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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35420?offset=270</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36812/porechop-tool-for-finding-and-removing-adapters-from-oxford-nanopore-reads</guid>
	<pubDate>Tue, 29 May 2018 07:33:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36812/porechop-tool-for-finding-and-removing-adapters-from-oxford-nanopore-reads</link>
	<title><![CDATA[Porechop:  tool for finding and removing adapters from Oxford Nanopore reads]]></title>
	<description><![CDATA[<p>Porechop is a tool for finding and removing adapters from <a href="https://nanoporetech.com/">Oxford Nanopore</a> reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity.</p>
<p>Porechop also supports demultiplexing of Nanopore reads that were barcoded with the <a href="https://store.nanoporetech.com/native-barcoding-kit-1d.html">Native Barcoding Kit</a>, <a href="https://store.nanoporetech.com/pcr-barcoding-kit-96.html">PCR Barcoding Kit</a> or <a href="https://store.nanoporetech.com/rapid-barcoding-sequencing-kit.html">Rapid Barcoding Kit</a>.</p><p>Address of the bookmark: <a href="https://github.com/rrwick/Porechop" rel="nofollow">https://github.com/rrwick/Porechop</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36907/higlass-a-tool-for-exploring-genomic-contact-matrices-and-tracks</guid>
	<pubDate>Mon, 11 Jun 2018 09:44:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36907/higlass-a-tool-for-exploring-genomic-contact-matrices-and-tracks</link>
	<title><![CDATA[HiGlass: a tool for exploring genomic contact matrices and tracks.]]></title>
	<description><![CDATA[HiGlass is a tool for exploring genomic contact matrices and tracks. Please take a look at the examples and documentation for a description of the ways that it can be configured to explore and compare contact matrices. To load private data, HiGlass can be run locally within a Docker container. The HiC data in the examples below is from Rao et al. (2014)

http://higlass.io/<p>Address of the bookmark: <a href="http://higlass.io/" rel="nofollow">http://higlass.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool</guid>
	<pubDate>Thu, 02 Aug 2018 07:39:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool</link>
	<title><![CDATA[LSC :a long read error correction tool]]></title>
	<description><![CDATA[<h2>Getting Started</h2>
<p>These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.</p>
<ul>
<li>Read the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_requirements.asp">LSC_requirements</a>&nbsp;for running LSC.</li>
<li><a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_download.asp">Download</a>&nbsp;and set-up the LSC package.</li>
<li>Follow the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_tutorial.asp">tutorial</a>&nbsp;to see how LSC works on some example data.</li>
<li>Read the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_manual.asp">manual</a>&nbsp;if anything is unclear.</li>
<li>You're ready, Happy LSCing!</li>
</ul>
<h2>Latest publication</h2>
<p><span>Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong&nbsp;</span><br><strong>Improving PacBio Long Read Accuracy by Short Read Alignment&nbsp;</strong><span>[</span><a href="http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0046679">Manuscript</a><span>]&nbsp;</span><br><em>PLoS ONE</em><span>&nbsp;2012. 7(10): e46679. doi:10.1371/journal.pone.0046679</span></p><p>Address of the bookmark: <a href="https://www.healthcare.uiowa.edu/labs/au/LSC/" rel="nofollow">https://www.healthcare.uiowa.edu/labs/au/LSC/</a></p>]]></description>
	<dc:creator>Jit</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/37800/heatmapper-web-enabled-heat-mapping-for-all</guid>
	<pubDate>Mon, 01 Oct 2018 08:34:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37800/heatmapper-web-enabled-heat-mapping-for-all</link>
	<title><![CDATA[Heatmapper: web-enabled heat mapping for all]]></title>
	<description><![CDATA[<p><span>Heatmapper is a freely available web server that allows users to interactively visualize their data in the form of heat maps through an easy-to-use graphical interface. Heatmapper is a versatile tool that allows users to easily create a wide variety of heat maps for many different data types and applications. Heatmapper allows users to generate, cluster and visualize: </span></p>
<p><span>1)&nbsp;</span><span>expression-based heat maps</span><span>&nbsp;from transcriptomic, proteomic and metabolomic experiments; 2)&nbsp;</span><span>pairwise distance maps</span><span>; </span></p>
<p><span>3)&nbsp;</span><span>correlation maps</span><span>; </span></p>
<p><span>4)&nbsp;</span><span>image overlay heat maps</span><span>; </span></p>
<p><span>5)&nbsp;</span><span>latitude and longitude heat maps</span><span>&nbsp;and </span></p>
<p><span>6)&nbsp;</span><span>geopolitical (choropleth) heat maps</span><span>. </span></p>
<p><span>Heatmapper offers a number of simple and intuitive customization options for easy adjustments to each heat map&rsquo;s appearance and plotting parameters. Heatmapper also allows users to interactively explore their numeric data values by hovering their cursor over each heat map, or by using a searchable/sortable data table view.</span></p>
<p><span>Ref&nbsp;https://www.ncbi.nlm.nih.gov/pubmed/27190236</span></p><p>Address of the bookmark: <a href="http://www2.heatmapper.ca/" rel="nofollow">http://www2.heatmapper.ca/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38449/koala-keggs-internal-annotation-tool-for-k-number-assignment-of-kegg-genes-using-ssearch-computation</guid>
	<pubDate>Wed, 12 Dec 2018 09:16:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38449/koala-keggs-internal-annotation-tool-for-k-number-assignment-of-kegg-genes-using-ssearch-computation</link>
	<title><![CDATA[KOALA: KEGG&#039;s internal annotation tool for K number assignment of KEGG GENES using SSEARCH computation]]></title>
	<description><![CDATA[<p>KOALA (KEGG Orthology And Links Annotation) is KEGG's internal annotation tool for&nbsp;<a href="https://www.kegg.jp/kegg/ko.html">K number</a>&nbsp;assignment of KEGG GENES using SSEARCH computation. BlastKOALA and GhostKOALA assign K numbers to the user's sequence data by&nbsp;<a href="http://www.ncbi.nlm.nih.gov/blast/">BLAST</a>&nbsp;and&nbsp;<a href="http://www.bi.cs.titech.ac.jp/ghostx/">GHOSTX</a>&nbsp;searches, respectively, against a nonredundant set of KEGG GENES. Annotate Sequence in KEGG Mapper and Pathogen Checker in KEGG Pathogen are special interfaces to the BlastKOALA server and can be executed in an interactive mode. &nbsp;&nbsp; See&nbsp;<a href="https://www.kegg.jp/blastkoala/help_blastkoala.html" target="_blastkoala">Step-by-step Instructions</a>.</p>
<div>Reference: Kanehisa, M., Sato, Y., and Morishima, K. (2016) BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726-731. [<a href="http://www.ncbi.nlm.nih.gov/pubmed/26585406">pubmed</a>] [<a href="https://doi.org/10.1016/j.jmb.2015.11.006">pdf</a>]</div><p>Address of the bookmark: <a href="https://www.kegg.jp/blastkoala/" rel="nofollow">https://www.kegg.jp/blastkoala/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39624/cogent-a-tool-for-reconstructing-the-coding-genome-using-high-quality-full-length-transcriptome-sequences</guid>
	<pubDate>Tue, 18 Jun 2019 05:33:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39624/cogent-a-tool-for-reconstructing-the-coding-genome-using-high-quality-full-length-transcriptome-sequences</link>
	<title><![CDATA[Cogent: a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences.]]></title>
	<description><![CDATA[<div id="yui_3_14_1_1_1560853173251_3865">Cogent is a tool that identifies gene&nbsp;families and reconstructs the coding genome using high-quality transcriptome data without a reference genome, and can be used to check&nbsp;assemblies&nbsp;for the presence of&nbsp;these known coding sequences.</div>
<div>&nbsp;</div>
<div>
<p>Cogent is a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences. It is designed to be used on&nbsp;<a href="https://github.com/PacificBiosciences/cDNA_primer/wiki">Iso-Seq data</a>&nbsp;and in cases where there is no reference genome or the ref genome is highly incomplete.</p>
<p>See a&nbsp;<a href="https://www.dropbox.com/s/mn6hwhguh0pqceu/20160106_Cogent_developers_conference_slides_Cuttlefish.pdf?dl=0">recent presentation</a>&nbsp;on Cogent being applied to the Cuttlefish Iso-Seq data.</p>
<p><a href="https://www.dropbox.com/s/kz0gi7qg0w82k9a/20161026_Cogent_manuscript_forGitHub.pdf?dl=0">Cogent preliminary draft paper (updated 2016Dec version)</a>,&nbsp;<a href="https://www.dropbox.com/s/37412o8glvnfhf9/20161026_Cogent_ManuscriptPlusSupplement_forGitHub.pdf?dl=0">Supplementary</a></p>
<p>Please see&nbsp;<a href="https://github.com/Magdoll/Cogent/wiki">wiki</a>&nbsp;for details on usage.</p>
</div><p>Address of the bookmark: <a href="https://github.com/Magdoll/Cogent" rel="nofollow">https://github.com/Magdoll/Cogent</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40721/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application</guid>
	<pubDate>Tue, 28 Jan 2020 05:12:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40721/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application</link>
	<title><![CDATA[EFS: an ensemble feature selection tool implemented as R-package and web-application]]></title>
	<description><![CDATA[<p><span>The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble.</span></p>
<p><a href="https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8">https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8</a></p><p>Address of the bookmark: <a href="http://efs.heiderlab.de/" rel="nofollow">http://efs.heiderlab.de/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</guid>
	<pubDate>Sun, 12 Apr 2020 10:02:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</link>
	<title><![CDATA[WEGO : simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results]]></title>
	<description><![CDATA[<p><span>WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. As the GO vocabulary became more and more popular, WEGO was widely adopted and used in many researches. Therefore we have updated WEGO 2.0 in 2018. Here are some changes we&rsquo;ve made:</span><br><span>1. The limit of input file numbers was cancelled. Now the users could upload as many files as they want with one operation.</span><br><span>2. We have added the reference data of 9 species for users selection.</span><br><span>3. Besides the traditional WEGO histogram, WEGO 2.0 outputs an additional type of bar graph showing GO terms with significant gene number differences.</span></p><p>Address of the bookmark: <a href="http://wego.genomics.org.cn/" rel="nofollow">http://wego.genomics.org.cn/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41991/sequence-ontology-bioinformatics-analysis-soba-tool-to-provide-a-simple-statistical-and-graphical-summary-of-an-annotated-genome</guid>
	<pubDate>Wed, 22 Jul 2020 10:11:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41991/sequence-ontology-bioinformatics-analysis-soba-tool-to-provide-a-simple-statistical-and-graphical-summary-of-an-annotated-genome</link>
	<title><![CDATA[Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome]]></title>
	<description><![CDATA[<p><span>We have developed the Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome. We envisage its use during annotation jamborees, genome comparison and for use by developers for rapid feedback during annotation software development and testing. SOBA also provides annotation consistency feedback to ensure correct use of terminology within annotations, and guides users to add new terms to the Sequence Ontology when required. SOBA is available at http://www.sequenceontology.org/cgi-bin/soba.cgi.</span></p>
<p><span>More at <a href="https://pubmed.ncbi.nlm.nih.gov/20494974/">https://pubmed.ncbi.nlm.nih.gov/20494974/</a></span></p><p>Address of the bookmark: <a href="http://www.sequenceontology.org/cgi-bin/soba.cgi" rel="nofollow">http://www.sequenceontology.org/cgi-bin/soba.cgi</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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