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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/32948?offset=690</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads</guid>
	<pubDate>Tue, 08 May 2018 04:27:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36512/hisat2-a-fast-and-sensitive-alignment-program-for-mapping-next-generation-sequencing-reads</link>
	<title><![CDATA[HISAT2: a fast and sensitive alignment program for mapping next-generation sequencing reads]]></title>
	<description><![CDATA[<p><strong>HISAT2</strong><span>&nbsp;is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs&nbsp;</span><a href="http://dl.acm.org/citation.cfm?id=2674828">[Sir&eacute;n et al. 2014]</a><span>, we designed and implemented a graph FM index (GFM), an original approach and its first implementation to the best of our knowledge. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM).&nbsp;</span></p>
<p><span>more at&nbsp;https://ccb.jhu.edu/software/hisat2/index.shtml</span></p><p>Address of the bookmark: <a href="https://github.com/infphilo/hisat2" rel="nofollow">https://github.com/infphilo/hisat2</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads</guid>
	<pubDate>Mon, 11 Jun 2018 05:14:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads</link>
	<title><![CDATA[npScarf: real-time scaffolder using SPAdes contigs and Nanopore sequencing reads]]></title>
	<description><![CDATA[npScarf (jsa.np.npscarf) is a program that connect contigs from a draft genomes to generate sequences that are closer to finish. These pipelines can run on a single laptop for microbial datasets. In real-time mode, it can be integrated with simple structural analyses such as gene ordering, plasmid forming.<p>Address of the bookmark: <a href="http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html" rel="nofollow">http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html</a></p>]]></description>
	<dc:creator>Shruti Paniwala</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/37835/variantbam-filtering-and-profiling-of-next-generational-sequencing-data-using-region-specific-rules</guid>
	<pubDate>Thu, 04 Oct 2018 16:30:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37835/variantbam-filtering-and-profiling-of-next-generational-sequencing-data-using-region-specific-rules</link>
	<title><![CDATA[VariantBam: Filtering and profiling of next-generational sequencing data using region-specific rules]]></title>
	<description><![CDATA[<p>VariantBam is a tool to extract/count specific sets of sequencing reads from next-generational sequencing files. To save money, disk space and I/O, one may not want to store an entire BAM on disk. In many cases, it would be more efficient to store only those read-pairs or reads who intersect some region around the variant locations. Alternatively, if your scientific question is focused on only one aspect of the data (e.g. breakpoints), many reads can be removed without losing the information relevant to the problem.</p>
<h5>&nbsp;</h5><p>Address of the bookmark: <a href="https://github.com/broadinstitute/VariantBam" rel="nofollow">https://github.com/broadinstitute/VariantBam</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38535/nanopack-visualizing-and-processing-long-read-sequencing-data</guid>
	<pubDate>Tue, 25 Dec 2018 21:20:50 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38535/nanopack-visualizing-and-processing-long-read-sequencing-data</link>
	<title><![CDATA[NanoPack: visualizing and processing long-read sequencing data]]></title>
	<description><![CDATA[The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.<p>Address of the bookmark: <a href="https://github.com/wdecoster/nanopack" rel="nofollow">https://github.com/wdecoster/nanopack</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39671/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Sat, 06 Jul 2019 03:48:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39671/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</link>
	<title><![CDATA[Flye: Fast and accurate de novo assembler for single molecule sequencing reads]]></title>
	<description><![CDATA[<p><span>Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PB / ONT reads as input and outputs polished contigs. Flye also includes a special mode for metagenome assembly.</span></p><p>Address of the bookmark: <a href="https://github.com/fenderglass/Flye" rel="nofollow">https://github.com/fenderglass/Flye</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40544/ngs-bits-short-read-sequencing-tools</guid>
	<pubDate>Thu, 16 Jan 2020 23:14:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40544/ngs-bits-short-read-sequencing-tools</link>
	<title><![CDATA[ngs-bits - Short-read sequencing tools]]></title>
	<description><![CDATA[<p>Binaries of&nbsp;<em>ngs-bits</em>&nbsp;are available via Bioconda. Alternatively,&nbsp;<em>ngs-bits</em>&nbsp;can be built from sources:</p>
<ul>
<li><span>Binaries</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_bioconda.md">Linux/macOS</a></li>
<li>From&nbsp;<span>sources</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_unix.md">Linux/macOS</a></li>
<li>From&nbsp;<span>sources</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_win.md">Windows</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/imgag/ngs-bits" rel="nofollow">https://github.com/imgag/ngs-bits</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42826/ktrim-an-extra-fast-and-accurate-adapter-and-quality-trimmer-for-sequencing-data</guid>
	<pubDate>Thu, 11 Feb 2021 21:39:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42826/ktrim-an-extra-fast-and-accurate-adapter-and-quality-trimmer-for-sequencing-data</link>
	<title><![CDATA[Ktrim: an extra-fast and accurate adapter- and quality-trimmer for sequencing data]]></title>
	<description><![CDATA[<p>Ktrim&nbsp;is written in&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">C++</code>&nbsp;for GNU Linux/Unix platforms. After uncompressing the source package, you can find an executable file&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">ktrim</code>&nbsp;under&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">bin/</code>&nbsp;directory compiled using&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">g++ v4.8.5</code>&nbsp;and linked with&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">libz v1.2.7</code>&nbsp;for Linux x86_64 system. If you could not run it (which is usually caused by low version of&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">libc++</code>&nbsp;or&nbsp;<code style="font-size: 13.6px; padding: 0.2em 0.4em; margin: 0px; background-color: var(--color-markdown-code-bg);">libz</code>&nbsp;library) or you want to build a version optimized for your system, you can re-compile the programs:</p>
<p>user@linux$ make clean &amp;&amp; make</p><p>Address of the bookmark: <a href="https://github.com/hellosunking/Ktrim" rel="nofollow">https://github.com/hellosunking/Ktrim</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/19087/dcgor</guid>
	<pubDate>Sat, 08 Nov 2014 14:54:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/19087/dcgor</link>
	<title><![CDATA[dcGOR]]></title>
	<description><![CDATA[<p>An R package for analysing ontologies and protein domain annotations has been published in PLoS Computational Biology (http://dx.doi.org/10.1371/journal.pcbi.1003929). The package is distributed as part of CRAN (http://cran.r-project.org/package=dcGOR), and also at GitHub for version control.<br /><br />The dedicated website is available in http://supfam.org/dcGOR, from which several demos are also provided:<br /><br />1. Analysing SCOP domains: http://supfam.org/dcGOR/demo-Fang.html<br /><br />2. Analysing Pfam domains: http://supfam.org/dcGOR/demo-Basu.html<br /><br />3. Analysing InterPro domains: http://supfam.org/dcGOR/demo-Customisation.html<br /><br />&nbsp;</p>]]></description>
	<dc:creator>Martin Jones</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>

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