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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/34685?offset=200</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data</guid>
	<pubDate>Fri, 02 Feb 2018 03:24:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35418/karyoploter-plot-whole-genomes-with-arbitrary-data</link>
	<title><![CDATA[karyoploteR: plot whole genomes with arbitrary data]]></title>
	<description><![CDATA[<p><span><a href="http://bioconductor.org/packages/karyoploteR">karyoploteR</a></span><span>&nbsp;is an R package to create karyoplots, that is, representations of whole genomes with arbitrary data plotted on them. It is inspired by the R base graphics system and does not depend on other graphics packages. The aim of karyoploteR is to offer the user an easy way to plot data along the genome to get broad genome-wide view to facilitate the identification of genome wide relations and distributions.</span></p><p>Address of the bookmark: <a href="https://bernatgel.github.io/karyoploter_tutorial/" rel="nofollow">https://bernatgel.github.io/karyoploter_tutorial/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/3925/genome-annotation</guid>
	<pubDate>Sun, 25 Aug 2013 10:53:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/3925/genome-annotation</link>
	<title><![CDATA[Genome Annotation]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/on4TMnuYTaU" frameborder="0" allowfullscreen></iframe>Dr. Rob Edwards describes some of the problems, challenges, and approches in genome annotation, with a particular emphasis on how the Fellowship for the Interpretation of Genomes (FIG) developed subsystems using the SEED database available at http://www.theseed.org/]]></description>
	
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26378/centurion</guid>
	<pubDate>Fri, 12 Feb 2016 04:45:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26378/centurion</link>
	<title><![CDATA[Centurion]]></title>
	<description><![CDATA[<p>Although centromeres are essential for life and are the subject of extensive research, centromere locations in yeast genomes are difficult to infer, and in most species they are still unknown. Recently, the chromatin conformation assay Hi-C has been re-purposed for diverse applications, including de novo genome assembly, deconvolution of metagenomic samples, and inference of centromere locations. We describe a method, Centurion, that jointly infers the locations of all centromeres in a single yeast genome by exploiting the centromeres&rsquo; tendency to cluster in 3D space. We first demonstrate the accuracy of Centurion in identifying known centromere locations from high coverage Hi-C data of budding yeast and a human malaria parasite. We then use two metagenomic samples with relatively low coverage Hi-C data to infer centromere locations for each chromosome in 14 different yeast species. For yeasts with large centromeres (e.g., S. pombe) Centurion predicts the exact centromere locations. For seven yeasts with point centromeres, Centurion predicts most of the centromeres at an average of 5~kb distance from their known locations. Finally, we predict centromere coordinates for six yeast species that currently lack centromere annotations. These results suggest that Centurion can be used for centromere identification for a large number of yeast species, even with a limited amount of Hi-C sequencing.</p>
<p>Paper:http://www.ncbi.nlm.nih.gov/pubmed/25940625</p>
<p>More at http://cbio.ensmp.fr/centurion/</p><p>Address of the bookmark: <a href="http://cbio.ensmp.fr/centurion/" rel="nofollow">http://cbio.ensmp.fr/centurion/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33741/diya-a-bacterial-annotation-pipeline-for-any-genomics-lab</guid>
	<pubDate>Fri, 30 Jun 2017 08:48:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33741/diya-a-bacterial-annotation-pipeline-for-any-genomics-lab</link>
	<title><![CDATA[DIYA: a bacterial annotation pipeline for any genomics lab]]></title>
	<description><![CDATA[<p><span>DIY Genomics is an open source bioinformatics consortium intended to bring a collection of tools and libraries into the hands of small scale genomics labs for the process of sequence assembly and annotation. Projects include DIYA, MGAP, CRISPR, and DIYGV</span></p>
<p><span>http://gmod.org/wiki/Diya</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/diyg/" rel="nofollow">https://sourceforge.net/projects/diyg/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39244/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes</guid>
	<pubDate>Fri, 12 Apr 2019 05:30:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39244/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes</link>
	<title><![CDATA[chromoMap-An R package for Interactive Visualization and Annotation of Chromosomes]]></title>
	<description><![CDATA[<p>Provides interactive, configurable and elegant graphics visualization of the chromosomes or chromosome regions of any living organism allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. The package provide multiple features like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling. The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R 'Shiny' applications.</p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html" rel="nofollow">https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44585/dram-distilled-and-refined-annotation-of-metabolism</guid>
	<pubDate>Sat, 06 Jul 2024 04:19:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44585/dram-distilled-and-refined-annotation-of-metabolism</link>
	<title><![CDATA[DRAM: Distilled and Refined Annotation of Metabolism]]></title>
	<description><![CDATA[<p><span>DRAM (Distilled and Refined Annotation of Metabolism) is a tool for annotating metagenomic assembled genomes and&nbsp;</span><a href="https://github.com/simroux/VirSorter">VirSorter</a><span>&nbsp;identified viral contigs. DRAM annotates MAGs and viral contigs using&nbsp;</span><a href="https://www.kegg.jp/">KEGG</a><span>&nbsp;(if provided by the user),&nbsp;</span><a href="https://www.uniprot.org/">UniRef90</a><span>,&nbsp;</span><a href="https://pfam.xfam.org/">PFAM</a><span>,&nbsp;</span><a href="http://bcb.unl.edu/dbCAN2/">dbCAN</a><span>,&nbsp;</span><a href="https://www.ncbi.nlm.nih.gov/genome/viruses/">RefSeq viral</a><span>,&nbsp;</span><a href="http://vogdb.org/">VOGDB</a><span>&nbsp;and the&nbsp;</span><a href="https://www.ebi.ac.uk/merops/">MEROPS</a><span>&nbsp;peptidase database as well as custom user databases. DRAM is run in two stages. First an annotation step to assign database identifiers to gene, and then a distill step to curate these annotations into useful functional categories. Additionally, viral contigs are further analyzed during to identify potential AMGs. This is done via assigning an auxiliary score and flags representing the confidence that a gene is both metabolic and viral.</span></p>
<p><img src="https://genomicsaotearoa.github.io/metagenomics_summer_school/figures/ex14_DRAM_annotation_rank.png" alt="image" style="border: 0px;"></p>
<p>Ref&nbsp;https://genomicsaotearoa.github.io/metagenomics_summer_school/day4/ex15_gene_annotation_part3/#overview-of-drampy-annotate-output&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/WrightonLabCSU/DRAM" rel="nofollow">https://github.com/WrightonLabCSU/DRAM</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34501/dnapipete-de-novo-assembly-annotation-pipeline-for-transposable-elements</guid>
	<pubDate>Sat, 02 Dec 2017 18:25:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34501/dnapipete-de-novo-assembly-annotation-pipeline-for-transposable-elements</link>
	<title><![CDATA[dnaPipeTE: de-novo assembly &amp; annotation Pipeline for Transposable Elements]]></title>
	<description><![CDATA[<p>dnaPipeTE (for de-novo assembly &amp; annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (&lt; 1X).</p>
<ul>
<li>
<p>dnaPipeTE is developped by Cl&eacute;ment Goubert, Laurent Modolo and the TREEP team of the LBBE:&nbsp;<a href="http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en">http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en</a></p>
</li>
<li>
<p>You can find the original publication in GBE here:&nbsp;<a href="https://academic.oup.com/gbe/article/7/4/1192/533768">https://academic.oup.com/gbe/article/7/4/1192/533768</a></p>
</li>
</ul>
<p><a href="https://github.com/clemgoub/dnaPipeTE/blob/dev/dnaPipefront.png" target="_blank"><img src="https://github.com/clemgoub/dnaPipeTE/raw/dev/dnaPipefront.png" alt="Front" style="border: 0px;"></a><em>output examples of quantification and TE landscape (relative age) produced by dnaPipeTE</em></p>
<p><em>&nbsp;</em></p><p>Address of the bookmark: <a href="https://github.com/clemgoub/dnaPipeTE" rel="nofollow">https://github.com/clemgoub/dnaPipeTE</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39370/multiphate-bioinformatics-pipeline-for-functional-annotation-of-phage-isolates</guid>
	<pubDate>Thu, 16 May 2019 00:17:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39370/multiphate-bioinformatics-pipeline-for-functional-annotation-of-phage-isolates</link>
	<title><![CDATA[multiPhATE: bioinformatics pipeline for functional annotation of phage isolates]]></title>
	<description><![CDATA[<p><span>multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a&nbsp;</span><em>de novo</em><span>&nbsp;phage gene-calling algorithm and assigns putative functions to gene calls using protein-, virus-, and phage-centric databases.&nbsp;</span></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/carolzhou/multiPhATE" rel="nofollow">https://github.com/carolzhou/multiPhATE</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</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/42530/shovill-assemble-bacterial-isolate-genomes-from-illumina-paired-end-reads</guid>
	<pubDate>Sat, 02 Jan 2021 07:05:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42530/shovill-assemble-bacterial-isolate-genomes-from-illumina-paired-end-reads</link>
	<title><![CDATA[shovill: Assemble bacterial isolate genomes from Illumina paired-end reads]]></title>
	<description><![CDATA[<p><span>Shovill is a pipeline which uses SPAdes at its core, but alters the steps before and after the primary assembly step to get similar results in less time. Shovill also supports other assemblers like SKESA, Velvet and Megahit, so you can take advantage of the pre- and post-processing the Shovill provides with those too.</span></p><p>Address of the bookmark: <a href="https://github.com/tseemann/shovill" rel="nofollow">https://github.com/tseemann/shovill</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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