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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41475?offset=70</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43815/kebabs-package-provides-functionality-for-kernel-based-analysis-of-biological-sequences-via-support-vector-machine-svm-based-methods</guid>
	<pubDate>Fri, 04 Mar 2022 00:14:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43815/kebabs-package-provides-functionality-for-kernel-based-analysis-of-biological-sequences-via-support-vector-machine-svm-based-methods</link>
	<title><![CDATA[kebabs: package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods]]></title>
	<description><![CDATA[<p><span>The&nbsp;</span><tt>kebabs</tt><span>&nbsp;package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods. Biological sequences include DNA, RNA, and amino acid (AA) sequences. Sequence kernels define similarity measures between sequences. The package implements some of the most important kernels for sequence analysis in a very flexible and efficient way and extends the standard position-independent functionality of these kernels in a novel way to take the position of patterns in the sequences into account for the similarity measure.</span></p>
<p>http://www.bioinf.jku.at/software/kebabs/</p>
<p>http://bioconductor.org/packages/release/bioc/vignettes/kebabs/inst/doc/kebabs.pdf</p><p>Address of the bookmark: <a href="http://www.bioinf.jku.at/software/kebabs/" rel="nofollow">http://www.bioinf.jku.at/software/kebabs/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44541/powerful-books-for-learning-data-analysis-with-r</guid>
	<pubDate>Tue, 28 May 2024 07:42:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44541/powerful-books-for-learning-data-analysis-with-r</link>
	<title><![CDATA[Powerful books for learning data analysis with R]]></title>
	<description><![CDATA[<p><span>R is powerful tool for data analysis, visualization, and machine learning. And it costs $0 to use! Here are six FREE books you can use to learn R today:</span></p>
<p><span>https://csgillespie.github.io/efficientR/</span></p>
<p><span>https://r-graphics.org/</span></p>
<p><span>https://rstudio-education.github.io/hopr/</span></p>
<p><span>https://r-pkgs.org/</span></p>
<p><span>https://r4ds.had.co.nz/</span></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://r-graphics.org/" rel="nofollow">https://r-graphics.org/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34420/rita-rapid-identification-of-high-confidence-taxonomic-assignments-for-metagenomic-data</guid>
	<pubDate>Mon, 27 Nov 2017 08:25:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34420/rita-rapid-identification-of-high-confidence-taxonomic-assignments-for-metagenomic-data</link>
	<title><![CDATA[RITA: Rapid identification of high-confidence taxonomic assignments for metagenomic data]]></title>
	<description><![CDATA[<p>RITA is a standalone software package and Web server for taxonomic assignment of metagenomic sequence reads. By combining homology predictions from BLAST or UBLAST with compositional classifications from a Naive Bayes classifier, RITA is able to achieve very high accuracy on short reads. Unlike other hybrid approaches which combine these predictions for all sequences to be classified, RITA uses a pipeline to first identify cases where both types of classifier are in agreement, which constitute the highest-confidence set. Sequences not classified in this manner are subjected to a series of downstream classification steps.</p>
<p>This work has been accepted for publication:</p>
<p>MacDonald NJ, Parks DH, and Beiko RG. Rapid identification of taxonomic assignments. Accepted to&nbsp;<em>Nucleic Acids Research</em>&nbsp;April 4, 2012.</p>
<p>If you have any questions or bug reports, please let us know at &lt;beiko@cs.dal.ca&gt;.</p><p>Address of the bookmark: <a href="http://kiwi.cs.dal.ca/Software/RITA" rel="nofollow">http://kiwi.cs.dal.ca/Software/RITA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39884/retrieving-taxonomic-information-with-r</guid>
	<pubDate>Thu, 29 Aug 2019 01:38:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39884/retrieving-taxonomic-information-with-r</link>
	<title><![CDATA[Retrieving Taxonomic Information with R]]></title>
	<description><![CDATA[<p>This vignette will introduce users to the retrieval of taxonomic information with&nbsp;<code>myTAI</code>. The&nbsp;<code>taxonomy()</code>&nbsp;function implemented in&nbsp;<code>myTAI</code>&nbsp;relies on the powerful package&nbsp;<a href="https://github.com/ropensci/taxize">taxize</a>. Nevertheless, taxonomic information retrieval has been customized for the&nbsp;<code>myTAI</code>&nbsp;standard and for organism specific information retrieval.</p>
<p>Specifically, the&nbsp;<code>taxonomy()</code>&nbsp;function implemented in&nbsp;<code>myTAI</code>&nbsp;can be used to classify genomes according to phylogenetic classification into Phylostrata (Phylostratigraphy) or to retrieve species specific taxonomic information when performing Divergence Stratigraphy (see&nbsp;<a href="https://cran.r-project.org/web/packages/myTAI/vignettes/Introduction.html">Introduction</a>&nbsp;for details).</p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/myTAI/vignettes/Taxonomy.html" rel="nofollow">https://cran.r-project.org/web/packages/myTAI/vignettes/Taxonomy.html</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33482/tardis-toolkit-for-automated-and-rapid-discovery-of-structural-variants</guid>
	<pubDate>Fri, 09 Jun 2017 04:43:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33482/tardis-toolkit-for-automated-and-rapid-discovery-of-structural-variants</link>
	<title><![CDATA[TARDIS: Toolkit for automated and rapid discovery of structural variants]]></title>
	<description><![CDATA[<p>tardis</p>
<p>Toolkit for Automated and Rapid DIscovery of Structural variants</p>
<p>Requirements</p>
<p>zlib (http://www.zlib.net)<br>mrfast (https://github.com/BilkentCompGen/mrfast)<br>htslib (included as submodule; http://htslib.org/)<br>Fetching tardis</p>
<p>git clone https://github.com/BilkentCompGen/tardis.git --recursive</p>
<p>&nbsp;</p>
<p>https://github.com/BilkentCompGen/tardis</p><p>Address of the bookmark: <a href="https://github.com/BilkentCompGen/tardis" rel="nofollow">https://github.com/BilkentCompGen/tardis</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36880/jvarkit-java-utilities-for-bioinformatics</guid>
	<pubDate>Fri, 08 Jun 2018 09:31:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36880/jvarkit-java-utilities-for-bioinformatics</link>
	<title><![CDATA[Jvarkit : Java utilities for Bioinformatics]]></title>
	<description><![CDATA[Collection of Java tool kits for bioinformatics works:

Jvarkit : Java utilities for Bioinformatics<p>Address of the bookmark: <a href="http://lindenb.github.io/jvarkit/" rel="nofollow">http://lindenb.github.io/jvarkit/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41207/blobtoolkit-a-toolkit-for-genome-assembly-qc</guid>
	<pubDate>Fri, 21 Feb 2020 00:17:50 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41207/blobtoolkit-a-toolkit-for-genome-assembly-qc</link>
	<title><![CDATA[BlobToolKit: A toolkit for genome assembly QC]]></title>
	<description><![CDATA[<p>Filtering raw genomic datasets is essential to avoid chimeric assemblies and to increase the validity of sequence-based biological inference. BlobToolKit extends the BlobTools<span>1</span>/Blobology<span>2</span>&nbsp;approach to simplify interactive and reproducible filtering.</p>
<p>BlobToolKit is comprised of four components:</p>
<ol>
<li><a href="https://blobtoolkit.genomehubs.org/btk-viewer/">BlobToolKit Viewer</a>&nbsp;allows browser-based interactive visualisation and filtering of preliminary or published genomic datasets even for highly fragmented assemblies.</li>
<li><a href="https://blobtoolkit.genomehubs.org/blobtools2/">BlobTools2</a>&nbsp;is a command-line program to convert assemblies and analysis results into datasets that can be further processed using&nbsp;<a href="https://blobtoolkit.genomehubs.org/blobtools2/">BlobTools2</a>&nbsp;and/or visualised in the Viewer.</li>
<li>The&nbsp;<a href="https://blobtoolkit.genomehubs.org/specification/">BlobToolKit Specification</a>&nbsp;features a formal schema and validator for the JSON-based BlobDir format used by&nbsp;<a href="https://blobtoolkit.genomehubs.org/blobtools2/">BlobTools2</a>&nbsp;and the&nbsp;<a href="https://blobtoolkit.genomehubs.org/btk-viewer/">Viewer</a>.</li>
<li>The&nbsp;<a href="https://blobtoolkit.genomehubs.org/pipeline/">BlobToolKit Pipeline</a>&nbsp;is a configurable Snakemake pipeline that automates all steps from retrieving public datasets through running analyses and generating a BlobDir dataset with&nbsp;<a href="https://blobtoolkit.genomehubs.org/blobtools2/">BlobTools2</a>, ready for visualisation in the&nbsp;<a href="https://blobtoolkit.genomehubs.org/btk-viewer/">Viewer</a>.</li>
</ol>
<p>Paper&nbsp;<a href="https://www.biorxiv.org/content/10.1101/844852v1.full.pdf">https://www.biorxiv.org/content/10.1101/844852v1.full.pdf</a></p><p>Address of the bookmark: <a href="https://blobtoolkit.genomehubs.org/" rel="nofollow">https://blobtoolkit.genomehubs.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45276/the-hidden-pages-of-our-dna</guid>
	<pubDate>Sat, 05 Sep 2026 01:04:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45276/the-hidden-pages-of-our-dna</link>
	<title><![CDATA[The Hidden Pages of Our DNA]]></title>
	<description><![CDATA[<p>Imagine opening the instruction manual for life.</p><p>It is enormous, billions of letters long. Some of those letters are easy to understand because they contain instructions for building proteins, the tiny machines that keep our cells alive.</p><p>But what if some of the most important instructions aren't in those obvious chapters?</p><p>Scientists have long suspected that the mysterious stretches of DNA between protein-coding genes may contain some of life's most interesting secrets. These regions can act like switches, telling genes when, where, and how strongly to turn on. Among them are conserved non-coding elements, or CNEs DNA sequences that have remained remarkably similar across different species.</p><p>Think of a CNE as a sentence that nature has copied into several editions of the same book.</p><p>A mouse has it.</p><p>A bird has it.</p><p>A human has it.</p><p>And despite millions of years of evolution, much of the sentence remains almost unchanged.</p><p>That conservation is a clue: this piece of DNA probably matters.</p><p>But there was a problem. Finding these hidden sentences across enormous genomes is difficult. Researchers needed to compare many genomes, identify conserved regions, and then determine which of those regions might have evolved unusually quickly in particular branches of the evolutionary tree.</p><p>Enter CNEwrap.&nbsp;https://github.com/YanCCscu/CNEwrap</p><p>The researchers developed CNEwrap as a streamlined toolkit capable of handling large-scale genome comparisons, conserved-element detection, and evolutionary analysis. Inside it is a new algorithm called EvoAcc, designed to identify CNEs that show accelerated evolution in specific species or lineages.</p><p>Why does that matter?</p><p>Because evolution isn't just about what stays the same.</p><p>Sometimes, the most interesting story is hidden in what changed.</p><p>Imagine that nearly every species carries the same regulatory sentence, but one lineage suddenly has several unusual edits. Perhaps those changes helped shape a new body structure, a different adaptation, or another distinctive trait.</p><p>EvoAcc is designed to help researchers find these evolutionary clues. In the study's tests, it performed particularly well in scenarios involving two or three accelerated lineages and showed improved sensitivity to insertion and deletion mutations compared with several existing approaches.</p><p>The researchers also tested CNEwrap using functional genomic regions from mammals and found that EvoAcc could recover human-specific accelerated regions while identifying some signals that other methods missed.</p><p>So the story of CNEwrap is really a story about looking beyond the obvious.</p><p>For decades, scientists have often focused on the DNA that directly encodes proteins. But the genome is more like a vast library: the protein-coding genes are the main text, while non-coding regions may contain punctuation, instructions, switches, annotations, and editing notes that tell the cell how to read the book.</p><p>CNEwrap gives researchers a new way to search that hidden material.</p><p>And perhaps somewhere inside those billions of DNA letters is an evolutionary story we haven't read yet.</p><p>The exciting part is that we now have better tools to find it.</p><p>Read more about it&nbsp;https://academic.oup.com/nar/article/54/14/gkag709/8738333?</p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/17176/arvados</guid>
	<pubDate>Sat, 20 Sep 2014 16:54:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/17176/arvados</link>
	<title><![CDATA[Arvados]]></title>
	<description><![CDATA[<p>Arvados is a free and open&nbsp;source bioinformatics&nbsp;platform for genomic and&nbsp;biomedical data. User can&nbsp;Store | Organize | Compute | Share the data for free.&nbsp;</p>
<p><img src="https://arvados.org/images/dax.png" width="400" height="535" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="https://arvados.org/" rel="nofollow">https://arvados.org/</a></p>]]></description>
	<dc:creator>Martin Jones</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27696/methylkit</guid>
	<pubDate>Fri, 03 Jun 2016 10:09:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27696/methylkit</link>
	<title><![CDATA[methylKit]]></title>
	<description><![CDATA[<p><em>methylKit</em> is an <a href="http://en.wikipedia.org/wiki/R_%28programming_language%29">R</a> package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from <a href="http://www.nature.com/nprot/journal/v6/n4/abs/nprot.2010.190.html">RRBS</a> and its variants, but also target-capture methods such as <a href="http://www.halogenomics.com/sureselect/methyl-seq">Agilent SureSelect methyl-seq</a>. In addition, methylKit can deal with base-pair resolution data for 5hmC obtained from Tab-seq or oxBS-seq. It can also handle whole-genome bisulfite sequencing data if proper input format is provided.</p><p>Address of the bookmark: <a href="https://github.com/al2na/methylKit" rel="nofollow">https://github.com/al2na/methylKit</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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