<?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/41125?offset=20</link>
	<atom:link href="https://bioinformaticsonline.com/related/41125?offset=20" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43795/anchorwave</guid>
	<pubDate>Wed, 23 Feb 2022 08:14:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43795/anchorwave</link>
	<title><![CDATA[AnchorWave]]></title>
	<description><![CDATA[<p dir="auto">AnchorWave (Anchored Wavefront Alignment) identifies collinear regions via conserved anchors (full-length CDS and full-length exon have been implemented currently) and breaks collinear regions into shorter fragments, i.e., anchor and inter-anchor intervals. By performing sensitive sequence alignment for each shorter interval via a 2-piece affine gap cost strategy and merging them together, AnchorWave generates a whole-genome alignment for each collinear block. AnchorWave implements commands to guide collinear block identification with or without chromosomal rearrangements and provides options to use known polyploidy levels or whole-genome duplications to inform alignment.</p><p>Address of the bookmark: <a href="https://github.com/baoxingsong/AnchorWave" rel="nofollow">https://github.com/baoxingsong/AnchorWave</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33586/genetic-mapper-svg-genetic-map-drawer</guid>
	<pubDate>Sun, 18 Jun 2017 14:11:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33586/genetic-mapper-svg-genetic-map-drawer</link>
	<title><![CDATA[Genetic-mapper: SVG Genetic Map Drawer]]></title>
	<description><![CDATA[<p><span>Genetic-mapper is a perl script able to draw publication-ready vectorial genetic maps.</span></p>
<p>Perl script for creating a publication-ready vectorial genetic/linkage map in Scalable Vector Graphics (SVG) format. The resulting file can either be submitted for publication and edited with any vectorial drawing software like&nbsp;<a href="https://inkscape.org/">Inkscape</a>&nbsp;and&nbsp;<a href="http://www.adobe.com/uk/products/illustrator.html">Abobe Illustrator(R)</a>.</p>
<p>The input file must be a text file with at least the marker name (ID), linkage group (LG) and the position (POS) separeted by tabulations. Additionally a logarithm of odds (LOD score) can be provided. Any extra parameter will be ignored.</p>
<pre><code>map.tsv

ID&lt;tab&gt;LG&lt;tab&gt;POS&lt;tab&gt;LOD
13519  12     0       0.250840894
2718   12     1.0     0.250840893
11040  12     1.6     0.252843341
...</code></pre>
<p>https://github.com/pseudogene/genetic-mapper</p><p>Address of the bookmark: <a href="https://github.com/pseudogene/genetic-mapper" rel="nofollow">https://github.com/pseudogene/genetic-mapper</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40611/deepvariant-an-analysis-pipeline-that-uses-a-deep-neural-network-to-call-genetic-variants-from-next-generation-dna-sequencing-data</guid>
	<pubDate>Sat, 25 Jan 2020 13:28:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40611/deepvariant-an-analysis-pipeline-that-uses-a-deep-neural-network-to-call-genetic-variants-from-next-generation-dna-sequencing-data</link>
	<title><![CDATA[DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.]]></title>
	<description><![CDATA[<p><span>DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.</span></p>
<p><span><span>DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on&nbsp;</span><a href="https://github.com/google/nucleus">Nucleus</a><span>, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the&nbsp;</span><a href="https://www.tensorflow.org/">TensorFlow</a><span>&nbsp;machine learning framework.</span></span></p>
<p><span><a href="https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html">https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html</a></span></p>
<p><span><a href="https://www.biorxiv.org/content/10.1101/092890v6">https://www.biorxiv.org/content/10.1101/092890v6</a></span></p>
<p><span><img src="https://4.bp.blogspot.com/-2KlXZO60sWE/WiGc8qlZfxI/AAAAAAAACOs/s1pNiKI8jsAvJLr1E_po5udDO8eObm_awCLcBGAs/s640/image3.png" width="640" height="427" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/google/deepvariant" rel="nofollow">https://github.com/google/deepvariant</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/2699/translational-bioinformatics-transforming-300-billion-points-of-data</guid>
	<pubDate>Tue, 20 Aug 2013 19:03:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/2699/translational-bioinformatics-transforming-300-billion-points-of-data</link>
	<title><![CDATA[Translational Bioinformatics: Transforming 300 Billion Points of Data]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/o4KNG7nd938" frameborder="0" allowfullscreen></iframe>Translational Bioinformatics: Transforming 300 Billion Points of Data into Diagnostics, Therapeutics, and New Insights into Disease      
      
Air date:  Wednesday, June 20, 2012, 3:00:00 PM
Time displayed is Eastern Time, Washington DC Local  
 
Description:  There is an urgent need to translate genome-era discoveries into clinical utility, but the difficulties in making bench-to-bedside translations haven't been well described. The nascent field of translational bioinformatics may help. Dr. Butte's lab at Stanford University builds and applies tools that convert more than 300 billion points of molecular, clinical, and epidemiological data (measured by researchers and clinicians over the past decade) into diagnostics, therapeutics, and new insights into disease. Dr. Butte, a bioinformatician and pediatric endocrinologist, will highlight his lab's work on using publicly available molecular measurements to find new uses for drugs, discovering new treatable mechanisms of disease in type 2 diabetes, and evaluating patients presenting with whole genomes sequenced. 

The NIH Wednesday Afternoon Lecture Series includes weekly scientific talks by some of the top researchers in the biomedical sciences worldwide. 

For more information, visit: 
The NIH Director's Wednesday Afternoon Lecture Series  
Author:  Atul Butte, M.D., Ph.D., Stanford University  
Runtime:  01:07:42  
Permanent link:  http://videocast.nih.gov/launch.asp?17321]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36597/gappadder-a-sensitive-approach-for-closing-gaps-on-draft-genomes-with-short-sequence-reads</guid>
	<pubDate>Mon, 14 May 2018 05:25:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36597/gappadder-a-sensitive-approach-for-closing-gaps-on-draft-genomes-with-short-sequence-reads</link>
	<title><![CDATA[GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads]]></title>
	<description><![CDATA[<p><span>This software is provided ``as is&rdquo; without warranty of any kind. In no event shall the author be held responsible for any damage resulting from the use of this software. The program package, including source codes, executables, and this documentation, is distributed free of charge. If you use this program in a publication, please cite the following reference:</span><br><span>Chong Chu, Xin Li, and Yufeng Wu. "GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads." bioRxiv (2017): 125534.</span></p><p>Address of the bookmark: <a href="https://github.com/Reedwarbler/GAPPadder" rel="nofollow">https://github.com/Reedwarbler/GAPPadder</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44599/p10k-the-protist-10000-genomes</guid>
	<pubDate>Sat, 06 Jul 2024 08:29:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44599/p10k-the-protist-10000-genomes</link>
	<title><![CDATA[P10K: The Protist 10,000 Genomes]]></title>
	<description><![CDATA[<p><span>The Protist 10,000 Genomes (P10K) Project aims to decipher the genome sequences and construct a comprehensive database resource containing over 10,000 species of protists, encompassing representatives from every major clade. Samples were collected from diverse habitats, and the genome information was acquired through de novo sequencing, genome re-annotation, and integration of publicly available data. Serving as a centralized data portal for the project, the P10K database primarily focuses on delivering high-quality curation and facilitating efficient retrieval of protist genome data.</span></p><p>Address of the bookmark: <a href="https://ngdc.cncb.ac.cn/p10k/" rel="nofollow">https://ngdc.cncb.ac.cn/p10k/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33887/gview-a-java-application-for-viewing-and-examining-prokaryotic-genomes-in-a-circular-or-linear-context</guid>
	<pubDate>Fri, 14 Jul 2017 07:47:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33887/gview-a-java-application-for-viewing-and-examining-prokaryotic-genomes-in-a-circular-or-linear-context</link>
	<title><![CDATA[GView: A Java application for viewing and examining prokaryotic genomes in a circular or linear context]]></title>
	<description><![CDATA[<p>GView is a Java application for viewing and examining prokaryotic genomes in a circular or linear context. It accepts standard sequence file formats and an optional style specification file to generate customizable, publication quality genome maps in bitmap and scalable vector graphics formats. GView features an interactive pan-and-zoom interface, a command-line interface for incorporation in genome analysis pipelines, and a public Application Programming Interface for incorporation in other Java applications.</p>
<p><strong>Availability:</strong>&nbsp;GView is a freely available application licensed under the GNU Public License. The application, source code, documentation, file specifications, tutorials and image galleries are available at&nbsp;<a href="http://gview.ca/" target="pmc_ext">http://gview.ca</a></p>
<p><strong>Contact:</strong>&nbsp;<a href="mailto:dev@null">ac.cg.cpsa-cahp@raalesmod.nav.yrag</a></p>
<p>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34569/ksnp30-snp-detection-and-phylogenetic-analysis-of-genomes-without-genome-alignment-or-reference-genome</guid>
	<pubDate>Fri, 08 Dec 2017 16:48:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34569/ksnp30-snp-detection-and-phylogenetic-analysis-of-genomes-without-genome-alignment-or-reference-genome</link>
	<title><![CDATA[kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome]]></title>
	<description><![CDATA[<p><span>Sept. 20, 2017 Version 3.1 released. Major upgrade. Version 3.1 fixes the problems with SNP annotation that arose when NCBI discontinued use of GI numbers. Please read carefully the Preface (page 3) and the File of annotated genomes section (pages 9-10) in the version 3.1 User Guide. Thanks to Tom Slezak for revsing the get_genbank_file3 script and to Tod Stuber (USDA) for testing version 3.1 even though he doesn't need the annotation feature. All users are encouraged to upgrade to version 3.1.&nbsp;<br></span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/ksnp/files/" rel="nofollow">https://sourceforge.net/projects/ksnp/files/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35619/tallymer-method-to-compute-k-mer-frequencies-and-its-application-to-annotate-large-repetitive-plant-genomes</guid>
	<pubDate>Thu, 15 Feb 2018 10:21:02 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35619/tallymer-method-to-compute-k-mer-frequencies-and-its-application-to-annotate-large-repetitive-plant-genomes</link>
	<title><![CDATA[Tallymer: method to compute K-mer frequencies and its application to annotate large repetitive plant genomes]]></title>
	<description><![CDATA[<p>Tallymer is based on enhanced suffix arrays. This gives a much larger flexibility concerning the choice of the&nbsp;<span>k</span>-mer size. Tallymer can process large data sizes of several billion bases. We used it in a variety of applications to study the genomes of maize and other plant species. In particular, Tallymer was used to index a set whole genome shotgun sequences from maize (B73) (total size 10<sup>9</sup>&nbsp;bp).&nbsp;<br>Tallymer was effective in a variety of applications to aid genome annotation in maize, despite limitations imposed by the relatively low coverage of sequence available.</p>
<p>A manual can be found&nbsp;<a href="https://www.zbh.uni-hamburg.de/fileadmin/gi/tallymer/tallymer.pdf" target="_blank" title="tallymer.pdf (111 KB)">here</a>.</p><p>Address of the bookmark: <a href="https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html" rel="nofollow">https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36800/genomemapper-simultaneous-alignment-of-short-reads-against-multiple-genomes</guid>
	<pubDate>Fri, 25 May 2018 09:29:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36800/genomemapper-simultaneous-alignment-of-short-reads-against-multiple-genomes</link>
	<title><![CDATA[GenomeMapper: Simultaneous alignment of short reads against multiple genomes]]></title>
	<description><![CDATA[GenomeMapper is a short read mapping tool designed for accurate read alignments. It quickly aligns millions of reads either with ungapped or gapped alignments. It can be used to align against multiple genomes simulanteously or against a single reference. If you are unsure which one is the appropriate GenomeMapper, you might want to use the latter

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2768987/<p>Address of the bookmark: <a href="http://1001genomes.org/software/genomemapper.html" rel="nofollow">http://1001genomes.org/software/genomemapper.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>