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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37223?</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43736/odgi-optimized-dynamic-genomegraph-implementation</guid>
	<pubDate>Tue, 01 Feb 2022 23:42:21 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43736/odgi-optimized-dynamic-genomegraph-implementation</link>
	<title><![CDATA[odgi: optimized dynamic genome/graph implementation]]></title>
	<description><![CDATA[<p dir="auto"><code>odgi</code>&nbsp;provides an efficient and succinct dynamic DNA sequence graph model, as well as a host of algorithms that allow the use of such graphs in bioinformatic analyses.</p>
<p dir="auto">Careful encoding of graph entities allows&nbsp;<code>odgi</code>&nbsp;to efficiently compute and transform&nbsp;<a href="https://pangenome.github.io/">pangenomes</a>&nbsp;with minimal overheads.&nbsp;<code>odgi</code>&nbsp;implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.</p>
<p dir="auto">The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.</p>
<p dir="auto">The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.</p><p>Address of the bookmark: <a href="https://github.com/pangenome/odgi" rel="nofollow">https://github.com/pangenome/odgi</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36594/fragscaff-genome-assembly-with-contiguity-preserving-transposition</guid>
	<pubDate>Mon, 14 May 2018 04:28:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36594/fragscaff-genome-assembly-with-contiguity-preserving-transposition</link>
	<title><![CDATA[fragScaff: Genome Assembly with Contiguity Preserving Transposition]]></title>
	<description><![CDATA[<p>Contiguity preserving transposition and sequencing (CPT-seq) is an entirely in vitro means of generating libraries comprised of 9216 indexed pools, each of which contains thousands of sparsely sequenced long fragments ranging from 5 kilobases to &gt;1 megabase. This software, fragScaff, leverages coincidences between the content of different pools as a source of contiguity information for scaffolding de novo genome assemblies. FragScaff is complementary to Lachesis, providing midrange contiguity to support robust, accurate chromosome-scale de novo genome assemblies without the need for laborious in vivo cloning steps.</p>
<p>Further information about fragScaff, including source code, is available at:<a href="https://sourceforge.net/projects/fragscaff/files/">https://sourceforge.net/projects/fragscaff/files</a>.</p>
<p>Manuscript describing fragScaff was published as: Adey A, Kitzman JO, Burton JN, Daza R, Kumar A, Christiansen L, Ronaghi M, Amini S, L Gunderson K, Steemers FJ, Shendure J#.&nbsp;<em>In vitro, long-range sequence information for de novo genome assembly via transposase contiguity.</em>&nbsp;Genome Research 2014 Dec;24(12):2041-9. doi:&nbsp;<a href="http://dx.doi.org/10.1101/gr.178319.114">10.1101/gr.178319.114</a>. PubMed PMID:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/25327137">25327137</a>.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/fragscaff/files/" rel="nofollow">https://sourceforge.net/projects/fragscaff/files/</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/bookmarks/view/36592/lachesis-genome-assembly-with-hi-c-based-contact-probability-maps-lachesis</guid>
	<pubDate>Mon, 14 May 2018 04:26:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36592/lachesis-genome-assembly-with-hi-c-based-contact-probability-maps-lachesis</link>
	<title><![CDATA[LACHESIS: Genome Assembly with Hi-C-based Contact Probability Maps (LACHESIS)]]></title>
	<description><![CDATA[<p>LACHESIS is method that exploits contact probability map data (e.g. from Hi-C) for chromosome-scale&nbsp;<em>de novo</em>&nbsp;genome assembly.</p>
<p>Further information about LACHESIS, including source code, documentation and a user's guide are available at:&nbsp;<a href="http://shendurelab.github.io/LACHESIS/">http://shendurelab.github.io/LACHESIS</a>.</p>
<p>Manuscript describing LACHESIS was published as: Burton JN#, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J#.&nbsp;<em>Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions.</em>&nbsp;Nature Biotechnology 2013 Dec;31(12):1119-25. doi:&nbsp;<a href="http://dx.doi.org/10.1038/nbt.2727">10.1038/nbt.272</a>. PubMed PMID:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/24185095">24185095</a>.</p>
<p>&nbsp;</p>
<p>http://shendurelab.github.io/LACHESIS/</p><p>Address of the bookmark: <a href="http://shendurelab.github.io/LACHESIS/" rel="nofollow">http://shendurelab.github.io/LACHESIS/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44503/entire-human-genome-sequencing</guid>
	<pubDate>Tue, 02 Apr 2024 01:19:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44503/entire-human-genome-sequencing</link>
	<title><![CDATA[Entire Human Genome Sequencing !]]></title>
	<description><![CDATA[<p>Cost-effective whole human genome sequencing has revolutionized the landscape of genetic research and personalized medicine by making comprehensive genetic analysis accessible to a wider population. Through advancements in sequencing technologies, such as next-generation sequencing (NGS), costs have significantly decreased, enabling researchers and healthcare providers to analyze an individual's complete genetic makeup with greater efficiency and affordability. This has profound implications for disease diagnosis, prognosis, and treatment, as it allows for the identification of genetic predispositions and the customization of healthcare interventions based on an individual's unique genetic profile. Moreover, as the cost continues to decline, the potential for population-scale genomic studies and large-scale screening programs becomes increasingly feasible, promising to further enhance our understanding of human genetics and improve healthcare outcomes on a global scale.</p><p>Here are few companies:</p><p>https://mynucleus.com/</p><p>https://myome.com/</p><p>https://nebula.org/whole-genome-sequencing-dna-test/</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26303/maker</guid>
	<pubDate>Sun, 07 Feb 2016 15:59:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26303/maker</link>
	<title><![CDATA[MAKER]]></title>
	<description><![CDATA[<p>MAKER is a portable and easily configurable genome annotation pipeline.Its purpose is to allow smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values.</p>
<p>More at http://www.yandell-lab.org/software/maker.html</p><p>Address of the bookmark: <a href="http://www.yandell-lab.org/software/maker.html" rel="nofollow">http://www.yandell-lab.org/software/maker.html</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32485/bacterial-genome-assembly</guid>
	<pubDate>Fri, 05 May 2017 06:11:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32485/bacterial-genome-assembly</link>
	<title><![CDATA[Bacterial genome assembly !!]]></title>
	<description><![CDATA[<p>This tutorial will serve as an example of how to use free and open-source genome assembly and secondary scaffolding tools to generate high quality assemblies of&nbsp;bacterial sequence data. The bacterial sample used in this tutorial will be referred&nbsp;to simply&nbsp;as &ldquo;Species&rdquo; since it is&nbsp;live data. This data is paired-end data, meaning that there are forward and reverse reads, which we will designate as Sample_R1.fastq and Sample_R2.fastq, respectively.</p>
<p>https://github.com/jennomics/WorkflowPaper/blob/master/Genome%20Assembly%20and%20Annotation.md</p><p>Address of the bookmark: <a href="http://bioinformatics.uconn.edu/bacterial-genome-assembly-tutorial/" rel="nofollow">http://bioinformatics.uconn.edu/bacterial-genome-assembly-tutorial/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34328/dfast-a-flexible-prokaryotic-genome-annotation-pipeline-for-faster-genome-publication</guid>
	<pubDate>Tue, 14 Nov 2017 10:26:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34328/dfast-a-flexible-prokaryotic-genome-annotation-pipeline-for-faster-genome-publication</link>
	<title><![CDATA[DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication]]></title>
	<description><![CDATA[<p>We developed a prokaryotic genome annotation pipeline, DFAST, that also supports genome submission to public sequence databases. DFAST was originally started as an on-line annotation server, and to date, over 7,000 jobs have been processed since its first launch in 2016. Here, we present a newly implemented background annotation engine for DFAST, which is also available as a standalone command-line program. The new engine can annotate a typical-sized bacterial genome within 10 minutes, with rich information such as pseudogenes, translation exceptions, and orthologous gene assignment between given reference genomes. In addition, the modular framework of DFAST allows users to customize the annotation workflow easily and will also facilitate extensions for new functions and incorporation of new tools in the future.</p>
<div>Availability and Implementation</div>
<p>The software is implemented in Python 3 and runs in both Python 2.7 and 3.4&ndash; on Macintosh and Linux systems. It is freely available at&nbsp;<a href="https://github.com/nigyta/dfast_core/" target="">https://github.com/nigyta/dfast_core/</a>&nbsp;under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at&nbsp;<a href="https://dfast.nig.ac.jp/" target="">https://dfast.nig.ac.jp/</a>.</p><p>Address of the bookmark: <a href="https://dfast.nig.ac.jp/" rel="nofollow">https://dfast.nig.ac.jp/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</guid>
	<pubDate>Mon, 04 May 2020 02:04:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</link>
	<title><![CDATA[HASLR: a hybrid assembler which uses both second and third generation sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples. Availability. HASLR is an open source tool available at https://github.com/vpc-ccg/haslr.</span></p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/haslr" rel="nofollow">https://github.com/vpc-ccg/haslr</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34925/rectangle-graph-for-repeat-resolution-in-genome-assembly</guid>
	<pubDate>Thu, 28 Dec 2017 09:43:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34925/rectangle-graph-for-repeat-resolution-in-genome-assembly</link>
	<title><![CDATA[Rectangle Graph for Repeat Resolution in Genome Assembly]]></title>
	<description><![CDATA[<p>Ultimate tool for resolving repeats in genome assemblies.</p>
<p>Though the specific implementation of the idea of the rectangle graph approach is already included into the&nbsp;<a href="http://bioinf.spbau.ru/spades">current SPAdes distribution</a>, we're also releasing the Rectangle Graph Module (RGM) as the separate code which can be run independently of SPAdes. Although RGM differs from the current implementation of the rectangle graph approach in SPAdes, in the future we plan to integrate RGM in SPAdes. RGM can be run with other genome assemblers if they use the graph format as SPAdes files.</p>
<p>For more details see: Nikolay Vyahhi, Son K. Pham, Pavel Pevzner.&nbsp;<a href="http://www.springerlink.com/content/e617788h25u36440/">From de Bruijn Graphs to Rectangle Graphs for Genome Assembly</a>,&nbsp;<em>Lecture Notes in Bioinformatics</em>&nbsp;7534 (2012), pp. 249-261.</p><p>Address of the bookmark: <a href="http://bioinf.spbau.ru/en/rectangles" rel="nofollow">http://bioinf.spbau.ru/en/rectangles</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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