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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/19786?offset=760</link>
	<atom:link href="https://bioinformaticsonline.com/related/19786?offset=760" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44914/predicting-pathogen-virulence-using-bioinformatics-tools</guid>
	<pubDate>Tue, 04 Nov 2025 07:55:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44914/predicting-pathogen-virulence-using-bioinformatics-tools</link>
	<title><![CDATA[Predicting Pathogen Virulence Using Bioinformatics Tools]]></title>
	<description><![CDATA[<p>In the genomic era, the ability to predict the virulence potential of pathogens has become an indispensable part of infectious disease research. With the exponential growth of microbial genome data, bioinformatics tools now enable scientists to identify virulence factors, model pathogen behavior, and even forecast outbreak risks &mdash; all from sequence data.</p><p>In an age where pathogens continue to evolve and cross boundaries, understanding <strong>what makes them virulent</strong>&mdash;that is, capable of causing disease&mdash;has become a critical focus in modern microbiology and genomics. <strong>Virulence prediction</strong> bridges computational biology, genomics, and machine learning to forecast the pathogenic potential of microbes before they strike.</p><h3>What Is Virulence?</h3><p><em>Virulence</em> refers to the degree of damage a pathogen can inflict on its host. It is determined by a combination of genetic factors&mdash;called <strong>virulence factors (VFs)</strong>&mdash;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.</p><p>Understanding virulence factors not only helps in deciphering the mechanisms of infection but also provides early warning signs for emerging threats.</p><h3>Why Predict Virulence?</h3><p>Traditional virulence studies relied heavily on experimental infection models, which, although accurate, are <strong>time-consuming, expensive, and ethically constrained</strong>.<br /> Today, the availability of whole-genome sequences and large-scale pathogen databases has paved the way for <strong>in silico virulence prediction</strong>&mdash;a computational approach that can screen thousands of genomes within hours.</p><p>This approach enables researchers to:</p><ul>
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<p>Rapidly identify potential <strong>high-risk strains</strong>.</p>
</li>
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<p>Prioritize pathogens for <strong>containment, surveillance, or further study</strong>.</p>
</li>
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<p>Guide <strong>vaccine development</strong> and <strong>drug target discovery</strong>.</p>
</li>
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<p>Support <strong>One Health frameworks</strong>, linking animal, human, and environmental health data.</p>
</li>
</ul><h3>How Is Virulence Predicted?</h3><p>Virulence prediction combines <strong>bioinformatics pipelines</strong> with <strong>machine learning</strong> and <strong>comparative genomics</strong>. The process generally involves:</p><ol>
<li>
<p><strong>Genome Annotation:</strong> Identifying genes and coding sequences in microbial genomes.</p>
</li>
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<p><strong>Feature Extraction:</strong> Comparing sequences with curated databases like <strong>VFDB (Virulence Factor Database)</strong>, <strong>PATRIC</strong>, or <strong>Victors</strong>.</p>
</li>
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<p><strong>Pattern Recognition:</strong> 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.</p>
</li>
<li>
<p><strong>Scoring and Visualization:</strong> Assigning a virulence score or confidence level and visualizing it through heatmaps or genome maps.</p>
</li>
</ol><h3>Tools and Resources for Virulence Prediction</h3><p>A number of tools and databases make virulence prediction accessible to the scientific community:</p><ul>
<li>
<p><strong>VFanalyzer</strong> &ndash; For identifying virulence genes based on VFDB.</p>
</li>
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<p><strong>PathoFact</strong> &ndash; Predicts virulence, antimicrobial resistance (AMR), and toxin genes from metagenomic data.</p>
</li>
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<p><strong>Pangenome-based models</strong> &ndash; Identify virulence-associated gene clusters across strains.</p>
</li>
<li>
<p><strong>Machine learning models</strong> &ndash; Use features like GC content, codon usage bias, or protein domains to predict pathogenicity.</p>
</li>
</ul><p>Emerging tools now integrate <strong>multi-omic data</strong>&mdash;including transcriptomics, proteomics, and metabolomics&mdash;to understand virulence in a systems biology framework.</p><h3>Applications in the Real World</h3><p>Virulence prediction has major implications across public health and research sectors:</p><ul>
<li>
<p><strong>Epidemic preparedness:</strong> Early identification of virulent strains in outbreak samples.</p>
</li>
<li>
<p><strong>AMR surveillance:</strong> Linking virulence profiles with antibiotic resistance determinants.</p>
</li>
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<p><strong>Environmental monitoring:</strong> Predicting pathogenic potential of soil or waterborne microbes.</p>
</li>
<li>
<p><strong>Clinical diagnostics:</strong> Supporting personalized treatment through pathogen profiling.</p>
</li>
</ul><p>For instance, integrating virulence prediction pipelines into <strong>national surveillance networks</strong> could enable faster risk assessment and response to infectious outbreaks.</p><h3>The Road Ahead</h3><p>As machine learning and genomics advance, virulence prediction will evolve from simple gene-based detection to <strong>dynamic, context-aware models</strong> that account for host&ndash;pathogen interactions, environmental signals, and evolutionary adaptation.</p><p>Future tools may predict <strong>not just if a strain is virulent</strong>, but <strong>under what conditions</strong> it expresses that virulence&mdash;bridging the gap between genotype and phenotype.</p><h3>In Summary</h3><p>Virulence prediction is redefining how we understand and anticipate infectious diseases. By coupling <strong>genomic insights</strong> with <strong>computational intelligence</strong>, researchers can identify potential threats earlier, design smarter interventions, and ultimately, strengthen our preparedness against emerging pathogens.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2631/what-junk-dna-it%E2%80%99s-an-operating-system</guid>
	<pubDate>Mon, 19 Aug 2013 15:24:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2631/what-junk-dna-it%E2%80%99s-an-operating-system</link>
	<title><![CDATA[What Junk DNA? It’s an Operating System]]></title>
	<description><![CDATA[<p>The report adds to growing experimental support for the idea that all that extra stuff in the human genes, once referred to as &ldquo;junk DNA,&rdquo; is more than functionless, space-filling material that happens to make up nearly 98% of the genome. The paper adds to a growing body of knowledge establishing a considerable role for this material in the regulation of gene expression and its potential role in human disease.</p><p>Address of the bookmark: <a href="http://www.genengnews.com/keywordsandtools/print/3/32115/" rel="nofollow">http://www.genengnews.com/keywordsandtools/print/3/32115/</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/6130/rna-bioinformatics-and-high-throughput-analysis-jena</guid>
  <pubDate>Sat, 09 Nov 2013 20:03:56 -0600</pubDate>
  <link></link>
  <title><![CDATA[RNA Bioinformatics and High Throughput Analysis Jena]]></title>
  <description><![CDATA[
<p>Research Topics:</p>

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

<p>http://www.rna.uni-jena.de/index.php</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/7362/junior-research-fellow-jrf-project-fellow-kalasalingam-university</guid>
  <pubDate>Thu, 19 Dec 2013 13:23:39 -0600</pubDate>
  <link></link>
  <title><![CDATA[Junior Research Fellow (JRF) / Project Fellow @ Kalasalingam University]]></title>
  <description><![CDATA[
<p>Applications are invited from interested candidates for the post of one Junior Research Fellow / Project Fellow on a purely temporary basis in a time bound research project (3 years) sponsored by Science and Engineering Research Board, Government of India, New Delhi.</p>

<p>Name of the fellowship: Junior Research Fellow (JRF) / Project Fellow</p>

<p>Title of the project: Genome-wide Mapping of Murine Specific Dengue T-cell Epitopes: Computational Prediction, Identification and use as Candidate Vaccines</p>

<p>Duration: 3 years</p>

<p>Fellowship: Rs. 18,000 for first 2 years and Rs. 20,000 for 3rdyear (for M.Tech. candidates)</p>

<p>Rs. 16,000 for first 2 years and Rs. 18,000 for 3rdyear (for M.Sc. candidates with NET qualification)</p>

<p>Rs. 8,000 for first 2 years and Rs. 10,000 for 3rdyear (for M.Sc. candidates without NET qualification)</p>

<p>Qualifications: M.Tech. in Biotechnology / M.Sc. in any branch of Life Sciences</p>

<p>Desirable Experience: Minimum of two years research experience in any of the following areas: Immunology / Microbiology / Gene Manipulation / Bioinformatics</p>

<p>Interested and eligible candidates may apply with their resume along with relevant documents and a passport size photograph to the Principal Investigator by post (or e-mail) on or before December 31, 2013. Only short listed candidates will be called for written test and/or interview. Selected candidate may register for PhD in Kalasalingam University. No TA/DA will be paid for attending interview.</p>

<p>Dr. K. Sundar<br />Principal Investigator (SERB Project)<br />Department of Biotechnology<br />Kalasalingam University<br />Krishnankoil – 626126, Tamil Nadu<br />sundarkr@klu.ac.in</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/9586/list-of-bioinformatics-companies-and-genomics-service-providers</guid>
	<pubDate>Wed, 02 Apr 2014 06:52:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/9586/list-of-bioinformatics-companies-and-genomics-service-providers</link>
	<title><![CDATA[List of bioinformatics companies and genomics service providers]]></title>
	<description><![CDATA[<p>Plz check out link for bioinformatics and genomics companies.&nbsp;</p><p>Address of the bookmark: <a href="http://grouthbio.com/Genome_Software_Service.php" rel="nofollow">http://grouthbio.com/Genome_Software_Service.php</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/17946/7th-international-conference-on-bioinformatics-and-computational-biology-bicob</guid>
	<pubDate>Mon, 06 Oct 2014 16:19:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/17946/7th-international-conference-on-bioinformatics-and-computational-biology-bicob</link>
	<title><![CDATA[7th International Conference on Bioinformatics and Computational Biology (BICoB)]]></title>
	<description><![CDATA[<p><span>In recent years, computational biology and medical informatics have seen significant advances driven by computational techniques in bioinformatics making bioinformatics and computational biology among the most vibrant research areas. The 7th international conference on Bioinformatics and Computational Biology (BICoB-2015) provides an excellent venue for researchers and practitioners in the fields of bioinformatics and computational biology to present and publish their research results and techniques. The BICoB conference seeks original and high quality papers in the fields of bioinformatics, computational biology, systems biology, medical informatics and the related disciplines. </span><span>We also encourage work in progress and research results in the emerging and evolutionary computational areas. Computational techniques have already enabled unprecedented advances in modern biology and medicine. Work in the computational methods related to, or with application in, bioinformatics is also encouraged including: data mining, text mining, machine learning, modeling and simulation, pattern recognition, data visualization, biostatistics, .etc. The topics of interest include (and are not limited to):&nbsp;</span><br><strong><span>Genome analysis:</span></strong><span>&nbsp;Genome assembly, genome annotation, gene finding, alternative splicing, EST analysis and comparative genomics.&nbsp;</span><br><strong><span>Sequence analysis:</span></strong><span>&nbsp;Multiple sequence alignment, sequence search and clustering, function prediction, motif discovery, functional site recognition in protein, RNA and DNA sequences.&nbsp;</span><br><strong><span>Phylogenetics:</span></strong><span>&nbsp;Phylogeny estimation, models of evolution, comparative biological methods, population genetics.&nbsp;</span><br><strong><span>Structural Bioinformatics:</span></strong><span>&nbsp;Structure matching, prediction, analysis and comparison; methods and tools for docking; protein design&nbsp;</span><br><strong><span>Analysis of high-throughput biological data:</span></strong><span>&nbsp;Microarrays (nucleic acid, protein, array CGH, genome tiling, and other arrays), EST, SAGE, MPSS, proteomics, mass spectrometry.&nbsp;</span><br><strong><span>Genetics and population analysis:</span></strong><span>&nbsp;Linkage analysis, association analysis, population simulation, haplotyping, marker discovery, genotype calling.&nbsp;</span><br><strong><span>Systems biology:</span></strong><span>&nbsp;Systems approaches to molecular biology, multiscale modeling, pathways,gene networks.&nbsp;</span><br><strong><span>Computational Proteomics:&nbsp;</span></strong><span>Filtering and indexing sequence databases, Peptide quantification and identification, Genome annotations via mass spectrometry, Identification of post-translational modifications, Structural genomics via mass spectrometry, Protein-protein interactions, Computational approaches to analysis of large scale Mass spectrometry data, Exploration and visualization of proteomic data, Data models and integration for proteomics and genomics, Querying and retrieval of proteomics and genomics data etc.</span></p>
<p><span><span>Authors of selected high quality papers in BICoB-2015 will be invited to submit extended version of their papers for possible publication in bioinformatics journals (</span><a href="http://www.worldscinet.com/jbcb/" target="_blank"><strong>Journal of Bioinformatics and Computational Biology JBCB).</strong></a></span></p>
<p><span><strong>Deadlines</strong>:</span></p>
<p><span></span></p>
<p>Paper Submission Deadline October 24, 2014<br>Notification of Acceptance December 15, 2014<br>Camera-Ready Manuscript January 16, 2015</p>
<p><span></span></p><p>Address of the bookmark: <a href="http://www.cs.umb.edu/bicob/" rel="nofollow">http://www.cs.umb.edu/bicob/</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22179/marie-curie-phd-position-available-immediately</guid>
  <pubDate>Fri, 24 Apr 2015 09:23:57 -0500</pubDate>
  <link></link>
  <title><![CDATA[Marie Curie PhD position available immediately]]></title>
  <description><![CDATA[
<p>Sub-project 10: Development of bioinformatic tools for the analysis of MACE data<br />Host Organizations GenXPRO (Germany)<br />Objectives : The ESR will be in charge of standardising pipelines that will be used for RNA-seq and MACE analyses by all the participants. He will be involved in performing next generation sequencing to characterise environmental adaptation. A single pipeline to analyse listerial transcriptomic and proteomic data will be developed and implemented by each partner for the sake of uniformity of all the data produced within List_MAPS. The ESR will be involved in the interpretation of transcriptomic and proteomic data for which pathway analyses and good data visualization will be required. A cytoscape app will be developed as visualization tool.<br />Expected Results: MACE analysis pipeline. Database. Transcriptome comparisons in selected habitats. Data visualization tool.<br />Duration (months) 24<br />Contact Dr. Bjorn ROTTER: rotter@genxpro.de </p>

<p>11. Development of innovative tools for rapid phenotypic characterisation of intraspecific diversity of Listeria monocytogenes (Joint supervision PhD)<br />Host Organizations BioFilm Control (France) and GenXPRO (Germany)<br />Objectives<br /> 1. The ESR will develop an assay to test biofilm phenotype in a large array of food processing-related environmental conditions (salt, acides, disinfectants, preservatives) in BFC facilities. He will be in charge of the development and validation of an in silico virulence assay. This assay will target specific mRNAs in order to estimate the virulence potential of strains of L. monocytogenes. Transcript targets will be selected and tested by qPCR in GXP premises. In the process of validation, virulence results of several strains collected in a humanised mouse model will be compared with the in silico analysis. Once these innovative tools will be validated, intraspecific phenotypic diversity (biofilm and virulence) will be assessed on a collection of environmental and clinical isolates of L. monocytogenes. Genotypic diversity will be assessed under the supervision of GPX.<br />Expected Results : Adaptation of the BioFilm Ring test R to test food processing environmental conditions. Development of an innovative in silico virulence assay surrogate to animal models. Diversity results will inform stakeholders on the level of health hazard according to the strain. This in turn will help secure food safety all along the shelf life of foodstuff.<br />Duration (months) 36<br />Contact : Dr. Thierry BERNARDI: thbe@biofilmcontrol.com <br />Dr. Bjorn ROTTER: rotter@genxpro.de<br />ELIGIBLE CRITERIA of Marie Sklokowska Curie actions:<br />Researchers may be of any nationality<br />Candidates shall at the time of recruitment by the host organization, be in the first four years (full-time equivalent research experience) of their research careers. Full-time equivalent research experience is measured from the date when a researcher obtained the degree which would formally entitle him or her to embark on a doctorate, either in the co</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/23122/candidates-required-in-bioinformatics-and-genomics-uk-only</guid>
  <pubDate>Fri, 03 Jul 2015 08:22:41 -0500</pubDate>
  <link></link>
  <title><![CDATA[Candidates required in Bioinformatics and Genomics UK ONLY]]></title>
  <description><![CDATA[
<p>I have various permanent positions available based in London, Manchester, Herftfordshire, Oxford and Belfast, as well as other areas throughout the UK.</p>

<p>If you are looking for a new opportunity and have skills within any sector of Bioinformatics with an IT skill then I would love to hear from you.  I have various exciting opportunities from programmers to researchers to scientists.</p>

<p>Call me now on 01772 278050 or email me your cv and requirements and I will call you back dareen.evans@itworkshealth.co.uk</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35108/mobyle-a-new-full-web-bioinformatics-framework</guid>
	<pubDate>Sun, 07 Jan 2018 19:33:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35108/mobyle-a-new-full-web-bioinformatics-framework</link>
	<title><![CDATA[Mobyle: a new full web bioinformatics framework]]></title>
	<description><![CDATA[<p><span>Mobyle, to provide a flexible and usable Web environment for defining and running bioinformatics analyses. It embeds simple yet powerful data management features that allow the user to reproduce analyses and to combine tools using a hierarchical typing system. Mobyle offers invocation of services distributed over remote Mobyle servers, thus enabling a federated network of curated bioinformatics portals without the user having to learn complex concepts or to install sophisticated software.</span></p><p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article/25/22/3005/179064" rel="nofollow">https://academic.oup.com/bioinformatics/article/25/22/3005/179064</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/8265/list-of-generic-simulation-softwaretoolsresource-with-brief-description-and-homepage</guid>
	<pubDate>Mon, 10 Feb 2014 05:57:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/8265/list-of-generic-simulation-softwaretoolsresource-with-brief-description-and-homepage</link>
	<title><![CDATA[List of generic simulation software/tools/resource with brief description and homepage !!!]]></title>
	<description><![CDATA[<p>List of generic simulation software/tools/resource with brief description and homepage</p><p><img src="http://www.evolution-of-life.com/fileadmin/images/carousel/genetic.PNG" alt="image" style="border: 0px;"></p><p>ALF <br />A Simulation Framework for Genome Evolution <br />http://www.cbrg.ethz.ch/alf<br /><br />Bayesian Serial SimCoal <br />Bayesian Serial SimCoal, (BayeSSC) is a modification of SIMCOAL 1.0, a program written by Laurent Excoffier, John Novembre, and Stefan Schneider. <br />http://www.stanford.edu/group/hadlylab/ssc/index.html<br /><br />BEERS <br />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 <br />http://cbil.upenn.edu/beers/<br /><br />BOTTLENECK <br />Bottleneck is a program for detecting recent effective population size reductions from allele data frequencies <br />http://www.ensam.inra.fr/urlb/bottleneck/bottleneck.html<br /><br />BottleSim <br />BottleSim is a computer simulation program for simulating the process of population bottlenecks <br />http://chkuo.name/software/bottlesim.html<br /><br />CASS <br />Protein Sequence Simulation <br />http://www.wyomingbioinformatics.org/liberlesgroup/cass/<br /><br />CDPOP <br />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. <br />http://cel.dbs.umt.edu/cdpop<br /><br />CoalFace <br />CoalFace is a simulation of the coalescent process with the visual display of gene genealogies. <br />http://web.up.ac.za/default.asp?ipkcategoryid=3283<br /><br />CoaSim <br />CoaSim is a tool for simulating the coalescent process with recombination and geneconversion under various demographic models. <br />http://users-birc.au.dk/mailund/coasim/index.html<br /><br />cosi <br />The cosi package is written in C and is available as a tar file. <br />http://www.broadinstitute.org/~sfs/cosi/<br /><br />CS-PSeq-Gen <br />A program to simulate the evolution of protein sequences under the constraints of the information of a particular reconstructed phylogeny <br />http://bioserv.rpbs.univ-paris-diderot.fr/software/cs-pseq-gen.html<br /><br />DAWG <br />An application designed to simulate the evolution of recombinant DNA sequences in continuous time <br />http://scit.us/projects/dawg<br /><br />Easypop <br />EASYPOP is an individual based model intended to simulate datasets under a very broad range of conditions <br />http://www.unil.ch/dee/page36926_fr.html<br /><br />EggLib <br />EggLib is a C++/Python library and program package for evolutionary genetics and genomics. <br />http://egglib.sourceforge.net/<br /><br />EvolSimulator <br />A simulation test bed for hypotheses of genome evolution <br />http://acb.qfab.org/acb/evolsim/<br /><br />EvolveAGene <br />A realistic coding sequence simulation program that separates mutation from selection and allows the user to set selection conditions <br />http://bellinghamresearchinstitute.com/software/index.html<br /><br />fastsimcoal <br />A continuous-&not;‐time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios <br />http://cmpg.unibe.ch/software/fastsimcoal/<br /><br />FastSLINK <br />Simulation of Marker and Phenotype Data in Pedigrees <br />http://watson.hgen.pitt.edu/<br /><br />FFPopSim <br />C++/Python library for population genetics. <br />http://webdav.tuebingen.mpg.de/ffpopsim/<br /><br />FLUX SIMULATOR <br />The Flux Simulator aims at providing a deterministic in silico reproduction of the experimental pipelines for RNA-Seq, employing a minimal set of parameters. <br />http://flux.sammeth.net/simulator.html<br /><br />ForSim <br />ForSim: A Forward Evolutionary Computer Simulation <br />http://www.anthro.psu.edu/weiss_lab/research.shtml<br /><br />ForwSim <br />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. <br />http://badri-populationgeneticsimulators.blogspot.com/<br /><br />FPG <br />Forward Population Genetic simulation <br />http://genfaculty.rutgers.edu/hey/software#fpg<br /><br />FREGENE <br />FREGENE is a C++ program that simulates sequence-like data over large genomic regions in large diploid populations. <br />http://www.ebi.ac.uk/projects/bargen/download/fregen/documentation_html.html<br /><br />GAMETES <br />Genetic Architecture Model Emulator for Testing and Evaluating Software: Simulates complex SNP models with pure, strict epistatic interactions with n-loci. <br />http://sourceforge.net/projects/gametes/?source=navbar<br /><br />GASP <br />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. <br />http://research.nhgri.nih.gov/gasp/<br /><br />GemSIM <br />Next generation sequencing read simulator <br />http://sourceforge.net/projects/gemsim/<br /><br />GeneArtisan <br />Simulation of Markers in Case-Control Study Designs <br />http://www.rannala.org/?page_id=241<br /><br />GENOME <br />A rapid coalescent-based whole genome simulator <br />http://www.sph.umich.edu/csg/liang/genome/<br /><br />GenomePop2 <br />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. <br />http://webs.uvigo.es/acraaj/genomepop2.htm<br /><br />GenomeSimla <br />GenomeSIMLA is currently under development- however, we have a beta release that we are asking to be tested <br />http://chgr.mc.vanderbilt.edu/genomesimla/<br /><br />GENS2 <br />Simulates interactions among two genetic and one environmental factor and also allows for epistatic interactions. <br />https://sourceforge.net/projects/gensim/<br /><br />GWAsimulator <br />A rapid whole genome simulation program <br />http://biostat.mc.vanderbilt.edu/wiki/main/gwasimulator<br /><br />HAP-SAMPLE <br />An association simulator for candidate regions or genome scans <br />http://www.hapsample.org/<br /><br />HAPGEN <br />A simulator for the simulation of case control datasets at SNP markers <br />https://mathgen.stats.ox.ac.uk/genetics_software/hapgen/hapgen2.html<br /><br />HapSim <br />A simulation tool for generating haplotype data with pre-specified allele frequencies and LD coefficients <br />http://cran.r-project.org/web/packages/hapsim/index.html<br /><br />HAPSIMU <br />A program that simulates heterogeneous populations with various known and controllable structures under the continuous migration model or the discrete model <br />http://l.web.umkc.edu/liujian/<br /><br />IBDsim <br />IBDSim is a computer package for the simulation of genotypic data under general isolation by distance models. <br />http://raphael.leblois.free.fr/<br /><br />indel-Seq-Gen <br />A biological sequence simulation program that simulates highly divergent DNA sequences and protein superfamilies <br />http://bioinfolab.unl.edu/~cstrope/isg/<br /><br />Indelible <br />A powerful and flexible simulator of biological evolution <br />http://abacus.gene.ucl.ac.uk/software/indelible/<br /><br />invertFREGENE <br />InvertFREGENE is a forward-in-time simulator of inversions in population genetic data <br />http://www.ebi.ac.uk/projects/bargen/<br /><br />kernalPop <br />A spatially explicit population genetic simulation engine <br />http://cran.r-project.org/src/contrib/archive/kernelpop/<br /><br />MaCS <br />Markovian Coalescent Simulator <br />http://www-hsc.usc.edu/~garykche/<br /><br />Mason <br />A package for the simulation of nucleotide data. <br />http://www.seqan.de/projects/mason/<br /><br />mbs <br />modifying Hudson's ms software to generate samples of DNA sequences with a biallelic site under selection <br />http://www.sendou.soken.ac.jp/esb/innan/innanlab/software.html<br /><br />Mendel's Accountant <br />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 <br />http://mendelsaccount.sourceforge.net/<br /><br />MetaSim <br />A tool to generate collections of synthetic reads that reflect the diverse taxonomical composition of typical metagenome data sets <br />http://ab.inf.uni-tuebingen.de/software/metasim/<br /><br />mlcoalsim <br />Multilocus Coalescent Simulations <br />http://code.google.com/p/mlcoalsim-v1/<br /><br />ms <br />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. <br />http://home.uchicago.edu/~rhudson1/source/mksamples.html<br /><br />msHOT <br />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. <br />http://home.uchicago.edu/~rhudson1/<br /><br />msms <br />A coalescent Simlation tool with selection. <br />http://www.mabs.at/ewing/msms/index.shtml<br /><br />MySSP <br />A program for the simulation of DNA sequence evolution across a phylogenetic tree <br />http://www.rosenberglab.net/software.php<br /><br />Nemo <br />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. <br />http://nemo2.sourceforge.net/<br /><br />NetRecodon <br />Coalescent simulation of coding DNA sequences with recombination (inter and intracodon), migration and demography <br />http://code.google.com/p/netrecodon/<br /><br />PEDAGOG <br />Software for simulating eco-evolutionary population dynamics <br />https://bcrc.bio.umass.edu/pedigreesoftware/node/5<br /><br />phenosim <br />A tool to add phenotypes to simulated genotypes <br />http://evoplant.uni-hohenheim.de/doku.php?id=software:software<br /><br />PhyloSim <br />An R package for the Monte Carlo simulation of sequence evolution <br />http://bit.ly/rlsim-git<br /><br />pIRS <br />Profile-based Illumina pair-end reads simulator <br />https://code.google.com/p/pirs/<br /><br />ProteinEvolver <br />Simulation of protein evolution along phylogenies under structure-based substitution models <br />http://code.google.com/p/proteinevolver/<br /><br />QMSim <br />QTL and Marker Simulator <br />http://www.aps.uoguelph.ca/~msargol/qmsim/<br /><br />quantiNEMO <br />An individual-based program for the analysis of quantitative traits with explicit genetic architecture potentially under selection in a structured population <br />http://www2.unil.ch/popgen/softwares/quantinemo/<br /><br />RECOAL <br />Simulates new haplotype data from a reference population of haplotypes. <br />ftp://popgen.usc.edu/<br /><br />Recodon <br />Coalescent simulation of coding DNA sequences with recombination, migration and demography <br />http://code.google.com/p/recodon/<br /><br />rlsim <br />A package for simulating RNA-seq library preparation with parameter estimation <br />http://bit.ly/rlsim-git<br /><br />Rmetasim <br />Rmetasim is a front-end for the metasim engine that is implemented as a package that runs in the statistical computing environment R <br />http://linum.cofc.edu/software.html#metasim<br /><br />RNA Seq Simulator <br />RSS takes SAM alignment files from RNA-Seq data and simulates over dispersed, multiple replica, differential, non-stranded RNA-Seq datasets. <br />http://useq.sourceforge.net/cmdlnmenus.html#rnaseqsimulator<br /><br />Rose <br />Random model of sequence evolution <br />http://bibiserv.techfak.uni-bielefeld.de/rose/<br /><br />SelSim <br />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 <br />http://www.well.ox.ac.uk/~spencer/selsim/<br /><br />Seq-Gen <br />An application for the Monte Carlo simulation of molecular sequence evolution along phylogenetic trees. <br />http://tree.bio.ed.ac.uk/software/seqgen/<br /><br />SEQPower <br />Statistical power analysis for sequence-based association studies <br />http://bioinformatics.org/spower/<br /><br />SeqSIMLA <br />SeqSIMLA can simulate sequence data with user-specified disease and quantitative trait models. Family or unrelated case-control data can be simulated. <br />http://seqsimla.sourceforge.net/<br /><br />Serial NetEvolve <br />A flexible utility for generating serially-sampled sequences along a tree or recombinant network <br />http://biorg.cis.fiu.edu/sne/<br /><br />SFS_CODE <br />SFS_CODE can perform forward population genetic simulations under a general Wright-Fisher model with arbitrary migration, demographic, selective, and mutational effects. <br />http://sfscode.sourceforge.net/sfs_code/index/index.html<br /><br />SIBSIM <br />Quantitative phenotype simulation in extended pedigrees <br />http://sourceforge.net/projects/sibsim/<br /><br />SIMCOAL2 <br />A coalescent program for the simulation of complex recombination patterns over large genomic regions under various demographic models <br />http://cmpg.unibe.ch/software/simcoal2/<br /><br />SimCopy <br />An R package simulating the evolution of copy number profiles along a tree. <br />http://bit.ly/simcopy<br /><br />SIMLA <br />SIMLA is a SIMuLAtion program that generates data sets of families for use in Linkage and Association studies. <br />http://www.chg.duke.edu/research/simla.html<br /><br />SimPed <br />A Simulation Program to Generate Haplotype and Genotype Data for Pedigree Structures <br />http://www.hgsc.bcm.tmc.edu/content/simped<br /><br />Simprot <br />A program to simulate protein evolution by substitution, insertion and deletion <br />http://www.uhnresearch.ca/labs/tillier/software.htm#3<br /><br />SimRare <br />Rare variant simulation and analysis tool <br />http://code.google.com/p/simrare/<br /><br />simuGWAS <br />A forward-time simulator that simulates realistic samples for genome-wide association studies. <br />http://simupop.sourceforge.net/cookbook/simucomplexdisease<br /><br />simuPOP <br />simuPOP is a general-purpose individual-based forward-time population genetics simulation environment. <br />http://simupop.sourceforge.net/<br /><br />SISSI <br />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. <br />http://www.cibiv.at/software/sissi/<br /><br />SNPsim <br />Coalescent simulation of hotspot recombination <br />http://code.google.com/p/phylosoftware/<br /><br />SPIP <br />SPIP simulates the transmission of genes from parents to offspring in a population having demographic structure defined by the user <br />http://swfsc.noaa.gov/textblock.aspx?division=fed&amp;id=3434<br /><br />Splatche <br />Spatial and Temporal Coalescences in Heterogeneous Environment <br />http://www.splatche.com/<br /><br />srv <br />Simulator of Rare Varaints (srv) is a simulator for the simulation of the introduction and evolution of (rare) genetic variants. <br />http://simupop.sourceforge.net/cookbook/simurarevariants<br /><br />SUP <br />SLINK/FastSLINK utility program <br />http://mlemire.freeshell.org/software.html<br /><br />TreesimJ <br />A flexible, forward-time population genetic simulator <br />http://code.google.com/p/treesimj/<br /><br />Vortex <br />VORTEX is an individual-based simulation model for population viability analysis (PVA). <br />http://www.vortex9.org/vortex.html<br /><br />References:</p><p>Image www.evolution-of-life.com</p><p>www.cancer.gov</p>]]></description>
	<dc:creator>Jit</dc:creator>
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