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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43057?offset=430</link>
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	<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/37221/asplice-a-scalable-and-memory-efficient-algorithm-for-de-novo-transcriptome-assembly</guid>
	<pubDate>Tue, 03 Jul 2018 04:09:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37221/asplice-a-scalable-and-memory-efficient-algorithm-for-de-novo-transcriptome-assembly</link>
	<title><![CDATA[ASplice: a scalable and memory-efficient algorithm for de novo transcriptome assembly]]></title>
	<description><![CDATA[With increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries.

Texas A&amp;M University researchers develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory.

Availability – A software program that implements the algorithm is available at: http://faculty.cse.tamu.edu/shsze/asplice.

Sze SH, Pimsler ML, Tomberlin JK, Jones CD, Tarone AM. (2017) A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms. BMC Genomics 18(Suppl 4):387.<p>Address of the bookmark: <a href="http://faculty.cse.tamu.edu/shsze/asplice/" rel="nofollow">http://faculty.cse.tamu.edu/shsze/asplice/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38212/megahit-an-ultra-fast-single-node-solution-for-large-and-complex-metagenomics-assembly-via-succinct-de-bruijn-graph</guid>
	<pubDate>Wed, 14 Nov 2018 04:50:27 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38212/megahit-an-ultra-fast-single-node-solution-for-large-and-complex-metagenomics-assembly-via-succinct-de-bruijn-graph</link>
	<title><![CDATA[MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph]]></title>
	<description><![CDATA[<p><span>MEGAHIT is a single node assembler for large and complex metagenomics NGS reads, such as soil. It makes use of succinct&nbsp;</span><em>de Bruijn</em><span>&nbsp;graph (SdBG) to achieve low memory assembly. MEGAHIT can&nbsp;</span><span>optionally</span><span>&nbsp;utilize a CUDA-enabled GPU to accelerate its SdBG contstruction. The GPU-accelerated version of MEGAHIT has been tested on NVIDIA GTX680 (4G memory) and Tesla K40c (12G memory) with CUDA 5.5, 6.0 and 6.5. MEGAHIT v1.0 or greater also supports IBM Power PC and has been tested on IBM POWER8.</span></p>
<p><span>https://academic.oup.com/bioinformatics/article/31/10/1674/177884</span></p><p>Address of the bookmark: <a href="https://github.com/voutcn/megahit" rel="nofollow">https://github.com/voutcn/megahit</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</guid>
	<pubDate>Thu, 24 Jan 2019 10:35:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</link>
	<title><![CDATA[NxRepair: error correction in de novo assemblies using Nextera Mate Pair Reads]]></title>
	<description><![CDATA[<p>NxRepair is a python module that automatically detects large structural errors in de novo assemblies using Nextera mate pair reads. The decector will break a contig at the site of an identified misassembly and will generate a new fasta file containing both the corrected contigs and the correct, unaffected contigs.</p>
<p>https://nxrepair.readthedocs.io/en/latest/tutorial.html</p>
<div>
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<div id="js-repo-pjax-container">
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<div id="readme">
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<div>
<pre>nxrepair aligned_matepairs.bam assemblyfasta.fasta error_locations.csv new_fasta.fasta</pre>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<div>&nbsp;</div><p>Address of the bookmark: <a href="https://github.com/rebeccaroisin/nxrepair" rel="nofollow">https://github.com/rebeccaroisin/nxrepair</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39213/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Tue, 02 Apr 2019 21:54:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39213/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</link>
	<title><![CDATA[Flye: Fast and accurate de novo assembler for single molecule sequencing reads]]></title>
	<description><![CDATA[<p><span>Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PB / ONT reads as input and outputs polished contigs. Flye also includes a special mode for metagenome assembly.</span></p><p>Address of the bookmark: <a href="https://github.com/fenderglass/Flye" rel="nofollow">https://github.com/fenderglass/Flye</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41843/stringtie-transcript-assembly-and-quantification-for-rna-seq</guid>
	<pubDate>Tue, 09 Jun 2020 05:21:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41843/stringtie-transcript-assembly-and-quantification-for-rna-seq</link>
	<title><![CDATA[StringTie Transcript assembly and quantification for RNA-Seq]]></title>
	<description><![CDATA[<p><strong>StringTie</strong><span>&nbsp;is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional&nbsp;</span><em>de novo</em><span>&nbsp;assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Its input can include not only alignments of short reads that can also be used by other transcript assemblers, but also alignments of longer sequences that have been assembled from those reads. In order to identify differentially expressed genes between experiments, StringTie's output can be processed by specialized software like&nbsp;</span><a href="https://github.com/alyssafrazee/ballgown">Ballgown</a><span>,&nbsp;</span><a href="http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html">Cuffdiff</a><span>&nbsp;or other programs (DESeq2, edgeR, etc.).</span></p><p>Address of the bookmark: <a href="https://ccb.jhu.edu/software/stringtie/" rel="nofollow">https://ccb.jhu.edu/software/stringtie/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40856/3d-de-novo-assembly-3d-dna-pipeline</guid>
	<pubDate>Sun, 02 Feb 2020 13:41:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40856/3d-de-novo-assembly-3d-dna-pipeline</link>
	<title><![CDATA[3D de novo assembly (3D DNA) pipeline]]></title>
	<description><![CDATA[<p>For a detailed description of the pipeline and how it integrates with other tools designed by the Aiden Lab see&nbsp;<a href="http://aidenlab.org/assembly/manual_180322.pdf">Genome Assembly Cookbook</a>&nbsp;on&nbsp;<a href="http://aidenlab.org/assembly">http://aidenlab.org/assembly</a>.</p>
<p>For the original version of the pipeline and to reproduce the Hs2-HiC and the AaegL4 genomes reported in&nbsp;<a href="http://science.sciencemag.org/content/356/6333/92">(Dudchenko et al.,&nbsp;<em>Science</em>, 2017)</a>&nbsp;see the&nbsp;<a href="https://github.com/theaidenlab/3d-dna/tree/745779bdf64db6e55bddb70c24e9b58825938c33">original commit</a>.</p>
<p>For the detailed description of the merge section see&nbsp;<a href="https://github.com/theaidenlab/AGWG-merge">https://github.com/theaidenlab/AGWG-merge</a>.</p><p>Address of the bookmark: <a href="https://github.com/theaidenlab/3d-dna" rel="nofollow">https://github.com/theaidenlab/3d-dna</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41501/hicanu-accurate-assembly-of-segmental-duplications-satellites-and-allelic-variants-from-high-fidelity-long-reads</guid>
	<pubDate>Fri, 27 Mar 2020 22:49:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41501/hicanu-accurate-assembly-of-segmental-duplications-satellites-and-allelic-variants-from-high-fidelity-long-reads</link>
	<title><![CDATA[HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads]]></title>
	<description><![CDATA[<p><span>HiCanu, a significant modification of the Canu assembler designed to leverage the full potential of HiFi reads via homopolymer compression, overlap-based error correction, and aggressive false overlap filtering.&nbsp;</span></p>
<p>More at&nbsp;<a href="https://www.biorxiv.org/content/10.1101/2020.03.14.992248v3?fbclid=IwAR2PaN4GLjvAZpWmCE2q0EWk2dtwY7wiKxVlXn9PPG7OBSP06PP2gcCrv3A">https://www.biorxiv.org/content/10.1101/2020.03.14.992248v3</a></p><p>Address of the bookmark: <a href="https://github.com/marbl/canu" rel="nofollow">https://github.com/marbl/canu</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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