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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/34396?offset=490</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36403/ngmlr-long-read-mapper-designed-to-align-pacbio-or-oxford-nanopore</guid>
	<pubDate>Wed, 25 Apr 2018 07:30:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36403/ngmlr-long-read-mapper-designed-to-align-pacbio-or-oxford-nanopore</link>
	<title><![CDATA[NGMLR: long-read mapper designed to align PacBio or Oxford Nanopore]]></title>
	<description><![CDATA[<p><span>CoNvex Gap-cost alignMents for Long Reads (ngmlr) is a long-read mapper designed to sensitively align PacBilo or Oxford Nanopore to (large) reference genomes. It was designed to quickly and correctly align the reads, including those spanning (complex) structural variations. Ngmlr uses an SV aware k-mer search to find approximate mapping locations for a read and then a banded Smith-Waterman alignment algorithm to compute the final alignment. Ngmlr uses a convex gap cost model that penalizes gap extensions for longer gaps less than for shorter ones to compute precise alignments. The gap model allows ngmlr to account for both the sequencing error and real genomic variations at the same time and makes it especially effective at more precisely identifying the position of breakpoints stemming from structural variations. The k-mer search helps to detect and split reads that cannot be aligned linearly, enabling ngmlr to reliably align reads to a wide range of different structural variations including nested SVs (e.g. inversions flanked by deletions).</span></p><p>Address of the bookmark: <a href="https://github.com/philres/ngmlr" rel="nofollow">https://github.com/philres/ngmlr</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36812/porechop-tool-for-finding-and-removing-adapters-from-oxford-nanopore-reads</guid>
	<pubDate>Tue, 29 May 2018 07:33:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36812/porechop-tool-for-finding-and-removing-adapters-from-oxford-nanopore-reads</link>
	<title><![CDATA[Porechop:  tool for finding and removing adapters from Oxford Nanopore reads]]></title>
	<description><![CDATA[<p>Porechop is a tool for finding and removing adapters from <a href="https://nanoporetech.com/">Oxford Nanopore</a> reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity.</p>
<p>Porechop also supports demultiplexing of Nanopore reads that were barcoded with the <a href="https://store.nanoporetech.com/native-barcoding-kit-1d.html">Native Barcoding Kit</a>, <a href="https://store.nanoporetech.com/pcr-barcoding-kit-96.html">PCR Barcoding Kit</a> or <a href="https://store.nanoporetech.com/rapid-barcoding-sequencing-kit.html">Rapid Barcoding Kit</a>.</p><p>Address of the bookmark: <a href="https://github.com/rrwick/Porechop" rel="nofollow">https://github.com/rrwick/Porechop</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore</guid>
	<pubDate>Mon, 12 Nov 2018 05:26:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore</link>
	<title><![CDATA[Pacasus: Correction of palindromes in long reads from PacBio and Nanopore]]></title>
	<description><![CDATA[<p><br>Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. Check the poster from the Revolutionizing Next-Generation Sequencing (2nd edition) conference in the source folder:&nbsp;<a href="https://github.com/swarris/Pacasus/blob/master/vib2017.pdf">https://github.com/swarris/Pacasus/blob/master/vib2017.pdf</a>.</p>
<p>The prepint version is found on&nbsp;<a href="http://www.biorxiv.org/content/early/2017/08/09/173872">http://www.biorxiv.org/content/early/2017/08/09/173872</a></p>
<p>It uses the pyPaSWAS framework for sequence alignment (<a href="https://github.com/swarris/pyPaSWAS">https://github.com/swarris/pyPaSWAS</a>)</p><p>Address of the bookmark: <a href="https://github.com/swarris/Pacasus" rel="nofollow">https://github.com/swarris/Pacasus</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/9673/now-time-is-come-to-revolutionize-amino-acid-sequencing-by-nanopore-technology</guid>
	<pubDate>Mon, 07 Apr 2014 08:01:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/9673/now-time-is-come-to-revolutionize-amino-acid-sequencing-by-nanopore-technology</link>
	<title><![CDATA[Now time is come to revolutionize amino acid sequencing by Nanopore technology]]></title>
	<description><![CDATA[<p>Amino acid sequencing by Nanopore recognition tunneling method</p><p>Address of the bookmark: <a href="http://www.eurekalert.org/multimedia/pub/71198.php" rel="nofollow">http://www.eurekalert.org/multimedia/pub/71198.php</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35059/lrcstats-long-read-correction-statistics</guid>
	<pubDate>Fri, 05 Jan 2018 04:04:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35059/lrcstats-long-read-correction-statistics</link>
	<title><![CDATA[LRCstats: Long Read Correction Statistics]]></title>
	<description><![CDATA[<p>LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.</p>
<p>Of course, some long read correction algorithms are better than others, and developers of long read correction algorithms will wish to compare their algorithm with others currently available. LRCstats benchmarks long read correction algorithms using long reads produced by simulators (such as SimLoRD or PBSim) where the two-way alignments between the uncorrected long reads (uLR) and the corresponding sequences in the reference genome (Ref) are given in some sort of alignment file and then aligning the corrected long reads (cLR) to the Ref-uLR two-way alignments to create three-way alignments using a dynamic programming algorithm. Statistics on these three-way alignments are then collected, such as the overall error rates of the corrected long reads.</p>
<p>https://www.healthcare.uiowa.edu/labs/au/LSC/</p><p>Address of the bookmark: <a href="https://github.com/cchauve/lrcstats" rel="nofollow">https://github.com/cchauve/lrcstats</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37524/fmlrc-a-long-read-error-correction-tool-using-the-multi-string-burrows-wheeler-transform</guid>
	<pubDate>Fri, 10 Aug 2018 13:29:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37524/fmlrc-a-long-read-error-correction-tool-using-the-multi-string-burrows-wheeler-transform</link>
	<title><![CDATA[FMLRC: a long-read error correction tool using the multi-string Burrows Wheeler Transform]]></title>
	<description><![CDATA[<p><span>FMLRC, or FM-index Long Read Corrector, is a tool for performing hybrid correction of long read sequencing using the BWT and FM-index of short-read sequencing data. Given a BWT of the short-read sequencing data, FMLRC will build an FM-index and use that as an implicit de Bruijn graph. Each long read is then corrected independently by identifying low frequency k-mers in the long read and replacing them with the closest matching high frequency k-mers in the implicit de Bruijn graph. In contrast to other de Bruijn graph based implementations, FMLRC is not restricted to a particular k-mer size and instead uses a two pass method with both a short "k-mer" and a longer "K-mer". This allows FMLRC to correct through low complexity regions that are computational difficult for short k-mers.</span></p><p>Address of the bookmark: <a href="https://github.com/holtjma/fmlrc" rel="nofollow">https://github.com/holtjma/fmlrc</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42974/list-of-bioinformatics-packages-for-ngs-analysis</guid>
	<pubDate>Sat, 20 Mar 2021 00:28:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42974/list-of-bioinformatics-packages-for-ngs-analysis</link>
	<title><![CDATA[List of bioinformatics packages for NGS analysis !]]></title>
	<description><![CDATA[<p>Package suites gather software packages and installation tools for specific languages or platforms. We have some for bioinformatics software.</p><ul>
<li><a href="https://github.com/Bioconductor">Bioconductor</a>&nbsp;&ndash; A plethora of tools for analysis and comprehension of high-throughput genomic data, including 1500+ software packages. [&nbsp;<a href="https://link.springer.com/article/10.1186/gb-2004-5-10-r80">paper-2004</a>&nbsp;|&nbsp;<a href="https://www.bioconductor.org/">web</a>&nbsp;]</li>
<li><a href="https://github.com/biopython/biopython">Biopython</a>&nbsp;&ndash; Freely available tools for biological computing in Python, with included cookbook, packaging and thorough documentation. Part of the&nbsp;<a href="http://open-bio.org/">Open Bioinformatics Foundation</a>. Contains the very useful&nbsp;<a href="https://biopython.org/DIST/docs/api/Bio.Entrez-module.html">Entrez</a>&nbsp;package for API access to the NCBI databases. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/19304878">paper-2009</a>&nbsp;|&nbsp;<a href="https://biopython.org/">web</a>&nbsp;]</li>
<li><a href="https://github.com/bioconda">Bioconda</a>&nbsp;&ndash; A channel for the&nbsp;<a href="http://conda.pydata.org/docs/intro.html">conda package manager</a>&nbsp;specializing in bioinformatics software. Includes a repository with 3000+ ready-to-install (with&nbsp;<code>conda install</code>) bioinformatics packages. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/29967506">paper-2018</a>&nbsp;|&nbsp;<a href="https://bioconda.github.io/">web</a>&nbsp;]</li>
<li><a href="https://github.com/BioJulia">BioJulia</a>&nbsp;&ndash; Bioinformatics and computational biology infastructure for the Julia programming language. [&nbsp;<a href="https://biojulia.net/">web</a>&nbsp;]</li>
<li><a href="https://github.com/rust-bio/rust-bio">Rust-Bio</a>&nbsp;&ndash; Rust implementations of algorithms and data structures useful for bioinformatics. [&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/early/2015/10/06/bioinformatics.btv573.short?rss=1">paper-2016</a>&nbsp;]</li>
<li><a href="https://github.com/seqan/seqan3">SeqAn</a>&nbsp;&ndash; The modern C++ library for sequence analysis.</li>
</ul>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32853/progressivecactus</guid>
	<pubDate>Thu, 18 May 2017 05:29:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32853/progressivecactus</link>
	<title><![CDATA[progressiveCactus]]></title>
	<description><![CDATA[<p><span>Progressive Cactus is a whole-genome alignment package.</span></p>
<p><span><span>Distribution package for the Prgressive Cactus multiple genome aligner. Dependencies are linked as submodules</span></span></p>
<p>https://github.com/glennhickey/progressiveCactus</p><p>Address of the bookmark: <a href="https://github.com/glennhickey/progressiveCactus" rel="nofollow">https://github.com/glennhickey/progressiveCactus</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35800/scikit-bio%E2%84%A2-is-an-open-source-bsd-licensed-python-package-providing-data-structures-algorithms-and-educational-resources-for-bioinformatics</guid>
	<pubDate>Fri, 02 Mar 2018 04:29:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35800/scikit-bio%E2%84%A2-is-an-open-source-bsd-licensed-python-package-providing-data-structures-algorithms-and-educational-resources-for-bioinformatics</link>
	<title><![CDATA[scikit-bio™ is an open-source, BSD-licensed, python package providing data structures, algorithms, and educational resources for bioinformatics.]]></title>
	<description><![CDATA[<p><span>scikit-bio is currently in beta. We are very actively developing it, and&nbsp;</span><strong>backward-incompatible interface changes can and will arise</strong><span>. To avoid these types of changes being a surprise to our users, our public APIs are decorated to make it clear to users when an API can be relied upon (stable) and when it may be subject to change (experimental). See the&nbsp;</span><a href="https://github.com/biocore/scikit-bio/blob/master/doc/source/user/api_stability.rst">API stability docs</a><span>&nbsp;for more details, including what we mean by&nbsp;</span><em>stable</em><span>&nbsp;and&nbsp;</span><em>experimental</em><span>&nbsp;in this context.</span></p><p>Address of the bookmark: <a href="http://scikit-bio.org/" rel="nofollow">http://scikit-bio.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40591/modelstudio-a-package-automates-the-explanation-of-machine-learning-predictive-models</guid>
	<pubDate>Wed, 22 Jan 2020 23:58:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40591/modelstudio-a-package-automates-the-explanation-of-machine-learning-predictive-models</link>
	<title><![CDATA[modelStudio: a package automates the explanation of machine learning predictive models]]></title>
	<description><![CDATA[<p>The&nbsp;<code>modelStudio</code>&nbsp;package automates the explanation of machine learning predictive models. This package generates advanced interactive and animated model explanations in the form of a serverless HTML site.</p>
<p>It combines&nbsp;<strong>R</strong>&nbsp;with&nbsp;<strong>D3.js</strong>&nbsp;to produce plots and descriptions for various local and global explanations. Tools for model exploration unite with tools for EDA (Exploratory Data Analysis) to give a broad overview of the model behavior.&nbsp;<code>modelStudio</code>&nbsp;is a fast and condensed way to get all the answers without much effort. Break down your model and look into its ingredients with only a few lines of code.</p><p>Address of the bookmark: <a href="https://modeloriented.github.io/modelStudio/index.html" rel="nofollow">https://modeloriented.github.io/modelStudio/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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