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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37602?offset=160</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data</guid>
	<pubDate>Tue, 22 Jan 2019 06:26:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data</link>
	<title><![CDATA[Katuali is a flexible consensus pipeline implemented in Snakemake to basecall, assemble, and polish Oxford Nanopore Technologies&#039; sequencing data]]></title>
	<description><![CDATA[<ul>
<li>Run a pipeline processing fast5s to a consensus in a single command.</li>
<li>Recommended fixed "standard" and "fast" pipelines.</li>
<li>Interchange basecaller, assembler, and consensus components of the pipelines simply by changing the target filepath.</li>
<li>Seemless distribution of tasks over local or distributed compute.</li>
<li>Highly configurable.</li>
<li>Open source (Mozilla Public License 2.0).</li>
</ul>
<p>Documentation can be found at&nbsp;<a href="https://nanoporetech.github.io/katuali/">https://nanoporetech.github.io/katuali/</a>.</p><p>Address of the bookmark: <a href="https://github.com/nanoporetech/katuali" rel="nofollow">https://github.com/nanoporetech/katuali</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</guid>
	<pubDate>Fri, 24 Jan 2020 04:09:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</link>
	<title><![CDATA[MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization]]></title>
	<description><![CDATA[<p><span>MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from&nbsp;</span><code>https://github.com/linzhi2013/MitoZ</code><span>.</span></p>
<p><span><a href="https://academic.oup.com/nar/article/47/11/e63/5377471">https://academic.oup.com/nar/article/47/11/e63/5377471</a></span></p><p>Address of the bookmark: <a href="https://github.com/linzhi2013/MitoZ" rel="nofollow">https://github.com/linzhi2013/MitoZ</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41039/phd-position-in-translational-medicine</guid>
  <pubDate>Sat, 15 Feb 2020 06:07:19 -0600</pubDate>
  <link></link>
  <title><![CDATA[PhD position in Translational Medicine]]></title>
  <description><![CDATA[
<p>https://www.jobvector.de/jobs-stellenangebote/biologie-life-sciences/wissenschaftliche-r-mitarbeiter-in/phd-position-translational-medicine-129981.html?suid=1b510358c7578e8f75cf04a464fc21a404a574ca</p>

<p>Essential experience / qualifications:<br />Master / Diploma in Biology, Biochemistry, Molecular Medicine or similar; solid knowledge of molecular and cell biological techniques; good English knowledge</p>

<p>Applications:<br />Please send your application (including CV, letter of motivation, contact information of two references, and list of publication) by 13.03.2020 at the latest to:</p>

<p>Universitätsklinikum Erlangen<br />Chirurgische Klinik<br />Translational Research Center<br />Prof. Dr. rer. nat. Michael Stürzl<br />Schwabachanlage 12<br />91054 Erlangen<br />E-Mail: michael.stuerzl@uk-erlangen.de</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data</guid>
	<pubDate>Fri, 25 Feb 2022 04:42:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data</link>
	<title><![CDATA[Smudgeplot: Inference of ploidy and heterozygosity structure using whole genome sequencing data]]></title>
	<description><![CDATA[<p dir="auto">This tool extracts heterozygous kmer pairs from kmer count databases and performs gymnastics with them. We are able to disentangle genome structure by comparing the sum of kmer pair coverages (CovA + CovB) to their relative coverage (CovB / (CovA + CovB)). Such an approach also allows us to analyze obscure genomes with duplications, various ploidy levels, etc.</p>
<p dir="auto">Smudgeplots are computed from raw or even better from trimmed reads and show the haplotype structure using heterozygous kmer pairs. For example:</p>
<p dir="auto"><a href="https://user-images.githubusercontent.com/8181573/45959760-f1032d00-c01a-11e8-8576-ff0512c33da9.png" target="_blank"><img src="https://user-images.githubusercontent.com/8181573/45959760-f1032d00-c01a-11e8-8576-ff0512c33da9.png" alt="smudgeexample" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/KamilSJaron/smudgeplot" rel="nofollow">https://github.com/KamilSJaron/smudgeplot</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35249/gpopsim-a-simulation-tool-for-whole-genome-genetic-data</guid>
	<pubDate>Wed, 17 Jan 2018 03:47:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35249/gpopsim-a-simulation-tool-for-whole-genome-genetic-data</link>
	<title><![CDATA[GPOPSIM: a simulation tool for whole-genome genetic data]]></title>
	<description><![CDATA[<p><span>GPOPSIM is a simulation tool for pedigree, phenotypes, and genomic data, with a variety of population and genome structures and trait genetic architectures. It provides flexible parameter settings for a wide discipline of users, especially can simulate multiple genetically correlated traits with desired genetic parameters and underlying genetic architectures.</span></p><p>Address of the bookmark: <a href="https://github.com/SCAU-AnimalGenetics/GPOPSIM" rel="nofollow">https://github.com/SCAU-AnimalGenetics/GPOPSIM</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41996/wgd%E2%80%94simple-command-line-tools-for-the-analysis-of-ancient-whole-genome-duplications</guid>
	<pubDate>Thu, 23 Jul 2020 05:49:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41996/wgd%E2%80%94simple-command-line-tools-for-the-analysis-of-ancient-whole-genome-duplications</link>
	<title><![CDATA[wgd—simple command line tools for the analysis of ancient whole-genome duplications]]></title>
	<description><![CDATA[<p><span>wgd is a easy to use command-line tool for<span>&nbsp;</span></span><em>K</em><sub>S</sub><span><span>&nbsp;</span>distribution construction named wgd. The wgd suite provides commonly used<span>&nbsp;</span></span><em>K</em><sub>S</sub><span><span>&nbsp;</span>and colinearity analysis workflows together with tools for modeling and visualization, rendering these analyses accessible to genomics researchers in a convenient manner.</span></p>
<p><a href="https://academic.oup.com/bioinformatics/article/35/12/2153/5162749">https://academic.oup.com/bioinformatics/article/35/12/2153/5162749</a></p><p>Address of the bookmark: <a href="https://github.com/arzwa/wgd" rel="nofollow">https://github.com/arzwa/wgd</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40986/cismuton-caller-that-detects-snvsindels-by-comparing-target-foreground-and-control-background-samples</guid>
	<pubDate>Tue, 11 Feb 2020 05:55:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40986/cismuton-caller-that-detects-snvsindels-by-comparing-target-foreground-and-control-background-samples</link>
	<title><![CDATA[cisMuton: caller that detects SNVs/indels by comparing target (foreground) and control (background) samples]]></title>
	<description><![CDATA[<p>cisMuton is a caller that detects SNVs/indels by comparing target (foreground) and control (background) samples.</p>
<p>cisMuton calls mutations from target capture regions, which are defined by the overlapping regions of&nbsp;<code>${GROUP}.target.bed</code>&nbsp;and&nbsp;<code>${GROUP}.gene.bed</code>.</p>
<p>Please read the&nbsp;<a href="https://static.ciscall.org/cisCall5_doc/doc/quick_start.html">Quick Start Guide for cisCall</a>&nbsp;first for an outline of how to run cisMuton.</p><p>Address of the bookmark: <a href="https://static.ciscall.org/cisCall5_doc/index.html" rel="nofollow">https://static.ciscall.org/cisCall5_doc/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27845/cnidaria-fast-reference-free-phylogenomic-clustering</guid>
	<pubDate>Thu, 16 Jun 2016 17:55:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27845/cnidaria-fast-reference-free-phylogenomic-clustering</link>
	<title><![CDATA[CNIDARIA: fast, reference-free phylogenomic clustering]]></title>
	<description><![CDATA[<p>Motivation: Identification of biological specimens is a major requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but these do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances.</p>
<p>Results: We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on ge-nome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100% accuracy at supra-species level and 78% accuracy for species level.</p>
<p>Availability and Implementation: Cnidaria is written in C++ and Python and is available at http://www.ab.wur.nl/cnidaria.</p>
<p>Contact: Saulo Aflitos - sauloal@gmail.com</p>
<p>Supplementary information: Supplementary data are available at Bioinformatics online.</p><p>Address of the bookmark: <a href="https://github.com/sauloal/cnidaria/wiki" rel="nofollow">https://github.com/sauloal/cnidaria/wiki</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41033/clark-fast-accurate-and-versatile-sequence-classification-system</guid>
	<pubDate>Sat, 15 Feb 2020 01:49:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41033/clark-fast-accurate-and-versatile-sequence-classification-system</link>
	<title><![CDATA[CLARK: Fast, accurate and versatile sequence classification system]]></title>
	<description><![CDATA[<p><span></span><a href="http://dx.doi.org/10.1186/s12864-015-1419-2"><strong>CLARK</strong></a><span>, a method based on a supervised sequence classification using discriminative&nbsp;</span><em>k</em><span>-mers. Considering two distinct specific classification problems (see the article for details), namely (1) the taxonomic classification of metagenomic reads to known bacterial genomes, and (2) the assignment of BAC clones and transcript to chromosome arms/centromeres (in the absence of a finished assembly for the reference genome), CLARK outperforms in classification speed and precision the best state-of-the-art methods.</span></p>
<p><span><a href="http://clark.cs.ucr.edu/Spaced/">http://clark.cs.ucr.edu/Spaced/</a></span></p><p>Address of the bookmark: <a href="http://clark.cs.ucr.edu/Spaced/" rel="nofollow">http://clark.cs.ucr.edu/Spaced/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes</guid>
	<pubDate>Tue, 12 Dec 2017 17:23:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34618/mashmap-a-fast-and-approximate-software-for-mapping-long-reads-pacbioont-or-assembly-to-reference-genomes</link>
	<title><![CDATA[MashMap: a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s)]]></title>
	<description><![CDATA[<p><span>MashMap is a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s). It maps a query sequence against a reference region if and only if its estimated alignment identity is above a specified threshold. It does not compute the alignments explicitly, but rather estimates a&nbsp;</span><em>k</em><span>-mer based&nbsp;</span><a href="https://en.wikipedia.org/wiki/Jaccard_index">Jaccard similarity</a><span>&nbsp;using a combination of&nbsp;</span><a href="http://www.cs.princeton.edu/courses/archive/spr05/cos598E/bib/p76-schleimer.pdf">Winnowing</a><span>&nbsp;and&nbsp;</span><a href="https://en.wikipedia.org/wiki/MinHash">MinHash</a><span>. This is then converted to an estimate of sequence identity using the&nbsp;</span><a href="http://mash.readthedocs.org/">Mash</a><span>&nbsp;distance. An appropriate&nbsp;</span><em>k</em><span>-mer sampling rate is automatically determined given minimum local alignment length and identity thresholds. The efficiency of the algorithm improves as both of these thresholds are increased.</span></p><p>Address of the bookmark: <a href="https://github.com/marbl/MashMap" rel="nofollow">https://github.com/marbl/MashMap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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