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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41843?offset=220</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35747/webinar-on-rna-seq-data-analysis-on-28-feb-2018</guid>
	<pubDate>Thu, 22 Feb 2018 06:38:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35747/webinar-on-rna-seq-data-analysis-on-28-feb-2018</link>
	<title><![CDATA[Webinar on RNA-Seq Data Analysis on 28 Feb 2018]]></title>
	<description><![CDATA[<p>Strand NGS is a biologist friendly NGS analysis tool that allows biologists to analyze their data using a very intuitive workflow for the analysis and visualization of RNA-Seq data. This webinar will give an overview of the workflow which includes Transcriptome/ Genome alignment, Differential expression analysis, Splicing events and gene fusion detection. Strand NGS also supports novel discovery like identification of novel genes, exons and novel splice junctions.<br />We will highlight the use of Strand NGS features such as PCA, sample correlation, clustering, Venn diagrams, CVA, UMI support and elastic genome browser used in RNA-Seq workflow that supports large scale RNA-Seq data analysis too. The tool also supports biological contextualization on the set of interesting genes from the data by allowing downstream analysis such as GO and pathway analysis. The product has an option to create pipelines for time consuming jobs which automates analysis and leaves more time for end data interpretation. This webinar will give an overview of the features in the RNA-Seq data analysis workflow in Strand NGS.</p><p>Details:<br /><a href="http://www.strand-ngs.com/webinar_registration">Session 1: </a>28 Feb 2018, 9 AM CET<br /><a href="http://www.strand-ngs.com/webinar_registration">Session 2:</a> 28 Feb 2018, 8 AM PST<br />Register here: http://www.strand-ngs.com/webinar_registration</p><p><span style="font-size: 12.8px;">About Speaker:</span></p><p>Dr. Suman Kapoor, Manager- Application Science at Strand Life Sciences, has over 11 years experience in molecular biology, next-generation sequencing based testing, clinical genomics, and personalized medicine for disease management and prenatal testing. Dr. Suman holds a Ph.D in Molecular and Cell Biology from Indian Institute of Science, Bangalore. Prior to joining Strand NGS team, Suman has worked extensively on protein synthesis in eubacteria and has experience working in CAP and NABL accredited lab validating and interpreting NGS based diagnostic tests.</p>]]></description>
	<dc:creator>Strand</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34398/ont-assembly-and-illumina-polishing-pipeline</guid>
	<pubDate>Thu, 23 Nov 2017 10:13:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34398/ont-assembly-and-illumina-polishing-pipeline</link>
	<title><![CDATA[ONT assembly and Illumina polishing pipeline]]></title>
	<description><![CDATA[<p>This pipeline performs the following steps:</p>
<ul>
<li>Assembly of nanopore reads using&nbsp;<a href="http://canu.readthedocs.io/">Canu</a>.</li>
<li>Polish canu contigs using&nbsp;<a href="https://github.com/isovic/racon">racon</a>&nbsp;(<em>optional</em>).</li>
<li>Map a paired-end Illumina dataset onto the contigs obtained in the previous steps using&nbsp;<a href="http://bio-bwa.sourceforge.net/">BWA</a>&nbsp;mem.</li>
<li>Perform correction of contigs using&nbsp;<a href="https://github.com/broadinstitute/pilon/wiki">pilon</a>&nbsp;and the Illumina dataset.</li>
</ul><p>Address of the bookmark: <a href="https://github.com/nanoporetech/ont-assembly-polish" rel="nofollow">https://github.com/nanoporetech/ont-assembly-polish</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34501/dnapipete-de-novo-assembly-annotation-pipeline-for-transposable-elements</guid>
	<pubDate>Sat, 02 Dec 2017 18:25:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34501/dnapipete-de-novo-assembly-annotation-pipeline-for-transposable-elements</link>
	<title><![CDATA[dnaPipeTE: de-novo assembly &amp; annotation Pipeline for Transposable Elements]]></title>
	<description><![CDATA[<p>dnaPipeTE (for de-novo assembly &amp; annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (&lt; 1X).</p>
<ul>
<li>
<p>dnaPipeTE is developped by Cl&eacute;ment Goubert, Laurent Modolo and the TREEP team of the LBBE:&nbsp;<a href="http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en">http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en</a></p>
</li>
<li>
<p>You can find the original publication in GBE here:&nbsp;<a href="https://academic.oup.com/gbe/article/7/4/1192/533768">https://academic.oup.com/gbe/article/7/4/1192/533768</a></p>
</li>
</ul>
<p><a href="https://github.com/clemgoub/dnaPipeTE/blob/dev/dnaPipefront.png" target="_blank"><img src="https://github.com/clemgoub/dnaPipeTE/raw/dev/dnaPipefront.png" alt="Front" style="border: 0px;"></a><em>output examples of quantification and TE landscape (relative age) produced by dnaPipeTE</em></p>
<p><em>&nbsp;</em></p><p>Address of the bookmark: <a href="https://github.com/clemgoub/dnaPipeTE" rel="nofollow">https://github.com/clemgoub/dnaPipeTE</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34914/ra-assembler-a-de-novo-dna-assembler-for-third-generation-sequencing-data</guid>
	<pubDate>Wed, 27 Dec 2017 20:36:54 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34914/ra-assembler-a-de-novo-dna-assembler-for-third-generation-sequencing-data</link>
	<title><![CDATA[Ra assembler - a de novo DNA assembler for third generation sequencing data]]></title>
	<description><![CDATA[<p>Integration of the Ra assembler - a de novo DNA assembler for third generation sequencing data developed on Faculty of Electrical Engineering and Computing (FER), Ruder Boskovic Institute (RBI) and Genome Institute of Singapore (GIS).</p>
<p>Ra is in development since 2014 in the form of several separate components that used to be run individually.<br>This project aims to ease the usage of Ra by integrating it into a complete de novo assembly tool.</p>
<p>Unlike other state-of-the-art assemblers,&nbsp;<span>Ra does not have an error correction step.</span>&nbsp;Instead, it relies on detecting overlaps using a very sensitive and specific overlapper ("graphmap -w owler",&nbsp;<a href="https://github.com/isovic/graphmap">https://github.com/isovic/graphmap</a>) and constructing and reducing an overlap graph (Ra layout,&nbsp;<a href="https://github.com/mariokostelac/ra">https://github.com/mariokostelac/ra</a>).</p><p>Address of the bookmark: <a href="https://github.com/mariokostelac/ra-integrate/" rel="nofollow">https://github.com/mariokostelac/ra-integrate/</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36257/aligngraph-algorithm-for-secondary-de-novo-genome-assembly-guided-by-closely-related-references</guid>
	<pubDate>Tue, 17 Apr 2018 16:21:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36257/aligngraph-algorithm-for-secondary-de-novo-genome-assembly-guided-by-closely-related-references</link>
	<title><![CDATA[AlignGraph: algorithm for secondary de novo genome assembly guided by closely related references]]></title>
	<description><![CDATA[<p>AlignGraph is a software that extends and joins contigs or scaffolds by reassembling them with help provided by a reference genome of a closely related organism.</p>
<p>Using AlignGraph</p>
<pre><code>AlignGraph --read1 reads_1.fa --read2 reads_2.fa --contig contigs.fa --genome genome.fa --distanceLow distanceLow --distanceHigh distancehigh --extendedContig extendedContigs.fa --remainingContig remainingContigs.fa [--kMer k --insertVariation insertVariation --coverage coverage --part p --fastMap --ratioCheck --iterativeMap --misassemblyRemoval --resume]</code></pre>
<h3>&nbsp;</h3><p>Address of the bookmark: <a href="https://github.com/baoe/AlignGraph" rel="nofollow">https://github.com/baoe/AlignGraph</a></p>]]></description>
	<dc:creator>Manisha Mishra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36861/eagler-a-scaffolding-tool-for-long-reads</guid>
	<pubDate>Mon, 04 Jun 2018 05:26:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36861/eagler-a-scaffolding-tool-for-long-reads</link>
	<title><![CDATA[EAGLER: a scaffolding tool for long reads.]]></title>
	<description><![CDATA[<p>EAGLER is a scaffolding tool for long reads. The scaffolder takes as input a draft genome created by any NGS assembler and a set of long reads. The long reads are used to extend the contigs present in the NGS draft and possibly join overlapping contigs. EAGLER supports both PacBio and Oxford Nanopore reads.</p>
<p>The tool should be compatible with most UNIX flavors and has been successfully tested on the following operating systems:</p>
<ul>
<li>Mac OS X 10.11.1</li>
<li>Mac OS X 10.10.3</li>
<li>Ubuntu 14.04 LTS</li>
</ul>

https://bib.irb.hr/datoteka/844447.Diplomski_2015_Luka_terbi.pdf<p>Address of the bookmark: <a href="https://github.com/mculinovic/EAGLER" rel="nofollow">https://github.com/mculinovic/EAGLER</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36985/swalo-scaffolding-with-assembly-likelihood-optimization</guid>
	<pubDate>Wed, 20 Jun 2018 02:45:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36985/swalo-scaffolding-with-assembly-likelihood-optimization</link>
	<title><![CDATA[SWALO: Scaffolding with assembly likelihood optimization]]></title>
	<description><![CDATA[SWALO (scaffolding with assembly likelihood optimization) is a method for scaffolding based on likelihood of genome assemblies computed using generative models for sequencing.

Please email your questions, comments, suggestions, and bug reports to atif.bd@gmail.com.<p>Address of the bookmark: <a href="https://atifrahman.github.io/SWALO/" rel="nofollow">https://atifrahman.github.io/SWALO/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/37396/converting-a-vcf-into-a-fasta-given-some-reference</guid>
	<pubDate>Fri, 20 Jul 2018 10:03:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/37396/converting-a-vcf-into-a-fasta-given-some-reference</link>
	<title><![CDATA[Converting a VCF into a FASTA given some reference !]]></title>
	<description><![CDATA[<p>Samtools/BCFtools (Heng Li) provides a Perl script&nbsp;<a href="https://github.com/lh3/samtools/blob/master/bcftools/vcfutils.pl"><code>vcfutils.pl</code></a>&nbsp;which does this, the function&nbsp;<code>vcf2fq</code>&nbsp;(lines 469-528)</p><p>This script has been modified by others to convert InDels as well, e.g.&nbsp;<a href="https://github.com/gringer/bioinfscripts/blob/master/vcf2fq.pl">this</a>&nbsp;by David Eccles</p><pre><code><span>./</span><span>vcf2fq</span><span>.</span><span>pl </span><span>-</span><span>f </span><span>&lt;</span><span>input</span><span>.</span><span>fasta</span><span>&gt;</span><span> </span><span>&lt;</span><span>all</span><span>-</span><span>site</span><span>.</span><span>vcf</span><span>&gt;</span><span> </span><span>&gt;</span><span> </span><span>&lt;</span><span>output</span><span>.</span><span>fastq</span><span>&gt;</span></code></pre><p>https://github.com/gringer/bioinfscripts/blob/master/vcf2fq.pl</p><p>https://github.com/lh3/samtools/blob/master/bcftools/vcfutils.pl</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37840/long-read-assembly-workshop</guid>
	<pubDate>Thu, 04 Oct 2018 17:23:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37840/long-read-assembly-workshop</link>
	<title><![CDATA[Long read assembly workshop !]]></title>
	<description><![CDATA[<p>This is a tutorial for a workshop on long-read (PacBio) genome assembly.</p>
<p>It demonstrates how to use long PacBio sequencing reads to assemble a bacterial genome, and includes additional steps for circularising, trimming, finding plasmids, and correcting the assembly with short-read Illumina data.</p>
<p>&nbsp;Please comment if you know any other long read addembly tutorial.</p><p>Address of the bookmark: <a href="http://sepsis-omics.github.io/tutorials/modules/cmdline_assembly_v2/" rel="nofollow">http://sepsis-omics.github.io/tutorials/modules/cmdline_assembly_v2/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38210/skesa-strategic-k-mer-extension-for-scrupulous-assemblies</guid>
	<pubDate>Wed, 14 Nov 2018 04:45:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38210/skesa-strategic-k-mer-extension-for-scrupulous-assemblies</link>
	<title><![CDATA[SKESA: strategic k-mer extension for scrupulous assemblies]]></title>
	<description><![CDATA[<p><span>SKESA is a DeBruijn graph-based de-novo assembler designed for assembling reads of microbial genomes sequenced using Illumina. Comparison with SPAdes and MegaHit shows that SKESA produces assemblies that have high sequence quality and contiguity, handles low-level contamination in reads, is fast, and produces an identical assembly for the same input when assembled multiple times with the same or different compute resources. </span></p>
<p><span>Source code for SKESA is freely available at&nbsp;</span><span><a href="https://github.com/ncbi/SKESA/releases"><span>https://github.com/ncbi/SKESA/releases</span></a></span><span>.</span></p>
<p>Research Paper&nbsp;@ <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1540-z">Link</a></p>
<p><span><span>SKESA algorithm are as follows:</span><br></span></p>
<p><span><img src="https://media.springernature.com/lw785/springer-static/image/art%3A10.1186%2Fs13059-018-1540-z/MediaObjects/13059_2018_1540_Fig4_HTML.png" alt="image" width="785" height="984" style="border: 0px; border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/ncbi/SKESA/releases" rel="nofollow">https://github.com/ncbi/SKESA/releases</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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