<?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/40703?offset=10</link>
	<atom:link href="https://bioinformaticsonline.com/related/40703?offset=10" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35896/phylographer-graph-visualization-tool</guid>
	<pubDate>Wed, 07 Mar 2018 18:11:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35896/phylographer-graph-visualization-tool</link>
	<title><![CDATA[PhyloGrapher - Graph Visualization Tool]]></title>
	<description><![CDATA[<p><strong>PhyloGrapher</strong><span>&nbsp;is a program designed to visualize and study evolutionary relationships within families of homologous genes or proteins (elements).&nbsp;</span><strong>PhyloGrapher</strong><span>&nbsp;is a drawing tool that generates custom graphs for a given set of elements. In general, it is possible to use&nbsp;</span><strong>PhyloGrapher</strong><span>&nbsp;to visualize any type of relations between elements.&nbsp;</span></p>
<p><span>https://www.youtube.com/watch?v=WgufqYMHCvM</span></p><p>Address of the bookmark: <a href="http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html" rel="nofollow">http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/34707/string-graph-based-genome-assembly-software-and-tools</guid>
	<pubDate>Tue, 19 Dec 2017 17:17:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/34707/string-graph-based-genome-assembly-software-and-tools</link>
	<title><![CDATA[String graph based genome assembly software and tools !]]></title>
	<description><![CDATA[<p>In&nbsp;<a href="https://en.wikipedia.org/wiki/Graph_theory" title="Graph theory">graph theory</a>, a&nbsp;<strong>string graph</strong>&nbsp;is an&nbsp;<a href="https://en.wikipedia.org/wiki/Intersection_graph" title="Intersection graph">intersection graph</a>&nbsp;of&nbsp;<a href="https://en.wikipedia.org/wiki/Curve" title="Curve">curves</a>&nbsp;in the plane; each curve is called a "string".&nbsp; String graphs were first proposed by E. W. Myers in a&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/21/suppl_2/ii79.full.pdf+html">2005 publication</a>.&nbsp;In&nbsp;recent&nbsp;<a href="http://genome.cshlp.org/content/early/2012/01/22/gr.126953.111">Genome Research paper</a>&nbsp;describing an innovative approach for assembling large genomes from NGS data caught our attention for several reasons. i) it give different "string graph" prospective of long lasting genome assembly problem ii) the&nbsp;paper is coauthored by Jared Simpson, the developer of&nbsp;<a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694472/">ABySS assembler</a>&nbsp;and Richard Durbin. iii)&nbsp;Simpson-Durbin algorithm is that it does not rely on de Bruijn graphs, and instead employs a different graph construction approach called &lsquo;string graph&rsquo;.</p><p>Following are the genome assembly tools based on string graph:</p><p>1.SGA (String Graph Assembler)&nbsp;https://github.com/jts/sga</p><p>Assembles large genomes from high coverage short read data. SGA is designed as a modular set of programs, which are used to form an assembly pipeline. SGA implements a set of assembly algorithms based on the FM-index. As the FM-index is a compressed data structure, the algorithms are very memory efficient. The SGA assembly has three distinct phases. The first phase corrects base calling errors in the reads. The second phase assembles contigs from the corrected reads. The third phase uses paired end and/or mate pair data to build scaffolds from the contigs. The output of this software is a PDF report that allows the properties of the genome and data quality to be visually explored. By providing more information to the user at the start of an assembly project, this software will help increase awareness of the factors that make a given assembly easy or difficult, assist in the selection of software and parameters and help to troubleshoot an assembly if it runs into problems.</p><p>2.&nbsp;SAGE: String-overlap Assembly of GEnomes&nbsp;https://github.com/lucian-ilie/SAGE2</p><p>SAGE, for de novo genome assembly. As opposed to most assemblers, which are de Bruijn graph based, SAGE uses the string-overlap graph. SAGE builds upon great existing work on string-overlap graph and maximum likelihood assembly, bringing an important number of new ideas, such as the efficient computation of the transitive reduction of the string overlap graph, the use of (generalized) edge multiplicity statistics for more accurate estimation of read copy counts, and the improved use of mate pairs and min-cost flow for supporting edge merging. The assemblies produced by SAGE for several short and medium-size genomes compared favourably with those of existing leading assemblers.</p><p>3. FSG: Fast String Graph</p><p>The new integrated assembler has been assessed on a standard benchmark, showing that fast string graph (FSG) is significantly faster than SGA while maintaining a moderate use of main memory, and showing practical advantages in running FSG on multiple threads. Moreover, we have studied the effect of coverage rates on the running times.</p><p>4.&nbsp;&nbsp;BASE&nbsp;https://github.com/dhlbh/BASE</p><p>It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.&nbsp;BASE is a practically efficient tool for constructing contig, with significant improvement in quality for long NGS reads. It is relatively easy to extend BASE to include scaffolding.</p><p>5.&nbsp;Fermi&nbsp;https://github.com/lh3/fermi/</p><p>Fermi is a de novo assembler with a particular focus on assembling Illumina&nbsp;short sequence reads from a mammal-sized genome. In addition to the role of a&nbsp;typical assembler, fermi also aims to preserve heterozygotes which are often&nbsp;collapsed by other assemblers. Its ultimate goal is to find a minimal set of&nbsp;unitigs to represent all the information in raw reads.</p><p>If you want to learn about String Graph assembler, please read the following papers -</p><p>i)&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/21/suppl_2/ii79.full.pdf+html">The Fragment Assembly String Graph - E. W. Myers</a></p><p>This paper describes the String Graph concept.</p><p>ii)&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/26/12/i367.full#ref-20">Efficient construction of an assembly string graph using the FM-index - Jared T. Simpson and Richard Durbin</a></p><p>This earlier paper from Simpson and Durbin</p><p>iii)&nbsp;<a href="http://genome.cshlp.org/content/early/2012/01/22/gr.126953.111">Efficient de novo assembly of large genomes using compressed data structures - Jared T. Simpson and Richard Durbin</a></p><p>&nbsp;</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38892/wtdbg2-a-fuzzy-bruijn-graph-approach-to-long-noisy-reads-assembly</guid>
	<pubDate>Mon, 04 Feb 2019 04:53:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38892/wtdbg2-a-fuzzy-bruijn-graph-approach-to-long-noisy-reads-assembly</link>
	<title><![CDATA[wtdbg2: A fuzzy Bruijn graph approach to long noisy reads assembly]]></title>
	<description><![CDATA[<p><span>Wtdbg2 is a&nbsp;</span><em>de novo</em><span>&nbsp;sequence assembler for long noisy reads produced by PacBio or Oxford Nanopore Technologies (ONT). It assembles raw reads without error correction and then builds the consensus from intermediate assembly output.&nbsp;</span></p>
<pre>./wtdbg2 -x rs -g 4.6m -t 16 -i reads.fa.gz -fo prefix
./wtpoa-cns -t 16 -i prefix.ctg.lay.gz -fo prefix.ctg.fa</pre><p>Address of the bookmark: <a href="https://github.com/ruanjue/wtdbg2" rel="nofollow">https://github.com/ruanjue/wtdbg2</a></p>]]></description>
	<dc:creator>BioStar</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/43445/parebrick-parallel-rearrangements-and-breaks-identification-toolkit</guid>
	<pubDate>Fri, 08 Oct 2021 10:20:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43445/parebrick-parallel-rearrangements-and-breaks-identification-toolkit</link>
	<title><![CDATA[PaReBrick: PArallel REarrangements and BReaks identification toolkit]]></title>
	<description><![CDATA[<p><span>PaReBrick. The tool takes a collection of strains represented as a sequence of oriented synteny blocks and a phylogenetic tree as input data. It identifies rearrangements, tests them for consistency with a tree, and sorts the events by their parallelism score. The tool provides diagrams of the neighbors for each block of interest, allowing the detection of horizontally transferred blocks or their extra copies and the inversions in which copied blocks are involved.We demonstrated PaReBrick&rsquo;s efficiency and accuracy and showed its potential to detect genome rearrangements responsible for pathogenicity and adaptation in bacterial genomes</span></p>
<p>More at&nbsp;https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab691/6380551</p>
<p><img src="https://github.com/ctlab/parallel-rearrangements/raw/master/figs/pipeline.svg" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="https://github.com/ctlab/parallel-rearrangements" rel="nofollow">https://github.com/ctlab/parallel-rearrangements</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41875/virtualflow-a-versatile-parallel-workflow-platform-for-carrying-out-virtual-screening</guid>
	<pubDate>Sat, 13 Jun 2020 13:03:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41875/virtualflow-a-versatile-parallel-workflow-platform-for-carrying-out-virtual-screening</link>
	<title><![CDATA[VirtualFlow: a versatile, parallel workflow platform for carrying out virtual screening]]></title>
	<description><![CDATA[<p>&nbsp;related tasks on Linux-based computer clusters of any type and size which are managed by a batchsystem&nbsp;(such as SLURM).&nbsp;</p>
<p>Currently, there exist two versions of VirtualFlow, which are tailored to different types of tasks:</p>
<ul>
<li><strong><a href="https://virtual-flow.org/#vflp">VFLP: VirtualFlow for Ligand Preparation</a></strong></li>
<li><strong><a href="https://virtual-flow.org/#vfvs"><strong>VFVS</strong><strong>&nbsp;:</strong>&nbsp;VirtualFlow for Virtual Screenings</a></strong></li>
</ul>
<p>They use the same core technology regarding the workflow management and parallelization, and they can be used individually&nbsp;or in concert with each other.&nbsp;Additional versions are expected to arrive in the future.&nbsp;</p>
<p><a href="https://github.com/VirtualFlow">https://github.com/VirtualFlow</a></p>
<p><a href="https://www.nature.com/articles/s41586-020-2117-z">https://www.nature.com/articles/s41586-020-2117-z?</a></p><p>Address of the bookmark: <a href="https://virtual-flow.org/" rel="nofollow">https://virtual-flow.org/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41831/merqury-reference-free-quality-and-phasing-assessment-for-genome-assemblies</guid>
	<pubDate>Sat, 06 Jun 2020 05:38:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41831/merqury-reference-free-quality-and-phasing-assessment-for-genome-assemblies</link>
	<title><![CDATA[Merqury: reference-free quality and phasing assessment for genome assemblies]]></title>
	<description><![CDATA[<p><span>Often, genome assembly projects have illumina whole genome sequencing reads available for the assembled individual. The k-mer spectrum of this read set can be used for independently evaluating assembly quality without the need of a high quality reference. Merqury provides a set of tools for this purpose.</span></p>
<p><span><a href="https://github.com/marbl/meryl">https://github.com/marbl/meryl</a></span></p><p>Address of the bookmark: <a href="https://github.com/marbl/merqury" rel="nofollow">https://github.com/marbl/merqury</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/11735/search-shell-command-history</guid>
	<pubDate>Thu, 12 Jun 2014 17:43:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/11735/search-shell-command-history</link>
	<title><![CDATA[Search Shell Command History]]></title>
	<description><![CDATA[<p>We use couple of hundreads of command in daily basis. Most of them are actually repeated several time. The question remain open how do I search old command history under bash shell and modify or reuse it? <br /><br />Now a days almost all modern shell allows you to search command history if enabled by user. Use history command to display the history list with line numbers. Lines listed with with a * have been modified by user.</p><p><br /><strong>Shell history search command</strong><br /><br />Type history at a shell prompt:<br />$ history</p><p>It will display the list of all used commandline history with an serial number.<br /><br />To search particular command, enter:<br />$ history | grep command-name<br />$ history | egrep -i 'scp|ssh|ftp'<br />Emacs Line-Edit Mode Command History Searching<br /><br />To get previous command containing string, hit [CTRL]+[r] followed by search string:<br /><br />(reverse-i-search): <br /><br />To get previous command, hit [CTRL]+[p]. You can also use up arrow key.<br /><br />CTRL-p<br /><br />To get next command, hit [CTRL]+[n]. You can also use down arrow key.<br /><br />CTRL-n<br /><br /></p><p><strong>fc command</strong></p><p>Apart from hostory command there are fc command to extract the command from history. The fc stands for either "find command" or "fix command.</p><p>For example list last 10 command, enter:<br />$ fc -l 10<br />To list commands 130 through 150, enter:<br />$ fc -l 130 150<br />To list all commands since the last command beginning with ssh, enter:<br />$ fc -l ssh<br />You can edit commands 1 through 5 using vi text editor, enter:<br />$ fc -e vi 1 5</p><p><strong>Delete command history</strong><br /><br />The -c option causes the history list to be cleared by deleting all of the entries:<br />$ history -c</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/44370/ncbiblast-2141-now-available</guid>
	<pubDate>Wed, 30 Aug 2023 02:36:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/44370/ncbiblast-2141-now-available</link>
	<title><![CDATA[NCBIBLAST+ 2.14.1 now available]]></title>
	<description><![CDATA[<p><a href="https://www.linkedin.com/feed/hashtag/?keywords=ncbiblast&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7101231946264924160">#NCBIBLAST</a><span>+ 2.14.1 now available with improved documentation, faster and more reliable database downloads, and some bug fixes.&nbsp;</span></p><p>Check out the changes they made.</p><p>They added the&nbsp;<code><span>cleanup-blastdb-volumes.py</span></code>&nbsp;script to remove unused BLAST database volumes. Read the documentation&nbsp;<a href="https://www.ncbi.nlm.nih.gov/books/NBK592857/">here</a>.</p><p>They also switched the protocol from&nbsp;<code><span>ftp</span></code>&nbsp;to&nbsp;<code><span>https</span></code>&nbsp;to access BLAST databases for increased performance and reliability when downloading data from the NCBI with the&nbsp;<code><span>update_blastdb.pl</span></code>&nbsp;script.</p><p>And fixed a few bugs related to downloading data from the NCBI, and&nbsp;<code><span>mt_mode</span></code>&nbsp;crashing&nbsp;<code><span>blastn</span></code>&nbsp;and&nbsp;<code><span>blastx</span></code>.</p><p>Check out the&nbsp;<a href="https://www.ncbi.nlm.nih.gov/books/NBK131777/">release notes</a>.</p><p>Download&nbsp;<a href="https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/2.14.1/">BLAST+ 2.14.1</a></p><p>Questions or comments? Please write the&nbsp;<a href="https://support.nlm.nih.gov/support/create-case/">BLAST help desk</a>.</p><p><span><span>More info and download:</span>&nbsp;https://blast.ncbi.nlm.nih.gov/doc/blast-news/2023-BLAST-News.html</span></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35131/giggle-a-search-engine-for-large-scale-integrated-genome-analysis</guid>
	<pubDate>Wed, 10 Jan 2018 03:10:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35131/giggle-a-search-engine-for-large-scale-integrated-genome-analysis</link>
	<title><![CDATA[GIGGLE: a search engine for large-scale integrated genome analysis]]></title>
	<description><![CDATA[<p><span>GIGGLE is a genomics search engine that identifies and ranks the significance of genomic loci shared between query features and thousands of genome interval files. GIGGLE (</span><a href="https://github.com/ryanlayer/giggle">https://github.com/ryanlayer/giggle</a><span>) scales to billions of intervals and is over three orders of magnitude faster than existing methods. Its speed extends the accessibility and utility of resources such as ENCODE, Roadmap Epigenomics, and GTEx by facilitating data integration and hypothesis generation.</span></p>
<p>https://www.nature.com/articles/nmeth.4556</p><p>Address of the bookmark: <a href="https://github.com/ryanlayer/giggle" rel="nofollow">https://github.com/ryanlayer/giggle</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>