<?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/37830?offset=260</link>
	<atom:link href="https://bioinformaticsonline.com/related/37830?offset=260" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26909/sequence-assembly-with-mira-4</guid>
	<pubDate>Wed, 06 Apr 2016 08:21:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26909/sequence-assembly-with-mira-4</link>
	<title><![CDATA[Sequence assembly with MIRA 4]]></title>
	<description><![CDATA[<p>MIRA is a multi-pass DNA sequence data assembler/mapper for whole genome and EST/RNASeq projects. MIRA assembles/maps reads gained by</p>
<div>
<ul>
<li>
<p>electrophoresis sequencing (aka Sanger sequencing)</p>
</li>
<li>
<p>454 pyro-sequencing (GS20, FLX or Titanium)</p>
</li>
<li>
<p>Ion Torrent</p>
</li>
<li>
<p>Solexa (Illumina) sequencing</p>
</li>
<li>
<p>(in development) Pacific Biosciences sequencing</p>
</li>
</ul>
</div>
<p>into contiguous sequences (called <span><em>contigs</em></span>). One can use the sequences of different sequencing technologies either in a single assembly run (a <span><em>true hybrid assembly</em></span>) or by mapping one type of data to an assembly of other sequencing type (a <span><em>semi-hybrid assembly (or mapping)</em></span>) or by mapping a data against consensus sequences of other assemblies (a <span><em>simple mapping</em></span>).</p>
<p>The MIRA acronym stands for <span><strong>M</strong></span>imicking <span><strong>I</strong></span>ntelligent <span><strong>R</strong></span>ead <span><strong>A</strong></span>ssembly and the program pretty well does what its acronym says (well, most of the time anyway). It is the Swiss army knife of sequence assembly that I've used and developed during the past 14 years to get assembly jobs I work on done efficiently - and especially accurately. That is, without me actually putting too much manual work into it.</p>
<p>More at http://mira-assembler.sourceforge.net/docs/DefinitiveGuideToMIRA.html</p><p>Address of the bookmark: <a href="http://mira-assembler.sourceforge.net/docs/DefinitiveGuideToMIRA.html" rel="nofollow">http://mira-assembler.sourceforge.net/docs/DefinitiveGuideToMIRA.html</a></p>]]></description>
	<dc:creator>Priya Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26975/trimmomatic-a-flexible-read-trimming-tool-for-illumina-ngs-data</guid>
	<pubDate>Fri, 15 Apr 2016 05:58:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26975/trimmomatic-a-flexible-read-trimming-tool-for-illumina-ngs-data</link>
	<title><![CDATA[Trimmomatic: A flexible read trimming tool for Illumina NGS data]]></title>
	<description><![CDATA[<h4>Paired End:</h4>
<p><code>java -jar trimmomatic-0.35.jar PE -phred33 input_forward.fq.gz input_reverse.fq.gz output_forward_paired.fq.gz output_forward_unpaired.fq.gz output_reverse_paired.fq.gz output_reverse_unpaired.fq.gz ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36</code></p>
<p>This will perform the following:</p>
<ul>
<li>Remove adapters (ILLUMINACLIP:TruSeq3-PE.fa:2:30:10)</li>
<li>Remove leading low quality or N bases (below quality 3) (LEADING:3)</li>
<li>Remove trailing low quality or N bases (below quality 3) (TRAILING:3)</li>
<li>Scan the read with a 4-base wide sliding window, cutting when the average quality per base drops below 15 (SLIDINGWINDOW:4:15)</li>
<li>Drop reads below the 36 bases long (MINLEN:36)</li>
</ul>
<p>More at http://www.usadellab.org/cms/?page=trimmomatic</p><p>Address of the bookmark: <a href="http://www.usadellab.org/cms/?page=trimmomatic" rel="nofollow">http://www.usadellab.org/cms/?page=trimmomatic</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27261/segemehl</guid>
	<pubDate>Tue, 10 May 2016 08:10:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27261/segemehl</link>
	<title><![CDATA[segemehl]]></title>
	<description><![CDATA[<p><span>segemehl is a software to map short sequencer reads to reference genomes. Unlike other methods, segemehl is able to detect not only mismatches but also insertions and deletions. Furthermore, segemehl is not limited to a specific read length and is able to map&nbsp;primer- or polyadenylation contaminated reads correctly.&nbsp; segemehl implements a matching strategy based on enhanced suffix arrays (ESA).&nbsp;</span></p>
<p><span>More at&nbsp;http://www.bioinf.uni-leipzig.de/Software/segemehl/</span></p>
<p><span>Manual&nbsp;http://www.bioinf.uni-leipzig.de/Software/segemehl/segemehl_manual_0_1_7.pdf</span></p><p>Address of the bookmark: <a href="http://hoffmann.bioinf.uni-leipzig.de/LIFE/segemehl.html" rel="nofollow">http://hoffmann.bioinf.uni-leipzig.de/LIFE/segemehl.html</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27092/medea-comparative-genomic-visualization-with-adobe-flash</guid>
	<pubDate>Tue, 26 Apr 2016 12:15:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27092/medea-comparative-genomic-visualization-with-adobe-flash</link>
	<title><![CDATA[MEDEA: Comparative Genomic Visualization with Adobe Flash]]></title>
	<description><![CDATA[<p><span>As the number of sequence and annotated genomes grows larger, the need to understand, compare, and contrast the data becomes increasingly important. Using the power of the human visual system to detect trends and spot outliers is necessary in such large and complex data sets.</span></p>
<p><span>More at&nbsp;http://www.broadinstitute.org/annotation/medea/</span></p><p>Address of the bookmark: <a href="http://www.broadinstitute.org/annotation/medea/" rel="nofollow">http://www.broadinstitute.org/annotation/medea/</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/27331/andi</guid>
	<pubDate>Fri, 13 May 2016 05:16:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27331/andi</link>
	<title><![CDATA[Andi]]></title>
	<description><![CDATA[<p>This is the <code>andi</code> program for estimating the evolutionary distance between closely related genomes. These distances can be used to rapidly infer phylogenies for big sets of genomes. Because <code>andi</code> does not compute full alignments, it is so efficient that it scales even up to thousands of bacterial genomes.</p>
<p>This readme covers all necessary instructions for the impatient to get <code>andi</code> up and running. For extensive instructions please consult the <a href="https://github.com/EvolBioInf/andi/blob/master/andi-manual.pdf">manual</a>.</p>
<p>More at https://github.com/evolbioinf/andi/</p><p>Address of the bookmark: <a href="http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27438/hagfish-assess-an-assembly-through-creative-use-of-coverage-plots</guid>
	<pubDate>Fri, 20 May 2016 19:08:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27438/hagfish-assess-an-assembly-through-creative-use-of-coverage-plots</link>
	<title><![CDATA[Hagfish - assess an assembly through creative use of coverage plots]]></title>
	<description><![CDATA[<p>Hagfish is a tool that is to be used in data analysis of Next Generation Sequencing (NGS) experiments. Hagfish builds on the concept of coverage plots and aims to assist (amongst others) in quality control of&nbsp;<em style="font-size: 12.8px;">de novo</em>&nbsp;genome assembly or identification of structural variation in a genome re-sequencing experiment.</p>
<p>Hagfish requires a reference sequence and a&nbsp;<span>paired end</span>&nbsp;re-sequencing data set. Hagfish has more power the larger the insert size of the paired end library is.</p>
<p>Quick links:&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Install">Installation</a>,<a href="https://github.com/mfiers/hagfish/wiki/Operation">Operation</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/ReadMappers">Read mappers</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Scripts">Hagfish scripts</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Plots">Hagfish plots</a></p><p>Address of the bookmark: <a href="https://github.com/mfiers/hagfish" rel="nofollow">https://github.com/mfiers/hagfish</a></p>]]></description>
	<dc:creator>Abhi</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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27845/cnidaria-fast-reference-free-phylogenomic-clustering</guid>
	<pubDate>Thu, 16 Jun 2016 17:55:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27845/cnidaria-fast-reference-free-phylogenomic-clustering</link>
	<title><![CDATA[CNIDARIA: fast, reference-free phylogenomic clustering]]></title>
	<description><![CDATA[<p>Motivation: Identification of biological specimens is a major requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but these do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances.</p>
<p>Results: We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on ge-nome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100% accuracy at supra-species level and 78% accuracy for species level.</p>
<p>Availability and Implementation: Cnidaria is written in C++ and Python and is available at http://www.ab.wur.nl/cnidaria.</p>
<p>Contact: Saulo Aflitos - sauloal@gmail.com</p>
<p>Supplementary information: Supplementary data are available at Bioinformatics online.</p><p>Address of the bookmark: <a href="https://github.com/sauloal/cnidaria/wiki" rel="nofollow">https://github.com/sauloal/cnidaria/wiki</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27973/wgsim</guid>
	<pubDate>Thu, 23 Jun 2016 07:26:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27973/wgsim</link>
	<title><![CDATA[WgSim]]></title>
	<description><![CDATA[<p>Reads simulator</p>
<p>Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.<br><br>Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.<br><br></p><p>Address of the bookmark: <a href="https://github.com/lh3/wgsim" rel="nofollow">https://github.com/lh3/wgsim</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>