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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43698?offset=30</link>
	<atom:link href="https://bioinformaticsonline.com/related/43698?offset=30" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times</guid>
	<pubDate>Sat, 02 Jun 2018 07:40:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times</link>
	<title><![CDATA[MCMCTREE: a phylogenetic program for Bayesian estimation of species divergence times]]></title>
	<description><![CDATA[<p><a href="http://abacus.gene.ucl.ac.uk/software/paml.html" target="_blank">MCMCTREE</a><span>&nbsp;is a phylogenetic program for Bayesian estimation of species divergence times using soft fossil constraints under various molecular clock models. This is part of the&nbsp;</span><a href="http://abacus.gene.ucl.ac.uk/software/paml.html" target="_blank">PAML</a><span>&nbsp;package. In this tutorial I will analyze an easy example modified from dataset of&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/20551041" target="_blank">Inoue et al. (2010)</a><span>. Here we conduct a commonly used time estimation method, "Approximate Likelihood Method", for the datasets including more than 10 species.</span></p><p>Address of the bookmark: <a href="http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html" rel="nofollow">http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44513/mike-an-ultrafast-assembly-and-alignment-free-approach-for-phylogenetic-tree-construction</guid>
	<pubDate>Mon, 08 Apr 2024 06:19:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44513/mike-an-ultrafast-assembly-and-alignment-free-approach-for-phylogenetic-tree-construction</link>
	<title><![CDATA[MIKE: an ultrafast, assembly-, and alignment-free approach for phylogenetic tree construction]]></title>
	<description><![CDATA[<p><span>MIKE (MinHash-based&nbsp;</span><em>k</em><span>-mer algorithm). This algorithm is designed for the swift calculation of the Jaccard coefficient directly from raw sequencing reads and enables the construction of phylogenetic trees based on the resultant Jaccard coefficient. Simulation results highlight the superior speed of MIKE compared to existing state-of-the-art methods. We used MIKE to reconstruct a phylogenetic tree, incorporating 238 yeast, 303&nbsp;</span><em>Zea</em><span>, 141&nbsp;</span><em>Ficus</em><span>, 67&nbsp;</span><em>Oryza</em><span>, and 43&nbsp;</span><em>Saccharum spontaneum</em><span>&nbsp;samples. MIKE demonstrated accurate performance across varying evolutionary scales, reproductive modes, and ploidy levels, proving itself as a powerful tool for phylogenetic tree construction.</span></p><p>Address of the bookmark: <a href="https://github.com/Argonum-Clever2/mike" rel="nofollow">https://github.com/Argonum-Clever2/mike</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42972/list-of-bioinformatics-workflow-management-tools</guid>
	<pubDate>Sat, 20 Mar 2021 00:15:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42972/list-of-bioinformatics-workflow-management-tools</link>
	<title><![CDATA[List of bioinformatics workflow management tools !]]></title>
	<description><![CDATA[<h3>Here are list of&nbsp;Workflow Managers</h3><ul>
<li><span><a href="https://github.com/pcingola/BigDataScript">BigDataScript</a></span>&nbsp;&ndash; A cross-system scripting language for working with big data pipelines in computer systems of different sizes and capabilities. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/25189778">paper-2014</a>&nbsp;|&nbsp;<a href="https://pcingola.github.io/BigDataScript">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/ssadedin/bpipe">Bpipe</a></span>&nbsp;&ndash; A small language for defining pipeline stages and linking them together to make pipelines. [&nbsp;<a href="http://docs.bpipe.org/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/common-workflow-language/common-workflow-language">Common Workflow Language</a></span>&nbsp;&ndash; a specification for describing analysis workflows and tools that are portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. [&nbsp;<a href="http://www.commonwl.org/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/broadinstitute/cromwell">Cromwell</a></span>&nbsp;&ndash; A Workflow Management System geared towards scientific workflows. [&nbsp;<a href="https://cromwell.readthedocs.io/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/galaxyproject">Galaxy</a></span>&nbsp;&ndash; a popular open-source, web-based platform for data intensive biomedical research. Has several features, from data analysis to workflow management to visualization tools. [&nbsp;<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030816">paper-2018</a>&nbsp;|&nbsp;<a href="https://galaxyproject.org/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/nextflow-io/nextflow">Nextflow</a>&nbsp;(recommended)</span>&nbsp;&ndash; A fluent DSL modelled around the UNIX pipe concept, that simplifies writing parallel and scalable pipelines in a portable manner. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/29412134">paper-2018</a>&nbsp;|&nbsp;<a href="http://nextflow.io/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/cgat-developers/ruffus">Ruffus</a></span>&nbsp;&ndash; Computation Pipeline library for python widely used in science and bioinformatics. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/20847218">paper-2010</a>&nbsp;|&nbsp;<a href="http://www.ruffus.org.uk/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/SeqWare/seqware">SeqWare</a></span>&nbsp;&ndash; Hadoop Oozie-based workflow system focused on genomics data analysis in cloud environments. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/21210981">paper-2010</a>&nbsp;|&nbsp;<a href="https://seqware.github.io/">web</a>&nbsp;]</li>
<li><span><a href="https://bitbucket.org/snakemake">Snakemake</a></span>&nbsp;&ndash; A workflow management system in Python that aims to reduce the complexity of creating workflows by providing a fast and comfortable execution environment. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/29788404">paper-2018</a>&nbsp;|&nbsp;<a href="https://snakemake.readthedocs.io/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/broadinstitute/wdl">Workflow Descriptor Language</a></span>&nbsp;&ndash; Workflow standard developed by the Broad. [&nbsp;<a href="https://software.broadinstitute.org/wdl">web</a>&nbsp;]</li>
</ul>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39720/snakemake-workflow-dna-seq-gatk-variant-calling</guid>
	<pubDate>Thu, 25 Jul 2019 12:55:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39720/snakemake-workflow-dna-seq-gatk-variant-calling</link>
	<title><![CDATA[Snakemake workflow: dna-seq-gatk-variant-calling]]></title>
	<description><![CDATA[<p><span>This Snakemake pipeline implements the&nbsp;</span><a href="https://software.broadinstitute.org/gatk/best-practices/workflow?id=11145">GATK best-practices workflow</a><span>&nbsp;for calling small genomic variants.</span></p><p>Address of the bookmark: <a href="https://github.com/snakemake-workflows/dna-seq-gatk-variant-calling" rel="nofollow">https://github.com/snakemake-workflows/dna-seq-gatk-variant-calling</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43797/gtotree-a-user-friendly-workflow-for-phylogenomics</guid>
	<pubDate>Wed, 23 Feb 2022 08:18:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43797/gtotree-a-user-friendly-workflow-for-phylogenomics</link>
	<title><![CDATA[GToTree: a user-friendly workflow for phylogenomics]]></title>
	<description><![CDATA[<p><a href="https://github.com/AstrobioMike/GToTree/wiki">GToTree</a><span>&nbsp;is a user-friendly workflow for phylogenomics intended to give more researchers the capability to create phylogenomic trees. The open-access Bioinformatics Journal publication is available&nbsp;</span><a href="https://doi.org/10.1093/bioinformatics/btz188">here</a><span>, and documentation and examples can be found&nbsp;</span><a href="https://github.com/AstrobioMike/GToTree/wiki">at the wiki here</a><span>.</span></p><p>Address of the bookmark: <a href="https://github.com/AstrobioMike/GToTree" rel="nofollow">https://github.com/AstrobioMike/GToTree</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33479/novelseq-novel-sequence-insertion-detection</guid>
	<pubDate>Fri, 09 Jun 2017 04:31:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33479/novelseq-novel-sequence-insertion-detection</link>
	<title><![CDATA[NovelSeq: Novel Sequence Insertion Detection]]></title>
	<description><![CDATA[<p><span>The NovelSeq framework is designed to detect novel sequence insertions using high throughput paired-end whole genome sequencing data.</span></p>
<p>http://novelseq.sourceforge.net/Home</p>
<p>Paper at&nbsp;https://www.ncbi.nlm.nih.gov/pubmed/20385726</p><p>Address of the bookmark: <a href="http://novelseq.sourceforge.net/Home" rel="nofollow">http://novelseq.sourceforge.net/Home</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41144/seqmule-automated-human-exomegenome-variants-detection</guid>
	<pubDate>Tue, 18 Feb 2020 03:22:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41144/seqmule-automated-human-exomegenome-variants-detection</link>
	<title><![CDATA[SeqMule: Automated human exome/genome variants detection]]></title>
	<description><![CDATA[<p>SeqMule takes single-end or paird-end FASTQ or BAM files, generates a script consisting of more than 10 popular alignment, analysis tools and runs the script line by line. Users can change the pipeline or fine-tune the parameters by modifying its configuration file.</p><p>Address of the bookmark: <a href="https://doc-openbio.readthedocs.io/projects/seqmule/en/latest/" rel="nofollow">https://doc-openbio.readthedocs.io/projects/seqmule/en/latest/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44527/alvis-a-tool-for-contig-and-read-alignment-visualisation-and-chimera-detection</guid>
	<pubDate>Wed, 08 May 2024 07:02:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44527/alvis-a-tool-for-contig-and-read-alignment-visualisation-and-chimera-detection</link>
	<title><![CDATA[Alvis: a tool for contig and read ALignment VISualisation and chimera detection]]></title>
	<description><![CDATA[<p><span>Alvis, a simple command line tool that can generate visualisations for a number of common alignment analysis tasks. Alvis is a fast and portable tool that accepts input in a variety of alignment formats and will output production ready vector images. Additionally, Alvis will highlight potentially chimeric reads or contigs, a common source of misassemblies.</span></p>
<p>More at&nbsp;https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04056-0</p><p>Address of the bookmark: <a href="https://github.com/SR-Martin/alvis" rel="nofollow">https://github.com/SR-Martin/alvis</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/859/boku-chair-of-bioinformatics</guid>
  <pubDate>Sun, 14 Jul 2013 12:37:23 -0500</pubDate>
  <link></link>
  <title><![CDATA[Boku Chair of Bioinformatics]]></title>
  <description><![CDATA[
<p>The Bioinformatics group at Boku University has two main areas of interest, underpinning a common goal, the study of complex systems in living organisms. To overcome the engineered redundancies and combinatorial effects prevalent in higher eukaryotes, novel views augmenting the classical gene by gene approaches are required. We combine<br />Work to establish improved quantitative experimental assays (such as microarrays or differential in-gel electrophoresis) and<br />Development of modern computational methods (such as hierarchical probabilistic models or integration of heterogeneous data sources)</p>

<p>Link @ http://bioinf.boku.ac.at/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/8798/list-of-gene-ontology-software-and-tools</guid>
	<pubDate>Sun, 09 Mar 2014 14:48:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/8798/list-of-gene-ontology-software-and-tools</link>
	<title><![CDATA[List of gene ontology software and tools]]></title>
	<description><![CDATA[<p>The Gene Ontology (GO) is a set of associations from biological phrases to specific genes that are either chosen by trained curators or generated automatically. GO is designed to rigorously encapsulate the known relationships between biological terms and and all genes that are instances of these terms. These Gene Ontology has become an extremely useful tool for the analysis of genomic data and structuring of biological knowledge. Several excellent software tools for navigating the gene ontology have been developed.</p><p><img src="http://ohnosequences.com/images/GoSlimBlog.svg" alt="image" width="500" height="380" style="border: 0px; border: 0px;"></p><p>The GO provides core biological knowledge representation for modern biologists, whether computationally or experimentally based. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Although extensively used in data analysis workflows, and widely incorporated into numerous data analysis platforms and applications, the general user of GO resources often misses fundamental distinctions about GO structures, GO annotations, and what can and can not be extrapolated from GO resources. Here are ten quick tips for using the Gene Ontology.</p><p>Read "Ten Quick Tips for Using the Gene Ontology" at http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003343</p><p>Following are the most commonly used old and new GO term enrichment determination tools. These tools are recommended to people working in a wet-lab.</p><p><strong>CLASSIFI (Department of Pathology, UT Southwestern Medical Center)</strong></p><p>CLASSIFI (Cluster Assignment for Biological Inference) is a data-mining tool that can be used to identify significant co-clustering of genes with similar functional properties (e.g. cellular response to DNA damage). Briefly, CLASSIFI uses the Gene OntologyTM (GO) gene annotation scheme to define the functional properties of all genes/probes in a microarray data set, and then applies a cumulative hypergeometric distribution analysis to determine if any statistically significant gene ontology co-clustering has occurred.</p><p><a href="http://pathcuric1.swmed.edu/pathdb/classifi.html">http://pathcuric1.swmed.edu/pathdb/classifi.html</a></p><p><strong>EasyGO (China Agricultural University)</strong></p><p>EasyGO is designed to automate enrichment job for experimental biologists to identify enriched Gene Ontology (GO) terms in a list of microarray probe sets or gene identifiers (with expression information for PAGE analysis). Also EasyGO is also a GO annotation database, especially focus on agronomical species, supporting 30 species. It is user friendly, with advanced result browsing format and in-time update.</p><p><a href="http://bioinformatics.cau.edu.cn/neweasygo/">http://bioinformatics.cau.edu.cn/neweasygo/</a></p><p><a href="http://bioinformatics.cau.edu.cn/easygo/">http://bioinformatics.cau.edu.cn/easygo/</a></p><p><strong>g:GOSt (Institute of Computer Science, University of Tartu)</strong></p><p>g:GOSt retrieves most significant Gene Ontology (GO) terms, KEGG and REACTOME pathways, and TRANSFAC motifs to a user-specified group of genes, proteins or microarray probes. g:GOSt also allows analysis of ranked or ordered lists of genes, visual browsing of GO graph structure, interactive visualisation of retrieved results, and many other features. Multiple testing corrections are applied to extract only statistically important results.</p><p><a href="http://biit.cs.ut.ee/gprofiler/">http://biit.cs.ut.ee/gprofiler/</a></p><p><strong>DAVID</strong> : Gene Functional Classification (Laboratory of Immunopathogenesis and Bioinformatics, NIAID)</p><p>The Functional Classification Tool provides a rapid means to organize large lists of genes into functionally related groups to help unravel the biological content captured by high throughput technologies.</p><p><a href="http://david.abcc.ncifcrf.gov/gene2gene.jsp">http://david.abcc.ncifcrf.gov/gene2gene.jsp</a></p><p><a href="http://david.abcc.ncifcrf.gov/">http://david.abcc.ncifcrf.gov/</a></p><p>API <a href="https://github.com/chrisamiller/davidapi">https://github.com/chrisamiller/davidapi</a></p><p><strong>GOEAST</strong> (Institute of Genetics and Developmental Biology, Chinese Academy of Sciences)</p><p>GOEAST is web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods.</p><p><a href="http://omicslab.genetics.ac.cn/GOEAST/">http://omicslab.genetics.ac.cn/GOEAST/</a></p><p><strong>GOstat</strong> (Walter and Eliza Hall Institute of Medical Research)</p><p>Find statistically overrepresented GO terms within a group of genes</p><p><a href="http://gostat.wehi.edu.au/">http://gostat.wehi.edu.au/</a></p><p><strong>GOrilla</strong> (Technion - Laboratory of Computational Biology , Israel Institute of Technology)</p><p>GOrilla is a tool for identifying and visualizing enriched GO terms in ranked lists of genes.<br /> It uses two approaches, first by searching for enriched GO terms that appear densely at the top of a ranked list of genes&nbsp; or by searching for enriched GO terms in a target list of genes compared to a background list of genes.</p><p><a href="http://cbl-gorilla.cs.technion.ac.il/">GOrilla</a> makes nice pictures !!!!</p><p><a href="http://cbl-gorilla.cs.technion.ac.il/">http://cbl-gorilla.cs.technion.ac.il/</a></p><p><strong>Gene Ontology for Functional Analysis (GOFFA)</strong></p><p>GOFFA is a tool developed for ArrayTrack&trade; that takes a list of genes and identifies terms in Gene Ontology (GO) disclaimer icon associated with those genes.</p><p>It provides several tools to view/access the GO term hierarchy, full listing of GO terms annotated with the genes associated with a given term with statically useful report.</p><p><a href="http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm233315.htm">http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm233315.htm</a></p><p><strong>GOAT</strong> (The University of Manchester)</p><p>The aim of the GOAT project is to create an application that will guide users, especially biomedical researchers, in the annotation of gene products with terms from the <a href="http://www.geneontology.org">Gene Ontology</a>.</p><p><a href="http://goat.man.ac.uk/">http://goat.man.ac.uk/</a></p><p>Script <a href="https://github.com/tanghaibao/goatools/">https://github.com/tanghaibao/goatools/</a></p><p><strong>REVIGO</strong> ( Rudjer Boskovic Institute, Croatia)</p><p>REViGO is a web server that can take long lists of Gene Ontology terms and summarize them by removing redundant GO terms. The remaining terms can be visualized in semantic similarity-based scatterplots, interactive graphs, or tag clouds.</p><p><a href="http://revigo.irb.hr/">http://revigo.irb.hr/</a></p><p><strong>QuickGo</strong> (EMBL-EBI Institute)</p><p>It uses extensive computational filters to allow the generation of specific subsets of GO annotations, mapped to sequence identifiers of your choice. Then GO slims are used which is collective list of GO full set of terms available from the Gene Ontology project.</p><p><a href="http://www.ebi.ac.uk/QuickGO/">http://www.ebi.ac.uk/QuickGO/</a></p><p><strong>GOLEM</strong></p><p>An interactive graph-based gene-ontology navigation and analysis tool. GOLEM is a userful tool which allows the viewer to navigate and explore a local portion of the <a href="http://www.geneontology.org/">Gene Ontology</a> (GO) hierarchy.</p><p><a href="http://reducio.princeton.edu/GOLEM/">http://reducio.princeton.edu/GOLEM/</a></p><p><strong>BGI Web Gene Ontology (WEGO)</strong> Annotation Plot (Beijing Genomics Institute)</p><p>WEGO () is a useful tool for plotting GO annotation results. It has been widely used in many important biological research projects, such as the rice genome project [<a href="http://wego.genomics.org.cn/pubs/rice_indica.pdf">Yu, J. et al. Science 296, 79-92 (2002);</a> <a href="http://wego.genomics.org.cn/pubs/rice_finish.pdf">Yu, J. et al. PLoS Biol 3, e38 (2005)</a>] and the silkworm genome project [<a href="http://wego.genomics.org.cn/pubs/combine_silkworm.pdf">Xia, Q. et al. Science 306, 1937-40 (2004)</a>]. It has become one of the daily tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. WEGO along with two other tools, namely <a href="http://wego.genomics.org.cn/cgi-bin/wego/External2GO.pl">External to GO Query</a> and <a href="http://wego.genomics.org.cn/cgi-bin/wego/GOArchive.pl">GO Archive Query</a>, are freely available for all users. Any suggestions are welcome at <a href="mailto:%20wego@genomics.org.cn">wego@genomics.org.cn</a>. Here is a sample output generated by WEGO</p><p><a href="http://wego.genomics.org.cn/cgi-bin/wego/index.pl">http://wego.genomics.org.cn/cgi-bin/wego/index.pl</a></p><p><strong>GeneGO MetaCore</strong> (MIT)</p><p>GeneGo is a leading provider of data mining &amp; analysis solutions in systems biology. MetaCore, GeneGo's flapship product, is an integrated software suite for functional analysis of experimental data. MetaCore is based on a curated database of human protein-protein, protein-DNA interactions, transcription factors, signaling and metabolic pathways, disease and toxicity, and the effects of bioactive molecules.</p><p><a href="https://portal.genego.com/">https://portal.genego.com/</a></p><p><strong>GOEx</strong> (Stony Brook University)</p><p>GOEx facilitates organism-specific studies by leveraging GO and providing a rich graphical user interface. It is a simple to use tool, specialized for biologists who wish to analyze spectral counting data from shotgun proteomics.</p><p><a href="http://pcarvalho.com/patternlab">http://pcarvalho.com/patternlab</a></p><p><strong>GOssTo</strong></p><p>GOssTo and GOssToWeb are tools to calculate the <a href="https://en.wikipedia.org/wiki/Semantic_similarity#Biomedical_Informatics">semantic similarity</a> between genes or terms in the <a href="http://www.geneontology.org/">Gene Ontology</a>.</p><p><a href="http://www.paccanarolab.org/gosstoweb/">http://www.paccanarolab.org/gosstoweb/</a></p><p><strong>GO Workbench</strong></p><p>The Gene Ontology Analysis Viewer allows direct browsing of the Gene Ontology, and also the visualization of GO Term analysis results.</p><p><a href="http://wiki.c2b2.columbia.edu/workbench/index.php/Gene_Ontology_Viewer">http://wiki.c2b2.columbia.edu/workbench/index.php/Gene_Ontology_Viewer</a></p><p>Some other useful list of GO software and tools is available at <a href="http://www.geneontology.org/GO.tools.shtml#browser">http://www.geneontology.org/GO.tools.shtml#browser</a></p><p>Yet another useful webpage with list of GO tools at <a href="http://neurolex.org/wiki/Category:Resource:Gene_Ontology_Tools">http://neurolex.org/wiki/Category:Resource:Gene_Ontology_Tools</a></p><p>&nbsp;</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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