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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35550?offset=80</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41482/magic-blast</guid>
	<pubDate>Fri, 20 Mar 2020 15:18:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41482/magic-blast</link>
	<title><![CDATA[Magic-BLAST]]></title>
	<description><![CDATA[<p>Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read-pairing is ignored.</p><p>Address of the bookmark: <a href="https://ncbi.github.io/magicblast/" rel="nofollow">https://ncbi.github.io/magicblast/</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43952/elastic-blast</guid>
	<pubDate>Tue, 06 Sep 2022 18:14:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43952/elastic-blast</link>
	<title><![CDATA[Elastic BLAST !]]></title>
	<description><![CDATA[<p><a href="https://blast.ncbi.nlm.nih.gov/doc/elastic-blast/elasticblast.html?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=elasticblast-top3-20220823">ElasticBLAST</a>&nbsp;is a new way to&nbsp;<a href="https://blast.ncbi.nlm.nih.gov/?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=elasticblast-top3-20220823">BLAST</a>&nbsp;large numbers of queries, faster and on the cloud. Here are the top three reasons you should use ElasticBLAST:</p>
<h6><strong><img src="https://i0.wp.com/ncbiinsights.ncbi.nlm.nih.gov/wp-content/uploads/2022/08/ElasticBLAST_Larger-e1659978198941.png?resize=150%2C120&amp;ssl=1" alt="" width="150" height="120" style="border: 0px;">1. ElasticBLAST can handle much LARGER queries!&nbsp;</strong></h6>
<p>ElasticBLAST can search query sets that have&nbsp;<em>hundreds to millions of sequences</em>&nbsp;and against BLAST databases of all sizes.</p>
<h6><span><img src="https://i0.wp.com/ncbiinsights.ncbi.nlm.nih.gov/wp-content/uploads/2022/08/ElasticBLAST_Faster.png?resize=150%2C120&amp;ssl=1" alt="" width="150" height="120" style="border: 0px;">2. ElasticBLAST is FASTER</span></h6>
<p>ElasticBLAST distributes your searches across multiple cloud instances to process them simultaneously. The ability to scale resources in this way allows you to process large numbers of queries in a shorter time than you could with BLAST+.</p>
<h6><img src="https://i0.wp.com/ncbiinsights.ncbi.nlm.nih.gov/wp-content/uploads/2022/08/ElasticBLAST_Easy.png?resize=150%2C120&amp;ssl=1" alt="" width="150" height="120" style="border: 0px;">3. ElasticBLAST is EASY to run on the cloud<strong><br></strong></h6>
<p>ElasticBLAST is easy to set up using our step-by-step instructions&nbsp;<span>(</span><a href="https://blast.ncbi.nlm.nih.gov/doc/elastic-blast/quickstart-aws.html?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=elasticblast-top3-20220823" target="_blank"><span><span>Amazon Web&nbsp;</span><span>Services (AWS)</span></span></a><span>,&nbsp;</span><a href="https://blast.ncbi.nlm.nih.gov/doc/elastic-blast/quickstart-gcp.html?utm_source=ncbi_insights&amp;utm_medium=referral&amp;utm_campaign=elasticblast-top3-20220823" target="_blank"><span>Google Cloud Platform (GCP)</span></a><span><span>)</span>&nbsp;<span>and</span>&nbsp;<span>allows&nbsp;</span><span>you&nbsp;</span><span>to leverage the power of</span><span>&nbsp;the&nbsp;</span><span>cloud. Once configured, i</span><span>t</span>&nbsp;<span>manages the software and database installation, handles partitioning of the BLAST workload among the various instances, and deallocates cloud resources when the searches are done.</span></span></p>
<p><span><span>ElasticBLAST</span>&nbsp;<span>also&nbsp;</span><span>selects the instance (</span><span>i.e.,</span><span>&nbsp;machine) type for you based on database size. Of course, you can also choose the instance type manually if you prefer</span><span>.&nbsp;</span></span></p><p>Address of the bookmark: <a href="https://blast.ncbi.nlm.nih.gov/doc/elastic-blast/" rel="nofollow">https://blast.ncbi.nlm.nih.gov/doc/elastic-blast/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39913/twinblast-when-two-is-better-than-one</guid>
	<pubDate>Sat, 07 Sep 2019 08:50:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39913/twinblast-when-two-is-better-than-one</link>
	<title><![CDATA[TwinBLAST: When Two Is Better than One]]></title>
	<description><![CDATA[<p>TwinBLAST is a web-based tool for viewing 2 BLAST reports simultaneouslyside-by-side. It uses ExtJS (www.sencha.com/products/extjs/) to provide 2independently scrollable panels. BioPerl (www.bioperl.org) is used to indexraw BLAST reports and Bio::Graphics is used to draw pictograms of the BLASThits.</p>
<p><a href="https://github.com/IGS/twinblast">https://github.com/IGS/twinblast</a></p>
<p><a href="https://mra.asm.org/content/8/35/e00842-19">https://mra.asm.org/content/8/35/e00842-19</a></p><p>Address of the bookmark: <a href="https://github.com/IGS/twinblast" rel="nofollow">https://github.com/IGS/twinblast</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4546/sowdhamini-lab</guid>
  <pubDate>Sun, 15 Sep 2013 09:19:12 -0500</pubDate>
  <link></link>
  <title><![CDATA[SOWDHAMINI Lab]]></title>
  <description><![CDATA[
<p>Genome sequencing projects have enormous potential for benefiting human endeavors. However, just as acquiring a language's vocabulary does not enable one to speak it, databases that list the amino acid composition of proteins do not directly tell us much about these proteins' higher-level structure and function. The most productive way to indirectly exploit these databases has been to start with the small number of proteins that are fully-characterised and to assume that other "similar" proteins will have a related structure and function. Proteins with very similar amino acid sequence are "no-brainers", but the real test, which our group largely focuses on, is to detect the "essential" similarity in proteins whose non-critical sections have experienced random rearrangements during evolution. In such cases functionally similar proteins may have less than 25% sequence overlap.</p>

<p>More @ http://www.ncbs.res.in/sowdhamini/groups_sowdhamini.htm</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44711/blast-5-key-updates-and-enhancements-for-modern-bioinformatics</guid>
	<pubDate>Sat, 07 Dec 2024 22:37:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44711/blast-5-key-updates-and-enhancements-for-modern-bioinformatics</link>
	<title><![CDATA[BLAST+ 5: Key Updates and Enhancements for Modern Bioinformatics]]></title>
	<description><![CDATA[<p>The BLAST+ 5 (Basic Local Alignment Search Tool) update has introduced several key enhancements aimed at improving performance, user experience, and compatibility with evolving genomic data standards. Here are the major updates:</p><ol>
<li>
<p><strong>Database Enhancements</strong>:</p>
<ul>
<li>The BLAST databases have shifted fully to the version 5 (v5) format, which integrates built-in taxonomy information. This allows for more detailed and efficient sequence annotation and analysis.</li>
<li>Protein databases in v5 are now accession-based, supporting a broader range of sequences, including those from high-throughput projects and the Pathogen Detection Project. These databases also accommodate structural proteins with multi-character chain identifiers.</li>
</ul>
</li>
<li>
<p><strong>Performance Improvements</strong>:</p>
<ul>
<li>Adaptive Composition-Based Statistics (CBS) is available as an experimental feature, enhancing the detection of novel results in protein-protein comparisons.</li>
<li>Updated algorithms improve the stability of search results, especially when fewer hits are requested than the default output.</li>
</ul>
</li>
<li>
<p><strong>Compatibility</strong>:</p>
<ul>
<li>Support for the older v4 databases has been discontinued. The v5 format is now the default for all BLAST database updates, ensuring alignment with current standards in bioinformatics.</li>
</ul>
</li>
<li>
<p><strong>User-Friendly Changes</strong>:</p>
<ul>
<li>Naming conventions for databases have been simplified to enhance clarity and ease of use. For example, database names no longer include version tags like "_v5".</li>
</ul>
</li>
<li>
<p><strong>Future-Proofing</strong>:</p>
<ul>
<li>BLAST+ 5 aligns with current and upcoming data requirements, ensuring that researchers have access to the most comprehensive and modern resources for sequence alignment.</li>
</ul>
</li>
</ol><p>These updates reflect NCBI's commitment to maintaining BLAST as a leading tool for sequence analysis. For detailed release notes and additional guidance, refer to NCBI Insights <a href="https://ncbiinsights.ncbi.nlm.nih.gov/">here</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44208/circos-visualization</guid>
	<pubDate>Mon, 06 Mar 2023 06:01:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44208/circos-visualization</link>
	<title><![CDATA[Circos visualization !]]></title>
	<description><![CDATA[<p>Circos visualization</p>
<p>https://wlcb.oit.uci.edu/modules/index.html</p><p>Address of the bookmark: <a href="https://wlcb.oit.uci.edu/NG-Circos" rel="nofollow">https://wlcb.oit.uci.edu/NG-Circos</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<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>
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