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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36478?offset=290</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/13267/the-genome-10k-project</guid>
	<pubDate>Tue, 29 Jul 2014 09:11:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/13267/the-genome-10k-project</link>
	<title><![CDATA[The Genome 10K Project]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/B57xDIGtCT0" frameborder="0" allowfullscreen></iframe>https://genome10k.soe.ucsc.edu

The Genome 10K project aims to assemble a genomic zoo—a collection of DNA sequences representing the genomes of 10,000 vertebrate species, approximately one for every vertebrate genus. The trajectory of cost reduction in DNA sequencing suggests that this project will be feasible within a few years. Capturing the genetic diversity of vertebrate species would create an unprecedented resource for the life sciences and for worldwide conservation efforts.

The growing Genome 10K Community of Scientists (G10KCOS), made up of leading scientists representing major zoos, museums, research centers, and universities around the world, is dedicated to coordinating efforts in tissue specimen collection that will lay the groundwork for a large-scale sequencing and analysis project.]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26322/liftover</guid>
	<pubDate>Mon, 08 Feb 2016 15:45:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26322/liftover</link>
	<title><![CDATA[liftover]]></title>
	<description><![CDATA[<p><span>Convenient conversions between genome assemblie.&nbsp;The liftover package makes it easy to remap genomic coordinates to a different genome assembly. </span></p>
<p><span>More at https://github.com/aaronwolen/liftover<br></span></p>
<p><span>https://www.bioconductor.org/help/workflows/liftOver/</span></p><p>Address of the bookmark: <a href="https://github.com/aaronwolen/liftover" rel="nofollow">https://github.com/aaronwolen/liftover</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26752/rna-seq-de-novo-assembly-using-trinity</guid>
	<pubDate>Wed, 23 Mar 2016 05:53:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26752/rna-seq-de-novo-assembly-using-trinity</link>
	<title><![CDATA[RNA-Seq De novo Assembly Using Trinity]]></title>
	<description><![CDATA[<p>Trinity, developed at the <a href="http://www.broadinstitute.org">Broad Institute</a> and the <a href="http://www.cs.huji.ac.il">Hebrew University of Jerusalem</a>, represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes. Briefly, the process works like so:</p>
<ul>
<li>
<p><em>Inchworm</em> assembles the RNA-seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts.</p>
</li>
<li>
<p><em>Chrysalis</em> clusters the Inchworm contigs into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs.</p>
</li>
<li>
<p><em>Butterfly</em> then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes.</p>
</li>
</ul>
<p>More at https://github.com/trinityrnaseq/trinityrnaseq/wiki</p>
<p>......................................................................................................................................</p>
<p>Download Trinity <a href="https://github.com/trinityrnaseq/trinityrnaseq/releases">here</a>.</p>
<p>Build Trinity by typing 'make' in the base installation directory.</p>
<p>Assemble RNA-Seq data like so:</p>
<pre><code> Trinity --seqType fq --left reads_1.fq --right reads_2.fq --CPU 6 --max_memory 20G 
</code></pre>
<p>Find assembled transcripts as: 'trinity_out_dir/Trinity.fasta'</p><p>Address of the bookmark: <a href="https://github.com/trinityrnaseq/trinityrnaseq/wiki" rel="nofollow">https://github.com/trinityrnaseq/trinityrnaseq/wiki</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26911/raca-reference-assisted-chromosome-assembly</guid>
	<pubDate>Wed, 06 Apr 2016 09:29:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26911/raca-reference-assisted-chromosome-assembly</link>
	<title><![CDATA[RACA: Reference-Assisted Chromosome Assembly]]></title>
	<description><![CDATA[<p>Rreference-Assisted Chromosome Assembly (RACA), an algorithm to reliably order and orient sequence scaffolds generated by NGS and assemblers into longer chromosomal fragments using comparative genome information and paired-end reads.</p>
<p>http://www.ncbi.nlm.nih.gov/pubmed/23307812</p>
<p>http://bioen-compbio.bioen.illinois.edu/RACA/</p><p>Address of the bookmark: <a href="http://bioen-compbio.bioen.illinois.edu/RACA/" rel="nofollow">http://bioen-compbio.bioen.illinois.edu/RACA/</a></p>]]></description>
	<dc:creator>Priya Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26999/discovar</guid>
	<pubDate>Mon, 18 Apr 2016 11:59:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26999/discovar</link>
	<title><![CDATA[DISCOVAR]]></title>
	<description><![CDATA[<p><strong>DISCOVAR</strong> is a new variant caller and <strong>DISCOVAR <em>de novo</em></strong> a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or longer &mdash; produced on MiSeq or HiSeq 2500 &mdash; and from a single PCR-free library. These data enable a level of completeness and continuity that was not previously possible.</p>
<p><strong>DISCOVAR</strong> can call variants on a region by region basis, potentially tiling an entire large genome. DISCOVAR variant calling is under active development and transitioning to VCF.</p>
<p><strong>DISCOVAR <em>de novo</em></strong> can generate <em>de novo</em> assemblies for both large and small genomes. It currently does not call variants.</p>
<p>More at https://www.broadinstitute.org/software/discovar/blog/?page_id=14</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/software/discovar/blog/" rel="nofollow">https://www.broadinstitute.org/software/discovar/blog/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28805/bambus</guid>
	<pubDate>Tue, 16 Aug 2016 08:09:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28805/bambus</link>
	<title><![CDATA[Bambus]]></title>
	<description><![CDATA[<div>
<div>
<div>
<p>Bambus 2.0, the second generation Bambus scaffolder available as an open source package. While most other scaffolders are closely tied to a specific assembly program, Bambus accepts the output from most current assemblers and provides the user with great flexibility in choosing the scaffolding parameters. In particular, Bambus is able to accept contig linking data other than specified by mate-pairs. Such sources of information include alignment to a reference genome (Bambus can directly use the output of MUMmer), physical mapping data, or information about gene synteny.</p>
</div>
</div>
</div>
<div>
<div>Home Page:&nbsp;</div>
<div>
<div><a href="http://sourceforge.net/apps/mediawiki/amos/index.php?title=Bambus2">http://sourceforge.net/apps/mediawiki/amos/index.php?title=Bambus2</a></div>
</div>
</div><p>Address of the bookmark: <a href="https://www.cbcb.umd.edu/software/bambus2" rel="nofollow">https://www.cbcb.umd.edu/software/bambus2</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32076/ngs-teaching-material</guid>
	<pubDate>Wed, 05 Apr 2017 04:29:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32076/ngs-teaching-material</link>
	<title><![CDATA[NGS teaching material]]></title>
	<description><![CDATA[<p><span>High throughput sequencing (HTS) technologies are being applied to a wide range of important topics in biology. However, the analyses of non-model organisms, for which little previous sequence information is available, pose specific problems. This course addresses the specific strengths and weaknesses of alternative HTS technologies, the computational resources needed for HTS, and how to analyze non-model species using HTS. The course consists of a practical training module, HTS bioinformatics training, and lecturing/seminars of HTS approaches specifically targeting non-model organisms.</span></p><p>Address of the bookmark: <a href="http://marinetics.org/teaching/hts/Assembly.html" rel="nofollow">http://marinetics.org/teaching/hts/Assembly.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34720/meraculous-haplotype-sensitive-assembly-of-highly-heterozygous-genomes</guid>
	<pubDate>Wed, 20 Dec 2017 18:59:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34720/meraculous-haplotype-sensitive-assembly-of-highly-heterozygous-genomes</link>
	<title><![CDATA[Meraculous: Haplotype-sensitive Assembly of Highly Heterozygous genomes.]]></title>
	<description><![CDATA[<p><span>Meraculous is a whole genome assembler for Next Generation Sequencing data geared for large genomes. It is a hybrid k-mer/read-based assembler that capitalizes on the high accuracy of Illumina sequence by eschewing an explicit error correction step which we argue to be redundant with the assembly process. Meraculous achieves high performance with large datasets by utilizing lightweight data structures and multi-threaded parallelization, allowing to assemble human-sized genomes on commodity clusters in under a day. The process pipeline implements a highly transparent and portable model of job control and monitoring where different assembly stages can be executed and re-executed separately or in unison on a wide variety of architectures.</span></p>
<p><span>https://jgi.doe.gov/data-and-tools/meraculous/</span></p>
<p><span>https://arxiv.org/ftp/arxiv/papers/1703/1703.09852.pdf</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/meraculous20/" rel="nofollow">https://sourceforge.net/projects/meraculous20/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36514/evidentialgene-tr2aacds-mrna-transcript-assembly-software</guid>
	<pubDate>Tue, 08 May 2018 04:39:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36514/evidentialgene-tr2aacds-mrna-transcript-assembly-software</link>
	<title><![CDATA[EvidentialGene: tr2aacds, mRNA Transcript Assembly Software]]></title>
	<description><![CDATA[<p><span>EvidentialGene is a genome informatics project, "Evidence Directed Gene Construction for Eukaryotes", to construct high quality, accurate gene sets for animals and plants, developed by Don Gilbert at Indiana University, see</span><br><a href="http://arthropods.eugenes.org/EvidentialGene/" target="_blank">http://arthropods.eugenes.org/EvidentialGene/<span></span></a><br><br><span>Construction refers to the combination of classical gene prediction, and more recent gene assembly (de-novo and genome-assisted) methods. The basic Evigene methods involve using available best-of-breed gene prediction and assembly software, combining all evidence for genes, from expressed sequences, genome assembly sequences, related species protein sequences, and any other, to annotate and score gene constructions. Over-produced constructions are classified by gene evidence for best qualities per "locus", including genome-aligned and gene-transcript aligned (genome-free) locus identification. All software developed for EvidentialGene is publicly available. See project wiki/blog for notes.</span></p>
<p><span>Download&nbsp;</span></p>
<p>http://arthropods.eugenes.org/EvidentialGene/trassembly.html</p>
<p>https://sourceforge.net/p/evidentialgene/blog/</p><p>Address of the bookmark: <a href="http://arthropods.eugenes.org/EvidentialGene/trassembly.html" rel="nofollow">http://arthropods.eugenes.org/EvidentialGene/trassembly.html</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41452/apollo-a-sequencing-technology-independent-scalable-and-accurate-assembly-polishing-algorithm</guid>
	<pubDate>Mon, 16 Mar 2020 10:09:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41452/apollo-a-sequencing-technology-independent-scalable-and-accurate-assembly-polishing-algorithm</link>
	<title><![CDATA[Apollo: A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm]]></title>
	<description><![CDATA[<p><span>Apollo is an assembly polishing algorithm that attempts to correct the errors in an assembly. It can take multiple set of reads in a single run and polish the assemblies of genomes of any size. Described by Firtina et al. (preliminary version at&nbsp;</span><a href="https://arxiv.org/pdf/1902.04341.pdf">https://arxiv.org/pdf/1902.04341.pdf</a></p>
<p>More at&nbsp;<a href="https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa179/5804978?rss=1">https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa179/5804978?rss=1</a></p><p>Address of the bookmark: <a href="https://github.com/CMU-SAFARI/Apollo" rel="nofollow">https://github.com/CMU-SAFARI/Apollo</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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