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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/4183?offset=30</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</guid>
	<pubDate>Fri, 24 Jan 2020 04:09:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</link>
	<title><![CDATA[MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization]]></title>
	<description><![CDATA[<p><span>MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from&nbsp;</span><code>https://github.com/linzhi2013/MitoZ</code><span>.</span></p>
<p><span><a href="https://academic.oup.com/nar/article/47/11/e63/5377471">https://academic.oup.com/nar/article/47/11/e63/5377471</a></span></p><p>Address of the bookmark: <a href="https://github.com/linzhi2013/MitoZ" rel="nofollow">https://github.com/linzhi2013/MitoZ</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</guid>
	<pubDate>Mon, 04 May 2020 02:04:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</link>
	<title><![CDATA[HASLR: a hybrid assembler which uses both second and third generation sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples. Availability. HASLR is an open source tool available at https://github.com/vpc-ccg/haslr.</span></p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/haslr" rel="nofollow">https://github.com/vpc-ccg/haslr</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43736/odgi-optimized-dynamic-genomegraph-implementation</guid>
	<pubDate>Tue, 01 Feb 2022 23:42:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43736/odgi-optimized-dynamic-genomegraph-implementation</link>
	<title><![CDATA[odgi: optimized dynamic genome/graph implementation]]></title>
	<description><![CDATA[<p dir="auto"><code>odgi</code>&nbsp;provides an efficient and succinct dynamic DNA sequence graph model, as well as a host of algorithms that allow the use of such graphs in bioinformatic analyses.</p>
<p dir="auto">Careful encoding of graph entities allows&nbsp;<code>odgi</code>&nbsp;to efficiently compute and transform&nbsp;<a href="https://pangenome.github.io/">pangenomes</a>&nbsp;with minimal overheads.&nbsp;<code>odgi</code>&nbsp;implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.</p>
<p dir="auto">The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.</p>
<p dir="auto">The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.</p><p>Address of the bookmark: <a href="https://github.com/pangenome/odgi" rel="nofollow">https://github.com/pangenome/odgi</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43806/genomicus-genome-browser-that-enables-users-to-navigate-in-genomes-in-several-dimensions</guid>
	<pubDate>Mon, 28 Feb 2022 23:27:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43806/genomicus-genome-browser-that-enables-users-to-navigate-in-genomes-in-several-dimensions</link>
	<title><![CDATA[Genomicus: genome browser that enables users to navigate in genomes in several dimensions]]></title>
	<description><![CDATA[<p>Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.</p>
<p>Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.</p>
<p>All the data on extant species displayed in this browser are from&nbsp;<a href="http://www.ensembl.org/">Ensembl</a>.</p>
<p><br><strong>Summary statistics of Genomicus version 105.01:</strong><span>&nbsp;(view species tree in&nbsp;</span><a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/data/SpeciesTree.pdf">pdf</a><span>&nbsp;or&nbsp;</span><a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/data/SpeciesTree.nwk">newick</a><span>)</span><br><br></p>
<table id="introstats">
<tbody>
<tr><th>Number of extant species</th>
<td>200</td>
</tr>
<tr><th>Number of extant genes</th>
<td>4303993</td>
</tr>
<tr><th>&nbsp;</th></tr>
<tr><th>Number of ancestral species</th>
<td>196</td>
</tr>
<tr><th>Number of ancestral genes</th>
<td>4624213</td>
</tr>
<tr><th>Number of ancestral synteny blocks</th>
<td>83342<br><br></td>
</tr>
</tbody>
</table><p>Address of the bookmark: <a href="https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl" rel="nofollow">https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43923/monkeypox-virus-isolate-mpxv-usa-2022-ma001-complete-genome</guid>
	<pubDate>Tue, 26 Jul 2022 06:21:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43923/monkeypox-virus-isolate-mpxv-usa-2022-ma001-complete-genome</link>
	<title><![CDATA[Monkeypox virus isolate MPXV_USA_2022_MA001, complete genome]]></title>
	<description><![CDATA[<pre>LOCUS       ON563414              197205 bp    DNA     linear   VRL 30-MAY-2022
DEFINITION  Monkeypox virus isolate MPXV_USA_2022_MA001, complete genome.
ACCESSION   ON563414
VERSION     ON563414.3
KEYWORDS    .
SOURCE      Monkeypox virus (monkeypox)
  ORGANISM  <a href="https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=10244">Monkeypox virus</a>
            Viruses; Varidnaviria; Bamfordvirae; Nucleocytoviricota;
            Pokkesviricetes; Chitovirales; Poxviridae; Chordopoxvirinae;
            Orthopoxvirus.</pre><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/nuccore/ON563414" rel="nofollow">https://www.ncbi.nlm.nih.gov/nuccore/ON563414</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel</guid>
	<pubDate>Fri, 30 May 2014 13:24:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel</link>
	<title><![CDATA[How to sequence the human genome - Mark J. Kiel]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/MvuYATh7Y74" frameborder="0" allowfullscreen></iframe>View full lesson: http://ed.ted.com/lessons/how-to-sequence-the-human-genome-mark-j-kiel

Your genome, every human's genome, consists of a unique DNA sequence of A's, T's, C's and G's that tell your cells how to operate. Thanks to technological advances, scientists are now able to know the sequence of letters that makes up an individual genome relatively quickly and inexpensively. Mark J. Kiel takes an in-depth look at the science behind the sequence.

Lesson by Mark J. Kiel, animation by Marc Christoforidis.]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/19560/alien-genome</guid>
	<pubDate>Sat, 13 Dec 2014 00:24:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/file/view/19560/alien-genome</link>
	<title><![CDATA[Alien Genome !!!]]></title>
	<description><![CDATA[<p>Genome sequencing, analysis and expression of Alien genome.</p><p>Note: This image/cartoon is create only for fun. It has nothing to do with any scientific findings.</p>]]></description>
	<dc:creator>Jit</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/19560" length="40389" type="image/jpeg" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29305/miro-mirna-omics</guid>
	<pubDate>Tue, 04 Oct 2016 14:50:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29305/miro-mirna-omics</link>
	<title><![CDATA[MIRO : miRNA omics]]></title>
	<description><![CDATA[<p><span>The MIRO (the miRNA omics) pipeline is a flexible and powerful tool for the analysis of miRNA (or more generall short RNA) expression using short-read deep sequencing data. In its present implementation MIRO is especially adapted for the analysis of reads generated with the Illumina sequencing platform. MIRO allows to preprocess the Solexa-reads, map them flexibly to several reference genomes using one of four different mappers, create differential gene (miRNA) expression profiles and cluster reads using one of several algorithm. MIRO output is furthermore compatible with software such as genome browsers and miRDeep.</span></p><p>Address of the bookmark: <a href="http://seq.crg.es/download/software/Miro/" rel="nofollow">http://seq.crg.es/download/software/Miro/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43714/hiv-genome-database</guid>
	<pubDate>Fri, 21 Jan 2022 05:40:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43714/hiv-genome-database</link>
	<title><![CDATA[HIV genome database !]]></title>
	<description><![CDATA[<p>HIV resources</p>
<p>https://www.hiv.lanl.gov/components/sequence/HIV/search/search.html</p><p>Address of the bookmark: <a href="https://www.hiv.lanl.gov/components/sequence/HIV/search/search.html" rel="nofollow">https://www.hiv.lanl.gov/components/sequence/HIV/search/search.html</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/17843/pathway-analysis</guid>
	<pubDate>Fri, 03 Oct 2014 08:51:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/17843/pathway-analysis</link>
	<title><![CDATA[Pathway Analysis]]></title>
	<description><![CDATA[<p>Pathway Analysis is usually performed with aim to enrich the genes with their functional information and reveal the underlying biological mechanisms pursue by genes. Pathway Analysis is not only limited to what biological pathways a particular set of expressed genes follow but also to disclose the relationships between these genes. With availability of more genomics, transcriptomics and proteomics data, interactions between genes involve in multiple pathways become more clear and also relationships between the genes, their transcripts, and their gene products. However, existing tools and dbs mainly based on knowledge driven approach in which pathways will be identified by finding the correlation between the&nbsp;<span>information in one of the pathway knowledge databases (KEGG,Reactome,Panther,BioCarta, Panther,GO,NCI,WikiPathways,etc) and gene expression result for a specific conditions for instance tumor, obesity , cold resistant crops/plants, etc.</span></p><p><span><strong>Introductory Articles/ppt/sources</strong>:</span></p><p><a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375"><span>http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375</span></a></p><p><a href="http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf"><span>http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf</span></a></p><p><a href="http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html"><span>http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html</span></a></p><p><a href="http://davetang.org/muse/tag/pathway/"><span>http://davetang.org/muse/tag/pathway/</span></a></p><p><a href="https://www.biostars.org/p/42219/"><span>https://www.biostars.org/p/42219/</span></a></p><p><a href="http://bioinformatics.ca//files/public/Pathways_2014_Module4_v2.pdf"><span>http://bioinformatics.ca//files/public/Pathways_2014_Module4_v2.pdf</span></a></p><p><a href="http://bioinformatics.ca//files/public/Pathways_2014_Module2.pdf"><span>http://bioinformatics.ca//files/public/Pathways_2014_Module2.pdf</span></a></p><p><span><strong>Impotant Database and Tools</strong>:</span></p><p>GeneMANIA, Cytoscape,&nbsp;<a href="http://www.ingenuity.com/products/ipa">IPA</a>&nbsp;and <a href="http://thomsonreuters.com/metacore/">Metacore</a> (Commerical ),&nbsp;<span>Pathway Commons, Reactome ,Panther, BioCyc, WikiPathways, Pathvisio, KEGG, NCI, Stringdb, Amigo,&nbsp;<span>WebGestalt ,<span>ConsensusPathDB ,GSEA,Blast2go</span></span></span></p><p><span><strong>Popular R based tools</strong>:</span></p><p><span>Reactome.db, ReactomePA, ClusterProfiler, Gage, SPIA, topGO, Pathview,DOSE,GOStat</span></p><p><span><strong>More</strong>:</span></p><p><a href="http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+"><span>http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+</span></a></p><p>&nbsp;</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>

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