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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44329?offset=0</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</guid>
	<pubDate>Mon, 27 Jun 2016 11:01:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</link>
	<title><![CDATA[Kraken: ultrafast metagenomic sequence classification using exact alignments]]></title>
	<description><![CDATA[<p>Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of <em>k</em>-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at <a href="http://ccb.jhu.edu/software/kraken/" target="pmc_ext">http://ccb.jhu.edu/software/kraken/</a>.</p>
<p>Krona</p>
<p>https://sourceforge.net/p/krona/home/krona/</p><p>Address of the bookmark: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/" rel="nofollow">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/</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/bookmarks/view/37512/purecn-copy-number-calling-and-snv-classification-using-targeted-short-read-sequencing</guid>
	<pubDate>Thu, 09 Aug 2018 04:09:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37512/purecn-copy-number-calling-and-snv-classification-using-targeted-short-read-sequencing</link>
	<title><![CDATA[PureCN: copy number calling and SNV classification using targeted short read sequencing]]></title>
	<description><![CDATA[<p>This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.</p>
<p>Author: Markus Riester [aut, cre], Angad P. Singh [aut]</p>
<p>Maintainer: Markus Riester &lt;markus.riester at novartis.com&gt;</p>
<div id="bioc_citation_outer">
<p>Citation (from within R, enter&nbsp;<code>citation("PureCN")</code>):</p>
<div id="bioc_citation">
<p>Riester M, Singh A, Brannon A, Yu K, Campbell C, Chiang D, Morrissey M (2016). &ldquo;PureCN: Copy number calling and SNV classification using targeted short read sequencing.&rdquo;&nbsp;<em>Source Code for Biology and Medicine</em>,&nbsp;<strong>11</strong>, 13. doi:&nbsp;<a href="http://doi.org/10.1186/s13029-016-0060-z">10.1186/s13029-016-0060-z</a>.</p>
</div>
</div><p>Address of the bookmark: <a href="http://bioconductor.org/packages/release/bioc/html/PureCN.html" rel="nofollow">http://bioconductor.org/packages/release/bioc/html/PureCN.html</a></p>]]></description>
	<dc:creator>Jit</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/32905/bigmac-breaking-inaccurate-genomes-and-merging-assembled-contigs-for-long-read-metagenomic-assembly</guid>
	<pubDate>Mon, 22 May 2017 05:43:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32905/bigmac-breaking-inaccurate-genomes-and-merging-assembled-contigs-for-long-read-metagenomic-assembly</link>
	<title><![CDATA[BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly]]></title>
	<description><![CDATA[<p>This tool is for users to upgrade their metagenomics assemblies using long reads. This includes fixing mis-assemblies and scaffolding/gap-filling. If you encounter any issues, please contact me at&nbsp;<a href="mailto:kklam@eecs.berkeley.edu">kklam@eecs.berkeley.edu</a>. My name is Ka-Kit Lam.</p>
<p>https://github.com/kakitone/MetaFinisherSC</p>
<p>https://github.com/kakitone/BIGMAC</p><p>Address of the bookmark: <a href="https://github.com/kakitone/BIGMAC" rel="nofollow">https://github.com/kakitone/BIGMAC</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37645/lsc-improving-pacbio-long-read-accuracy-by-short-read-alignment</guid>
	<pubDate>Thu, 06 Sep 2018 16:27:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37645/lsc-improving-pacbio-long-read-accuracy-by-short-read-alignment</link>
	<title><![CDATA[LSC: Improving PacBio Long Read Accuracy by Short Read Alignment]]></title>
	<description><![CDATA[<ul>
<li>Added Command line argument support.</li>
<li>Multi-stage execution modes.</li>
<li>Support for parallelization. Now execution proceeds in batches of long reads the size of which can be set by --long_read_batch_size N.</li>
<li>Better compressed intermediate files.</li>
<li>Added utilities folder.</li>
<li>Added support for multiple short read files.</li>
<li>Removed use of configuration file.</li>
</ul><p>Address of the bookmark: <a href="https://www.healthcare.uiowa.edu/labs/au/LSC/" rel="nofollow">https://www.healthcare.uiowa.edu/labs/au/LSC/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data</guid>
	<pubDate>Tue, 27 Nov 2018 04:43:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data</link>
	<title><![CDATA[lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data]]></title>
	<description><![CDATA[<p><span>lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.</span></p>
<p>lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/lordfast" rel="nofollow">https://github.com/vpc-ccg/lordfast</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43607/classification-of-sars-cov2-variant</guid>
	<pubDate>Fri, 26 Nov 2021 12:53:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43607/classification-of-sars-cov2-variant</link>
	<title><![CDATA[Classification of SARS-CoV2 Variant !]]></title>
	<description><![CDATA[<p>The scientists established some guidelines for determining whether a variant is a legitimate branch of an existing lineage:</p><p>The variant should be transmitted from its original location to another "geographically distinct population"&mdash;say, another country or a province of a large and populous country.<br />It should differ from its ancestor by at least one nucleotide.<br />At least 95% of its genetic code should have been sequenced at least five times from different samples.</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36644/tacoa-taxonomic-classification-of-environmental-genomic-fragments-using-a-kernelized-nearest-neighbor-approach</guid>
	<pubDate>Tue, 15 May 2018 09:52:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36644/tacoa-taxonomic-classification-of-environmental-genomic-fragments-using-a-kernelized-nearest-neighbor-approach</link>
	<title><![CDATA[TACOA: Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach]]></title>
	<description><![CDATA[TACOA is a software that can accurately predict the taxonomic origin of genomic fragments from metagenomic data sets by combining the advantages of the k -NN approach with a smoothing kernel function. 

TACOA can be easily installed and run on a desktop computer, therefore allowing researchers to locally analyze their metagenomic sequence data or integrate it into their pipelines.<p>Address of the bookmark: <a href="http://www.cebitec.uni-bielefeld.de/index.php/2-uncategorised/99-tacoa" rel="nofollow">http://www.cebitec.uni-bielefeld.de/index.php/2-uncategorised/99-tacoa</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43319/k-mers-tutorial-classification-and-taxonomy</guid>
	<pubDate>Thu, 26 Aug 2021 10:28:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43319/k-mers-tutorial-classification-and-taxonomy</link>
	<title><![CDATA[k-mers tutorial - classification and taxonomy]]></title>
	<description><![CDATA[<p>DNA k-mers underlie much of our assembly work, and we (along with many others!) have spent a lot of time thinking about how to&nbsp;<a href="http://www.pnas.org/content/109/33/13272">store k-mer graphs efficiently</a>,&nbsp;<a href="http://ivory.idyll.org/blog/what-is-diginorm.html">discard redundant data</a>, and&nbsp;<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0101271">count them efficiently</a>.</p>
<p>More recently, we've been enthused about&nbsp;<a href="http://joss.theoj.org/papers/3d793c6e7db683bee7c03377a4a7f3c9">using k-mer based similarity measures</a>&nbsp;and&nbsp;<a href="http://ivory.idyll.org/blog/2016-sourmash-sbt.html">computing and searching k-mer-based sketch search databases for all the things</a>.</p>
<p>But I haven't spent too much talking about using k-mers for taxonomy, although that has become an&nbsp;<em>ahem</em>&nbsp;area of interest recently,&nbsp;<a href="http://www.biorxiv.org/content/early/2017/07/03/155358">if you read into our papers a bit</a>.</p>
<p>In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!</p><p>Address of the bookmark: <a href="http://ivory.idyll.org/blog/2017-something-about-kmers.html" rel="nofollow">http://ivory.idyll.org/blog/2017-something-about-kmers.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

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