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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38413?offset=250</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26409/ucsc-genome-browser-and-blat-software</guid>
	<pubDate>Thu, 18 Feb 2016 03:18:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26409/ucsc-genome-browser-and-blat-software</link>
	<title><![CDATA[UCSC Genome Browser and Blat software !]]></title>
	<description><![CDATA[<p>This directory contains Genome Browser and Blat application binaries built for standalone <br>command-line use on various supported Linux and UNIX platforms. To determine which set of binaries <br>to download, type "uname -a" on the command line to display your machine type. In most cases the <br>usage statement for the application can be viewed by running the binary with no arguments. <br><br>The UCSC Genome Browser and Blat software are free for academic, nonprofit, and personal use. A <br>license is required for commercial download and installation of these binaries, with the exception <br>of items built from the following source code directories, which are freely available for all uses:<br><br>&nbsp;- kent/src/utils (includes big* tools)<br>&nbsp;- kent/src/lib<br>&nbsp;- kent/src/hg/autoSql<br>&nbsp;- kent/src/hg/autoXml<br><br>For information about commercial licensing of the Genome Browser software, see <br>http://genome.ucsc.edu/license/. The Blat and In-Silico PCR software may be commercially<br>licensed through Kent Informatics (http://www.kentinformatics.com).</p>
<p>More at http://hgdownload.cse.ucsc.edu/admin/exe/</p><p>Address of the bookmark: <a href="http://hgdownload.cse.ucsc.edu/admin/exe/" rel="nofollow">http://hgdownload.cse.ucsc.edu/admin/exe/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27216/yass-genomic-similarity-search-tool</guid>
	<pubDate>Mon, 02 May 2016 09:26:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27216/yass-genomic-similarity-search-tool</link>
	<title><![CDATA[YASS :: genomic similarity search tool]]></title>
	<description><![CDATA[<p>YASS is a genomic similarity search tool, for nucleic (DNA/RNA) sequences in fasta or plain text format (<em>it produces local pairwise alignments</em>). Like most of the heuristic pairwise local alignment tools for DNA sequences (FASTA, BLAST, PATTERNHUNTER, BLASTZ/LASTZ, LAST ...), YASS uses <em>seeds</em> to detect potential similarity regions, and then tries to extend them to local alignments. This genomic search tool uses <em>multiple transition constrained spaced seeds</em> that enable to search more fuzzy repeats, as non-coding DNA/RNA. Another simple, but interesting feature is that you can specify the seed pattern used in the search step (as provided for example by <a href="http://bioinfo.lifl.fr/yass/iedera.php">iedera</a>).</p>
<p>Main features of YASS are:</p>
<ul>
<li>multiple, possibly overlapping seeds and a new hit criterion to ensure a good sensitivity/selectivity trade-off</li>
<li>transition-constrained spaced seeds to improve sensitivity (transition mutations are purine to purine [<code>A&lt;-&gt;G</code>] or pyrimidine to pyrimidine [<code>C&lt;-&gt;T</code>])</li>
<li>using different scoring schemes with bit-score and E-value evaluated according to the sequence background frequencies</li>
<li>parameterizable <em>output</em> filter for low complexity repeats</li>
<li>reporting of various alignment statistical parameters (mutation bias along triplets, transition/transversion)</li>
<li>post-processing step to group gapped alignments</li>
</ul><p>Address of the bookmark: <a href="http://bioinfo.lifl.fr/yass/" rel="nofollow">http://bioinfo.lifl.fr/yass/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28809/kissplice</guid>
	<pubDate>Tue, 16 Aug 2016 08:34:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28809/kissplice</link>
	<title><![CDATA[KisSplice]]></title>
	<description><![CDATA[<p>KisSplice is a software that enables to analyse RNA-seq data with or without a reference genome. It is an exact local transcriptome assembler that allows to identify SNPs, indels and alternative splicing events. It can deal with an arbitrary number of biological conditions, and will quantify each variant in each condition. It has been tested on Illumina datasets of up to 1G reads. Its memory consumption is around 5Gb for 100M reads.</p>
<p>KisSplice is not a full-length transcriptome assembler. This means that it will output the variable regions of the transcripts, not reconstruct them entirely.</p>
<p>KisSplice comes as a workflow, with several possible post-treatments meant to facilitate the analysis of the results. The choice of the post-treatment depends on the availability of a reference genome/transcriptome and on the need to perform a differential analysis, as summarised in the following table.</p><p>Address of the bookmark: <a href="http://kissplice.prabi.fr/" rel="nofollow">http://kissplice.prabi.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</guid>
	<pubDate>Mon, 04 Jul 2016 00:44:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</link>
	<title><![CDATA[4DGenome]]></title>
	<description><![CDATA[<p><span>Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies.&nbsp;</span><br><br><span>All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology.&nbsp;</span></p><p>Address of the bookmark: <a href="http://4dgenome.research.chop.edu/" rel="nofollow">http://4dgenome.research.chop.edu/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29235/valet</guid>
	<pubDate>Thu, 22 Sep 2016 04:27:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29235/valet</link>
	<title><![CDATA[valet]]></title>
	<description><![CDATA[<div>
<div>
<div>VALET is a pipeline for performing&nbsp;<em>de novo</em>&nbsp;validation of metagenomic assemblies. VALET checks a number of properties that should hold true for a correct assembly (e.g., mate-pairs are aligned at the correct distance from each other in the assembly, the depth of coverage is fairly uniform along contigs, etc.). The violations of these invariants are reported allowing one to pinpoint areas that were potentially mis-assembled, or to compare the quality of different assemblies. For comparing multiple assemblies of the same data-sets, VALET also reports an overall estimate of the likelihood a particular assembly is correct.</div>
</div>
</div>
<div>
<div>Home Page:&nbsp;</div>
<div>
<div><a href="https://github.com/jgluck/VALET">VALET code repository</a></div>
</div>
</div><p>Address of the bookmark: <a href="https://www.cbcb.umd.edu/software/valet" rel="nofollow">https://www.cbcb.umd.edu/software/valet</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29208/srf-bioinformatics-job-position-in-national-institute-of-plant-genome-research-nipgr</guid>
  <pubDate>Mon, 19 Sep 2016 05:43:38 -0500</pubDate>
  <link></link>
  <title><![CDATA[SRF Bioinformatics job position in National Institute of Plant Genome Research (NIPGR)]]></title>
  <description><![CDATA[
<p>SRF Bioinformatics job position in National Institute of Plant Genome Research (NIPGR)<br />Title : “Transcriptome and small RNA diversity analysis of developing seed contrasting rice varieties” <br />Qualification : Candidates having M.Sc./M.Tech. degree or equivalent (with minimum 60% marks) in Bioinformatics with a minimum of two years of post M.Sc./M.Tech research experience are eligible to apply.<br />No. of Post : 01<br />How to apply<br />Application should reach to Dr. Pinky Agarwal, Staff Scientist, National Institute of Plant Genome Research (NIPGR) Aruna Asaf Ali Marg, P.O. Box NO. 10531, New Delhi - 110067 on or before 30/09/2016</p>

<p>More at http://www.nipgr.res.in/careers/vacancies_latest.php#</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29272/decipher</guid>
	<pubDate>Fri, 30 Sep 2016 09:33:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29272/decipher</link>
	<title><![CDATA[DECIPHER]]></title>
	<description><![CDATA[<p>DECIPHER is a software toolset that can be used to maintain, analyze, and decipher large amounts of DNA sequence data. To install DECIPHER, see the <a href="http://DECIPHER.cee.wisc.edu/Download.html">Downloads</a> page.<br><br> To begin using DECIPHER read the "Getting Started DECIPHERing" tutorial. Refer to the PDF documents below for instructions on how to use DECIPHER for various tasks.</p><p>Address of the bookmark: <a href="http://decipher.cee.wisc.edu/Documentation.html" rel="nofollow">http://decipher.cee.wisc.edu/Documentation.html</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30153/e-mem-efficient-computation-of-maximal-exact-matches</guid>
	<pubDate>Thu, 15 Dec 2016 09:30:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30153/e-mem-efficient-computation-of-maximal-exact-matches</link>
	<title><![CDATA[E-MEM: Efficient computation of Maximal Exact Matches]]></title>
	<description><![CDATA[<p>E-MEM is a C++/OpenMP program designed to efficiently compute MEMs between large genomes. See the README file for instructions on how to use E-MEM.&nbsp;<br><br>E-MEM source code</p>
<p>The source code can be downloaded&nbsp;<a href="http://www.csd.uwo.ca/~ilie/E-MEM/e-mem.zip">here</a>.&nbsp;<br><br>If you use E-MEM, please cite:</p>
<ul>
<li>N. Khiste, L. Ilie, E-MEM: Efficient computation of Maximal Exact Matches for very large genomes,&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/31/4/509.short">Bioinformatics</a>&nbsp;<strong>31</strong>(4) (2015) 509 -- 514.</li>
</ul>
<p>For any questions, please contact Lucian Ilie:&nbsp;<a href="mailto:ilie@uwo.ca">ilie@uwo.ca</a>&nbsp;</p><p>Address of the bookmark: <a href="http://www.csd.uwo.ca/~ilie/E-MEM/" rel="nofollow">http://www.csd.uwo.ca/~ilie/E-MEM/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30540/progressive-cactus</guid>
	<pubDate>Tue, 17 Jan 2017 03:40:06 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30540/progressive-cactus</link>
	<title><![CDATA[Progressive Cactus]]></title>
	<description><![CDATA[<h1><em style="font-size: 12.8px; font-weight: normal;">v0.0 by Glenn Hickey (<a href="mailto:hickey@soe.ucsc.edu">hickey@soe.ucsc.edu</a>)</em></h1>
<p>Progressive Cactus is a whole-genome alignment package.</p>
<h3><a href="https://github.com/glennhickey/progressiveCactus#requirements"></a>Requirements</h3>
<ul>
<li>git</li>
<li>gcc 4.2 or newer</li>
<li>python 2.7</li>
<li>wget</li>
<li>64bit processor and build environment</li>
<li>150GB+ of memory on at least one machine when aligning mammal-sized genomes; less memory is needed for smaller genomes.</li>
<li>Parasol or SGE for cluster support.</li>
<li>750M disk space</li>
</ul>
<h3><a href="https://github.com/glennhickey/progressiveCactus#instructions"></a>Instructions</h3>
<p>IMPORTANT NOTE: Progressive Cactus does not presently support installation into paths that contain spaces. Until this is resolved, you can use a softlink as a workaround: ln -s "path with spaces" "installation path without spaces"</p>
<p>In the parent directory of where you want Progressive Cactus installed:</p>
<pre><code>git clone git://github.com/glennhickey/progressiveCactus.git
cd progressiveCactus
git pull
git submodule update --init
make
</code></pre>
<p>It is also convenient to add the location of&nbsp;<code>progressiveCactus/bin</code>&nbsp;to your PATH environment variable. In order to run the included tools (ex hal2maf) in the submodules/ directory structure, first source&nbsp;<code>progressiveCactus/environment</code>&nbsp;to load the installed environment.</p>
<p>If any errors occur during the build process, you are unlikely to be able to use the tool. Please submit a GitHub issue so we can help out: not only will you help yourself, but others who wish to use the tool as well.</p>
<p><em>Note that all dependencies are also built and included in the submodules/ directory. This increases the size and build time but greatly simplifies installation and version management. The installation does not create or modify any files outside the progressiveCactus/ directory.</em></p><p>Address of the bookmark: <a href="https://github.com/glennhickey/progressiveCactus" rel="nofollow">https://github.com/glennhickey/progressiveCactus</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31300/clgenomics</guid>
	<pubDate>Fri, 03 Mar 2017 09:57:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31300/clgenomics</link>
	<title><![CDATA[CLgenomics]]></title>
	<description><![CDATA[<p>CLgenomics is a standalone desktop software specifically designed for bacterial genome analysis. This program has a powerful multi-genome browser, which enables rapid and responsive exploration of bacterial genomes.</p>
<p>To use CLgenomics, individual genome data (genome sequences + annotation details) are compiled and saved in a specially formatted file called CLG (ChunLab Genomics).&nbsp;Each CLG file corresponds with one bacterial genome. If multiple genomes are being considered and compared, multiple CLG files are needed. ChunLab offers &gt;40,000 CLG files of publicly available Bacterial and Archaeal genomes.</p><p>Address of the bookmark: <a href="https://chunlab.wordpress.com/clgenomics-software/" rel="nofollow">https://chunlab.wordpress.com/clgenomics-software/</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>

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