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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38210?offset=340</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29995/hga</guid>
	<pubDate>Tue, 29 Nov 2016 07:25:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29995/hga</link>
	<title><![CDATA[HGA]]></title>
	<description><![CDATA[<p>HGA tool version 1.0 This tool helps to apply the Hierarchical Genome Assembly (HGA) method. The tool will apply: 1. Partitioning a given reads dataset into a given number of partitions. 2. Assembling each partitions using a pre-specified assembler (Velvet or SPAdes in this version) and using a given kmer size. 3. Merging all the assemblies of the partition. 4. Combining all the assemblies of the partition (using velvet with kmer value of 31). 5. Finaly, re-assembling the whole dataset with the merged contigs or the combined contigs, using a given kmer size.</p>
<p>https://github.com/aalokaily/Hierarchical-Genome-Assembly-HGA</p><p>Address of the bookmark: <a href="https://github.com/aalokaily/Hierarchical-Genome-Assembly-HGA" rel="nofollow">https://github.com/aalokaily/Hierarchical-Genome-Assembly-HGA</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/36476/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Fri, 04 May 2018 19:16:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36476/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 long and noisy reads, such as those produced by PacBio and Oxford Nanopore Technologies. The algorithm uses an A-Bruijn graph to find the overlaps between reads and does not require them to be error-corrected. After the initial assembly, Flye performs an extra repeat classification and analysis step to improve the structural accuracy of the resulting sequence. The package also includes a polisher module, which produces the final assembly of high nucleotide-level quality.</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>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36837/ranbow-a-haplotype-assembler-for-polyploid-genomes</guid>
	<pubDate>Fri, 01 Jun 2018 07:21:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36837/ranbow-a-haplotype-assembler-for-polyploid-genomes</link>
	<title><![CDATA[Ranbow: a haplotype assembler for polyploid genomes]]></title>
	<description><![CDATA[Ranbow is a haplotype assembler for polyploid genomes. It has been developed for the haplotype assembly of the hexaploid sweet potato genome, which is highly heterozygous. Ranbow can also be applied to other polyploid genomes. After a first phasing, Ranbow utilizes the assembled haplotypes to improve the accuracy of variant calling results and to infer the evolutionary history of the organism´s genome. Ranbow has three main modes of function:

ranbow hap: for haplotyping
ranbow eval: for evaluating of the assemble haplotypes by gold standard (long) reads 
ranbow phylo: for the phylogenetic analysis<p>Address of the bookmark: <a href="https://www.molgen.mpg.de/ranbow" rel="nofollow">https://www.molgen.mpg.de/ranbow</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44559/metagraph-ultra-scalable-framework-for-dna-search-alignment-assembly</guid>
	<pubDate>Sat, 08 Jun 2024 16:15:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44559/metagraph-ultra-scalable-framework-for-dna-search-alignment-assembly</link>
	<title><![CDATA[MetaGraph: Ultra Scalable Framework for DNA Search, Alignment, Assembly]]></title>
	<description><![CDATA[<p><span>The MetaGraph framework</span><span>&nbsp;is designed to work with a wide range of input data sets, indexing from a few samples up to the contents of entire archives with hundreds of thousands of records. The indexing workflow always follows the same principle, transforming single input samples into error-removed, refined sample graphs, which are then merged into a joint metagraph index. Each input sample is annotated in the joint index as a subgraph. This graph index enriched with metadata can then be used for downstream applications such as&nbsp;</span><a href="https://metagraph.ethz.ch/#query">sequence search</a><span>&nbsp;or&nbsp;</span><a href="https://metagraph.ethz.ch/#assembly">differential assembly</a><span>.</span></p>
<p><span>Searcg link&nbsp;https://metagraph.ethz.ch/search&nbsp;</span></p>
<p><span>Pre-print&nbsp;https://www.biorxiv.org/content/10.1101/2020.10.01.322164v4&nbsp;</span></p><p>Address of the bookmark: <a href="https://metagraph.ethz.ch/" rel="nofollow">https://metagraph.ethz.ch/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41814/gggenes-a-ggplot2-extension-for-drawing-gene-arrow-maps</guid>
	<pubDate>Tue, 02 Jun 2020 11:43:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41814/gggenes-a-ggplot2-extension-for-drawing-gene-arrow-maps</link>
	<title><![CDATA[gggenes: a ggplot2 extension for drawing gene arrow maps.]]></title>
	<description><![CDATA[<p>Install the stable version of gggenes from CRAN:</p>
<p><code><a href="https://www.rdocumentation.org/packages/utils/topics/install.packages">install.packages("gggenes")</a></code></p>
<p>If you want the development version, install it from GitHub:</p>
<p><code><a href="https://www.rdocumentation.org/packages/devtools/topics/reexports">devtools::install_github("wilkox/gggenes")</a></code></p>
<p>More at&nbsp;<a href="https://github.com/wilkox/gggenes">https://github.com/wilkox/gggenes</a></p><p>Address of the bookmark: <a href="http://wilkox.org/gggenes" rel="nofollow">http://wilkox.org/gggenes</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33960/mgra-breakpoint-graphs-and-ancestral-genome-reconstructions</guid>
	<pubDate>Tue, 25 Jul 2017 08:48:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33960/mgra-breakpoint-graphs-and-ancestral-genome-reconstructions</link>
	<title><![CDATA[MGRA: Breakpoint graphs and ancestral genome reconstructions]]></title>
	<description><![CDATA[<p>MGRA (Multiple Genome Rearrangements and Ancestors) is a tool for reconstruction of ancestor genomes and evolutionary history of extant genomes.</p>
<p>It takes as an input a set of genomes represented as sequences of genes (or synteny blocks) and produces such sequences for ancestral genomes at the internal nodes of the phylogenetic tree.</p>
<p>The phylogenetic tree may be also specified completely or partially, in the latter case MGRA can reconstruct conserved ancestral regions (CARs) of the ancestral genome of interest.</p>
<p>Since version 2 MGRA supports gene insertion and deletions in addition to genome rearrangements and allows the input genomes to have different gene content.</p>
<p>It also can reconstruct most plausible phylogenetic tree based on the rearrangement characters.</p><p>Address of the bookmark: <a href="http://mgra.cblab.org/" rel="nofollow">http://mgra.cblab.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34482/ribbon-visualizing-complex-genome-alignments-and-structural-variation</guid>
	<pubDate>Wed, 29 Nov 2017 07:40:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34482/ribbon-visualizing-complex-genome-alignments-and-structural-variation</link>
	<title><![CDATA[Ribbon: Visualizing complex genome alignments and structural variation:]]></title>
	<description><![CDATA[<p>Ribbon can be used for long reads, short reads, paired-end reads, and assembly/genome alignments. Instructions for each data format are available by clicking on "instructions" in each tab on the right.</p>
<p>Local installation:</p>
<p>You can install Ribbon locally from Github by following the instructions here:&nbsp;<a href="https://github.com/MariaNattestad/ribbon" target="_blank">https://github.com/MariaNattestad/Ribbon</a></p><p>Address of the bookmark: <a href="http://genomeribbon.com/" rel="nofollow">http://genomeribbon.com/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34571/mugsy-multiple-whole-genome-alignment-tool</guid>
	<pubDate>Fri, 08 Dec 2017 17:41:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34571/mugsy-multiple-whole-genome-alignment-tool</link>
	<title><![CDATA[Mugsy: multiple whole genome alignment tool]]></title>
	<description><![CDATA[<p><span>Mugsy is a multiple whole genome aligner. Mugsy uses Nucmer for pairwise alignment, a custom graph based segmentation procedure for identifying collinear regions, and the segment-based progressive multiple alignment strategy from Seqan::TCoffee. Mugsy accepts draft genomes in the form of multi-FASTA files and does not require a reference genome.</span></p>
<p>To cite Mugsy, use:</p>
<p>Angiuoli SV and Salzberg SL.&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/27/3/334">Mugsy: Fast multiple alignment of closely related whole genomes.</a><em>Bioinformatics</em>&nbsp;2011 27(3):334-4</p><p>Address of the bookmark: <a href="http://mugsy.sourceforge.net/" rel="nofollow">http://mugsy.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34867/magic-blast-a-tool-for-mapping-large-next-generation-rna-or-dna-sequencing-runs-against-a-whole-genome-or-transcriptome</guid>
	<pubDate>Tue, 26 Dec 2017 22:23:39 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34867/magic-blast-a-tool-for-mapping-large-next-generation-rna-or-dna-sequencing-runs-against-a-whole-genome-or-transcriptome</link>
	<title><![CDATA[Magic-BLAST: a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome.]]></title>
	<description><![CDATA[<p>Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read-pairing is ignored.</p>
<p>Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pubmed/26109056">(http://www.ncbi.nlm.nih.gov/pubmed/26109056)</a>, and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.</p><p>Address of the bookmark: <a href="https://ncbi.github.io/magicblast/" rel="nofollow">https://ncbi.github.io/magicblast/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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