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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/32855?offset=60</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44525/synorth-exploring-the-evolution-of-synteny-and-long-range-regulatory-interactions-in-vertebrate-genomes</guid>
	<pubDate>Mon, 06 May 2024 06:21:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44525/synorth-exploring-the-evolution-of-synteny-and-long-range-regulatory-interactions-in-vertebrate-genomes</link>
	<title><![CDATA[Synorth: exploring the evolution of synteny and long-range regulatory interactions in vertebrate genomes]]></title>
	<description><![CDATA[<p><span>Genomic regulatory blocks are chromosomal regions spanned by long clusters of highly conserved noncoding elements devoted to long-range regulation of developmental genes, often immobilizing other, unrelated genes into long-lasting syntenic arrangements. Synorth&nbsp;</span><a href="http://synorth.genereg.net/" target="_blank">http://synorth.genereg.net/</a><span>&nbsp;is a web resource for exploring and categorizing the syntenic relationships in genomic regulatory blocks across multiple genomes, tracing their evolutionary fate after teleost whole genome duplication at the level of genomic regulatory block loci, individual genes, and their phylogenetic context.</span></p>
<p><span>More at&nbsp;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2745767/</span></p><p>Address of the bookmark: <a href="http://synorth.genereg.net/" rel="nofollow">http://synorth.genereg.net/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27110/easyfig</guid>
	<pubDate>Fri, 29 Apr 2016 05:49:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27110/easyfig</link>
	<title><![CDATA[Easyfig]]></title>
	<description><![CDATA[<p>Easyfig has moved to github, for newer releases of Easyfig please visit our new webpage - https://mjsull.github.io/Easyfig.&nbsp; Easyfig is a Python application for creating linear comparison figures of multiple genomic loci with an easy-to-use graphical user interface (GUI).</p>
<p>More at http://easyfig.sourceforge.net/</p><p>Address of the bookmark: <a href="http://easyfig.sourceforge.net/" rel="nofollow">http://easyfig.sourceforge.net/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/27311/release-notes-for-genome-workbench-2105</guid>
	<pubDate>Thu, 12 May 2016 13:49:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/27311/release-notes-for-genome-workbench-2105</link>
	<title><![CDATA[Release Notes for Genome Workbench 2.10.5]]></title>
	<description><![CDATA[<p>New Features in latest release</p><ul>
<li>New ProSplign tool integrated with Genome Workbench (<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial13">Tutorial</a>,&nbsp;<a href="https://www.youtube.com/watch?v=V9UqKJprzAg&amp;feature=youtu.be" target="_blank">Video</a>)</li>
<li>New export function for BAM/cSRA coverage graphs (<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial14">Tutorial</a>)</li>
<li>New export function for alignments GFF3 format ((<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial15">Tutorial</a>))</li>
<li>Tree View: implemented new export mode based on selections (tutorial coming)</li>
<li>Tree View: added support for&nbsp;<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial3/#distance_based_circular_trees">distance based circular trees</a></li>
<li>Tree View: new rooting mode (Midpoint Root) results in more balanced trees (<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial3#reroot_tree">Tutorial</a>)</li>
<li>Tree View: added possibility to right-click on an edge between two nodes and "Place Root at Middle of Branch" &ndash; to re-root at mid-branch (<a href="https://www.ncbi.nlm.nih.gov/tools/gbench/tutorial3#reroot_tree">Tutorial</a>)</li>
</ul>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27432/gkno</guid>
	<pubDate>Fri, 20 May 2016 18:56:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27432/gkno</link>
	<title><![CDATA[GKNO]]></title>
	<description><![CDATA[<p><span>gkno opens the world of complex bioinformatic analysis to people of all level of computational expertise. This site contains documentation, tutorials and information on all the tools that comprise gkno.</span></p>
<p><span>http://gkno.me/how-to/install.html</span></p>
<p><span>http://gkno.me/software.html</span></p><p>Address of the bookmark: <a href="http://gkno.me/" rel="nofollow">http://gkno.me/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27839/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads-such-those-produced-by-pacific-biosciences-sequencing-machines</guid>
	<pubDate>Wed, 15 Jun 2016 17:18:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27839/lorma-a-tool-for-correcting-sequencing-errors-in-long-reads-such-those-produced-by-pacific-biosciences-sequencing-machines</link>
	<title><![CDATA[LoRMA: a tool for correcting sequencing errors in long reads such those produced by Pacific Biosciences sequencing machines]]></title>
	<description><![CDATA[<p>LoRMA is a tool for correcting sequencing errors in long reads such those produced by Pacific Biosciences sequencing machines.</p>
<p>Publication:</p>
<ul>
<li>L. Salmela, R. Walve, E. Rivals, and E. Ukkonen: Accurate selfcorrection of errors in long reads using de Bruijn graphs. Accepted to RECOMB-Seq 2016.</li>
</ul>
<p>Download:</p>
<ul>
<li><a href="https://www.cs.helsinki.fi/u/lmsalmel/LoRMA/LoRMA-0.3.tar.gz">LoRMA 0.3 source files</a></li>
<li><a href="https://www.cs.helsinki.fi/u/lmsalmel/LoRMA/README.txt">README</a></li>
</ul><p>Address of the bookmark: <a href="https://www.cs.helsinki.fi/u/lmsalmel/LoRMA/" rel="nofollow">https://www.cs.helsinki.fi/u/lmsalmel/LoRMA/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30971/hiveplot</guid>
	<pubDate>Thu, 16 Feb 2017 11:39:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30971/hiveplot</link>
	<title><![CDATA[HivePlot]]></title>
	<description><![CDATA[<p>The&nbsp;<em>hive plot</em>&nbsp;is a rational visualization method for drawing networks. Nodes are mapped to and positioned on radially distributed linear axes &mdash; this mapping is based on network structural properties. Edges are drawn as curved links. Simple and interpretable.</p>
<p>The purpose of the hive plot is to establish a new baseline for visualization of large networks &mdash; a method that is both general and tunable and useful as a starting point in visually exploring network structure.</p>
<p>More at&nbsp;http://www.hiveplot.com/</p><p>Address of the bookmark: <a href="http://www.hiveplot.com/" rel="nofollow">http://www.hiveplot.com/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35033/bbsplit-read-binning-tool-for-metagenomes-and-contaminated-libraries</guid>
	<pubDate>Wed, 03 Jan 2018 00:25:27 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35033/bbsplit-read-binning-tool-for-metagenomes-and-contaminated-libraries</link>
	<title><![CDATA[BBSplit: Read Binning Tool for Metagenomes and Contaminated Libraries]]></title>
	<description><![CDATA[<p>BBSplit internally uses BBMap to map reads to multiple genomes at once, and determine which genome they match best. This is different than with ordinary mapping. If a genome (say, human) contains an exact repeat somewhere, reads mapping to it will be mapped ambiguously. But if you want to determine whether reads are mouse or human, it does not matter whether they map ambiguously within human, only whether they are ambiguous between human and mouse. BBSplit tracks this additional ambiguity information and decides how to use it based on the &ldquo;ambig2&rdquo; flag. The normal use of BBSplit is like Seal, either quantifying how many reads go to each reference, or splitting the reads into multiple output files, one per reference. BBSplit can only be run using references indexed with BBSplit, as they contain additional information regarding which sequences came from which reference file.</p><p><span>BBSplit is a tool that bins reads by mapping to multiple references simultaneously, using&nbsp;</span><a href="http://seqanswers.com/forums/showthread.php?t=41057" target="_blank">BBMap</a><span>. The reads go to the bin of the reference they map to best. There are also disambiguation options, such that reads that map to multiple references can be binned with all of them, none of them, one of them, or put in a special "ambiguous" file for each of them. Paired reads will always be kept together.</span><br /><br /><span>For example, if you had a library of something that was contaminated with e.coli and salmonella, you could do this:</span><br /><br /><strong>bbsplit.sh in=reads.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu=clean.fq int=t</strong><br /><br /><span>This will produce 3 output files:</span><br /><strong>out_ecoli.fq</strong><span>&nbsp;(ecoli reads)</span><br /><strong>out_salmonella.fq</strong><span>&nbsp;(salmonella reads)</span><br /><strong>clean.fq</strong><span>&nbsp;(unmapped reads)</span><br /><br /><span>In this case, "int=t" means that the input file is paired and interleaved. For single-end reads you would leave that out. For paired reads in 2 files, you would do this:</span><br /><strong>bbsplit.sh in1=reads1.fq in2=reads2.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu1=clean1.fq outu2=clean2.fq</strong></p><p><strong><span>BBSplit is available here:</span><br /><a href="https://sourceforge.net/projects/bbmap/" target="_blank">https://sourceforge.net/projects/bbmap/</a></strong></p><p><span>The sensitivity can be raised to be equivalent to BBMap with these flags: "minratio=0.56 minhits=1 maxindel=16000"</span></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38581/cvit-chromosome-viewing-tool</guid>
	<pubDate>Wed, 02 Jan 2019 04:10:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38581/cvit-chromosome-viewing-tool</link>
	<title><![CDATA[CViT: Chromosome Viewing Tool]]></title>
	<description><![CDATA[<p><span>CViT - Chromosome Viewing Tool. A collection of Perl scripts that enable quick visualizations of features on linkage groups, psuedochromosomes or cytogenetic maps. Intended for whole-genome views of data but can be used to create images of single chromosomes/linkage groups, contigs, or BACs, or even proteins -- any feature that has a location on a backbone. Handles most standard genetic/genomic coordinate systems. Reads GFF3 data and produces a PNG or SVG image.</span></p>
<p><span>https://www.hindawi.com/journals/ijpg/2011/373875/</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/cvit/" rel="nofollow">https://sourceforge.net/projects/cvit/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44487/r-package-for-pca-analysis</guid>
	<pubDate>Sun, 24 Mar 2024 20:06:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44487/r-package-for-pca-analysis</link>
	<title><![CDATA[R Package for PCA Analysis]]></title>
	<description><![CDATA[<p><span>An R package for performing principal component analysis (PCA) of genomics data. The package performs PCA, generates the publication-ready plots, and identifies population-specific outlier individuals. The package can be accessed on GitHub:&nbsp;https://github.com/Devashish13/PopulationStructure</span></p><p>Address of the bookmark: <a href="https://rpubs.com/Devashish13/PCAGenomics" rel="nofollow">https://rpubs.com/Devashish13/PCAGenomics</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45249/from-dna-chaos-to-a-clear-microbial-story</guid>
	<pubDate>Fri, 21 Aug 2026 11:22:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45249/from-dna-chaos-to-a-clear-microbial-story</link>
	<title><![CDATA[From DNA Chaos to a Clear Microbial Story]]></title>
	<description><![CDATA[<p>Imagine opening a metagenomic dataset and finding millions of DNA fragments, each carrying a tiny piece of information about the microbial world inside a sample. The challenge is not generating the data &mdash; it is turning that data into a meaningful story.</p><p>This is where MeTAline comes in.</p><p>MeTAline is a reproducible workflow designed to simplify shotgun metagenomic analysis. It brings together important steps such as quality control, host-DNA removal, taxonomic profiling and functional analysis, using tools including Kraken2, MetaPhlAn4 and HUMAnN.</p><p>What makes it especially useful is its ability to handle large datasets while keeping analyses organized and reproducible. Built with Snakemake and supporting containerized environments, MeTAline can also be used on high-performance computing systems.</p><p>In simple terms, MeTAline helps researchers move from millions of confusing DNA fragments to a clearer picture of who is there, what they might be doing, and what the data could mean.</p><p>Sometimes, the biggest challenge in science isn't collecting more data &mdash; it's finding the story hidden inside it.</p><p>Read more at&nbsp;https://academic.oup.com/nargab/article/7/4/lqaf158/8340158?login=false</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

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