<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44887?offset=60</link>
	<atom:link href="https://bioinformaticsonline.com/related/44887?offset=60" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33942/mulan-multiple-sequence-local-alignment-and-conservation-visualization-tool</guid>
	<pubDate>Thu, 20 Jul 2017 08:02:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33942/mulan-multiple-sequence-local-alignment-and-conservation-visualization-tool</link>
	<title><![CDATA[Mulan: MUltiple sequence Local AligNment and conservation visualization tool]]></title>
	<description><![CDATA[<p><span>Mulan performs multiple (2 or more) sequence alignments with an efficient and rapid "full local" alignment strategy that ensures a recapitulation of evolutionary sequence rearrangements (such as inversions and reshuffling) in any of the species. It combines&nbsp;</span><a href="http://www.bx.psu.edu/miller_lab/" target="_new"><em>refine</em>&nbsp;and&nbsp;<em>tba</em></a><span>&nbsp;tools to align either "draft" or "finished" quality sequences. Mulan provides a dynamic graphical interface to align and visualize conservation profiles for evolutionarily distant and closely related species.</span><br><span></span></p>
<p><span>Input formats, automated data upload from the&nbsp;</span><a href="http://genome.ucsc.edu/" target="_new">UCSC Genome Browser</a><span>, gene annotation, annotation of repetitive elements, and progress report were previously described in the&nbsp;</span><a href="https://zpicture.dcode.org/zpInstructions.html" target="_zp">zPicture instructions</a><span>&nbsp;and we refer the users to these materials for more details. This introduction is mainly focused on some novel features unique to the Mulan.</span><span><br></span></p><p>Address of the bookmark: <a href="https://mulan.dcode.org/mulanInstructions.php" rel="nofollow">https://mulan.dcode.org/mulanInstructions.php</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37987/ropebwt2-incremental-construction-of-fm-index-for-dna-sequences</guid>
	<pubDate>Thu, 25 Oct 2018 04:48:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37987/ropebwt2-incremental-construction-of-fm-index-for-dna-sequences</link>
	<title><![CDATA[RopeBWT2: Incremental construction of FM-index for DNA sequences]]></title>
	<description><![CDATA[<p><span>RopeBWT2 is an tool for constructing the FM-index for a collection of DNA sequences. It works by incrementally inserting one or multiple sequences into an existing pseudo-BWT position by position, starting from the end of the sequences. This algorithm can be largely considered a mixture of&nbsp;</span><a href="http://dx.doi.org/10.1007/978-3-642-21458-5_20">BCR</a><span>&nbsp;and&nbsp;</span><a href="http://dfmi.sourceforge.net/">dynamic FM-index</a><span>. Nonetheless, ropeBWT2 is unique in that it may&nbsp;</span><em>implicitly</em><span>sort the input into reverse lexicographical order (RLO) or reverse-complement lexicographical order (RCLO) while building the index.</span></p><p>Address of the bookmark: <a href="https://github.com/lh3/ropebwt2" rel="nofollow">https://github.com/lh3/ropebwt2</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29583/graph-genome-suite</guid>
	<pubDate>Fri, 28 Oct 2016 07:59:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29583/graph-genome-suite</link>
	<title><![CDATA[Graph Genome Suite]]></title>
	<description><![CDATA[<p><span>Seven Bridges is the biomedical data analysis company accelerating breakthroughs in genomics research for cancer, drug development and precision medicine. We build self-improving systems to analyze millions of genomes, including the&nbsp;</span><strong>Graph Genome Suite</strong><span>&nbsp;&mdash; the most advanced population genomics tools in the world.</span></p><p>Address of the bookmark: <a href="https://www.sbgenomics.com/graph/" rel="nofollow">https://www.sbgenomics.com/graph/</a></p>]]></description>
	<dc:creator>Jit</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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40713/glia-a-graphsmith-waterman-partial-order-alignerrealigner</guid>
	<pubDate>Tue, 28 Jan 2020 04:02:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40713/glia-a-graphsmith-waterman-partial-order-alignerrealigner</link>
	<title><![CDATA[Glia: a Graph/Smith-Waterman (partial order) aligner/realigner]]></title>
	<description><![CDATA[<p><span>glia's main use is as a local realigner. It will realign reads to a set of known (or putative) variants in a VCF, both consuming and producing an ordered stream of BAM alignments.&nbsp;</span></p>
<p><span>More at&nbsp;<a href="https://github.com/ekg/glia">https://github.com/ekg/glia</a></span></p>
<pre><code>glia -f ~/human_g1k_v37.fasta -t 20:62900077-62902077 -v variants.vcf.gz \
     -s AAATGTAAACATTTTATAGGGGATTCCCCTAAAAACAAAAAAACTTTCTGGGAAAGATTTTTCAAAAAATAAAA</code></pre><p>Address of the bookmark: <a href="https://github.com/ekg/glia" rel="nofollow">https://github.com/ekg/glia</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37223/chopstitch-exon-annotation-and-splice-graph-construction-using-transcriptome-assembly-and-whole-genome-sequencing-data</guid>
	<pubDate>Tue, 03 Jul 2018 04:14:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37223/chopstitch-exon-annotation-and-splice-graph-construction-using-transcriptome-assembly-and-whole-genome-sequencing-data</link>
	<title><![CDATA[ChopStitch: exon annotation and splice graph construction using transcriptome assembly and whole genome sequencing data]]></title>
	<description><![CDATA[ChopStitch is a new method for finding putative exons and constructing splice graphs using an assembled transcriptome and whole genome shotgun sequencing (WGSS) data. ChopStitch identifies exon-exon boundaries in de novo assembled RNA-seq data with the help of a Bloom filter that represents the k-mer spectrum of WGSS reads. The algorithm also detects base substitutions in transcript sequences corresponding to sequencing or assembly errors, haplotype variations, or putative RNA editing events. The primary output of our tool is a FASTA file containing putative exons. Further, exon edges are interrogated for alternative exon-exon boundaries to detect transcript isoforms, which are reported as splice graphs in dot output format.<p>Address of the bookmark: <a href="https://github.com/bcgsc/ChopStitch" rel="nofollow">https://github.com/bcgsc/ChopStitch</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42806/graphunzip-phases-an-assembly-graph-using-hi-c-data-andor-long-reads</guid>
	<pubDate>Fri, 05 Feb 2021 21:22:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42806/graphunzip-phases-an-assembly-graph-using-hi-c-data-andor-long-reads</link>
	<title><![CDATA[GraphUnzip: Phases an assembly graph using Hi-C data and/or long reads.]]></title>
	<description><![CDATA[<p>GraphUnzip, a fast, memory-efficient and accurate tool to unzip assembly graphs into their constituent haplotypes using long reads and/or Hi-C data. As GraphUnzip only connects sequences in the assembly graph that already had a potential link based on overlaps, it yields high-quality gap-less supercontigs. To demonstrate the efficiency of GraphUnzip, we tested it on a simulated diploid Escherichia coli genome, and on two real datasets for the genomes of the rotifer Adineta vaga and the potato Solanum tuberosum. In all cases, GraphUnzip yielded highly continuous phased assemblies.</p>
<p>https://www.biorxiv.org/content/biorxiv/early/2021/02/01/2021.01.29.428779.full.pdf</p><p>Address of the bookmark: <a href="https://github.com/nadegeguiglielmoni/GraphUnzip" rel="nofollow">https://github.com/nadegeguiglielmoni/GraphUnzip</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44889/gfaffix-identifies-walk-preserving-shared-affixes-in-variation-graphs-and-collapses-them-into-a-non-redundant-graph-structure</guid>
	<pubDate>Thu, 28 Aug 2025 03:11:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44889/gfaffix-identifies-walk-preserving-shared-affixes-in-variation-graphs-and-collapses-them-into-a-non-redundant-graph-structure</link>
	<title><![CDATA[GFAffix : Identifies walk-preserving shared affixes in variation graphs and collapses them into a non-redundant graph structure.]]></title>
	<description><![CDATA[<p><span>GFAffix identifies walk-preserving shared affixes in variation graphs and collapses them into a non-redundant graph structure.</span></p>
<p>&nbsp;</p>
<p><span><img src="https://github.com/codialab/GFAffix/raw/main/doc/gfaffix-illustration.png?raw=true" alt="image" style="border: 0px; border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/codialab/GFAffix" rel="nofollow">https://github.com/codialab/GFAffix</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36884/halc-high-throughput-algorithm-for-long-read-error-correction</guid>
	<pubDate>Fri, 08 Jun 2018 10:47:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36884/halc-high-throughput-algorithm-for-long-read-error-correction</link>
	<title><![CDATA[HALC: High throughput algorithm for long read error correction]]></title>
	<description><![CDATA[HALC, a high throughput algorithm for long read error correction. HALC aligns the long reads to short read contigs from the same species with a relatively low identity requirement so that a long read region can be aligned to at least one contig region, including its true genome region’s repeats in the contigs sufficiently similar to it (similar repeat based alignment approach)

HALC was able to obtain 6.7-41.1% higher throughput than the existing algorithms while maintaining comparable accuracy. The HALC corrected long reads can thus result in 11.4-60.7% longer assembled contigs than the existing algorithms.<p>Address of the bookmark: <a href="https://github.com/lanl001/halc" rel="nofollow">https://github.com/lanl001/halc</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38445/orthoani-an-improved-algorithm-and-software-for-calculating-average-nucleotide-identity</guid>
	<pubDate>Wed, 12 Dec 2018 08:36:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38445/orthoani-an-improved-algorithm-and-software-for-calculating-average-nucleotide-identity</link>
	<title><![CDATA[OrthoANI: An improved algorithm and software for calculating average nucleotide identity]]></title>
	<description><![CDATA[<p><span>OAT uses OrthoANI to measure the overall similarity between two genome sequences. ANI and OrthoANI are comparable algorithms: they share the same species demarcation cut-off at 95~96% and large comparison studies have demonstrated both algorithms to produce near identical reciprocal similarities. Details of the OrthoANI algorithm is given in (Lee et al. 2015). OAT employs an easy-to-follow Graphical User Interface that allow researchers to calculate OrthoANI values between genomes of interest without unfamiliar Command Line Environments. Moreover, the OAT_cmd command-line software can be integrated into preexisting bioinformatics pipelines.&nbsp;</span></p><p>Address of the bookmark: <a href="https://www.ezbiocloud.net/tools/orthoani" rel="nofollow">https://www.ezbiocloud.net/tools/orthoani</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

</channel>
</rss>