<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/32868?offset=30</link>
	<atom:link href="https://bioinformaticsonline.com/related/32868?offset=30" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes</guid>
	<pubDate>Thu, 13 Aug 2020 10:06:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes</link>
	<title><![CDATA[PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes]]></title>
	<description><![CDATA[<p>PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring&nbsp;O(n2)&nbsp;resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.</p><p>Address of the bookmark: <a href="https://github.com/ryanmelnyk/PyParanoid" rel="nofollow">https://github.com/ryanmelnyk/PyParanoid</a></p>]]></description>
	<dc:creator>BioStar</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/44561/bactopia-a-flexible-pipeline-for-complete-analysis-of-bacterial-genomes</guid>
	<pubDate>Sat, 08 Jun 2024 16:25:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44561/bactopia-a-flexible-pipeline-for-complete-analysis-of-bacterial-genomes</link>
	<title><![CDATA[Bactopia: a flexible pipeline for complete analysis of bacterial genomes]]></title>
	<description><![CDATA[<p>Bactopia is a flexible pipeline for complete analysis of bacterial genomes. The goal of Bactopia is process your data with a broad set of tools, so that you can get to the fun part of analyses quicker!</p>
<p>Bactopia was inspired by&nbsp;<a href="https://staphopia.github.io/">Staphopia</a>, a workflow we (Tim Read and myself) released that is targeted towards&nbsp;<em>Staphylococcus aureus</em>&nbsp;genomes. Using what we learned from Staphopia and user feedback, Bactopia was developed from scratch with usability, portability, and speed in mind from the start.</p>
<p>Bactopia uses&nbsp;<a href="https://www.nextflow.io/">Nextflow</a>&nbsp;to manage the workflow, allowing for support of many types of environments (e.g. cluster or cloud). Bactopia allows for the usage of many public datasets as well as your own datasets to further enhance the analysis of your sequencing. Bactopia only uses software packages available from&nbsp;<a href="https://bioconda.github.io/">Bioconda</a>&nbsp;and&nbsp;<a href="https://conda-forge.org/">Conda-Forge</a>&nbsp;to make installation as simple as possible for&nbsp;<em>all</em>&nbsp;users.</p>
<p>To highlight the use of&nbsp;<a href="https://bactopia.github.io/latest/full-guide/">Bactopia</a>&nbsp;and&nbsp;<a href="https://bactopia.github.io/latest/bactopia-tools/">Bactopia Tools</a>, we performed an analysis of 1,664 public&nbsp;<em>Lactobacillus</em>&nbsp;genomes, focusing on&nbsp;<em>Lactobacillus crispatus</em>, a species that is a common part of the human vaginal microbiome. The results from this analysis are published in mSystems under the title:&nbsp;<em><a href="https://doi.org/10.1128/mSystems.00190-20">Bactopia: a flexible pipeline for complete analysis of bacterial genomes</a></em></p>
<p><a href="https://bactopia.github.io/latest/assets/bactopia-workflow.png"><img src="https://bactopia.github.io/latest/assets/bactopia-workflow.png" alt="Bactopia Workflow" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://bactopia.github.io/latest/" rel="nofollow">https://bactopia.github.io/latest/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27477/cytoscape</guid>
	<pubDate>Mon, 23 May 2016 02:32:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27477/cytoscape</link>
	<title><![CDATA[Cytoscape]]></title>
	<description><![CDATA[<p>Cytoscape is an <a href="http://www.cytoscape.org/download.php">open source</a> software platform for visualizing complex networks and integrating these with any type of attribute data. A lot of <a href="http://apps.cytoscape.org/"><em>Apps</em></a> are available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web.</p><p>Address of the bookmark: <a href="http://www.cytoscape.org/" rel="nofollow">http://www.cytoscape.org/</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41875/virtualflow-a-versatile-parallel-workflow-platform-for-carrying-out-virtual-screening</guid>
	<pubDate>Sat, 13 Jun 2020 13:03:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41875/virtualflow-a-versatile-parallel-workflow-platform-for-carrying-out-virtual-screening</link>
	<title><![CDATA[VirtualFlow: a versatile, parallel workflow platform for carrying out virtual screening]]></title>
	<description><![CDATA[<p>&nbsp;related tasks on Linux-based computer clusters of any type and size which are managed by a batchsystem&nbsp;(such as SLURM).&nbsp;</p>
<p>Currently, there exist two versions of VirtualFlow, which are tailored to different types of tasks:</p>
<ul>
<li><strong><a href="https://virtual-flow.org/#vflp">VFLP: VirtualFlow for Ligand Preparation</a></strong></li>
<li><strong><a href="https://virtual-flow.org/#vfvs"><strong>VFVS</strong><strong>&nbsp;:</strong>&nbsp;VirtualFlow for Virtual Screenings</a></strong></li>
</ul>
<p>They use the same core technology regarding the workflow management and parallelization, and they can be used individually&nbsp;or in concert with each other.&nbsp;Additional versions are expected to arrive in the future.&nbsp;</p>
<p><a href="https://github.com/VirtualFlow">https://github.com/VirtualFlow</a></p>
<p><a href="https://www.nature.com/articles/s41586-020-2117-z">https://www.nature.com/articles/s41586-020-2117-z?</a></p><p>Address of the bookmark: <a href="https://virtual-flow.org/" rel="nofollow">https://virtual-flow.org/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36739/blasr-mapping-single-molecule-sequencing-reads-using-basic-local-alignment-with-successive-refinement-blasr-theory-and-application</guid>
	<pubDate>Wed, 23 May 2018 06:54:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36739/blasr-mapping-single-molecule-sequencing-reads-using-basic-local-alignment-with-successive-refinement-blasr-theory-and-application</link>
	<title><![CDATA[BlasR Mapping single molecule sequencing reads using Basic Local Alignment with Successive Refinement (BLASR): Theory and Application,]]></title>
	<description><![CDATA[<p><span>BLASR (Basic Local Alignment with Successive Refinement) for mapping Single Molecule Sequencing (SMS) reads that are thousands to tens of thousands of bases long with divergence between the read and genome dominated by insertion and deletion error.</span></p>
<p>Here is how I use the blasr to align PacBio reads to the contigs (target.fasta). The &ldquo;target.fasta.sa&rdquo; is the suffix array from &ldquo;target.fasta&rdquo; generated by sawriter.</p>
<blockquote>
<p>blasr query.fa ./target.fasta -sa ./target.fasta.sa -bestn 40 -maxScore -500 -m 4 -nproc 24 -out target.m4 -maxLCPLength 15</p>
</blockquote>
<p>the output format option &ldquo;-m 4&Prime; generate the alignment coordinate. Not fully documented, but I can explain that to you.&nbsp;</p>
<p>I use a 24 cores / 48G ram server for the alignment. It took about 2 to 3 hours aligning 3G PacBio Reads to 10^6 sequences of short read contigs with a mean 3.5kbp length.</p><p>Address of the bookmark: <a href="http://bix.ucsd.edu/projects/blasr/" rel="nofollow">http://bix.ucsd.edu/projects/blasr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore</guid>
	<pubDate>Mon, 12 Nov 2018 05:26:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore</link>
	<title><![CDATA[Pacasus: Correction of palindromes in long reads from PacBio and Nanopore]]></title>
	<description><![CDATA[<p><br>Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. Check the poster from the Revolutionizing Next-Generation Sequencing (2nd edition) conference in the source folder:&nbsp;<a href="https://github.com/swarris/Pacasus/blob/master/vib2017.pdf">https://github.com/swarris/Pacasus/blob/master/vib2017.pdf</a>.</p>
<p>The prepint version is found on&nbsp;<a href="http://www.biorxiv.org/content/early/2017/08/09/173872">http://www.biorxiv.org/content/early/2017/08/09/173872</a></p>
<p>It uses the pyPaSWAS framework for sequence alignment (<a href="https://github.com/swarris/pyPaSWAS">https://github.com/swarris/pyPaSWAS</a>)</p><p>Address of the bookmark: <a href="https://github.com/swarris/Pacasus" rel="nofollow">https://github.com/swarris/Pacasus</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</guid>
	<pubDate>Wed, 22 Aug 2018 11:05:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37576/lrcstats-a-tool-for-evaluating-long-reads-correction-methods</link>
	<title><![CDATA[LRCstats: a tool for evaluating long reads correction methods]]></title>
	<description><![CDATA[<p><span>LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.</span></p><p>Address of the bookmark: <a href="https://github.com/cchauve/lrcstats" rel="nofollow">https://github.com/cchauve/lrcstats</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41689/medaka-sequence-correction-provided-by-ont-research</guid>
	<pubDate>Mon, 18 May 2020 16:28:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41689/medaka-sequence-correction-provided-by-ont-research</link>
	<title><![CDATA[medaka: Sequence correction provided by ONT Research]]></title>
	<description><![CDATA[<p><code>medaka</code><span>&nbsp;is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster.</span></p><p>Address of the bookmark: <a href="https://github.com/nanoporetech/medaka" rel="nofollow">https://github.com/nanoporetech/medaka</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40893/quorum-an-error-corrector-for-illumina-reads</guid>
	<pubDate>Tue, 04 Feb 2020 23:26:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40893/quorum-an-error-corrector-for-illumina-reads</link>
	<title><![CDATA[QuorUM: An Error Corrector for Illumina Reads]]></title>
	<description><![CDATA[<p><span>We produce trimmed and error-corrected reads that result in assemblies with longer contigs and fewer errors. We compared QuorUM against several published error correctors and found that it is the best performer in most metrics we use. QuorUM is efficiently implemented making use of current multi-core computing architectures and it is suitable for large data sets (1 billion bases checked and corrected per day per core)</span></p><p>Address of the bookmark: <a href="http://www.genome.umd.edu/" rel="nofollow">http://www.genome.umd.edu/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

</channel>
</rss>