<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35386?offset=300</link>
	<atom:link href="https://bioinformaticsonline.com/related/35386?offset=300" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27104/gatb-genome-analysis-toolbox-with-de-bruijn-graph</guid>
	<pubDate>Thu, 28 Apr 2016 11:16:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27104/gatb-genome-analysis-toolbox-with-de-bruijn-graph</link>
	<title><![CDATA[GATB : Genome Analysis Toolbox with de-Bruijn graph]]></title>
	<description><![CDATA[<p>The&nbsp;<strong><strong>Genome Analysis Toolbox with de-Bruijn graph</strong> (GATB)</strong> provides a set of <a href="https://gatb.inria.fr/gatb-global-architecture/">highly efficient algorithms to analyse NGS data sets</a>. These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (<em>e.g.</em> metagenomes).</p>
<p>More at https://gatb.inria.fr/</p><p>Address of the bookmark: <a href="https://gatb.inria.fr/" rel="nofollow">https://gatb.inria.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</guid>
	<pubDate>Wed, 22 Jun 2016 05:37:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</link>
	<title><![CDATA[DarkHorse]]></title>
	<description><![CDATA[<p><em>DarkHorse</em>&nbsp;is a bioinformatic method for rapid, automated identification and ranking of phylogenetically atypical proteins on a genome-wide basis. It works by selecting potential ortholog matches from a reference database of amino acid sequences, then using these matches to calculate a lineage probability index (LPI) score for each genome protein.</p>
<p>LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scale<em>de novo</em>&nbsp;predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.</p>
<p>One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.</p>
<p><em>DarkHorse</em>&nbsp;is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the&nbsp;<a href="http://darkhorse.ucsd.edu/#references">references</a>&nbsp;cited below for a complete explanation of parameter selection and result interpretation. A brief&nbsp;<a href="http://darkhorse.ucsd.edu/tutorial.html">tutorial</a>&nbsp;page is also available on-line.</p><p>Address of the bookmark: <a href="http://darkhorse.ucsd.edu/download.html" rel="nofollow">http://darkhorse.ucsd.edu/download.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30966/maftools</guid>
	<pubDate>Thu, 16 Feb 2017 11:16:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30966/maftools</link>
	<title><![CDATA[MafTools]]></title>
	<description><![CDATA[<p>maftools - An R package to summarize, analyze and visualize MAF files. <a href="https://github.com/PoisonAlien/maftools#introduction"></a>Introduction.</p>
<p>With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widley accepted and used to store variants detected. <a href="http://cancergenome.nih.gov">The Cancer Genome Atlas</a> Project has seqenced over 30 different cancers with sample size of each cancer type being over 200. The <a href="https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files">resulting data</a> consisting of genetic variants is stored in the form of <a href="https://wiki.nci.nih.gov/display/TCGA/Mutation+Annotation+Format+%28MAF%29+Specification">Mutation Annotation Format</a>. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner either from TCGA sources or any in-house studies as long as the data is in MAF format. Maftools can also handle ICGC Simple Somatic Mutation format.</p>
<p>maftools is on <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f449.png" alt=":point_right:" width="20" height="20" style="border: 0px;"> <a href="http://biorxiv.org/content/early/2016/05/11/052662">bioRxiv</a> <img src="https://assets-cdn.github.com/images/icons/emoji/bowtie.png" alt=":bowtie:" title=":bowtie:" width="20" height="20" style="border: 0px; text-align: absmiddle;"></p>
<p>Please cite the below if you find this tool useful for you.</p>
<p>Mayakonda, A. and H.P. Koeffler, Maftools: Efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. bioRxiv, 2016. doi: <a href="http://dx.doi.org/10.1101/052662">http://dx.doi.org/10.1101/052662</a></p><p>Address of the bookmark: <a href="https://github.com/PoisonAlien/maftools" rel="nofollow">https://github.com/PoisonAlien/maftools</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34685/tools-for-bacterial-whole-genome-annotation</guid>
	<pubDate>Sat, 16 Dec 2017 17:37:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34685/tools-for-bacterial-whole-genome-annotation</link>
	<title><![CDATA[Tools for bacterial whole genome annotation]]></title>
	<description><![CDATA[<p><a href="http://rast.nmpdr.org/">RAST</a>&nbsp;&ndash;&nbsp;Web tool (upload contigs), uses the subsystems in the SEED database and&nbsp;provides detailed annotation and pathway analysis. Takes several hours per genome but I think this is the best way to get a high quality annotation (if you have only a few genomes to annotate).</p><p><a href="http://www.vicbioinformatics.com/software.prokka.shtml">Prokka</a>&nbsp;&ndash;&nbsp;Standalone command line tool, takes just a few minutes per genome.&nbsp;This is the best way to get good quality annotation in a flash, which is particularly useful if you have loads of genomes or need to annotate a pangenome or metagenome. Note however that the quality of functional information is not as good as RAST, and you&nbsp;will need several extra steps if you want to do&nbsp;functional profiling and pathway analysis of your genome(s)&hellip; which is in-built in RAST.</p><p>NCBI Prokaryotic Genome Annotation Pipeline is designed to annotate bacterial and archaeal genomes (chromosomes and plasmids).</p><p>Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.</p><p><a href="https://www.ncbi.nlm.nih.gov/genome/annotation_prok/">PGAP</a>: NCBI has developed an automatic prokaryotic genome annotation pipeline that combines&nbsp;<em>ab initio</em>&nbsp;gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP;&nbsp;<a href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=pubmed&amp;dopt=Abstract&amp;list_uids=18416670">see Pubmed Article</a>) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume.&nbsp; NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.</p><p><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC453985">BEACON</a> (automated tool for Bacterial GEnome Annotation ComparisON), a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at:&nbsp;<a href="http://www.cbrc.kaust.edu.sa/BEACON/" target="pmc_ext">http://www.cbrc.kaust.edu.sa/BEACON/</a>.</p><p><a href="http://www.kegg.jp/blastkoala/">BlastKOLA</a>: Assigns K numbers to the user's sequence data by BLAST searches, respectively, against a nonredundant set of KEGG GENES. KOALA (KEGG Orthology And Links Annotation) is KEGG's internal annotation tool for K number assignment of KEGG GENES using SSEARCH computation. Annotate Sequence in KEGG Mapper and Pathogen Checker in KEGG Pathogen are special interfaces to this server and can be executed in an interactive mode. BlastKOALA is suitable for annotating fully sequenced genomes.</p><p><a href="http://www.sanger.ac.uk/science/tools/pagit">PAGIT</a>: Provides a toolkit for improving the quality of genome assemblies created via an assembly software. PAGIT compiled four tools: (i) ABACAS which classifies and orientates contigs and estimates the sizes of gaps between them; (ii) IMAGE uses paired-end reads to extend contigs and close gaps within the scaffolds; (iii) ICORN for identifying and correcting small errors in consensus sequences and; (iv) RATT for help annotation. The software was mainly created to analyze parasite genomes of up to about 300 Mb.</p><p><a href="http://www.yandell-lab.org/software/maker.html">MAKER: </a>A portable and easily configurable genome annotation pipeline. MAKER allows smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. It identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values. MAKER's inputs are minimal and its ouputs can be directly loaded into a Generic Model Organism Database (GMOD). They can also be viewed in the Apollo genome browser; this feature of MAKER provides an easy means to annotate, view and edit individual contigs and BACs without the overhead of a database. MAKER is available for download and can be tested online via the MAKER Web Annotation Service (MWAS).</p><p><a href="https://www.sciencedirect.com/science/article/pii/S0167701215001207">MyPro</a> is a software pipeline for high-quality prokaryotic genome assembly and annotation. It was validated on 18 oral streptococcal strains to produce submission-ready, annotated draft genomes. MyPro installed as a virtual machine and supported by updated databases will enable biologists to perform quality prokaryotic genome assembly and annotation with ease.</p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36392/protein-protein-interaction-sites-predictions</guid>
	<pubDate>Wed, 25 Apr 2018 04:53:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36392/protein-protein-interaction-sites-predictions</link>
	<title><![CDATA[Protein-Protein Interaction Sites Predictions !]]></title>
	<description><![CDATA[<p><span>The study of Protein&ndash;Protein Interactions (PPIs) has a crucial role in biology, medicine and the pharmaceutical industry. PPIs can be investigated from two aspects: The interaction partners of a specific protein and the amino acid residues participating in a given PPI. Information about a protein&rsquo;s interaction partners allows scientists to construct protein interaction networks, such as signaling pathways, which in turn facilitate the understanding of many biological and clinical observations.&nbsp;</span></p><p><span>Following are the list of tools commonly used to PPIs predictions:</span></p><p>Protein-Protein Interaction Sites</p><p><a href="http://pipe.scs.fsu.edu/ppisp.html" target="_blank">PPISP</a></p><p>A consensus neural network method for predicting protein-protein interaction sites</p><p><a href="http://biunit.naist.jp/homcos/" target="_blank">HOMCOS</a></p><p>A server to predict interacting protein pairs and interacting sites by homology modeling of complex structures</p><p><a href="http://prism.ccbb.ku.edu.tr/hotpoint/" target="_blank">HotPOINT</a></p><p>Prediction of protein interfaces using an empirical model</p><p><a href="http://cubic.bioc.columbia.edu/services/isis/" target="_blank">ISIS</a></p><p>Prediction of interaction hotspots from sequence</p><p><a href="http://kfc.mitchell-lab.org/" target="_blank">KFC server</a></p><p>Automated decision-tree approach to predicting protein-protein interaction hot spots</p><p><a href="http://pipe.scs.fsu.edu/meta-ppisp.html" target="_blank">meta-PPISP</a></p><p>A meta server for predicting protein-protein interaction sites. meta-PPISP is built on three individual web servers:&nbsp;<a href="https://bip.weizmann.ac.il/toolbox/structure/binding.htm#cons">cons-PPISP</a>,&nbsp;<a href="https://bip.weizmann.ac.il/toolbox/structure/binding.htm#pin">PINUP</a>, and&nbsp;<a href="https://bip.weizmann.ac.il/toolbox/structure/binding.htm#pro">Promate</a></p><p><a href="http://www.molsoft.com/oda.html" target="_blank">ODA</a></p><p>Identification of optimal surface patches with the lowest docking desolvation energy values</p><p><a href="http://sparks.informatics.iupui.edu/PINUP/" target="_blank">PINUP</a></p><p>Protein binding site prediction with an empirical scoring function</p><p>Other Sites (DNA, RNA, Metals)</p><p><a href="http://ligin.weizmann.ac.il/~lpgerzon/mbs4/mbs.cgi" target="_blank">CHED</a>&nbsp;</p><p>Web server for predicting soft metal binding sites in proteins</p><p><a href="http://cssb.biology.gatech.edu/skolnick/webservice/DBD-Hunter/" target="_blank">DBD-Hunter</a></p><p>A knowledge-based method for the prediction of DNA-protein interactions</p><p><a href="http://pipe.scs.fsu.edu/displar.html" target="_blank">DISPLAR</a></p><p>Given the structure of a protein known to bind DNA, the method predicts residues that contact DNA using neural network method</p><p><a href="http://idbps.tau.ac.il/" target="_blank">iDBPs</a></p><p>Predicts DNA binding proteins for proteins with known 3D structure.</p><p><a href="http://pfp.technion.ac.il/" target="_blank">PFplus</a></p><div style="text-align: left;">A tool for extracting and displaying positive electrostatic patches on protein surfaces which can be indicative of nucleic acid binding interfaces.</div>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping</guid>
	<pubDate>Tue, 08 May 2018 04:15:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping</link>
	<title><![CDATA[MITObim - mitochondrial baiting and iterative mapping]]></title>
	<description><![CDATA[<p>This document contains instructions on how to use the MITObim pipeline described in Hahn et al. 2013. The full article can be found&nbsp;<a href="http://nar.oxfordjournals.org/content/41/13/e129" title="MITObim full article at NAR">here</a>. Kindly cite the article if you are using MITObim in your work. The pipeline was originally developed for&nbsp;<span>Illumina</span>&nbsp;data, but thanks to the versatility of the MIRA assembler, MITObim supports in principle also data from the&nbsp;<span>Iontorrent</span>,&nbsp;<span>454</span>&nbsp;and&nbsp;<span>PacBio</span>&nbsp;sequencing platforms.</p>
<p>Below you can find a few basic tutorials for how to run MITObim and I encorage you to give them a try with the testdata that comes with this Repo, just to make sure everything is running smoothly on your system. It'll only take a few minutes and will potentially safe you a lot of time down the line.</p>
<p>I provide further examples&nbsp;<a href="https://github.com/chrishah/MITObim/tree/master/examples">here</a>&nbsp;as Jupyter notebooks. Get in touch if you feel like sharing your particular MITObim solution and I'd be happy to put it up here, too!</p><p>Address of the bookmark: <a href="https://github.com/chrishah/MITObim" rel="nofollow">https://github.com/chrishah/MITObim</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36533/mecat-fast-mapping-error-correction-and-de-novo-assembly-for-single-molecule-sequencing-reads</guid>
	<pubDate>Fri, 11 May 2018 05:07:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36533/mecat-fast-mapping-error-correction-and-de-novo-assembly-for-single-molecule-sequencing-reads</link>
	<title><![CDATA[MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads]]></title>
	<description><![CDATA[<p>MECAT is an ultra-fast Mapping, Error Correction and de novo Assembly Tools for single molecula sequencing (SMRT) reads. MECAT employs novel alignment and error correction algorithms that are much more efficient than the state of art of aligners and error correction tools. MECAT can be used for effectively de novo assemblying large genomes. For example, on a 32-thread computer with 2.0 GHz CPU , MECAT takes 9.5 days to assemble a human genome based on 54x SMRT data, which is 40 times faster than the current&nbsp;<a href="http://cbcb.umd.edu/software/pbcr/mhap/">PBcR-Mhap pipeline</a>. MECAT performance were compared with&nbsp;<a href="http://cbcb.umd.edu/software/pbcr/mhap/">PBcR-Mhap pipeline</a>,&nbsp;<a href="https://github.com/PacificBiosciences/falcon">FALCON</a>&nbsp;and&nbsp;<a href="http://canu.readthedocs.io/en/latest/">Canu(v1.3)</a>&nbsp;in five real datasets. The quality of assembled contigs produced by MECAT is the same or better than that of the&nbsp;<a href="http://cbcb.umd.edu/software/pbcr/mhap/">PBcR-Mhap pipeline</a>&nbsp;and&nbsp;<a href="https://github.com/PacificBiosciences/falcon">FALCON</a>.&nbsp;</p>
<p>https://www.nature.com/articles/nmeth.4432</p><p>Address of the bookmark: <a href="https://github.com/xiaochuanle/MECAT" rel="nofollow">https://github.com/xiaochuanle/MECAT</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40604/gapfinisher-a-reliable-gap-filling-pipeline-for-sspace-longread-scaffolder-output</guid>
	<pubDate>Fri, 24 Jan 2020 06:04:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40604/gapfinisher-a-reliable-gap-filling-pipeline-for-sspace-longread-scaffolder-output</link>
	<title><![CDATA[gapFinisher: A reliable gap filling pipeline for SSPACE-LongRead scaffolder output]]></title>
	<description><![CDATA[<p><span>gapFinisher is based on the controlled use of a previously published gap filling tool FGAP and works on all standard Linux/UNIX command lines. They compare the performance of gapFinisher against two other published gap filling tools PBJelly and GMcloser. </span></p>
<p><span>gapFinisher can fill gaps in draft genomes quickly and reliably.</span></p><p>Address of the bookmark: <a href="https://github.com/kammoji/gapFinisher" rel="nofollow">https://github.com/kammoji/gapFinisher</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43997/tools-for-rna-classification</guid>
	<pubDate>Tue, 08 Nov 2022 03:39:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43997/tools-for-rna-classification</link>
	<title><![CDATA[Tools for RNA classification]]></title>
	<description><![CDATA[<p><span>barrnap</span>&nbsp;-&nbsp;<a href="https://github.com/tseemann/barrnap" target="_blank">https://github.com/tseemann/barrnap</a></p><p><span>CPAT</span>&nbsp;-&nbsp;<a href="https://github.com/liguowang/cpat" target="_blank">https://github.com/liguowang/cpat</a>,&nbsp;<a href="http://lilab.research.bcm.edu/" target="_blank">http://lilab.research.bcm.edu/</a>&nbsp;(web server)</p><p><span>CPC2</span>&nbsp;-&nbsp;<a href="https://github.com/gao-lab/CPC2_standalone" target="_blank">https://github.com/gao-lab/CPC2_standalone</a>,&nbsp;<a href="http://cpc2.gao-lab.org/" target="_blank">http://cpc2.gao-lab.org/</a>&nbsp;(web server)</p><p><span>Infernal</span>&nbsp;-&nbsp;<a href="http://eddylab.org/infernal/" target="_blank">http://eddylab.org/infernal/</a>,&nbsp;<a href="https://github.com/EddyRivasLab/infernal" target="_blank">https://github.com/EddyRivasLab/infernal</a></p><p><span>NCBI RefSeq</span>&nbsp;-&nbsp;<a href="https://www.ncbi.nlm.nih.gov/refseq/" target="_blank">https://www.ncbi.nlm.nih.gov/refseq/</a></p><p><span>Rfam</span>&nbsp;-&nbsp;<a href="http://rfam.xfam.org/" target="_blank">http://rfam.xfam.org/</a>,&nbsp;<a href="https://docs.rfam.org/en/latest/index.html" target="_blank">https://docs.rfam.org/en/latest/index.html</a></p><p><span>SILVA</span>&nbsp;-&nbsp;<a href="https://www.arb-silva.de/" target="_blank">https://www.arb-silva.de/</a></p><p><span>RNAmmer</span>&nbsp;-&nbsp;<a href="http://www.cbs.dtu.dk/services/RNAmmer/" target="_blank">http://www.cbs.dtu.dk/services/RNAmmer/</a>&nbsp;(web server, standalone download link)</p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44401/bioinformatics-tools-for-phylogeny</guid>
	<pubDate>Mon, 06 Nov 2023 03:09:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44401/bioinformatics-tools-for-phylogeny</link>
	<title><![CDATA[Bioinformatics Tools for Phylogeny !]]></title>
	<description><![CDATA[<p><span>Direct access to the individual tools available on this server.</span></p><table summary="list of individual tools">
<thead>
<tr><th>Multiple Alignment:</th><th>Phylogeny:</th><th>Tree viewers:</th><th>Utilities:</th></tr>
</thead>
<tbody>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=muscle">MUSCLE</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=phyml">PhyML</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=treedyn">TreeDyn</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=gblocks">Gblocks</a></td>
</tr>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=tcoffee">T-Coffee</a>&nbsp;/&nbsp;<a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=expresso">3DCoffee</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=tnt">TNT</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=drawgram">Drawgram</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=jalview">Jalview</a></td>
</tr>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=clustalw">ClustalW</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=bionj">BioNJ</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=drawtree">Drawtree</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=readseq">Readseq</a></td>
</tr>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=probcons">ProbCons</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=mrbayes">MrBayes</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=atv">ATV (A Tree Viewer)</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/data_converter.cgi">Built-in converter</a></td>
</tr>
</tbody>
</table>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

</channel>
</rss>