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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/2518?offset=80</link>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/35752/hejnol-group</guid>
  <pubDate>Thu, 22 Feb 2018 16:02:53 -0600</pubDate>
  <link></link>
  <title><![CDATA[Hejnol Group]]></title>
  <description><![CDATA[
<p>The group studies a broad range of animal taxa using morphological and molecular tools to unravel the evolution and development of animal organ systems.</p>

<p>To understand the evolution of the biodiversity seen on planet earth is one of the major goals in biology. How animals explored new habitats from only being confined to the marine environment and the how the forms diversified is still one of the most tremendous questions to be answered.</p>

<p>http://www.sars.no/research/HejnolGrp.php</p>
]]></description>
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<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/26309/ratt</guid>
	<pubDate>Sun, 07 Feb 2016 16:09:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26309/ratt</link>
	<title><![CDATA[RATT]]></title>
	<description><![CDATA[<p><strong>RATT</strong> is software to transfer annotation from a reference (annotated) genome to an unannotated query genome.</p>
<p>It was first developed to transfer annotations between different genome assembly versions. However, it can also transfer annotations between strains and even different species, like <em>Plasmodium chabaudi</em> onto <em> P. berghei</em>, between different Leishmania species or <em>Salmonella enterica</em> onto other Salmonella serotypes. <strong>RATT</strong> is able to transfer any entries present on a reference sequence, such as the systematic id or an annotator's notes; such information would be lost in a <em>de novo</em> annotation.</p>
<p>More at http://ratt.sourceforge.net/</p><p>Address of the bookmark: <a href="http://ratt.sourceforge.net/" rel="nofollow">http://ratt.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26380/hicdat</guid>
	<pubDate>Fri, 12 Feb 2016 05:23:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26380/hicdat</link>
	<title><![CDATA[HiCdat]]></title>
	<description><![CDATA[<p>HiCdat: a fast and easy-to-use Hi-C data analysis tool</p>
<p>HiCdat is easy-to-use and provides solutions starting from aligned reads up to in-depth analyses. Importantly, HiCdat is focussed on the analysis of larger structural features of chromosomes, their correlation to genomic and epigenomic features, and on comparative studies. It uses simple input and output formats and can therefore easily be integrated into existing workflows or combined with alternative tools.</p>
<p>More at http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0678-x</p><p>Address of the bookmark: <a href="https://github.com/MWSchmid/HiCdat" rel="nofollow">https://github.com/MWSchmid/HiCdat</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30149/mypro-a-seamless-pipeline-for-automated-prokaryotic-genome-assembly-and-annotation</guid>
	<pubDate>Thu, 15 Dec 2016 05:47:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30149/mypro-a-seamless-pipeline-for-automated-prokaryotic-genome-assembly-and-annotation</link>
	<title><![CDATA[MyPro: A seamless pipeline for automated prokaryotic genome assembly and annotation]]></title>
	<description><![CDATA[<p>MyPro is an improved genomics software pipeline for prokaryotic genomes. MyPro is user-friendly and requires minimal programming skills. High-quality prokaryotic genome assembly and annotation can be obtained with ease. It performed better than de novo assemblers and contig integration software. Produces more contiguous assemblies, higher N50 values and lower number of contigs.</p>
<p>More at https://sourceforge.net/projects/sb2nhri/files/MyPro/</p><p>Address of the bookmark: <a href="http://www.sciencedirect.com/science/article/pii/S0167701215001207" rel="nofollow">http://www.sciencedirect.com/science/article/pii/S0167701215001207</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32730/ncbi-prokaryotic-genome-annotation-pipeline</guid>
	<pubDate>Tue, 16 May 2017 08:56:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32730/ncbi-prokaryotic-genome-annotation-pipeline</link>
	<title><![CDATA[NCBI Prokaryotic Genome Annotation Pipeline]]></title>
	<description><![CDATA[<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>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. You can find a more detailed description of the new version of&nbsp;the pipeline in&nbsp;<a href="https://www.ncbi.nlm.nih.gov/books/NBK174280/">NCBI Handbook chapter</a>. 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>https://www.ncbi.nlm.nih.gov/genome/annotation_prok/</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/genome/annotation_prok/" rel="nofollow">https://www.ncbi.nlm.nih.gov/genome/annotation_prok/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39453/fuma-gwas-functional-mapping-and-annotation-of-genome-wide-association-studies</guid>
	<pubDate>Sat, 01 Jun 2019 03:11:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39453/fuma-gwas-functional-mapping-and-annotation-of-genome-wide-association-studies</link>
	<title><![CDATA[FUMA GWAS: Functional Mapping and Annotation of Genome-Wide Association Studies]]></title>
	<description><![CDATA[<p><span>FUMA is a platform that can be used to annotate, prioritize, visualize and interpret GWAS results.&nbsp;</span><br><span>The&nbsp;</span><a href="https://fuma.ctglab.nl/snp2gene">SNP2GENE</a><span>&nbsp;function takes GWAS summary statistics as an input, and provides extensive functional annotation for all SNPs in genomic areas identified by lead SNPs.&nbsp;</span><br><span>The&nbsp;</span><a href="https://fuma.ctglab.nl/gene2func">GENE2FUNC</a><span>&nbsp;function takes a list of gene IDs (as identified by SNP2GENE or as provided manually) and annotates genes in biological context&nbsp;</span></p><p>Address of the bookmark: <a href="https://fuma.ctglab.nl/" rel="nofollow">https://fuma.ctglab.nl/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44892/grannot</guid>
	<pubDate>Sun, 31 Aug 2025 06:21:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44892/grannot</link>
	<title><![CDATA[GrAnnoT]]></title>
	<description><![CDATA[<p>GrAnnoT is an annotation transfer tool for pangenome graphs. It can transfer linear genome annotations to a pangenome graph containing the genome, and also transfer the pangenome graph's annotations on the genomes it contains. It also outputs complementary information such as the alignments of the transfered genes, or a presence-absence matrix.</p><p>Address of the bookmark: <a href="https://forge.ird.fr/diade/dynadiv/grannot" rel="nofollow">https://forge.ird.fr/diade/dynadiv/grannot</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<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>
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