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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43815?</link>
	<atom:link href="https://bioinformaticsonline.com/related/43815?" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42130/shaman-a-user-friendly-website-for-metataxonomic-analysis-from-raw-reads-to-statistical-analysis</guid>
	<pubDate>Mon, 17 Aug 2020 05:21:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42130/shaman-a-user-friendly-website-for-metataxonomic-analysis-from-raw-reads-to-statistical-analysis</link>
	<title><![CDATA[SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis]]></title>
	<description><![CDATA[<p><span>SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, &hellip;).</span><br><span>The bioinformatics treatment is based on Vsearch [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/27781170">Rognes 2016</a><span>] which showed to be both accurate and fast [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/26664811">Wescott 2015</a><span>].The statistical analysis is based on DESeq2 R package [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/20979621">Anders and Huber 2010</a><span>] which robustly identifies the differential abundant features as suggested in [</span><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974642/">McMurdie and Holmes 2014</a><span>] and [</span><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4727335/">Jonsson2016</a><span>]. SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2 [</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/25516281">Love 2014</a><span>].</span><br><span>SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats. A presentation about SHAMAN is available&nbsp;</span><a href="https://github.com/aghozlane/shaman/blob/master/www/shaman_presentation.pdf">here</a><span>&nbsp;and a poster&nbsp;</span><a href="https://github.com/aghozlane/shaman/blob/master/www/shaman_poster.pdf">here</a><span>.&nbsp;</span></p>
<p><span>More at&nbsp;<a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4</a></span></p><p>Address of the bookmark: <a href="https://github.com/aghozlane/shaman" rel="nofollow">https://github.com/aghozlane/shaman</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44227/common-methods-to-discover-tandem-repeats</guid>
	<pubDate>Thu, 09 Mar 2023 02:40:52 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44227/common-methods-to-discover-tandem-repeats</link>
	<title><![CDATA[Common methods to discover tandem repeats]]></title>
	<description><![CDATA[<div><div><div><div><div><div><div><div><div><div><p>Tandem repeats are DNA sequences that are repeated in a contiguous manner in the genome. These sequences are often used as genetic markers and are important in many areas of genetics and genomics research. Here are some methods for discovering tandem repeats in genomes:</p><ol>
<li>
<p>Tandem Repeat Finder: Tandem Repeat Finder is a software tool that identifies tandem repeats in DNA sequences. It is available for free download and can be used on both nucleotide and protein sequences. The tool uses a statistical algorithm to identify repeats based on their length, copy number, and overall composition.</p>
</li>
<li>
<p>RepeatMasker: RepeatMasker is another software tool that can identify tandem repeats in DNA sequences. It works by comparing the input sequence to a database of known repeats and then identifies any tandem repeats that match those in the database.</p>
</li>
<li>
<p>PCR-based methods: Polymerase chain reaction (PCR) can be used to amplify and detect tandem repeats in genomic DNA. PCR primers are designed to flank the tandem repeat region, and amplification of the target DNA fragment can be visualized on a gel. This method can be useful for detecting novel tandem repeats and for genotyping.</p>
</li>
<li>
<p>Southern blotting: Southern blotting is a classic method for detecting DNA fragments in a sample. It can be used to detect tandem repeats by digesting genomic DNA with a restriction enzyme, separating the fragments by gel electrophoresis, and then probing the blot with a tandem repeat-specific probe.</p>
</li>
</ol><p>Overall, a combination of these methods can be used to comprehensively identify tandem repeats in genomes.</p></div></div></div></div></div></div></div></div></div></div>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40948/bio7-an-integrated-development-environment-for-ecological-modeling-scientific-image-analysis-and-statistical-analysis</guid>
	<pubDate>Fri, 07 Feb 2020 23:32:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40948/bio7-an-integrated-development-environment-for-ecological-modeling-scientific-image-analysis-and-statistical-analysis</link>
	<title><![CDATA[Bio7: an integrated development environment for ecological modeling, scientific image analysis and statistical analysis]]></title>
	<description><![CDATA[<p><span>The application Bio7 is an integrated development environment for ecological modeling, scientific image analysis and statistical analysis. The application itself is based on an RCP-Eclipse-Environment (Rich-Client-Platform) which offers a huge flexibility in configuration and extensibility because of its plug-in structure and the possibility of customization.</span></p>
<p><a href="https://bio7.org/about/">https://bio7.org/about/</a></p><p>Address of the bookmark: <a href="https://bio7.org/home-2/" rel="nofollow">https://bio7.org/home-2/</a></p>]]></description>
	<dc:creator>Nidhi Rajput</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42359/dnasp-dna-sequence-polymorphism-is-a-software-package-for-the-analysis-of-dna-polymorphisms</guid>
	<pubDate>Wed, 25 Nov 2020 19:51:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42359/dnasp-dna-sequence-polymorphism-is-a-software-package-for-the-analysis-of-dna-polymorphisms</link>
	<title><![CDATA[DnaSP: DNA Sequence Polymorphism, is a software package for the analysis of DNA polymorphisms]]></title>
	<description><![CDATA[<p><span>DnaSP, DNA Sequence Polymorphism, is a software package for the analysis of DNA polymorphisms using data from a single locus (a multiple sequence aligned -MSA data), or from several loci (a Multiple-MSA data, such as formats generated by some assembler RAD-seq software). DnaSP can estimate several measures of DNA sequence variation within and between populations in noncoding, synonymous or nonsynonymous sites, or in various sorts of codon positions), as well as linkage disequilibrium, recombination, gene flow and gene conversion parameters.</span></p><p>Address of the bookmark: <a href="http://www.ub.edu/dnasp/" rel="nofollow">http://www.ub.edu/dnasp/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38385/decipher-a-software-toolset-for-deciphering-and-managing-biological-sequences-efficiently-using-the-r</guid>
	<pubDate>Sun, 09 Dec 2018 19:06:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38385/decipher-a-software-toolset-for-deciphering-and-managing-biological-sequences-efficiently-using-the-r</link>
	<title><![CDATA[DECIPHER; a software toolset for deciphering and managing biological sequences efficiently using the R]]></title>
	<description><![CDATA[<p><span>DECIPHER is a software toolset that can be used for deciphering and managing biological sequences efficiently using the&nbsp;</span><a href="http://www.r-project.org/">R</a><span>&nbsp;programming language. The&nbsp;</span><a href="http://www.r-project.org/">R</a><span>&nbsp;package is distributed as platform independent source code under the&nbsp;</span><a href="http://www.gnu.org/copyleft/gpl.html">GPL version 3 license</a><span>. Some functionality of the program is accessible online through web tools.</span></p>
<p><span style="font-size: medium; text-align: justify;">&nbsp;</span></p><p>Address of the bookmark: <a href="http://www2.decipher.codes/" rel="nofollow">http://www2.decipher.codes/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40591/modelstudio-a-package-automates-the-explanation-of-machine-learning-predictive-models</guid>
	<pubDate>Wed, 22 Jan 2020 23:58:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40591/modelstudio-a-package-automates-the-explanation-of-machine-learning-predictive-models</link>
	<title><![CDATA[modelStudio: a package automates the explanation of machine learning predictive models]]></title>
	<description><![CDATA[<p>The&nbsp;<code>modelStudio</code>&nbsp;package automates the explanation of machine learning predictive models. This package generates advanced interactive and animated model explanations in the form of a serverless HTML site.</p>
<p>It combines&nbsp;<strong>R</strong>&nbsp;with&nbsp;<strong>D3.js</strong>&nbsp;to produce plots and descriptions for various local and global explanations. Tools for model exploration unite with tools for EDA (Exploratory Data Analysis) to give a broad overview of the model behavior.&nbsp;<code>modelStudio</code>&nbsp;is a fast and condensed way to get all the answers without much effort. Break down your model and look into its ingredients with only a few lines of code.</p><p>Address of the bookmark: <a href="https://modeloriented.github.io/modelStudio/index.html" rel="nofollow">https://modeloriented.github.io/modelStudio/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44234/steps-to-find-palindrome-in-genomes</guid>
	<pubDate>Thu, 09 Mar 2023 02:56:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44234/steps-to-find-palindrome-in-genomes</link>
	<title><![CDATA[Steps to find palindrome in genomes !]]></title>
	<description><![CDATA[<div><div><div><div><div><div><div><div><div><div><p>Palindromes are sequences of nucleotides that read the same backward as forward. They can be present in genomes and have various biological functions. Here are some methods for discovering palindromes in genomes:</p><ol>
<li>
<p>Direct sequence search: One of the simplest ways to discover palindromes is to search the genome sequence directly for palindromic sequences using pattern matching tools, such as regular expressions or string algorithms. This approach can be useful for discovering simple palindromes, but may miss more complex palindromic structures.</p>
</li>
<li>
<p>Dot plot analysis: Dot plot analysis is a graphical method that can be used to identify palindromic regions in a genome. It involves plotting the genome sequence against itself and examining the diagonal patterns that emerge. Palindromic regions will appear as symmetrical patterns along the diagonal.</p>
</li>
<li>
<p>Restriction enzyme analysis: Some restriction enzymes, such as EcoRI and HindIII, recognize palindromic sequences and cleave DNA at these sites. By digesting the genome with these enzymes and examining the resulting fragments, palindromic regions can be identified.</p>
</li>
<li>
<p>Next-generation sequencing: High-throughput sequencing technologies, such as PacBio and Oxford Nanopore, can generate long reads that can span entire palindromic regions. By mapping these reads to the genome, palindromic regions can be identified and characterized.</p>
</li>
<li>
<p>Comparative genomics: Comparing the genomes of related species can also reveal palindromic regions that are conserved across evolutionarily divergent lineages. This approach can help identify functional palindromes that are under selective pressure.</p>
</li>
</ol><p>Overall, the discovery of palindromic sequences in genomes can be accomplished using a variety of methods, each with their own advantages and limitations. A combination of these methods can provide a comprehensive understanding of the palindromic landscape of a genome.</p></div></div></div></div></div></div></div></div></div></div>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42965/nucl2vec-local-alignment-of-dna-sequences-using-distributed-vector-representation</guid>
	<pubDate>Tue, 16 Mar 2021 05:45:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42965/nucl2vec-local-alignment-of-dna-sequences-using-distributed-vector-representation</link>
	<title><![CDATA[Nucl2Vec: Local alignment of DNA sequences using Distributed Vector Representation]]></title>
	<description><![CDATA[<p><span>We demonstrate a novel approach for</span><span>local alignment of DNA reads with respect to reference genome.</span><span>For this process we have used Skip-gram model for creating</span><span>encoding(Nucl2Vec) and k-nearest neighbor for the alignment.</span><span>With our new approach we have reduced computation cost for</span><span>local alignment , while achieving accuracy comparable to existing</span><span>defacto standard BWA-MEM tool.</span> </p>
<p><em>https://prakharg24.github.io/papers/401851.full.pdf</em></p><p>Address of the bookmark: <a href="https://prakharg24.github.io/papers/401851.full.pdf" rel="nofollow">https://prakharg24.github.io/papers/401851.full.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34049/libsvm-a-library-for-support-vector-machines</guid>
	<pubDate>Wed, 02 Aug 2017 06:49:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34049/libsvm-a-library-for-support-vector-machines</link>
	<title><![CDATA[LIBSVM -- A Library for Support Vector Machines]]></title>
	<description><![CDATA[<p><strong>LIBSVM&nbsp;</strong>is an integrated software for support vector classification, (C-SVC,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#nuandone">nu-SVC</a>), regression (epsilon-SVR,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#nuandone">nu-SVR</a>) and distribution estimation (<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#nuandone">one-class SVM</a>). It supports multi-class classification.</p>
<p>Since version 2.8, it implements an SMO-type algorithm proposed in this paper:<br>R.-E. Fan, P.-H. Chen, and C.-J. Lin.&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/papers/quadworkset.pdf">Working set selection using second order information for training SVM</a>. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#f203">how to cite LIBSVM</a>)</p>
<p><span style="color: #ff0000;">Our goal is to help users from other fields to easily use SVM as a tool.&nbsp;</span><strong>LIBSVM&nbsp;</strong>provides a simple interface where users can easily link it with their own programs. Main features of&nbsp;<strong>LIBSVM</strong>&nbsp;include</p>
<ul>
<li>Different SVM formulations</li>
<li>Efficient multi-class classification</li>
<li>Cross validation for model selection</li>
<li>Probability estimates</li>
<li>Various kernels (including precomputed kernel matrix)</li>
<li>Weighted SVM for unbalanced data</li>
<li>Both C++ and&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#java">Java</a>&nbsp;sources</li>
<li><a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#GUI">GUI</a>&nbsp;demonstrating SVM classification and regression</li>
<li><a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#python">Python</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#R">R</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab">MATLAB</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#perl">Perl</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#ruby">Ruby</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#weka">Weka</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#lisp">Common LISP</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#clisp">CLISP</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#haskell">Haskell</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#ocaml">OCaml</a>,&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#labview">LabVIEW</a>, and&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#PHP">PHP</a>&nbsp;interfaces.&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#csharp">C# .NET</a>&nbsp;code and&nbsp;<a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/#cuda">CUDA</a>&nbsp;extension is available.&nbsp;<br>It's also included in some data mining environments:&nbsp;<a href="http://rapid-i.com/">RapidMiner</a>,&nbsp;<a href="http://pcp.sourceforge.net/">PCP</a>, and&nbsp;<a href="http://lionoso.org/">LIONsolver</a>.</li>
<li>Automatic model selection which can generate contour of cross validation accuracy.</li>
<li></li>
</ul>
<p>https://www.csie.ntu.edu.tw/~cjlin/libsvm/</p><p>Address of the bookmark: <a href="https://www.csie.ntu.edu.tw/~cjlin/libsvm/" rel="nofollow">https://www.csie.ntu.edu.tw/~cjlin/libsvm/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36746/soap2-short-oligonucleotide-analysis-package-2</guid>
	<pubDate>Wed, 23 May 2018 10:09:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36746/soap2-short-oligonucleotide-analysis-package-2</link>
	<title><![CDATA[SOAP2 : Short Oligonucleotide Analysis Package 2]]></title>
	<description><![CDATA[SOAPaligner/soap2 is a member of the SOAP (Short Oligonucleotide Analysis Package). It is an updated version of SOAP software for short oligonucleotide alignment. The new program features in super fast and accurate alignment for huge amounts of short reads generated by Illumina/Solexa Genome Analyzer. Compared to soap v1, it is one order of magnitude faster. It require only 2 minutes aligning one million single-end reads onto the human reference genome. Another remarkable improvement of SOAPaligner is that it now supports a wide range of the read length.

SOAPaligner benefitted in time and space efficiency by a revolution in the basic data structures and algorithms used.The core algorithms and the indexing data structures (2way-BWT) are developed by the algorithms research group of the Department of Computer Science, the University of Hong Kong (T.W. Lam, Alan Tam, Simon Wong, Edward Wu and S.M. Yiu).<p>Address of the bookmark: <a href="http://soap.genomics.org.cn/soapaligner.html" rel="nofollow">http://soap.genomics.org.cn/soapaligner.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>

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