<?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/33901?</link>
	<atom:link href="https://bioinformaticsonline.com/related/33901?" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43384/lncpipea-nextflow-based-pipeline-for-comprehensive-analyses-of-long-non-coding-rnas-from-rna-seq-datasets</guid>
	<pubDate>Fri, 17 Sep 2021 01:57:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43384/lncpipea-nextflow-based-pipeline-for-comprehensive-analyses-of-long-non-coding-rnas-from-rna-seq-datasets</link>
	<title><![CDATA[LncPipe:A Nextflow-based pipeline for comprehensive analyses of long non-coding RNAs from RNA-seq datasets]]></title>
	<description><![CDATA[<p><span>The pipeline was developed based on a popular workflow framework&nbsp;</span><a href="https://github.com/nextflow-io/nextflow">Nextflow</a><span>, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report.&nbsp;</span><a href="https://github.com/likelet/LncPipe">LncPipe</a><span>&nbsp;allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.</span></p>
<p>Ref&nbsp;https://www.lncrnablog.com/lncpipe-a-nextflow-based-pipeline-for-identification-and-analysis-of-long-non-coding-rnas-from-rna-seq-data/</p>
<p><img src="https://ars.els-cdn.com/content/image/1-s2.0-S1673852718301176-gr1.jpg" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="https://github.com/likelet/LncPipe" rel="nofollow">https://github.com/likelet/LncPipe</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</guid>
	<pubDate>Mon, 27 Jun 2016 11:01:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</link>
	<title><![CDATA[Kraken: ultrafast metagenomic sequence classification using exact alignments]]></title>
	<description><![CDATA[<p>Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of <em>k</em>-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at <a href="http://ccb.jhu.edu/software/kraken/" target="pmc_ext">http://ccb.jhu.edu/software/kraken/</a>.</p>
<p>Krona</p>
<p>https://sourceforge.net/p/krona/home/krona/</p><p>Address of the bookmark: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/" rel="nofollow">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43607/classification-of-sars-cov2-variant</guid>
	<pubDate>Fri, 26 Nov 2021 12:53:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43607/classification-of-sars-cov2-variant</link>
	<title><![CDATA[Classification of SARS-CoV2 Variant !]]></title>
	<description><![CDATA[<p>The scientists established some guidelines for determining whether a variant is a legitimate branch of an existing lineage:</p><p>The variant should be transmitted from its original location to another "geographically distinct population"&mdash;say, another country or a province of a large and populous country.<br />It should differ from its ancestor by at least one nucleotide.<br />At least 95% of its genetic code should have been sequenced at least five times from different samples.</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37512/purecn-copy-number-calling-and-snv-classification-using-targeted-short-read-sequencing</guid>
	<pubDate>Thu, 09 Aug 2018 04:09:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37512/purecn-copy-number-calling-and-snv-classification-using-targeted-short-read-sequencing</link>
	<title><![CDATA[PureCN: copy number calling and SNV classification using targeted short read sequencing]]></title>
	<description><![CDATA[<p>This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.</p>
<p>Author: Markus Riester [aut, cre], Angad P. Singh [aut]</p>
<p>Maintainer: Markus Riester &lt;markus.riester at novartis.com&gt;</p>
<div id="bioc_citation_outer">
<p>Citation (from within R, enter&nbsp;<code>citation("PureCN")</code>):</p>
<div id="bioc_citation">
<p>Riester M, Singh A, Brannon A, Yu K, Campbell C, Chiang D, Morrissey M (2016). &ldquo;PureCN: Copy number calling and SNV classification using targeted short read sequencing.&rdquo;&nbsp;<em>Source Code for Biology and Medicine</em>,&nbsp;<strong>11</strong>, 13. doi:&nbsp;<a href="http://doi.org/10.1186/s13029-016-0060-z">10.1186/s13029-016-0060-z</a>.</p>
</div>
</div><p>Address of the bookmark: <a href="http://bioconductor.org/packages/release/bioc/html/PureCN.html" rel="nofollow">http://bioconductor.org/packages/release/bioc/html/PureCN.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43552/understanding-pango-networks</guid>
	<pubDate>Sat, 16 Oct 2021 14:02:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43552/understanding-pango-networks</link>
	<title><![CDATA[Understanding pango networks !]]></title>
	<description><![CDATA[<p><span>In the vast majority of instances it is expected that Pango lineage names and designations will conform to the following rules. These rules also act as guidelines for the decisions made by the Lineage Designation Committee.</span></p>
<p>https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/</p>
<p>https://www.pango.network/how-does-the-system-work/what-are-pango-lineages/</p>
<p>Reference paper</p>
<p>https://www.nature.com/articles/s41564-020-0770-5</p><p>Address of the bookmark: <a href="https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/" rel="nofollow">https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44236/type-of-ssr</guid>
	<pubDate>Thu, 09 Mar 2023 04:35:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44236/type-of-ssr</link>
	<title><![CDATA[Type of SSR]]></title>
	<description><![CDATA[<div><div><div><div><div><div><div><div><div><div><p>Types of SSRs (simple sequence repeats), SSRs are short DNA sequences consisting of a tandem repeat of a few nucleotides, typically 2-6 nucleotides in length. There are different types of SSRs based on the length and pattern of the repeated sequence, as well as the presence or absence of interruptions of non-repeated nucleotides within the repeat array. The four types of SSRs are:</p><ol>
<li>
<p>Perfect SSR: This is the simplest type of SSR, where the same repeat motif is present adjacent to each other without any interruption of any other nucleotide. For example, a perfect SSR with the repeat motif "CAT" would be "CATCATCATCAT", where the "CAT" sequence is repeated four times.</p>
</li>
<li>
<p>Imperfect SSR: This type of SSR contains repeat motifs that are interrupted by one or a few non-repeat nucleotides. For example, an imperfect SSR with the repeat motif "CAT" would be "CATCATGGCATCATCAT", where the "CAT" sequence is repeated twice, but interrupted by "GG".</p>
</li>
<li>
<p>Compound perfect SSR: This type of SSR contains two or more repeat motifs lying adjacent to each other, separated by no or very few intervening nucleotides. For example, a compound perfect SSR with the repeat motifs "CAT" and "GTC" would be "CATCATCATGTCGTC", where the "CAT" sequence is repeated three times, followed by the "GTC" sequence repeated twice.</p>
</li>
<li>
<p>Compound imperfect SSR: This type of SSR contains two or more repeat motifs interrupted by several non-repeat nucleotides. For example, a compound imperfect SSR with the repeat motifs "CAT" and "GTC" would be "CATCATCATNNNNNNNGTCGTCGTC", where the "CAT" sequence is repeated three times, interrupted by several non-repeat nucleotides, followed by the "GTC" sequence repeated three times.</p>
</li>
</ol></div></div></div></div></div></div></div></div></div></div>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/25993/hoffman-lab</guid>
  <pubDate>Tue, 12 Jan 2016 02:47:41 -0600</pubDate>
  <link></link>
  <title><![CDATA[Hoffman Lab]]></title>
  <description><![CDATA[
<p>They develop machine learning techniques to better understand chromatin biology. These models and algorithms transform high-dimensional functional genomics data into interpretable patterns and lead to new biological insight.</p>

<p>https://www.pmgenomics.ca/hoffmanlab/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27479/biogps</guid>
	<pubDate>Mon, 23 May 2016 03:15:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27479/biogps</link>
	<title><![CDATA[BioGPS]]></title>
	<description><![CDATA[<p>A free&nbsp;<em>extensible</em>&nbsp;and&nbsp;<em>customizable</em>&nbsp;<strong>gene annotation portal</strong>, a complete resource for learning about&nbsp;<strong>gene and protein function</strong>.</p>
<p>http://biogps.org/#goto=welcome</p><p>Address of the bookmark: <a href="http://biogps.org/#goto=welcome" rel="nofollow">http://biogps.org/#goto=welcome</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34940/jpred4-a-protein-secondary-structure-prediction-server</guid>
	<pubDate>Fri, 29 Dec 2017 16:14:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34940/jpred4-a-protein-secondary-structure-prediction-server</link>
	<title><![CDATA[JPred4: A Protein Secondary Structure Prediction Server]]></title>
	<description><![CDATA[<p><span>JPred4 (</span><a href="http://www.compbio.dundee.ac.uk/jpred4" target="">http://www.compbio.dundee.ac.uk/jpred4</a><span>) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction.</span></p><p>Address of the bookmark: <a href="http://www.compbio.dundee.ac.uk/jpred4/" rel="nofollow">http://www.compbio.dundee.ac.uk/jpred4/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37827/genomethreader-gene-prediction-software</guid>
	<pubDate>Wed, 03 Oct 2018 15:34:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37827/genomethreader-gene-prediction-software</link>
	<title><![CDATA[GenomeThreader: Gene Prediction Software]]></title>
	<description><![CDATA[<p><em>GenomeThreader</em><span>&nbsp;is a software tool to compute gene structure predictions. The gene structure predictions are calculated using a similarity-based approach where additional cDNA/EST and/or protein sequences are used to predict gene structures via spliced alignments.&nbsp;</span><em>GenomeThreader</em><span>&nbsp;was motivated by disabling limitations in&nbsp;</span><a href="http://bioinformatics.iastate.edu/cgi-bin/gs.cgi"><em>GeneSeqer</em></a><span>, a popular gene prediction program which is widely used for plant genome annotation.</span></p><p>Address of the bookmark: <a href="http://genomethreader.org/" rel="nofollow">http://genomethreader.org/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

</channel>
</rss>