<?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/40594?offset=30</link>
	<atom:link href="https://bioinformaticsonline.com/related/40594?offset=30" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/22793/sequencing-by-xpansion</guid>
	<pubDate>Wed, 17 Jun 2015 20:58:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/22793/sequencing-by-xpansion</link>
	<title><![CDATA[Sequencing By Xpansion]]></title>
	<description><![CDATA[<p>Sequencing By Xpansion (SBX) is a DNA sequencing method that uses a simple biochemical reaction to encode the sequence of a DNA molecule into a highly measurable surrogate called an Xpandomer. This single molecule approach produces enough Xpandomer in a single drop reaction to sequence an entire human genome 1000X over. To achieve this, an Xpandomer replaces each DNA sequence with a sequence of large, high signal reporter molecules using the SBX molecular expansion technology. The DNA sequence is then read out as the Xpandomer reporters pass sequentially through a nanopore detector. SBX is a molecular engineering platform that benefits from core design principles that separate the multiple molecular functions. This systems approach enables efficient development and incorporation of improvements to SBX and is key to reconfiguring and optimizing Xpandomer measurement for different detection platforms.</p><p>http://www.stratosgenomics.com/stratos-genomics-technology</p>]]></description>
	<dc:creator>Jitendra Narayan</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/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/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</guid>
	<pubDate>Wed, 15 Mar 2017 14:31:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads</link>
	<title><![CDATA[Software and Tools to detect structure variation with long reads !!]]></title>
	<description><![CDATA[<p>Uncovering the connection between genetics and heritable diseases requires an approach that looks at all the variant bases and types in a genome. While a PacBio&nbsp;<em>de novo</em>&nbsp;assembly resolves the most novel SV variants. 8-10X PacBio coverage of single genomes or trios reveals triple the SVs detectable by short-read data.</p><p>With&nbsp;<span style="text-decoration: underline;"><a href="http://www.pacb.com/smrt-science/">Single Molecule, Real-Time (SMRT) Sequencing</a></span>, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:</p><ul>
<li>Uncover missing heritability linked to structural variation</li>
<li>Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease</li>
<li>Resolve structural variation across the complete size spectrum with basepair resolution</li>
</ul><p>Following are the SV tools, which can assist you to achieve your goal.</p><p><strong>Sniffles:</strong>&nbsp;Structural variation caller using third generation sequencing</p><p>Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled!&nbsp;</p><p>More at&nbsp;https://github.com/fritzsedlazeck/Sniffles</p><p><strong style="font-size: 12.8px;"><br />MultiBreak-SV:</strong> It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.</p><p>There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.</p><p>More at&nbsp;https://github.com/raphael-group/multibreak-sv</p><p><strong style="font-size: 12.8px;"><br />Parliament:</strong>&nbsp;A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?</p><p>Publication about the algorithm and &ldquo;&hellip;the first long-read characterization of structural variation in a diploid human personal genome&hellip;&rdquo; (HS1011) -&nbsp;<a href="http://www.biomedcentral.com/1471-2164/16/286">&ldquo;Assessing structural variation in a personal genome&mdash;towards a human reference diploid genome&rdquo;</a></p><p>More at&nbsp;https://sourceforge.net/projects/parliamentsv/</p><p>https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf</p><p><br /><strong>PBHoney:</strong>&nbsp;the structural variation discovery tool&nbsp;<br /><br />PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.</p><p>Read The Paper&nbsp;<a href="http://www.biomedcentral.com/1471-2105/15/180/abstract" target="_blank">http://www.biomedcentral.com/1471-2105/15/180/abstract</a></p><p>More at&nbsp;https://sourceforge.net/projects/pb-jelly/</p><p><strong><br />SMRT-SV:</strong> Structural variant and indel caller for PacBio reads</p><p>Structural variant (SV) and indel caller for PacBio reads based on methods from&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>.</p><p>SMRT-SV provides an official software package for tools described in&nbsp;<a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13907.html">Chaisson et al. 2014</a>&nbsp;and adds several key features including the following.</p><ul>
<li>Unified variant calling user interface with built-in cluster compute support</li>
<li>Small indel calling (2-49 bp)</li>
<li>Improved inversion calling (<code>screenInversions</code>)</li>
<li>Quality metric for SV calls based on number of local assemblies supporting each call</li>
<li>Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions</li>
<li>Genotyping of SVs with Illumina paired-end reads from WGS samples</li>
</ul><p>More at&nbsp;https://github.com/EichlerLab/pacbio_variant_caller</p>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
</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/40940/consed-a-finishing-package-bam-file-viewer-assembly-editor-autofinish-autoreport-autoedit-and-align-reads-to-reference-sequence</guid>
	<pubDate>Fri, 07 Feb 2020 07:16:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40940/consed-a-finishing-package-bam-file-viewer-assembly-editor-autofinish-autoreport-autoedit-and-align-reads-to-reference-sequence</link>
	<title><![CDATA[Consed--A Finishing Package (BAM File Viewer, Assembly Editor, Autofinish, Autoreport, Autoedit, and Align Reads To Reference Sequence)]]></title>
	<description><![CDATA[<ul>
<li>Supports Illumina, 454, other Next-Gen and Sanger Reads and allows mixtures of these read types</li>
<li>Consed includes BamScape which can view bam files with unlimited numbers of reads. BamScape can bring up consed to edit reads and the reference sequence in targeted regions.</li>
<li>Consed is compatible with Newbler, Cross_match, Phrap, MIRA, Velvet and PCAP output.</li>
<li>Quickly takes the user to each variant site for viewing (also available as an automated report)</li>
<li>Overview of assembly can help detect and fix misassemblies</li>
<li>Editing time reduced by the program's ability to pin-point problem areas</li>
<li>Editing is guided by error probabilities</li>
</ul><p>Address of the bookmark: <a href="http://www.phrap.org/consed/consed.html" rel="nofollow">http://www.phrap.org/consed/consed.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42359/dnasp-dna-sequence-polymorphism-is-a-software-package-for-the-analysis-of-dna-polymorphisms</guid>
	<pubDate>Wed, 25 Nov 2020 19:51:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42359/dnasp-dna-sequence-polymorphism-is-a-software-package-for-the-analysis-of-dna-polymorphisms</link>
	<title><![CDATA[DnaSP: DNA Sequence Polymorphism, is a software package for the analysis of DNA polymorphisms]]></title>
	<description><![CDATA[<p><span>DnaSP, DNA Sequence Polymorphism, is a software package for the analysis of DNA polymorphisms using data from a single locus (a multiple sequence aligned -MSA data), or from several loci (a Multiple-MSA data, such as formats generated by some assembler RAD-seq software). DnaSP can estimate several measures of DNA sequence variation within and between populations in noncoding, synonymous or nonsynonymous sites, or in various sorts of codon positions), as well as linkage disequilibrium, recombination, gene flow and gene conversion parameters.</span></p><p>Address of the bookmark: <a href="http://www.ub.edu/dnasp/" rel="nofollow">http://www.ub.edu/dnasp/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44555/ultra-ultra-locates-tandemly-repetitive-areas-effective-labeling-of-repetitive-genomic-sequence</guid>
	<pubDate>Sat, 08 Jun 2024 16:03:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44555/ultra-ultra-locates-tandemly-repetitive-areas-effective-labeling-of-repetitive-genomic-sequence</link>
	<title><![CDATA[ULTRA (ULTRA Locates Tandemly Repetitive Areas) : Effective Labeling of Repetitive Genomic Sequence]]></title>
	<description><![CDATA[<p dir="auto">ULTRA is a tool to find and annotate tandem repeats inside genomic sequence. It is able to find repeats of any length and of any period (up to a maximum period of 4000). It can find highly decayed repeats missed by other software, and it will also be able to find very large repeats in highly repetitive sequence, regardless of the size of sequence or length of repeats. ULTRA offers meaningful annotation scores and can produce annotation P-values at user request.</p>
<p dir="auto">More at&nbsp;https://www.biorxiv.org/content/10.1101/2024.06.03.597269v1</p><p>Address of the bookmark: <a href="https://github.com/TravisWheelerLab/ULTRA" rel="nofollow">https://github.com/TravisWheelerLab/ULTRA</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34391/taxoblast-taxoblast-is-a-pipeline-to-identify-contamination-in-genomic-sequence</guid>
	<pubDate>Thu, 23 Nov 2017 08:37:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34391/taxoblast-taxoblast-is-a-pipeline-to-identify-contamination-in-genomic-sequence</link>
	<title><![CDATA[Taxoblast : Taxoblast is a pipeline to identify contamination in genomic sequence]]></title>
	<description><![CDATA[<p><span>Modern genome sequencing strategies are highly sensitive to contamination making the detection of foreign DNA sequences an important part of analysis pipelines. Here we use Taxoblast, a simple pipeline with a graphical user interface, for the post-assembly detection of contaminating sequences in the published genome of the kelp&nbsp;</span><em>Saccharina japonica</em><span>. Analyses were based on multiple blastn searches with short sequence fragments. They revealed a number of probable bacterial contaminations as well as hybrid scaffolds that contain both bacterial and algal sequences. This or similar types of analysis, in combination with manual curation, may thus constitute a useful complement to standard bioinformatics analyses prior to submission of genomic data to public repositories. Our analysis pipeline is open-source and freely available at&nbsp;</span><a href="http://sdittami.altervista.org/taxoblast" title="">http://sdittami.altervista.org/taxoblast</a><span>&nbsp;and via SourceForge (</span><a href="https://sourceforge.net/projects/taxoblast" title="">https://sourceforge.net/projects/taxoblast</a><span>).</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/taxoblast/files/" rel="nofollow">https://sourceforge.net/projects/taxoblast/files/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35550/circoletto-visualizing-sequence-similarity-with-circos</guid>
	<pubDate>Fri, 09 Feb 2018 10:23:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35550/circoletto-visualizing-sequence-similarity-with-circos</link>
	<title><![CDATA[Circoletto: visualizing sequence similarity with Circos]]></title>
	<description><![CDATA[<p><span>Circoletto, an online visualization tool based on Circos, which provides a fast, aesthetically pleasing and informative overview of sequence similarity search results.</span></p>
<p>Online version and downloadable software package for offline use (source code in PERL) freely available at&nbsp;<a href="http://bat.ina.certh.gr/tools/circoletto/" target="">http://bat.ina.certh.gr/tools/circoletto/</a></p>
<p><strong>Contact:</strong><a href="mailto:ndarz@certh.gr" target="">ndarz@certh.gr</a></p><p>Address of the bookmark: <a href="http://tools.bat.infspire.org/circoletto/" rel="nofollow">http://tools.bat.infspire.org/circoletto/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>