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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/27967?offset=190</link>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27099/rasttk-algorithm-for-building-custom-annotation-pipelines-and-annotating-batches-of-genomes</guid>
	<pubDate>Wed, 27 Apr 2016 11:07:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27099/rasttk-algorithm-for-building-custom-annotation-pipelines-and-annotating-batches-of-genomes</link>
	<title><![CDATA[RASTtk : algorithm for building custom annotation pipelines and annotating batches of genomes]]></title>
	<description><![CDATA[<p>The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.</p>
<p>More at http://www.nature.com/articles/srep08365</p><p>Address of the bookmark: <a href="http://rast.nmpdr.org/" rel="nofollow">http://rast.nmpdr.org/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27328/platanus</guid>
	<pubDate>Fri, 13 May 2016 05:12:40 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27328/platanus</link>
	<title><![CDATA[Platanus]]></title>
	<description><![CDATA[<p>Platanus is a novel <em>de novo</em> sequence assembler that can reconstruct genomic sequences of<br> highly heterozygous diploids from massively parallel shotgun sequencing data.</p>
<p>The latest version is <a href="http://platanus.bio.titech.ac.jp/platanus/?page_id=14">1.2.4</a>.</p>
<p>To cite Platanus, please use the following:</p>
<p>Kajitani R, Toshimoto K, Noguchi H, Toyoda A, Ogura Y, Okuno M, Yabana M, Harada M, Nagayasu E, Maruyama H, Kohara Y, Fujiyama A, Hayashi T, Itoh T, &ldquo;Efficient de novo assembly of highly heterozygous genomes from whole-genome shotgun short reads&rdquo;.&nbsp;Genome Res. 2014 Aug;24(8):1384-95. doi: 10.1101/gr.170720.113. [<a href="http://www.ncbi.nlm.nih.gov/pubmed/24755901">abstract</a> |<a href="http://genome.cshlp.org/content/24/8/1384.long"> full text</a>]</p><p>Address of the bookmark: <a href="http://platanus.bio.titech.ac.jp/" rel="nofollow">http://platanus.bio.titech.ac.jp/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27430/mosaik-a-hash-based-algorithm-for-accurate-next-generation-sequencing-short-read-mapping</guid>
	<pubDate>Fri, 20 May 2016 18:53:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27430/mosaik-a-hash-based-algorithm-for-accurate-next-generation-sequencing-short-read-mapping</link>
	<title><![CDATA[MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping]]></title>
	<description><![CDATA[<p><span>MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery.</span></p><p>Address of the bookmark: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581" rel="nofollow">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27806/blobology</guid>
	<pubDate>Mon, 13 Jun 2016 10:18:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27806/blobology</link>
	<title><![CDATA[Blobology]]></title>
	<description><![CDATA[<p><span>Tools for making blobplots or Taxon-Annotated-GC-Coverage plots (TAGC plots) to visualise the contents of genome assembly data sets as a QC step</span></p>
<p>Blaxter Lab, Institute of Evolutionary Biology, University of Edinburgh</p>
<p><span>Goal</span>: To create blobplots or Taxon-Annotated-GC-Coverage plots (TAGC plots) to visualise the contents of genome assembly data sets as a QC step.</p>
<p>This repository accompanies the paper:<br><span>Blobology: exploring raw genome data for contaminants, symbionts and parasites using taxon-annotated GC-coverage plots.</span>&nbsp;<em>Sujai Kumar, Martin Jones, Georgios Koutsovoulos, Michael Clarke, Mark Blaxter</em><br>(submitted 2013-10-01 to&nbsp;<em>Frontiers in Bioinformatics and Computational Biology special issue : Quality assessment and control of high-throughput sequencing data</em>).</p>
<p>It contains bash/perl/R scripts for running the analysis presented in the paper to create a preliminary assembly, and to create and collate GC content, read coverage and taxon annotation for the preliminary assembly, which can be visualised, such as Figure 2a from the paper showing TAGC plots/blobplots for&nbsp;<em>Caenorhabditis</em>&nbsp;sp. 5:&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/blaxterlab/blobology" rel="nofollow">https://github.com/blaxterlab/blobology</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28168/sam-flags</guid>
	<pubDate>Wed, 29 Jun 2016 15:38:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28168/sam-flags</link>
	<title><![CDATA[SAM flags]]></title>
	<description><![CDATA[<p>Decoding SAM flags</p>
<p>This utility makes it easy to identify what are the properties of a read based on its SAM flag value, or conversely, to find what the SAM Flag value would be for a given combination of properties.</p>
<p>To decode a given SAM flag value, just enter the number in the field below. The encoded properties will be listed under Summary below, to the right.</p><p>Address of the bookmark: <a href="https://broadinstitute.github.io/picard/explain-flags.html" rel="nofollow">https://broadinstitute.github.io/picard/explain-flags.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28121/kaiju</guid>
	<pubDate>Mon, 27 Jun 2016 11:23:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28121/kaiju</link>
	<title><![CDATA[Kaiju]]></title>
	<description><![CDATA[<p>Kaiju is a program for the taxonomic classification of metagenomic high-throughput sequencing reads. Each read is directly assigned to a taxon within the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences.</p>
<p>By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database <em>nr</em> used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.</p>
<p>Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.</p>
<p>Kaiju is described in <a href="http://www.nature.com/ncomms/2016/160413/ncomms11257/full/ncomms11257.html">Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. <em>Nat. Commun.</em> 7:11257</a> (open access).</p><p>Address of the bookmark: <a href="http://kaiju.binf.ku.dk/" rel="nofollow">http://kaiju.binf.ku.dk/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28415/scarpa</guid>
	<pubDate>Wed, 13 Jul 2016 07:59:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28415/scarpa</link>
	<title><![CDATA[Scarpa]]></title>
	<description><![CDATA[<p><strong>Scarpa</strong>&nbsp;is a stand-alone scaffolding tool for NGS data. It can be used together with virtually any genome assembler and any NGS read mapper that supports SAM format. Other features include support for multiple libraries and an option to estimate insert size distributions from data. Scarpa is available free of charge for academic and commercial use under the GNU General Public License (GPL).</p>
<p>See the&nbsp;<a href="http://compbio.cs.toronto.edu/hapsembler/hapsembler-2.21_manual.pdf">user manual</a>&nbsp;or the&nbsp;<a href="http://compbio.cs.toronto.edu/hapsembler/scarpa_paper.pdf">paper</a>&nbsp;for more information about Scarpa. Click&nbsp;<a href="http://compbio.cs.toronto.edu/hapsembler/ScarpaSupplementary.pdf">here</a>&nbsp;for the supplementary material.</p><p>Address of the bookmark: <a href="http://compbio.cs.toronto.edu/hapsembler/scarpa.html" rel="nofollow">http://compbio.cs.toronto.edu/hapsembler/scarpa.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</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/29574/beagle</guid>
	<pubDate>Thu, 27 Oct 2016 11:19:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29574/beagle</link>
	<title><![CDATA[Beagle]]></title>
	<description><![CDATA[<p>Beagle is a software package that performs genotype calling, genotype phasing, imputation of ungenotyped markers, and identity-by-descent segment detection.</p>
<p>Beagle version 4.1 has a more accurate genotype phasing algorithm and a very fast and accurate genotype imputation algorithm. Version 4.1 also has several changes to the command line arguments which are described in the&nbsp;<a href="http://faculty.washington.edu/browning/beagle/release_notes" target="_blank">release notes</a>. The "ped" argument has no effect in version 4.1. If your data contains nuclear families and you want to model the parent-offspring relationships when phasing genotypes, please use&nbsp;<a href="https://faculty.washington.edu/browning/beagle/b4_0.html">version 4.0</a>.</p>
<p>If you use Beagle 4.1 in a published analysis, please report the program version and cite the appropriate article.</p>
<p>The citation for Beagle's phasing algorithm is:</p>
<p>S R Browning and B L Browning (2007) Rapid and accurate haplotype phasing and missing data inference for whole genome association studies by use of localized haplotype clustering. Am J Hum Genet 81:1084-1097.<a href="http://dx.doi.org/doi:10.1086/521987" target="_blank">doi:10.1086/521987</a></p>
<p>The citation for Beagle's genotype imputation algorithm is:</p>
<p>B L Browning and S R Browning (2016). Genotype imputation with millions of reference samples. Am J Hum Genet 98:116-126.<a href="http://dx.doi.org/doi:10.1016/j.ajhg.2015.11.020" target="_blank">doi:10.1016/j.ajhg.2015.11.020</a></p>
<p>The citation for Beagle's IBD detection algorithm is:</p>
<p>B L Browning and S R Browning (2013). Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194(2):459-71.<a href="http://dx.doi.org/doi:10.1534/genetics.113.150029" target="_blank">doi:10.1534/genetics.113.150029</a></p><p>Address of the bookmark: <a href="http://faculty.washington.edu/browning/beagle/beagle.html" rel="nofollow">http://faculty.washington.edu/browning/beagle/beagle.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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