<?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/36476?offset=240</link>
	<atom:link href="https://bioinformaticsonline.com/related/36476?offset=240" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43652/peregrine-shimmer-genome-assembly-toolkit</guid>
	<pubDate>Thu, 16 Dec 2021 02:50:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43652/peregrine-shimmer-genome-assembly-toolkit</link>
	<title><![CDATA[Peregrine &amp; SHIMMER Genome Assembly Toolkit]]></title>
	<description><![CDATA[<p><span>Peregrine is a fast genome assembler for accurate long reads (length &gt; 10kb, accuracy &gt; 99%). It can assemble a human genome from 30x reads within 20 cpu hours from reads to polished consensus. It uses Sparse HIereachical MimiMizER (SHIMMER) for fast read-to-read overlaping without quadratic comparisions used in other OLC assemblers.</span></p><p>Address of the bookmark: <a href="https://github.com/cschin/Peregrine" rel="nofollow">https://github.com/cschin/Peregrine</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44474/claw-chloroplast-long-read-assembly-workflow</guid>
	<pubDate>Wed, 21 Feb 2024 12:37:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44474/claw-chloroplast-long-read-assembly-workflow</link>
	<title><![CDATA[CLAW: Chloroplast Long-read Assembly Workflow]]></title>
	<description><![CDATA[<p dir="auto">CLAW (Chloroplast Long-read Assembly Workflow) is an mostly-automated Snakemake-based workflow for the assembly of chloroplast genomes. CLAW uses chloroplast long-reads, which are baited out of larger read libraries (e.g., an Oxford Nanopore Technologies MinION read library derived from photosynthetic tissue), for assembly with Flye and/or Unicycler. CLAW was designed with the novice bioinformatician in mind - it is easy to install and easy to use, requiring only minimal user input.</p><p>Address of the bookmark: <a href="https://github.com/aaronphillips7493/CLAW" rel="nofollow">https://github.com/aaronphillips7493/CLAW</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34519/bandage-interactive-visualization-of-de-novo-genome-assemblies</guid>
	<pubDate>Mon, 04 Dec 2017 10:09:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34519/bandage-interactive-visualization-of-de-novo-genome-assemblies</link>
	<title><![CDATA[Bandage: interactive visualization of de novo genome assemblies]]></title>
	<description><![CDATA[<p>Bandage (a Bioinformatics Application for Navigating&nbsp;<em>De&nbsp;novo</em>&nbsp;Assembly Graphs Easily) is a tool for visualizing assembly graphs with connections. Users can zoom in to specific areas of the graph and interact with it by moving nodes, adding labels, changing colors and extracting sequences. BLAST searches can be performed within the Bandage graphical user interface and the hits are displayed as highlights in the graph. By displaying connections between contigs, Bandage presents new possibilities for analyzing&nbsp;<em>de novo</em>&nbsp;assemblies that are not possible through investigation of contigs alone.</p>
<p><strong>Availability and implementation:</strong>&nbsp;Source code and binaries are freely available at&nbsp;<a href="https://github.com/rrwick/Bandage" target="pmc_ext">https://github.com/rrwick/Bandage</a>. Bandage is implemented in C++ and supported on Linux, OS X and Windows. A full feature list and screenshots are available at&nbsp;<a href="http://rrwick.github.io/Bandage" target="pmc_ext">http://rrwick.github.io/Bandage</a>.</p><p>Address of the bookmark: <a href="http://rrwick.github.io/Bandage/" rel="nofollow">http://rrwick.github.io/Bandage/</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37842/rapclust-accurate-lightweight-clustering-of-de-novo-transcriptomes-using-fragment-equivalence-classes</guid>
	<pubDate>Thu, 04 Oct 2018 17:57:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37842/rapclust-accurate-lightweight-clustering-of-de-novo-transcriptomes-using-fragment-equivalence-classes</link>
	<title><![CDATA[RapClust: Accurate, Lightweight Clustering of de novo Transcriptomes using Fragment Equivalence Classes]]></title>
	<description><![CDATA[<p><span>RapClust is a tool for clustering contigs from&nbsp;</span><em>de novo</em><span>&nbsp;transcriptome assemblies. RapClust is designed to be run downstream of the&nbsp;</span><a href="https://github.com/kingsfordgroup/sailfish">Sailfish</a><span>&nbsp;or&nbsp;</span><a href="https://github.com/COMBINE-lab/salmon">Salmon</a><span>&nbsp;tools for rapid transcript-level quantification. Specifically, RapClust relies on the&nbsp;</span><em>fragment equivalence classes</em><span>&nbsp;computed by these tools in order to determine how seqeunce is shared across the transcriptome, and how reads map to potentially-related contigs across different conditions.</span></p><p>Address of the bookmark: <a href="https://github.com/COMBINE-lab/RapClust" rel="nofollow">https://github.com/COMBINE-lab/RapClust</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34704/nanosim-nanopore-sequence-read-simulator-based-on-statistical-characterization</guid>
	<pubDate>Mon, 18 Dec 2017 04:16:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34704/nanosim-nanopore-sequence-read-simulator-based-on-statistical-characterization</link>
	<title><![CDATA[NanoSim: nanopore sequence read simulator based on statistical characterization.]]></title>
	<description><![CDATA[<p><span>NanoSim, a fast and scalable read simulator that captures the technology-specific features of ONT data and allows for adjustments upon improvement of nanopore sequencing technology. The first step of NanoSim is read characterization, which provides a comprehensive alignment-based analysis and generates a set of read profiles serving as the input to the next step, the simulation stage. The simulation stage uses the model built in the previous step to produce in silico reads for a given reference genome. NanoSim is written in Python and R. The source files and manual are available at the Genome Sciences Centre website: http://www.bcgsc.ca/platform/bioinfo/software/nanosim</span></p>
<p><span>https://github.com/bcgsc/NanoSim</span></p><p>Address of the bookmark: <a href="http://www.bcgsc.ca/platform/bioinfo/software/nanosim" rel="nofollow">http://www.bcgsc.ca/platform/bioinfo/software/nanosim</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/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus</guid>
	<pubDate>Fri, 05 Jan 2018 04:12:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus</link>
	<title><![CDATA[proovread : large-scale high-accuracy PacBio correction through iterative short read consensus]]></title>
	<description><![CDATA[<p>proovread : large-scale high-accuracy PacBio correction through iterative short read consensus</p>
<ul>
<li>outperforms PacBioToCA/LSC in terms of accuracy and contiguity/sensitivity (<a href="http://dx.doi.org/10.1093/bioinformatics/btu392">http://dx.doi.org/10.1093/bioinformatics/btu392</a>)</li>
<li>is easy to install/run/configure</li>
<li>supports various types of dat
<ul>
<li><strong>HiSeq/MiSeq&nbsp;</strong>(100-500bp)</li>
<li><strong>Unitigs</strong></li>
<li>454, ...</li>
</ul>
</li>
</ul>
<p>proovread maps high coverage data to pacbio reads (bwa mem, blasr, daligner) in multiple iterations.</p><p>Address of the bookmark: <a href="https://github.com/BioInf-Wuerzburg/proovread" rel="nofollow">https://github.com/BioInf-Wuerzburg/proovread</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/blog/view/38277/understating-pacbio-reads-name</guid>
	<pubDate>Fri, 23 Nov 2018 07:36:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/38277/understating-pacbio-reads-name</link>
	<title><![CDATA[Understating pacbio reads name !]]></title>
	<description><![CDATA[<pre>m140415_143853_42175_c100635972550000001823121909121417_s1_p0/553/3100_11230 0.99 24
└1┘└─────2─────┘└──3─┘└────────────────4────────────────┘└5┘└6┘└7┘└────8────┘└─9─┘└10┘
</pre><ol>
<li>"<code>m</code>" =&nbsp;<em>movie</em></li>
<li>Time of Run Start (<code>yymmdd_hhmmss</code>)</li>
<li>Instrument Serial Number</li>
<li>SMRT Cell Barcode</li>
<li>Set Number (a.k.a. "Look Number". Deprecated field, used in earlier version of RS)</li>
<li>Part Number (usually "<code>p0</code>", "<code>X0</code>" when using expired reagents)</li>
<li>ZMW hole number</li>
<li>Subread Region (<code>start_stop</code>&nbsp;using polymerase read coordinates)</li>
<li>readScore</li>
<li>barcodeScore</li>
</ol>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35899/reference-free-prediction-of-rearrangement-breakpoint-reads</guid>
	<pubDate>Thu, 08 Mar 2018 05:05:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35899/reference-free-prediction-of-rearrangement-breakpoint-reads</link>
	<title><![CDATA[Reference-free prediction of rearrangement breakpoint reads]]></title>
	<description><![CDATA[<p><span>lideSort-BPR (&nbsp;</span><span>b</span><span>&nbsp;reak&nbsp;</span><span>p</span><span>&nbsp;oint&nbsp;</span><span>r</span><span>&nbsp;eads) is based on a fast algorithm for all-against-all comparisons of short reads and theoretical analyses of the number of neighboring reads. When applied to a dataset with a sequencing depth of 100&times;, it finds &sim;88% of the breakpoints correctly with no false-positive reads. Moreover, evaluation on a real prostate cancer dataset shows that the proposed method predicts more fusion transcripts correctly than previous approaches, and yet produces fewer false-positive reads. To our knowledge, this is the first method to detect breakpoint reads without using a reference genome.</span></p>
<p><span>https://github.com/ewijaya/slidesort-bpr</span></p><p>Address of the bookmark: <a href="https://code.google.com/archive/p/slidesort-bpr/" rel="nofollow">https://code.google.com/archive/p/slidesort-bpr/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

</channel>
</rss>