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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41459?offset=490</link>
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	<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>
</item>
<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/38659/detail-annotation-of-genes</guid>
	<pubDate>Fri, 11 Jan 2019 05:23:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38659/detail-annotation-of-genes</link>
	<title><![CDATA[Detail annotation of genes !]]></title>
	<description><![CDATA[<p>gene_info recalculated daily<br>---------------------------------------------------------------------------<br> tab-delimited<br> one line per GeneID<br> Column header line is the first line in the file.<br> Note: subsets of gene_info are available in the DATA/GENE_INFO<br> directory (described later)<br>---------------------------------------------------------------------------</p>
<p>tax_id:<br> the unique identifier provided by NCBI Taxonomy<br> for the species or strain/isolate</p>
<p>GeneID:<br> the unique identifier for a gene<br> ASN1: geneid</p>
<p>Symbol:<br> the default symbol for the gene<br> ASN1: gene-&gt;locus</p>
<p>LocusTag:<br> the LocusTag value<br> ASN1: gene-&gt;locus-tag</p>
<p>Synonyms:<br> bar-delimited set of unofficial symbols for the gene</p>
<p>dbXrefs:<br> bar-delimited set of identifiers in other databases<br> for this gene. The unit of the set is database:value.<br> Note that HGNC and MGI include 'HGNC' and 'MGI', respectively,<br> in the value part of their identifier. Consequently,<br> dbXrefs for these databases will appear like:<br> HGNC:HGNC:1100<br> This would be interpreted as database='HGNC', value='HGNC:1100'<br> Example for MGI:<br> MGI:MGI:104537<br> This would be interpreted as database='MGI', value='MGI:104537'</p>
<p>chromosome:<br> the chromosome on which this gene is placed.<br> for mitochondrial genomes, the value 'MT' is used.</p>
<p>map location:<br> the map location for this gene</p>
<p>description:<br> a descriptive name for this gene</p>
<p>type of gene:<br> the type assigned to the gene according to the list of options<br> provided in https://www.ncbi.nlm.nih.gov/IEB/ToolBox/CPP_DOC/lxr/source/src/objects/entrezgene/entrezgene.asn</p>
<p><br>Symbol from nomenclature authority:<br> when not '-', indicates that this symbol is from a<br> a nomenclature authority</p>
<p>Full name from nomenclature authority:<br> when not '-', indicates that this full name is from a<br> a nomenclature authority</p>
<p>Nomenclature status:<br> when not '-', indicates the status of the name from the <br> nomenclature authority (O for official, I for interim)</p>
<p>Other designations:<br> pipe-delimited set of some alternate descriptions that<br> have been assigned to a GeneID<br> '-' indicates none is being reported.</p>
<p>Modification date:<br> the last date a gene record was updated, in YYYYMMDD format</p>
<p>Feature type:<br> pipe-delimited set of annotated features and their classes or <br> controlled vocabularies, displayed as feature_type:feature_class <br> or feature_type:controlled_vocabulary, when appropriate; derived <br> from select feature annotations on RefSeq(s) associated with the <br> GeneID</p><p>Address of the bookmark: <a href="ftp://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/" rel="nofollow">ftp://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2726/comparison-of-short-read-de-novo-alignment-algorithms</guid>
	<pubDate>Wed, 21 Aug 2013 07:56:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2726/comparison-of-short-read-de-novo-alignment-algorithms</link>
	<title><![CDATA[Comparison of Short Read De Novo Alignment Algorithms]]></title>
	<description><![CDATA[<p>Excellent article to introduce different sequencing methods along with tools for de novo assembly of sequencing reads and their relevant references.</p>
<p>Title:&nbsp;<strong>Comparison of Short Read De Novo Alignment Algorithms&nbsp;</strong></p>
<p>Author<strong>: Nikhil Gopal</strong></p><p>Address of the bookmark: <a href="http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf" rel="nofollow">http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes</guid>
	<pubDate>Tue, 12 Dec 2017 17:23:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes</link>
	<title><![CDATA[MashMap: a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s)]]></title>
	<description><![CDATA[<p><span>MashMap is a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s). It maps a query sequence against a reference region if and only if its estimated alignment identity is above a specified threshold. It does not compute the alignments explicitly, but rather estimates a&nbsp;</span><em>k</em><span>-mer based&nbsp;</span><a href="https://en.wikipedia.org/wiki/Jaccard_index">Jaccard similarity</a><span>&nbsp;using a combination of&nbsp;</span><a href="http://www.cs.princeton.edu/courses/archive/spr05/cos598E/bib/p76-schleimer.pdf">Winnowing</a><span>&nbsp;and&nbsp;</span><a href="https://en.wikipedia.org/wiki/MinHash">MinHash</a><span>. This is then converted to an estimate of sequence identity using the&nbsp;</span><a href="http://mash.readthedocs.org/">Mash</a><span>&nbsp;distance. An appropriate&nbsp;</span><em>k</em><span>-mer sampling rate is automatically determined given minimum local alignment length and identity thresholds. The efficiency of the algorithm improves as both of these thresholds are increased.</span></p><p>Address of the bookmark: <a href="https://github.com/marbl/MashMap" rel="nofollow">https://github.com/marbl/MashMap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35345/rgfa-powerful-and-convenient-handling-of-assembly-graphs</guid>
	<pubDate>Thu, 25 Jan 2018 05:47:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35345/rgfa-powerful-and-convenient-handling-of-assembly-graphs</link>
	<title><![CDATA[RGFA: powerful and convenient handling of assembly graphs]]></title>
	<description><![CDATA[<p><span>RGFA, an implementation of the proposed GFA specification in Ruby. It allows the user to conveniently parse, edit and write GFA files. Complex operations such as the separation of the implicit instances of repeats and the merging of linear paths can be performed. A typical application of RGFA is the editing of a graph, to finish the assembly of a sequence, using information not available to the assembler. We illustrate a use case, in which the assembly of a repetitive metagenomic fosmid insert was completed using a script based on RGFA.</span></p>
<p><span>https://github.com/ggonnella/rgfa</span></p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5103826/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5103826/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36867/cerulean-a-hybrid-assembly-using-high-throughput-short-and-long-reads</guid>
	<pubDate>Tue, 05 Jun 2018 10:10:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36867/cerulean-a-hybrid-assembly-using-high-throughput-short-and-long-reads</link>
	<title><![CDATA[Cerulean: A hybrid assembly using high throughput short and long reads]]></title>
	<description><![CDATA[Cerulean extends contigs assembled using short read datasets like Illumina paired-end reads using long reads like PacBio RS long reads.

Cerulean v0.1 has been implemented with bacterial genomes in mind.

The method is fully described in Deshpande, V., Fung, E. D., Pham, S., &amp; Bafna, V. (2013). Cerulean: A hybrid assembly using high throughput short and long reads. arXiv preprint arXiv:1307.7933.
http://arxiv.org/abs/1307.7933<p>Address of the bookmark: <a href="https://sourceforge.net/projects/ceruleanassembler/" rel="nofollow">https://sourceforge.net/projects/ceruleanassembler/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37291/transrate-understanding-your-transcriptome-assembly</guid>
	<pubDate>Fri, 13 Jul 2018 07:49:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37291/transrate-understanding-your-transcriptome-assembly</link>
	<title><![CDATA[transrate: Understanding your transcriptome assembly]]></title>
	<description><![CDATA[<p><span>Transrate is software for&nbsp;</span><em>de-novo</em><span>&nbsp;transcriptome assembly quality analysis. It examines your assembly in detail and compares it to experimental evidence such as the sequencing reads, reporting quality scores for contigs and assemblies. This allows you to choose between assemblers and parameters, filter out the bad contigs from an assembly, and help decide when to stop trying to improve the assembly.</span></p><p>Address of the bookmark: <a href="http://hibberdlab.com/transrate/index.html" rel="nofollow">http://hibberdlab.com/transrate/index.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

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