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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40598?offset=100</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27323/cutadapt</guid>
	<pubDate>Fri, 13 May 2016 04:54:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27323/cutadapt</link>
	<title><![CDATA[cutadapt]]></title>
	<description><![CDATA[<p>Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.</p>
<p>Cleaning your data in this way is often required: Reads from small-RNA sequencing contain the 3&rsquo; sequencing adapter because the read is longer than the molecule that is sequenced. Amplicon reads start with a primer sequence. Poly-A tails are useful for pulling out RNA from your sample, but often you don&rsquo;t want them to be in your reads.</p>
<p>Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.</p>
<p>Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.</p>
<p>If you use cutadapt, please cite <a href="http://dx.doi.org/10.14806/ej.17.1.200">DOI:10.14806/ej.17.1.200</a> .</p><p>Address of the bookmark: <a href="https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart" rel="nofollow">https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27847/anvio</guid>
	<pubDate>Thu, 16 Jun 2016 18:15:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27847/anvio</link>
	<title><![CDATA[Anvio]]></title>
	<description><![CDATA[<p>In a nutshell</p>
<p>Anvi&rsquo;o is an analysis and visualization platform for &lsquo;omics data.</p>
<p>Please find the methods paper here: https://peerj.com/articles/1319/</p>
<p>Anvi&rsquo;o would not have been possible without the help of many people who directly or indirectly contributed to its development. Here is the acknowledgements section of our methods paper</p>
<p><span>An analysis and visualization platform for 'omics data</span><span>&nbsp;</span><span><a href="http://merenlab.org/projects/anvio">http://merenlab.org/projects/anvio</a></span></p>
<p><span>Paper&nbsp;https://peerj.com/articles/1839/</span></p><p>Address of the bookmark: <a href="https://github.com/meren/anvio" rel="nofollow">https://github.com/meren/anvio</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28290/bioinformatics-tools-and-software</guid>
	<pubDate>Tue, 05 Jul 2016 10:02:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28290/bioinformatics-tools-and-software</link>
	<title><![CDATA[Bioinformatics tools and software]]></title>
	<description><![CDATA[<p><a href="http://drive5.com/usearch">USEARCH &gt;</a><br><span>Extreme high-throughput sequence analysis. Orders of magnitude faster than BLAST.</span>&nbsp;<a href="http://drive5.com/muscle">MUSCLE &gt;</a><br><span>Multiple sequence alignment. Faster and more accurate than CLUSTALW.</span></p>
<p>&nbsp;<a href="http://drive5.com/uparse">UPARSE &gt;</a><br><span>OTU clustering for 16S and other marker genes. Highly accurate OTU sequences and improved diversity measures.</span>&nbsp;<a href="http://drive5.com/uchime">UCHIME &gt;</a><br><span>Chimeric sequence detection.</span>&nbsp;<a href="http://drive5.com/piler">PILER &gt;</a><br><span>De novo genome repeat finder.</span>&nbsp;<a href="http://drive5.com/pilercr">PILER-CR &gt;</a><br><span>Detection of CRISPR repeats in bacterial genomes.</span>&nbsp;<a href="http://drive5.com/qscore">QSCORE &gt;</a><br><span>Compare two multiple alignments for benchmarking.</span>&nbsp;<a href="http://drive5.com/pals">PALS &gt;</a><br><span>Whole-genome alignment.</span>&nbsp;<a href="http://drive5.com/muscle/prefab.htm">PREFAB &gt;</a><br><span>Protein Reference Alignment Database.</span>&nbsp;<a href="http://drive5.com/bench">MSA benchmark collection &gt;</a><br><span>Selected multiple alignment benchmarks in a standardized FASTA format.</span></p><p>Address of the bookmark: <a href="http://drive5.com/software.html" rel="nofollow">http://drive5.com/software.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32946/grass-a-generic-algorithm-for-scaffolding-next-generation-sequencing-assemblies</guid>
	<pubDate>Tue, 23 May 2017 05:20:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32946/grass-a-generic-algorithm-for-scaffolding-next-generation-sequencing-assemblies</link>
	<title><![CDATA[GRASS: a generic algorithm for scaffolding next-generation sequencing assemblies.]]></title>
	<description><![CDATA[<p><span>GRASS (GeneRic ASsembly Scaffolder)-a novel algorithm for scaffolding second-generation sequencing assemblies capable of using diverse information sources. GRASS offers a mixed-integer programming formulation of the contig scaffolding problem, which combines contig order, distance and orientation in a single optimization objective. The resulting optimization problem is solved using an expectation-maximization procedure and an unconstrained binary quadratic programming approximation of the original problem. We compared GRASS with existing HTS scaffolders using Illumina paired reads of three bacterial genomes. Our algorithm constructs a comparable number of scaffolds, but makes fewer errors. This result is further improved when additional data, in the form of related genome sequences, are used.</span></p><p>Address of the bookmark: <a href="https://github.com/AlexeyG/GRASS" rel="nofollow">https://github.com/AlexeyG/GRASS</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37830/nquire-a-statistical-framework-for-ploidy-estimation-using-next-generation-sequencing</guid>
	<pubDate>Thu, 04 Oct 2018 05:23:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37830/nquire-a-statistical-framework-for-ploidy-estimation-using-next-generation-sequencing</link>
	<title><![CDATA[nQuire: a statistical framework for ploidy estimation using next generation sequencing]]></title>
	<description><![CDATA[<p>nQuire provides a statistical framework to study organisms with intraspecific variation in ploidy. nQuire is likely to be useful in epidemiological studies of pathogens, artificial selection experiments, and for historical or ancient samples where intact nuclei are not preserved. It is implemented as a stand-alone Linux command line tool in the C programming language and is available at https://github.com/clwgg/nQuireunder the MIT license.</p><p>Address of the bookmark: <a href="https://github.com/clwgg/nQuireunder" rel="nofollow">https://github.com/clwgg/nQuireunder</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/40770/scientist-bioinformatics-positions</guid>
  <pubDate>Thu, 30 Jan 2020 06:53:40 -0600</pubDate>
  <link></link>
  <title><![CDATA[Scientist Bioinformatics Positions]]></title>
  <description><![CDATA[
<p>Bioinformatics-Multi_Omics_Integration</p>

<p>https://www.researchgate.net/job/939073_Senior_Scientist_Bioinformatics-Multi_Omics_Integration</p>

<p> <br />Senior_Scientist_Bioinformatics-Transcriptomics_Analysis     </p>

<p>https://www.researchgate.net/job/939075_Senior_Scientist_Bioinformatics-Transcriptomics_Analysis-Belgium_France_Switzerland_The_Netherlands</p>

<p>Senior Scientist Bioinformatics - Network Analytics</p>

<p>https://www.researchgate.net/job/939070_Senior_Scientist_Bioinformatics-Network_Analytics_Belgium_France_Switzerland_the_Netherlands</p>

<p>Team Leader Bioinformatics Data Sciences - Mechelen, Belgium</p>

<p>https://www.researchgate.net/job/938787_Team_Leader_Bioinformatics_Data_Sciences-Mechelen_Belgium</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41730/parliament2-runs-a-combination-of-tools-to-generate-structural-variant-calls-on-whole-genome-sequencing-data</guid>
	<pubDate>Thu, 28 May 2020 21:57:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41730/parliament2-runs-a-combination-of-tools-to-generate-structural-variant-calls-on-whole-genome-sequencing-data</link>
	<title><![CDATA[Parliament2: Runs a combination of tools to generate structural variant calls on whole-genome sequencing data]]></title>
	<description><![CDATA[<p>Parliament2 identifies structural variants in a given sample relative to a reference genome. These structural variants cover large deletion events that are called as Deletions of a region, Insertions of a sequence into a region, Duplications of a region, Inversions of a region, or Translocations between two regions in the genome.</p>
<p>Parliament2 runs a combination of tools to generate structural variant calls on whole-genome sequencing data. It can run the following callers: Breakdancer, Breakseq2, CNVnator, Delly2, Manta, and Lumpy. Because of synergies in how the programs use computational resources, these are all run in parallel. Parliament2 will produce the outputs of each of the tools for subsequent investigation.</p><p>Address of the bookmark: <a href="https://github.com/dnanexus/parliament2" rel="nofollow">https://github.com/dnanexus/parliament2</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37674/qualimap2-evaluating-next-generation-sequencing-alignment-data</guid>
	<pubDate>Tue, 11 Sep 2018 04:44:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37674/qualimap2-evaluating-next-generation-sequencing-alignment-data</link>
	<title><![CDATA[Qualimap2: Evaluating next generation sequencing alignment data]]></title>
	<description><![CDATA[<p><strong>Qualimap 2</strong><span>&nbsp;is a platform-independent application written in Java and R that provides both a Graphical User Inteface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.&nbsp;</span><br><br><span>Supported types of experiments include:</span></p>
<ul>
<li>Whole-genome sequencing</li>
<li>Whole-exome sequencing</li>
<li>RNA-seq (speical mode available)</li>
<li>ChIP-seq</li>
</ul><p>Address of the bookmark: <a href="http://qualimap.bioinfo.cipf.es/" rel="nofollow">http://qualimap.bioinfo.cipf.es/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39726/jackalope-a-swift-versatile-phylogenomic-and-high-throughput-sequencing-simulator</guid>
	<pubDate>Fri, 26 Jul 2019 00:58:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39726/jackalope-a-swift-versatile-phylogenomic-and-high-throughput-sequencing-simulator</link>
	<title><![CDATA[jackalope: A swift, versatile phylogenomic and high-throughput sequencing simulator]]></title>
	<description><![CDATA[<p><code>jackalope</code> simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations&mdash;the latter of which can include selection, recombination, and demographic fluctuations. <code>jackalope</code> can simulate single, paired-end, or mate-pair Illumina reads, as well as reads from Pacific Biosciences These simulations include sequencing errors, mapping qualities, multiplexing, and optical/PCR duplicates. All outputs can be written to standard file formats.</p>
<p><span>A swift, versatile phylogenomic and high-throughput sequencing simulator </span> <span><a href="https://jackalope.lucasnell.com">https://jackalope.lucasnell.com</a></span></p><p>Address of the bookmark: <a href="https://github.com/lucasnell/jackalope" rel="nofollow">https://github.com/lucasnell/jackalope</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37527/nanopack-visualizing-and-processing-long-read-sequencing-data</guid>
	<pubDate>Fri, 10 Aug 2018 18:41:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37527/nanopack-visualizing-and-processing-long-read-sequencing-data</link>
	<title><![CDATA[NanoPack: visualizing and processing long-read sequencing data]]></title>
	<description><![CDATA[<p>The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at&nbsp;<a href="https://github.com/wdecoster/nanopack" target="">https://github.com/wdecoster/nanopack</a>, 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&nbsp;<a href="http://nanoplot.bioinf.be/" target="">http://nanoplot.bioinf.be</a>&nbsp;and command line tools.</p>
<p>&nbsp;https://academic.oup.com/bioinformatics/article/34/15/2666/4934939</p><p>Address of the bookmark: <a href="https://github.com/wdecoster/nanoQC" rel="nofollow">https://github.com/wdecoster/nanoQC</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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