetic tree
http://www.rosenberglab.net/software.php

Nemo
A forward-time, individual-based, genetically explicit, and stochastic simulation program designed to study the evolution of genetic markers, life history traits, and phenotypic traits in a flexible (meta-)population framework.
http://nemo2.sourceforge.net/

NetRecodon
Coalescent simulation of coding DNA sequences with recombination (inter and intracodon), migration and demography
http://code.google.com/p/netrecodon/

PEDAGOG
Software for simulating eco-evolutionary population dynamics
https://bcrc.bio.umass.edu/pedigreesoftware/node/5

phenosim
A tool to add phenotypes to simulated genotypes
http://evoplant.uni-hohenheim.de/doku.php?id=software:software

PhyloSim
An R package for the Monte Carlo simulation of sequence evolution
http://bit.ly/rlsim-git

pIRS
Profile-based Illumina pair-end reads simulator
https://code.google.com/p/pirs/

ProteinEvolver
Simulation of protein evolution along phylogenies under structure-based substitution models
http://code.google.com/p/proteinevolver/

QMSim
QTL and Marker Simulator
http://www.aps.uoguelph.ca/~msargol/qmsim/

quantiNEMO
An individual-based program for the analysis of quantitative traits with explicit genetic architecture potentially under selection in a structured population
http://www2.unil.ch/popgen/softwares/quantinemo/

RECOAL
Simulates new haplotype data from a reference population of haplotypes.
ftp://popgen.usc.edu/

Recodon
Coalescent simulation of coding DNA sequences with recombination, migration and demography
http://code.google.com/p/recodon/

rlsim
A package for simulating RNA-seq library preparation with parameter estimation
http://bit.ly/rlsim-git

Rmetasim
Rmetasim is a front-end for the metasim engine that is implemented as a package that runs in the statistical computing environment R
http://linum.cofc.edu/software.html#metasim

RNA Seq Simulator
RSS takes SAM alignment files from RNA-Seq data and simulates over dispersed, multiple replica, differential, non-stranded RNA-Seq datasets.
http://useq.sourceforge.net/cmdlnmenus.html#rnaseqsimulator

Rose
Random model of sequence evolution
http://bibiserv.techfak.uni-bielefeld.de/rose/

SelSim
SelSim is a program for Monte Carlo simulation of DNA polymorphism data for a recom- bining region within which a single bi-allelic site has experienced natural selection
http://www.well.ox.ac.uk/~spencer/selsim/

Seq-Gen
An application for the Monte Carlo simulation of molecular sequence evolution along phylogenetic trees.
http://tree.bio.ed.ac.uk/software/seqgen/

SEQPower
Statistical power analysis for sequence-based association studies
http://bioinformatics.org/spower/

SeqSIMLA
SeqSIMLA can simulate sequence data with user-specified disease and quantitative trait models. Family or unrelated case-control data can be simulated.
http://seqsimla.sourceforge.net/

Serial NetEvolve
A flexible utility for generating serially-sampled sequences along a tree or recombinant network
http://biorg.cis.fiu.edu/sne/

SFS_CODE
SFS_CODE can perform forward population genetic simulations under a general Wright-Fisher model with arbitrary migration, demographic, selective, and mutational effects.
http://sfscode.sourceforge.net/sfs_code/index/index.html

SIBSIM
Quantitative phenotype simulation in extended pedigrees
http://sourceforge.net/projects/sibsim/

SIMCOAL2
A coalescent program for the simulation of complex recombination patterns over large genomic regions under various demographic models
http://cmpg.unibe.ch/software/simcoal2/

SimCopy
An R package simulating the evolution of copy number profiles along a tree.
http://bit.ly/simcopy

SIMLA
SIMLA is a SIMuLAtion program that generates data sets of families for use in Linkage and Association studies.
http://www.chg.duke.edu/research/simla.html

SimPed
A Simulation Program to Generate Haplotype and Genotype Data for Pedigree Structures
http://www.hgsc.bcm.tmc.edu/content/simped

Simprot
A program to simulate protein evolution by substitution, insertion and deletion
http://www.uhnresearch.ca/labs/tillier/software.htm#3

SimRare
Rare variant simulation and analysis tool
http://code.google.com/p/simrare/

simuGWAS
A forward-time simulator that simulates realistic samples for genome-wide association studies.
http://simupop.sourceforge.net/cookbook/simucomplexdisease

simuPOP
simuPOP is a general-purpose individual-based forward-time population genetics simulation environment.
http://simupop.sourceforge.net/

SISSI
A software tool to generate data of related sequences along a given phylogeny, taking into account user defined system of neighbourhoods and instantaneous rate matrices.
http://www.cibiv.at/software/sissi/

SNPsim
Coalescent simulation of hotspot recombination
http://code.google.com/p/phylosoftware/

SPIP
SPIP simulates the transmission of genes from parents to offspring in a population having demographic structure defined by the user
http://swfsc.noaa.gov/textblock.aspx?division=fed&id=3434

Splatche
Spatial and Temporal Coalescences in Heterogeneous Environment
http://www.splatche.com/

srv
Simulator of Rare Varaints (srv) is a simulator for the simulation of the introduction and evolution of (rare) genetic variants.
http://simupop.sourceforge.net/cookbook/simurarevariants

SUP
SLINK/FastSLINK utility program
http://mlemire.freeshell.org/software.html

TreesimJ
A flexible, forward-time population genetic simulator
http://code.google.com/p/treesimj/

Vortex
VORTEX is an individual-based simulation model for population viability analysis (PVA).
http://www.vortex9.org/vortex.html

References:

Image www.evolution-of-life.com

www.cancer.gov

Ra is in development since 2014 in the form of several separate components that used to be run individually.
This project aims to ease the usage of Ra by integrating it into a complete de novo assembly tool.

Unlike other state-of-the-art assemblers, Ra does not have an error correction step. Instead, it relies on detecting overlaps using a very sensitive and specific overlapper ("graphmap -w owler", https://github.com/isovic/graphmap) and constructing and reducing an overlap graph (Ra layout, https://github.com/mariokostelac/ra).

Address of the bookmark: https://github.com/mariokostelac/ra-integrate/

Computational methods used by the Shasta assembler include:

• Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
• Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

More at https://chanzuckerberg.github.io/shasta/index.html

Address of the bookmark: https://github.com/chanzuckerberg/shasta

1. RNA-Seq Analysis Pipelines

RNA-seq is one of the most popular techniques for studying RNA. These tools streamline processing raw sequence data:

• FASTQC: For quality control of raw RNA-seq reads.
• Trimmomatic: For trimming and filtering RNA-seq reads.
• HISAT2/STAR: High-performance aligners for RNA-seq reads.
• FeatureCounts: For quantifying gene expression.
• DESeq2/EdgeR: For differential expression analysis.

2. Transcriptome Assembly and Annotation

For analyzing transcriptomes from non-model organisms or assembling novel transcripts:

• Trinity: For de novo transcriptome assembly.
• StringTie: For transcript assembly and quantification from RNA-seq alignments.
• TransDecoder: To predict coding regions within assembled transcripts.
• TAU: Tools for annotating non-coding and coding RNAs.

3. Exploring Non-Coding RNA (ncRNA)

Non-coding RNAs play critical regulatory roles. Dedicated tools for studying them include:

• Infernal: For identifying ncRNA sequences based on covariance models.
• Rfam: Database and tools for ncRNA families.
• miRDeep: For identifying microRNAs in RNA-seq datasets.

4. RNA Structure and Motif Analysis

Structural biology of RNA helps in understanding its function:

• RNAfold (ViennaRNA): Predicts secondary structures from RNA sequences.
• RNAstructure: Tools for RNA secondary structure prediction and analysis.
• MEME Suite: For identifying motifs in RNA sequences.
• IntaRNA: For RNA-RNA interaction prediction.

5. RNA Editing and Modifications

Epitranscriptomics is a growing field focusing on RNA modifications:

• REDItools: For RNA editing analysis.
• m6Aboost: For identifying m6A modifications in RNA.

6. Long-Read RNA Sequencing Analysis

Long-read technologies like Nanopore and PacBio are transforming RNA research:

• FLAIR: For isoform-level analysis of long-read RNA-seq data.
• NanoMod: For detecting modifications in RNA from Nanopore sequencing.

7. RNA-Protein Interactions

To study RNA-protein interactions and complexes:

• RBPmap: For identifying RNA-binding protein motifs.
• PARalyzer: For analyzing PAR-CLIP data.

8. Functional Enrichment Analysis

Understanding biological functions and pathways from RNA-seq data:

• getENRICH: A tool designed for pathway enrichment analysis of non-model organisms (hypergeometric P-value calculation with FDR correction).
• ClusterProfiler: For GO and KEGG pathway enrichment analysis.

9. Visualization and Data Sharing

Presenting and sharing RNA sequence analysis results effectively:

• IGV: Genome browser for visualizing RNA-seq alignments.
• Circos: Circular visualization of RNA-seq data.
• DashBio: A Python library for creating bioinformatics visualizations.

Conclusion

The bioinformatics landscape for RNA sequence analysis is vast, with tools catering to specific needs. Whether you’re studying coding RNAs, non-coding RNAs, or exploring RNA-protein interactions, the right tools can transform your data into biological insights.

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.

Following are the comprehensive list of visualization tools for biological pathways:

BiNA

Drawings of metabolic networks supporting hiding of cofactors and drawing of chemical structures

http://bina.unipax.info/

BioTapestry

Interactive tool for building, visualizing and sharing gene regulatory network models over the web

http://www.biotapestry.org/

Caleydo

Visual analysis framework targeted at biomolecular data. Visualization of interdependencies between multiple datasets

http://www.caleydo.org/

CellDesigner

A modeling tool for biochemical networks

http://www.celldesigner.org/

Edinburgh Pathway Editor

Edit and draw pathway diagrams

http://epe.sourceforge.net/SourceForge/EPE.html

GenMAPP

Visualization of gene expression and other genomic data on maps representing biological pathways and groupings of genes

http://www.genmapp.org/

Ingenuity IPA

Data integration platform and manually annotated pathways

http://tinyurl.com/IngenuityPath

JDesigner

Graphical modeling environment for biochemical reaction networks

http://jdesigner.sourceforge.net/Site/JDesigner.html

KaPPA View

Plant pathways

http://kpv.kazusa.or.jp/

KEGG Atlas

Interactive Kyoto Encyclopedia of Genes and Genomes pathways

http://www.genome.jp/kegg/

Omix 

Visualizing multi-omics data in metabolic networks

https://www.omix-visualization.com

PathVisio 

Biological pathway analysis software that allows drawing, editing and analysis of biological pathways

http://www.pathvisio.org/

VitaPad 

Application to visualize biological pathways and map experimental data to them

http://tinyurl.com/vitapad/

Web tools for pathways

ArrayXPath 

Mapping and visualizing microarray gene-expression data and integrated biological pathway resources using SVG

http://tinyurl.com/ArrayXPath/

GEPAT 

Integrated analysis of transcriptome data in genomic, proteomic and metabolic contexts

http://gepat.sourceforge.net/

iPath 

Web-based tool for the visualization, analysis and customization of pathway maps

http://pathways.embl.de/

Kegg-Based Viewer 

KEGG-based pathway visualization tool for complex high-throughput data

http://www.g-language.org/data/marray/

MapMan 

User-driven tool that displays large datasets onto diagrams of metabolic pathways or other processes

http://mapman.gabipd.org/web/guest/mapman

MetPA 

Analysis and visualization of metabolomic data within the biological context of metabolic pathways

http://metpa.metabolomics.ca

Omics Viewer 

Data mapping on BioCyc pathways (collection of 5500 pathway/genome databases)

http://www.biocyc.org/

Pathway Explorer

Interactive Java drawing tool for the construction of biological pathway diagrams in a visual way and the annotation of the components and interactions between them

http://genome.tugraz.at/pathwayexplorer/pathwayexplorer_description.shtml

Pathway projector 

Zoomable pathway browser using KEGG atlas and Google Maps API

http://www.g-language.org/PathwayProjector/

PATIKA 

Integrated environment composed of a central database and a visual editor, built around an extensive ontology and an integration framework

http://www.cs.bilkent.edu.tr/~patikaweb/

Reactome SkyPainter 

Visualization of over-represented pathways and reactions from gene lists

http://www.reactome.org/skypainter-2

WikiPathways

Wiki-based, open, public platform dedicated to the curation of biological pathways by and for the scientific community

http://www.wikipathways.org/

This package is a loose collection of scripts. To run the correction
routine see the section below. Descriptions of the other scripts
are at the bottom of this file.

Contact: gurtowsk@cshl.edu

In short, the correction algorithm takes as input the unitigs from a short read assembly and uses them to correct long read data. More background information for the algorithm can be found:
http://schatzlab.cshl.edu/presentations/2013-06-18.PBUserMeeting.pdf

Address of the bookmark: https://github.com/jgurtowski/ectools