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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42619?</link>
	<atom:link href="https://bioinformaticsonline.com/related/42619?" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</guid>
	<pubDate>Sun, 12 Apr 2020 10:02:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</link>
	<title><![CDATA[WEGO : simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results]]></title>
	<description><![CDATA[<p><span>WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. As the GO vocabulary became more and more popular, WEGO was widely adopted and used in many researches. Therefore we have updated WEGO 2.0 in 2018. Here are some changes we&rsquo;ve made:</span><br><span>1. The limit of input file numbers was cancelled. Now the users could upload as many files as they want with one operation.</span><br><span>2. We have added the reference data of 9 species for users selection.</span><br><span>3. Besides the traditional WEGO histogram, WEGO 2.0 outputs an additional type of bar graph showing GO terms with significant gene number differences.</span></p><p>Address of the bookmark: <a href="http://wego.genomics.org.cn/" rel="nofollow">http://wego.genomics.org.cn/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37965/kobas-a-web-server-for-geneprotein-functional-annotation-and-functional-gene-set-enrichment</guid>
	<pubDate>Fri, 19 Oct 2018 09:36:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37965/kobas-a-web-server-for-geneprotein-functional-annotation-and-functional-gene-set-enrichment</link>
	<title><![CDATA[KOBAS: a web server for gene/protein functional annotation and functional gene set enrichment]]></title>
	<description><![CDATA[<p><span>KOBAS 3.0 is a web server for gene/protein functional annotation (</span><a href="http://kobas.cbi.pku.edu.cn/annotate.php">Annotate</a><span>&nbsp;module) and functional gene set enrichment(Enrichment module). For Annotate module, it accepts gene list as input, including IDs or sequences, and generates annotations for each gene based on multiple databases about pathways, diseases, and Gene Ontology. For Enrichment module, it can accept either gene list or gene expression data as input, and generates enriched gene sets, corresponding name, p-value or a probability of enrichment and enrichment score based on results of multiple methods.</span></p><p>Address of the bookmark: <a href="http://kobas.cbi.pku.edu.cn/" rel="nofollow">http://kobas.cbi.pku.edu.cn/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42035/pannzer-a-fully-automated-service-for-functional-annotation-of-prokaryotic-and-eukaryotic-proteins-of-unknown-function</guid>
	<pubDate>Thu, 13 Aug 2020 09:57:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42035/pannzer-a-fully-automated-service-for-functional-annotation-of-prokaryotic-and-eukaryotic-proteins-of-unknown-function</link>
	<title><![CDATA[PANNZER: a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function.]]></title>
	<description><![CDATA[<p><strong>PANNZER</strong>&nbsp;(Protein ANNotation with Z-scoRE) is a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function.</p>
<p><strong>PANNZER</strong>&nbsp;(Protein ANNotation with Z-scoRE) is a fully automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function. The tool is designed to predict the functional description (DE) and GO classes.</p>
<p>PANNZER2 processes bacterial proteomes in minutes and eukaryotic proteomes in an hour. You can use&nbsp;<a href="http://ekhidna2.biocenter.helsinki.fi/AAI/">AAI-profiler</a>&nbsp;to summarize a proteome's species neighbors and reveal taxonomic identity or contamination.</p><p>Address of the bookmark: <a href="http://ekhidna2.biocenter.helsinki.fi/sanspanz/#" rel="nofollow">http://ekhidna2.biocenter.helsinki.fi/sanspanz/#</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38752/hgtector-an-automated-method-facilitating-genome-wide-discovery-of-putative-horizontal-gene-transfers</guid>
	<pubDate>Mon, 21 Jan 2019 06:50:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38752/hgtector-an-automated-method-facilitating-genome-wide-discovery-of-putative-horizontal-gene-transfers</link>
	<title><![CDATA[HGTector: an automated method facilitating genome-wide discovery of putative horizontal gene transfers]]></title>
	<description><![CDATA[<p>A computational pipeline for genome-wide detection of putative horizontal gene transfer (HGT) events based on sequence homology search hit distribution statistics</p>
<p>Authors: Qiyun Zhu (<a href="mailto:qiyunzhu@gmail.com">qiyunzhu@gmail.com</a>), Katharina Dittmar (<a href="mailto:katharinad@gmail.com">katharinad@gmail.com</a>)</p>
<p>Affiliation: Department of Biological Sciences, University at Buffalo, State University of New York, Buffalo, USA</p>
<p>Zhu Q, Kosoy M, Dittmar K. HGTector: an automated method facilitating genome-wide discovery of putative horizontal gene transfers.&nbsp;<em style="font-size: 12.8px;">BMC Genomics</em>. 2014. 15:717.</p>
<p>Usage: Simply execute&nbsp;<span style="font-size: 12.8px;">perl HGTector.pl</span>, or, open&nbsp;<span style="font-size: 12.8px;">GUI.html</span>&nbsp;in a web browser to see a step-by-step wizard.</p>
<p>Download&nbsp;<a href="https://github.com/DittmarLab/HGTector/archive/0.2.2.zip">HGTector 0.2.2</a>.</p><p>Address of the bookmark: <a href="https://github.com/DittmarLab/HGTector" rel="nofollow">https://github.com/DittmarLab/HGTector</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</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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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37746/funannotate-eukaryotic-genome-annotation-pipeline</guid>
	<pubDate>Wed, 19 Sep 2018 07:47:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37746/funannotate-eukaryotic-genome-annotation-pipeline</link>
	<title><![CDATA[funannotate: Eukaryotic Genome Annotation Pipeline]]></title>
	<description><![CDATA[<p><span>Funannotate is a genome prediction, annotation, and comparison software package. It was originally written to annotate fungal genomes (small eukaryotes ~ 30 Mb genomes), but has evolved over time to accomodate larger genomes. The impetus for this software package was to be able to accurately and easily annotate a genome for submission to NCBI GenBank. Existing tools (such as Maker) require significant manually editing to comply with GenBank submission rules, thus funannotate is aimed at simplifying the genome submission process.</span></p><p>Address of the bookmark: <a href="https://github.com/nextgenusfs/funannotate" rel="nofollow">https://github.com/nextgenusfs/funannotate</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42491/eukulele-taxonomic-annotation-of-the-unsung-eukaryotic-microbes</guid>
	<pubDate>Sat, 26 Dec 2020 12:10:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42491/eukulele-taxonomic-annotation-of-the-unsung-eukaryotic-microbes</link>
	<title><![CDATA[EUKulele: Taxonomic annotation of the unsung eukaryotic microbes]]></title>
	<description><![CDATA[<p><span><span>&nbsp;</span>EUKulele, an open-source software tool designed to assign taxonomy to microeukaryotes detected in meta-omic samples, and complement analysis approaches in other domains by accommodating assembly output and providing concrete metrics reporting the taxonomic completeness of each sample.</span></p><p>Address of the bookmark: <a href="https://github.com/AlexanderLabWHOI/EUKulele" rel="nofollow">https://github.com/AlexanderLabWHOI/EUKulele</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43877/crowdgo-machine-learning-and-semantic-similarity-guided-consensus-gene-ontology-annotation</guid>
	<pubDate>Thu, 26 May 2022 00:59:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43877/crowdgo-machine-learning-and-semantic-similarity-guided-consensus-gene-ontology-annotation</link>
	<title><![CDATA[CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation]]></title>
	<description><![CDATA[<p dir="auto">CrowdGO is a protein Gene Ontology predictor using a meta approach, analyzing the predictions of other tools in order to get an improved precision and recall.</p>
<p dir="auto">Please note that the CrowdGO snakemake workflow is currently only tested on Ubuntu. It should work on OSX, but please report any errors to <a href="mailto:maarten.reijnders@unil.ch">maarten.reijnders@unil.ch</a> or create an issue.</p>
<p>https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010075</p><p>Address of the bookmark: <a href="https://gitlab.com/mreijnders/crowdgo" rel="nofollow">https://gitlab.com/mreijnders/crowdgo</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34862/pasa-gene-structure-annotation-and-analysis</guid>
	<pubDate>Tue, 26 Dec 2017 21:14:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34862/pasa-gene-structure-annotation-and-analysis</link>
	<title><![CDATA[PASA: Gene Structure Annotation and Analysis]]></title>
	<description><![CDATA[<p><span>PASA, acronym for Program to Assemble Spliced Alignments, is a eukaryotic genome annotation tool that exploits spliced alignments of expressed transcript sequences to automatically model gene structures, and to maintain gene structure annotation consistent with the most recently available experimental sequence data. PASA also identifies and classifies all splicing variations supported by the transcript alignments.</span></p><p>Address of the bookmark: <a href="http://pasapipeline.github.io/" rel="nofollow">http://pasapipeline.github.io/</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36730/bprna-large-scale-automated-annotation-and-analysis-of-rna-secondary-structure</guid>
	<pubDate>Wed, 23 May 2018 03:24:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36730/bprna-large-scale-automated-annotation-and-analysis-of-rna-secondary-structure</link>
	<title><![CDATA[bpRNA: large-scale automated annotation and analysis of RNA secondary structure]]></title>
	<description><![CDATA[<p>bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature.</p>
<p>The bpRNA code is written in perl and requires the Graph perl module. Several additional scripts for analysis are included. The source code is available at http://github.com/hendrixlab/bpRNA.</p><p>Address of the bookmark: <a href="http://github.com/hendrixlab/bpRNA" rel="nofollow">http://github.com/hendrixlab/bpRNA</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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