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	<title><![CDATA[BOL: Related items]]></title>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/33869/import-r-data</guid>
	<pubDate>Wed, 12 Jul 2017 08:30:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/33869/import-r-data</link>
	<title><![CDATA[Import R Data]]></title>
	<description><![CDATA[<p>It is often necessary to import sample textbook data into R before you start working on your homework.</p><div id="node-69"><div><p><strong>Excel File</strong></p><p>Quite frequently, the sample data is in&nbsp;<span>Excel&nbsp;</span>format, and needs to be imported into R prior to use. For this, we can use the function&nbsp;<span>read.xls&nbsp;</span>from the&nbsp;<span>gdata&nbsp;</span>package. It reads from an Excel spreadsheet and returns a&nbsp;<a href="http://www.r-tutor.com/r-introduction/data-frame">data frame</a>. The following shows how to load an Excel spreadsheet named&nbsp;<span>"mydata.xls"</span>. This method requires Perl runtime to be present in the system.</p><blockquote><div id="listing-68"><span><a></a></span>&gt;&nbsp;library(gdata)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;load&nbsp;gdata&nbsp;package&nbsp;<br /><span><a></a></span>&gt;&nbsp;help(read.xls)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;documentation&nbsp;<br /><span><a></a></span>&gt;&nbsp;mydata&nbsp;=&nbsp;read.xls("mydata.xls")&nbsp;&nbsp;#&nbsp;read&nbsp;from&nbsp;first&nbsp;sheet</div></blockquote><p>Alternatively, we can use the function&nbsp;<span>loadWorkbook&nbsp;</span>from the&nbsp;<span>XLConnect&nbsp;</span>package to read the entire workbook, and then load the worksheets with&nbsp;<span>readWorksheet</span>. The&nbsp;<span>XLConnect&nbsp;</span>package requires Java to be pre-installed.</p><blockquote><div id="listing-69"><span><a></a></span>&gt;&nbsp;library(XLConnect)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;load&nbsp;XLConnect&nbsp;package&nbsp;<br /><span><a></a></span>&gt;&nbsp;wk&nbsp;=&nbsp;loadWorkbook("mydata.xls")&nbsp;<br /><span><a></a></span>&gt;&nbsp;df&nbsp;=&nbsp;readWorksheet(wk,&nbsp;sheet="Sheet1")</div></blockquote><p>&nbsp;</p><h4><a></a>Minitab File</h4><p>If the data file is in&nbsp;<span>Minitab Portable Worksheet&nbsp;</span>format, it can be opened with the function&nbsp;<span>read.mtp&nbsp;</span>from the&nbsp;<span>foreign&nbsp;</span>package. It returns a&nbsp;<a href="http://www.r-tutor.com/r-introduction/list">list</a>&nbsp;of components in the Minitab worksheet.</p><blockquote><div id="listing-70"><span><a></a></span>&gt;&nbsp;library(foreign)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;load&nbsp;the&nbsp;foreign&nbsp;package&nbsp;<br /><span><a></a></span>&gt;&nbsp;help(read.mtp)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;documentation&nbsp;<br /><span><a></a></span>&gt;&nbsp;mydata&nbsp;=&nbsp;read.mtp("mydata.mtp")&nbsp;&nbsp;#&nbsp;read&nbsp;from&nbsp;.mtp&nbsp;file</div></blockquote><p>&nbsp;</p><h4><a></a>SPSS File</h4><p>For the data files in&nbsp;<span>SPSS&nbsp;</span>format, it can be opened with the function&nbsp;<span>read.spss&nbsp;</span>also from the&nbsp;<span>foreign&nbsp;</span>package. There is a&nbsp;<span>"to.data.frame"&nbsp;</span>option for choosing whether a data frame is to be returned. By default, it returns a list of components instead.</p><blockquote><div id="listing-71"><span><a></a></span>&gt;&nbsp;library(foreign)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;load&nbsp;the&nbsp;foreign&nbsp;package&nbsp;<br /><span><a></a></span>&gt;&nbsp;help(read.spss)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;documentation&nbsp;<br /><span><a></a></span>&gt;&nbsp;mydata&nbsp;=&nbsp;read.spss("myfile",&nbsp;to.data.frame=TRUE)</div></blockquote><p>&nbsp;</p><h4><a></a>Table File</h4><p>A data table can resides in a text file. The cells inside the table are separated by blank characters. Here is an example of a table with 4 rows and 3 columns.</p><blockquote><div id="listing-72"><span><a></a></span>100&nbsp;&nbsp;&nbsp;a1&nbsp;&nbsp;&nbsp;b1&nbsp;<br /><span><a></a></span>200&nbsp;&nbsp;&nbsp;a2&nbsp;&nbsp;&nbsp;b2&nbsp;<br /><span><a></a></span>300&nbsp;&nbsp;&nbsp;a3&nbsp;&nbsp;&nbsp;b3&nbsp;<br /><span><a></a></span>400&nbsp;&nbsp;&nbsp;a4&nbsp;&nbsp;&nbsp;b4</div></blockquote><p>Now copy and paste the table above in a file named&nbsp;<span>"mydata.txt"&nbsp;</span>with a text editor. Then load the data into the workspace with the function&nbsp;<span>read.table</span>.</p><blockquote><div id="listing-73"><span><a></a></span>&gt;&nbsp;mydata&nbsp;=&nbsp;read.table("mydata.txt")&nbsp;&nbsp;#&nbsp;read&nbsp;text&nbsp;file&nbsp;<br /><span><a></a></span>&gt;&nbsp;mydata&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;print&nbsp;data&nbsp;frame&nbsp;<br /><span><a></a></span>&nbsp;&nbsp;&nbsp;V1&nbsp;V2&nbsp;V3&nbsp;<br /><span><a></a></span>1&nbsp;100&nbsp;a1&nbsp;b1&nbsp;<br /><span><a></a></span>2&nbsp;200&nbsp;a2&nbsp;b2&nbsp;<br /><span><a></a></span>3&nbsp;300&nbsp;a3&nbsp;b3&nbsp;<br /><span><a></a></span>4&nbsp;400&nbsp;a4&nbsp;b4</div></blockquote><p>For further detail of the function&nbsp;<span>read.table</span>, please consult the R documentation.</p><blockquote><div id="listing-74"><span><a></a></span>&gt;&nbsp;help(read.table)</div></blockquote><p>&nbsp;</p><h4><a></a>CSV File</h4><p>The sample data can also be in&nbsp;<span>comma separated values&nbsp;</span>(CSV) format. Each cell inside such data file is separated by a special character, which usually is a comma, although other characters can be used as well.</p><p>The first row of the data file should contain the column names instead of the actual data. Here is a sample of the expected format.</p><blockquote><div id="listing-75"><span><a></a></span>Col1,Col2,Col3&nbsp;<br /><span><a></a></span>100,a1,b1&nbsp;<br /><span><a></a></span>200,a2,b2&nbsp;<br /><span><a></a></span>300,a3,b3</div></blockquote><p>After we copy and paste the data above in a file named&nbsp;<span>"mydata.csv"&nbsp;</span>with a text editor, we can read the data with the function&nbsp;<span>read.csv</span>.</p><blockquote><div id="listing-76"><span><a></a></span>&gt;&nbsp;mydata&nbsp;=&nbsp;read.csv("mydata.csv")&nbsp;&nbsp;#&nbsp;read&nbsp;csv&nbsp;file&nbsp;<br /><span><a></a></span>&gt;&nbsp;mydata&nbsp;<br /><span><a></a></span>&nbsp;&nbsp;Col1&nbsp;Col2&nbsp;Col3&nbsp;<br /><span><a></a></span>1&nbsp;&nbsp;100&nbsp;&nbsp;&nbsp;a1&nbsp;&nbsp;&nbsp;b1&nbsp;<br /><span><a></a></span>2&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;a2&nbsp;&nbsp;&nbsp;b2&nbsp;<br /><span><a></a></span>3&nbsp;&nbsp;300&nbsp;&nbsp;&nbsp;a3&nbsp;&nbsp;&nbsp;b3</div></blockquote><p>In various European locales, as the comma character serves as the decimal point, the function&nbsp;<span>read.csv2&nbsp;</span>should be used instead. For further detail of the&nbsp;<span>read.csv&nbsp;</span>and&nbsp;<span>read.csv2&nbsp;</span>functions, please consult the R documentation.</p><blockquote><div id="listing-77"><span><a></a></span>&gt;&nbsp;help(read.csv)</div></blockquote><p>&nbsp;</p><h4><a></a>Working Directory</h4><p>Finally, the code samples above assume the data files are located in the R&nbsp;<span>working</span>&nbsp;<span>directory</span>, which can be found with the function&nbsp;<span>getwd</span>.</p><blockquote><div id="listing-78"><span><a></a></span>&gt;&nbsp;getwd()&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;get&nbsp;current&nbsp;working&nbsp;directory</div></blockquote><p>You can select a different working directory with the function&nbsp;<span>setwd()</span>, and thus avoid entering the full path of the data files.</p><blockquote><div id="listing-79"><span><a></a></span>&gt;&nbsp;setwd("")&nbsp;&nbsp;&nbsp;#&nbsp;set&nbsp;working&nbsp;directory</div></blockquote><p>Note that the forward slash should be used as the path separator even on Windows platform.</p><blockquote><div id="listing-80"><span><a></a></span>&gt;&nbsp;setwd("C:/MyDoc")</div></blockquote></div></div>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/38226/ncbi-to-assist-in-virus-hunting-data-science-hackathon</guid>
	<pubDate>Thu, 15 Nov 2018 12:55:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/38226/ncbi-to-assist-in-virus-hunting-data-science-hackathon</link>
	<title><![CDATA[NCBI to assist in Virus Hunting Data Science Hackathon]]></title>
	<description><![CDATA[<p>NCBI Hackathon are pleased to announce the second installment of the&nbsp;<a href="https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/30/ncbi-southern-california-genomics-hackathon-january/" target="_blank">SoCal Bioinformatics Hackathon</a>. From January 9-11, 2019, the&nbsp;<a href="https://www.ncbi.nlm.nih.gov/" target="_blank">NCBI</a>&nbsp;will help run a bioinformatics hackathon in Southern California hosted by the&nbsp;<a href="http://www.csrc.sdsu.edu/" target="_blank">Computational Sciences Research Center</a>&nbsp;at&nbsp;<a href="http://www.sdsu.edu/" target="_blank">San Diego State University</a>!</p><p><span>NCBI Hackathon</span>&nbsp;specifically looking for folks who have experience in computational virus hunting or adjacent fields to identify known, taxonomically-definable and novel viruses from a few hundred thousand metagenomic datasets that we&rsquo;ll put on cloud infrastructure. This event is for researchers, including students and postdocs, who are already engaged in the use of bioinformatics data or in the development of pipelines for virological analyses from high-throughput experiments. If this describes you, please&nbsp;<a href="https://goo.gl/forms/kDnSG0IAZD62XQRe2" target="_blank">apply</a>! The event is open to anyone selected for the hackathon and willing to travel to SDSU (see below).</p><p>https://ncbiinsights.ncbi.nlm.nih.gov/2018/11/09/ncbi-sdsu-virus-hunting-data-science-hackathon-january-2019/</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40573/de-novo-genome-assembly-for-illumina-data</guid>
	<pubDate>Mon, 20 Jan 2020 05:13:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40573/de-novo-genome-assembly-for-illumina-data</link>
	<title><![CDATA[De novo Genome Assembly for Illumina Data]]></title>
	<description><![CDATA[<p>Written and maintained by <a href="mailto:simon.gladman@unimelb.edu.au">Simon Gladman</a> - Melbourne Bioinformatics (formerly VLSCI)</p>
<p>Protocol Overview / Introduction</p>
<p>In this protocol we discuss and outline the process of de novo assembly for small to medium sized genomes.</p>
<p>https://www.melbournebioinformatics.org.au/tutorials/tutorials/assembly/assembly-protocol/</p><p>Address of the bookmark: <a href="https://www.melbournebioinformatics.org.au/tutorials/tutorials/assembly/assembly-protocol/" rel="nofollow">https://www.melbournebioinformatics.org.au/tutorials/tutorials/assembly/assembly-protocol/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42419/biojupies-automatically-generates-rna-seq-data-analysis-notebooks</guid>
	<pubDate>Sun, 20 Dec 2020 11:43:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42419/biojupies-automatically-generates-rna-seq-data-analysis-notebooks</link>
	<title><![CDATA[BioJupies: Automatically Generates RNA-seq Data Analysis Notebooks]]></title>
	<description><![CDATA[<p>With BioJupies you can produce in seconds a customized, reusable, and interactive report from your own raw or processed RNA-seq data through a simple user interface</p>
<p>BioJupies now supports user accounts! Sign in from the top right corner of the page for access to unlimited private notebooks, RNA-seq datasets and alignment jobs.</p><p>Address of the bookmark: <a href="https://amp.pharm.mssm.edu/biojupies/" rel="nofollow">https://amp.pharm.mssm.edu/biojupies/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44557/fundamentals-of-data-visualization-by-claus-o-wilke</guid>
	<pubDate>Sat, 08 Jun 2024 16:07:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44557/fundamentals-of-data-visualization-by-claus-o-wilke</link>
	<title><![CDATA[Fundamentals of Data Visualization by Claus O. Wilke]]></title>
	<description><![CDATA[<p><span><span>The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.</span></span></p>
<p><span>The entire book is written in R Markdown, using RStudio as my text editor and the&nbsp;</span><span>bookdown</span><span>&nbsp;package to turn a collection of markdown documents into a coherent whole. The book&rsquo;s source code is hosted on GitHub, at&nbsp;</span><a href="https://github.com/clauswilke/dataviz">https://github.com/clauswilke/dataviz</a><span>.&nbsp;</span></p><p>Address of the bookmark: <a href="https://clauswilke.com/dataviz/" rel="nofollow">https://clauswilke.com/dataviz/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38702/quick-tour-of-genetic-algorithms</guid>
	<pubDate>Thu, 17 Jan 2019 03:42:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38702/quick-tour-of-genetic-algorithms</link>
	<title><![CDATA[Quick tour of Genetic Algorithms !]]></title>
	<description><![CDATA[<p><span>The R package&nbsp;</span><strong>GA</strong><span>&nbsp;provides a collection of general purpose functions for optimization using genetic algorithms. The package includes a flexible set of tools for implementing genetic algorithms search in both the continuous and discrete case, whether constrained or not. Users can easily define their own objective function depending on the problem at hand.&nbsp;</span></p>
<p><span>https://cran.r-project.org/web/packages/GA/vignettes/GA.html</span></p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/GA/vignettes/GA.html" rel="nofollow">https://cran.r-project.org/web/packages/GA/vignettes/GA.html</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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34470/simngs-and-simlibrary-%E2%80%93-software-for-simulating-next-gen-sequencing-data</guid>
	<pubDate>Tue, 28 Nov 2017 06:49:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34470/simngs-and-simlibrary-%E2%80%93-software-for-simulating-next-gen-sequencing-data</link>
	<title><![CDATA[simNGS and simLibrary – Software for Simulating Next-Gen Sequencing Data]]></title>
	<description><![CDATA[<p>simNGS is software for simulating observations from Illumina sequencing machines using the statistical models behind the AYB base-calling software. By default, observations only incorporate noise due to sequencing and do not incorporate effects from more esoteric sources of noise that may be present in real data ("dust", bubbles, merged clusters, sequence-heterogeneous clusters, etc). Many of these additional sources may optionally applied.</p>
<p>simNGS takes fasta format sequences and a file describing the covariance of noise between bases and cycles observed in an actual run of the machine, randomly generates noisy intensities representing the signals for the sequence at each cycle and calculates likelihoods for all possible base calls.</p><p>Address of the bookmark: <a href="https://www.ebi.ac.uk/goldman-srv/simNGS/" rel="nofollow">https://www.ebi.ac.uk/goldman-srv/simNGS/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26252/recombination-detection-tool</guid>
	<pubDate>Tue, 02 Feb 2016 10:11:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26252/recombination-detection-tool</link>
	<title><![CDATA[Recombination detection tool]]></title>
	<description><![CDATA[<p>A program to detect recombination hotspots using population genetic data.</p>
<p>More at https://github.com/auton1/LDhot</p><p>Address of the bookmark: <a href="https://github.com/auton1/LDhot" rel="nofollow">https://github.com/auton1/LDhot</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27099/rasttk-algorithm-for-building-custom-annotation-pipelines-and-annotating-batches-of-genomes</guid>
	<pubDate>Wed, 27 Apr 2016 11:07:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27099/rasttk-algorithm-for-building-custom-annotation-pipelines-and-annotating-batches-of-genomes</link>
	<title><![CDATA[RASTtk : algorithm for building custom annotation pipelines and annotating batches of genomes]]></title>
	<description><![CDATA[<p>The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.</p>
<p>More at http://www.nature.com/articles/srep08365</p><p>Address of the bookmark: <a href="http://rast.nmpdr.org/" rel="nofollow">http://rast.nmpdr.org/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

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