<?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/44497?offset=30</link>
	<atom:link href="https://bioinformaticsonline.com/related/44497?offset=30" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads</guid>
	<pubDate>Wed, 22 Aug 2018 10:40:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads</link>
	<title><![CDATA[SimLoRD: A read simulator for third generation sequencing reads]]></title>
	<description><![CDATA[<p>SimLoRD is a read simulator for third generation sequencing reads and is currently focused on the Pacific Biosciences SMRT error model.</p>
<p>Reads are simulated from both strands of a provided or randomly generated reference sequence.</p>
<div id="rst-header-features">
<ul>
<li>The reference can be read from a FASTA file or randomly generated with a given GC content. It can consist of several chromosomes, whose structure is respected when drawing reads. (Simulation of genome rearrangements may be incorporated at a later stage.)</li>
<li>The read lengths can be determined in four ways: drawing from a log-normal distribution (typical for genomic DNA), sampling from an existing FASTQ file (typical for RNA), sampling from a a text file with integers (RNA), or using a fixed length</li>
<li>Quality values and number of passes depend on fragment length.</li>
<li>Provided subread error probabilities are modified according to number of passes</li>
<li>Outputs reads in FASTQ format and alignments in SAM format</li>
</ul>
</div><p>Address of the bookmark: <a href="https://bitbucket.org/genomeinformatics/simlord/" rel="nofollow">https://bitbucket.org/genomeinformatics/simlord/</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29343/accnet</guid>
	<pubDate>Fri, 07 Oct 2016 05:22:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29343/accnet</link>
	<title><![CDATA[AccNET]]></title>
	<description><![CDATA[<p><span>AccNET is a Perl application that presents a new way to study the accessory genome of a given set of organisms. Using the proteomes of these organisms, AccNET create a bipartite network compatible with common network analysis platforms. AccNET collects phylogenetic and functional information in a network improving the analysis capability. Networks offer a new perspective of organism organization through elements acquired by horizontal gene transfers and not constricted by hierarchical structures.</span></p>
<p><span>More at&nbsp;https://www.youtube.com/watch?v=vdGuy1GAJrQ</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/accnet/" rel="nofollow">https://sourceforge.net/projects/accnet/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38745/osprey-network-visualization-system</guid>
	<pubDate>Sun, 20 Jan 2019 05:34:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38745/osprey-network-visualization-system</link>
	<title><![CDATA[Osprey: Network Visualization System]]></title>
	<description><![CDATA[<p>Osprey is a software platform for the visualization of complex biological interaction networks. Osprey builds data-rich graphical representations from&nbsp;<a href="http://geneontology.org/" title="GENE ONTOLOGY CONSORTIUM">Gene Ontology (GO)</a>&nbsp;annotated interaction data maintained by the&nbsp;<a href="https://thebiogrid.org/" title="The BioGRID">BioGRID</a>.</p>
<p>Osprey is developed by the&nbsp;<a href="http://www.tyerslab.com/">TyersLab</a>&nbsp;and is a part of the&nbsp;<a href="https://thebiogrid.org/" title="The BioGRID">BioGRID</a>&nbsp;family of software. It utilizes both&nbsp;<a href="https://www.mysql.com/" title="MySQL Database">MySQL</a>&nbsp;and&nbsp;<a href="http://openjdk.java.net/" title="OpenJDK">Java</a>&nbsp;to operate and is compatible with&nbsp;<a href="https://www.microsoft.com/en-us/windows/" title="Microsoft Windows">Windows</a>,&nbsp;<a href="http://www.ubuntu.com/">Linux</a>, and&nbsp;<a href="http://www.apple.com/" title="Apple">Apple</a>&nbsp;operating systems.</p>
<p>These works were published in&nbsp;<strong>Breitkreutz, BJ., Stark, C., Tyers M. "Osprey: A Network Visualization System." Genome Biology 2003 4(3):R22</strong>&nbsp;<a href="http://genomebiology.com/2003/4/3/R22" title="Genome Biology">[Genome Biology]</a>&nbsp;<a href="http://genomebiology.com/content/pdf/gb-2003-4-3-r22.pdf" title="Osprey PDF">[PDF]</a>&nbsp;<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;list_uids=12620107&amp;dopt=Abstract" title="Pubmed">[PubMed]</a>&nbsp;and supported by the&nbsp;<a href="http://www.nih.gov/" title="NIH">National Institutes of Health</a>,&nbsp;<a href="http://www.cihr-irsc.gc.ca/" title="CIHR">Canadian Institutes of Health Research</a>, and&nbsp;<a href="http://www.genomecanada.ca/en/" title="Genome Canada">Genome Canada</a>.</p><p>Address of the bookmark: <a href="https://osprey.thebiogrid.org/" rel="nofollow">https://osprey.thebiogrid.org/</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35386/list-of-visualization-tools-for-network-biology</guid>
	<pubDate>Mon, 29 Jan 2018 05:12:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35386/list-of-visualization-tools-for-network-biology</link>
	<title><![CDATA[List of visualization tools for network biology]]></title>
	<description><![CDATA[<p>Network analysis&nbsp;is any structured technique used to mathematically analyze a circuit (a &ldquo;network&rdquo; of interconnected components). The&nbsp;<span>Network analysis provides the ability to quantify associations between individuals, which makes it possible to infer details about the network as a whole at the species and/or population level.&nbsp;</span>Few tools published in BMC are listed here https://bmcbioinformatics.biomedcentral.com/articles/sections/networks-analysis.</p><p><img src="https://www.dropbox.com/pri/get/Public/Link%20to%20network.gif?_subject_uid=85115969&amp;raw=1&amp;revision_id=BBqs9eYx7G_faj5J33ExdjmtF8nXK2xrN5dUBsKyTLZQ9RB_hGM-YFmWZMBzbQZfRvjYzfs65HbQYrHRyoikxsQscSFTn1Nud2QeJ8KGfVI5wv4Kzp6froKOmPZu8ZygfKo&amp;size=1280x960&amp;size_mode=3&amp;w=AABQaErsFIz5ZjVZSxXvKaSVUkY5ob1Yjk0x7dghy0X7zw" alt="image" style="border: 0px; border: 0px;"></p><p>Following are the list of standalone applications for network analysis:</p><p>Arena 3D</p><p>3D visualization of multi-layer networks</p><p>http://www.arena3d.org</p><p>Biana</p><p>Data integration and network management</p><p>http://sbi.imim.es/web/BIANA.php</p><p>BioLayout Express 3D&nbsp;</p><p>2D/3D network visualization</p><p>http://www.biolayout.org/</p><p>BiologicalNetworks&nbsp;</p><p>Efficient integrated multi-level analysis of microarray, sequence, regulatory and other data</p><p>http://www.biologicalnetworks.org</p><p>BioMiner</p><p>Modeling, analyzing and visualizing biochemical pathways and networks</p><p>http://www.zbi.uni-saarland.de/chair/projects/BioMiner</p><p>Cell Illustrator&nbsp;</p><p>Petri nets for modeling and simulating biological networks</p><p>http://www.cellillustrator.com</p><p>COPASI</p><p>Analysis of biochemical networks and their dynamics</p><p>http://www.copasi.org/</p><p>Cytoscape&nbsp;</p><p>Network visualization and analysis. Over 200 plugins [60]</p><p>http://www.cytoscape.org/</p><p>Dizzy</p><p>Chemical kinetics stochastic simulation software</p><p>http://magnet.systemsbiology.net/software/Dizzy/</p><p>DyCoNet</p><p>Gephi plugin that can be used to identify dynamic communities in networks</p><p>https://github.com/juliemkauffman/DyCoNet</p><p>GENeVis&nbsp;</p><p>Network and pathway visualization</p><p>http://tinyurl.com/genevis/</p><p>GEPHI&nbsp;</p><p>Interactive visualization and exploration for any network and complex system, dynamic and hierarchical graph.</p><p>https://gephi.org</p><p>Igraph</p><p>Collection of network analysis tools with the emphasis on efficiency, portability and ease of use</p><p>http://igraph.sourceforge.net</p><p>Medusa</p><p>Semantic and multi-edged simple networks</p><p>https://sites.google.com/site/medusa3visualization/</p><p>NAViGaTOR</p><p>Visualizing and analyzing protein-protein interaction networks</p><p>http://tinyurl.com/navigator1/</p><p>N-Browse</p><p>Interactive graphical browser for biological networks</p><p>http://www.gnetbrowse.org/</p><p>NeAT</p><p>Topological and clustering analysis of networks</p><p>http://rsat.ulb.ac.be/neat/</p><p>Ondex&nbsp;</p><p>Data integration and visualization of large networks</p><p>http://www.ondex.org/</p><p>Osprey</p><p>Visualization and annotation of biological networks</p><p>http://biodata.mshri.on.ca/osprey/servlet/Index</p><p>Pajek&nbsp;</p><p>Analysis and visualization of large networks and social network analysis</p><p>http://vlado.fmf.uni-lj.si/pub/networks/pajek/</p><p>PathwayAssist&nbsp;</p><p>Navigation and analysis of biological pathways, gene regulation networks and protein interaction maps.</p><p>http://www.ariadnegenomics.com/downloads/</p><p>PIVOT&nbsp;</p><p>Layout algorithms for visualizing protein interactions and families</p><p>http://acgt.cs.tau.ac.il/pivot/</p><p>ProCope&nbsp;</p><p>Prediction and evaluation of protein complexes from purification data experiments</p><p>http://www.bio.ifi.lmu.de/Complexes/ProCope/</p><p>ProViz&nbsp;</p><p>Visualization and exploration of interaction networks. Gene Ontology and PSI-MI formats supported</p><p>http://cbi.labri.fr/eng/proviz.htm</p><p>SpectralNET&nbsp;</p><p>Network analysis and visualizations. Scatter plots and dimensionality reduction algorithms</p><p>https://www.broadinstitute.org/software/spectralnet</p><p>Tulip&nbsp;</p><p>Enables the development of algorithms, visual encodings, interaction techniques, data models and domain-specific visualizations</p><p>http://tulip.labri.fr/TulipDrupal/</p><p>VANESA&nbsp;</p><p>Automatic reconstruction and analysis of biological networks and Petri nets based on life-science database information</p><p>http://agbi.techfak.uni-bielefeld.de/vanesa/</p><p>VANTED&nbsp;</p><p>Network reconstruction, data visualization, integration of various data types, network simulation</p><p>http://tinyurl.com/vanted/</p><p>yEd</p><p>Creation of diagrams manually and import external data</p><p>http://tinyurl.com/yEdGraph/</p><p>Web tools for network analysis</p><p>APID&nbsp;</p><p>Unified protein-protein interactions from BIND, BioGRID, DIP, HPRD, IntAct and MINT</p><p>http://bioinfow.dep.usal.es/apid/</p><p>Arcadia&nbsp;</p><p>Translates text-based descriptions of biological networks (SBML files) into standardized diagrams (Systems Biology Graphical Notation Process Description maps)</p><p>http://arcadiapathways.sourceforge.net/</p><p>AVIS&nbsp;</p><p>Viewer for signaling networks</p><p>http://actin.pharm.mssm.edu/AVIS2</p><p>bioPIXIE&nbsp;</p><p>Discovery of biological networks from diverse functional genomic data</p><p>http://pixie.princeton.edu/pixie</p><p>CellPublisher</p><p>Interactive representations of biochemical processes</p><p>http://cellpublisher.gobics.de/</p><p>Graphle</p><p>Distributed network exploration and visualization of interactive large, dense graphs</p><p>http://tinyurl.com/graphle/</p><p>GraphWeb&nbsp;</p><p>Web server for graph-based analysis of biological networks</p><p>http://biit.cs.ut.ee/graphweb/</p><p>Hubba</p><p>Web-based service to explore the essential nodes in a network</p><p>http://hub.iis.sinica.edu.tw/Hubba</p><p>NetworkBLAST&nbsp;</p><p>Analysis of protein interaction networks across species to infer protein complexes that are conserved in evolution</p><p>http://www.cs.tau.ac.il/~bnet/networkblast.htm</p><p>Pathview&nbsp;</p><p>Tool set for pathway-based data integration and visualization</p><p>http://Pathview.r-forge.r-project.org/</p><p>PINA&nbsp;</p><p>Integrated platform for protein interaction network construction, filtering, analysis, visualization and management</p><p>http://cbg.garvan.unsw.edu.au/pina/home.do</p><p>ReMatch&nbsp;</p><p>Web-based tool for integration of user-given stoichiometric metabolic models into a database collected from public data sources</p><p>http://www.cs.helsinki.fi/group/sysfys/software/rematch/</p><p>SNOW&nbsp;</p><p>Gene mapping on a reference or human protein-protein interaction network that SNOW hosts</p><p>http://snow.bioinfo.cipf.es</p><p>STITCH&nbsp;</p><p>Resource to explore known and predicted interactions of chemicals and proteins</p><p>http://stitch.embl.de/</p><p>STRING</p><p>Protein interaction networks and integration of data such as genomic context, high-throughput experiments, conserved coexpression and previous knowledge derived from the literature</p><p>http://string-db.org</p><p>TVNViewer&nbsp;</p><p>An interactive visualization tool for exploring networks that change over time or space</p><p>http://www.sailing.cs.cmu.edu/main/?page_id=545</p><p>tYNA&nbsp;</p><p>System for managing, comparing and mining multiple networks</p><p>http://tyna.gersteinlab.org/tyna/</p><p>VisANT&nbsp;</p><p>Visualization, mining, analysis and modeling of biological networks, metabolic networks and ecosystems</p><p>http://visant.bu.edu/</p><p>&nbsp;</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39728/patterns-a-modeling-tool-dedicated-to-biological-network-modeling</guid>
	<pubDate>Fri, 26 Jul 2019 01:11:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39728/patterns-a-modeling-tool-dedicated-to-biological-network-modeling</link>
	<title><![CDATA[Patterns: a modeling tool dedicated to biological network modeling]]></title>
	<description><![CDATA[<p>It is designed to work with <strong>patterned data</strong>. Famous examples of problems related to patterned data are:</p>
<ul>
<li>recovering <strong>signals</strong> in networks after a <strong>stimulation</strong> (cascade network reverse engineering),</li>
<li>analysing <strong>periodic signals</strong>.</li>
</ul><p>Address of the bookmark: <a href="https://github.com/fbertran/Patterns" rel="nofollow">https://github.com/fbertran/Patterns</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/41804/useful-links-to-therapy-disease-drug-and-drug-target-network-data</guid>
	<pubDate>Mon, 01 Jun 2020 11:47:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/41804/useful-links-to-therapy-disease-drug-and-drug-target-network-data</link>
	<title><![CDATA[Useful links to therapy, disease, drug and drug-target network data:]]></title>
	<description><![CDATA[<p>Useful links to therapy, disease, drug and drug-target network data:</p><p><strong>DrugBank:</strong></p><p>a bioinformatics- cheminformatics resource combining detailed drug data with comprehensive drug target information with &gt;4900 drug (~3500 experimental) and &gt;1500 non-redundant protein entries http://www.drugbank.ca/</p><p><strong>Drug-Target Network:</strong></p><p>network data of 890 drugs and 394 target human proteins http://www.nature.com/nbt/journal/v25/ n10/suppinfo/nbt1338_S1.html</p><p><strong>Drug-Therapy Network:</strong></p><p>three layers of drug-therapy networks according to the ATC classification http://www.biomedcentral.com/1471-2210/8/5/additional/</p><p><strong>FDA Orange Book:</strong></p><p>approved drug products with therapeutic equivalence evaluations http://www.fda.gov/cder/ob/HIDdb: Thomson Investigational drugs database including information on 107000 patents, 25000 investigational drugs and 80000 chemical structures http://scientific.thomson.com/products/iddb/HOMIM: a knowledgebase of human genes and genetic disorders http://www.ncbi.nlm.nih.gov/ sites/entrez?db=omim</p><p><strong>PDTD:</strong></p><p>3D drug target structure database with a target identification option http://www.dddc.ac.cn/pdtd/</p><p><strong>Predicted drug targets:</strong></p><p>a set of 1383 predicted drug targets http://www.biomedcentral.com/1471-2105/8/353/additional/ [25] Protein ligand network: a network of 4208 ligands and ~15000 binding sites http://pbil.kaist.ac.kr/~parkkw/Lnet/</p><p><strong>TDR Targets Database:</strong></p><p>identification and ranking targets against neglected tropical diseases http://tdrtargets.org/</p><p><strong>Therapeutic Target Database:</strong></p><p>lists &gt;1500 therapeutic targets, disease conditions and corresponding drugs http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31714/krona</guid>
	<pubDate>Wed, 22 Mar 2017 04:47:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31714/krona</link>
	<title><![CDATA[Krona]]></title>
	<description><![CDATA[<p>Krona allows hierarchical data to be explored with zooming, multi-layered pie charts. Krona charts can be created using an <a href="https://github.com/marbl/Krona/wiki/ExcelTemplate">Excel template</a> or <a href="https://github.com/marbl/Krona/wiki/KronaTools">KronaTools</a>, which includes support for several bioinformatics tools and raw data formats. The interactive charts are self-contained and can be viewed with any modern web browser (see <a href="https://github.com/marbl/Krona/wiki/Browser%20support">Browser support</a>).</p>
<p><a href="http://marbl.github.io/Krona/img/screen_mgrast.png"><img src="https://camo.githubusercontent.com/27b71b1f1832523723c3d14dec764e7ad098438c/687474703a2f2f6d6172626c2e6769746875622e696f2f4b726f6e612f696d672f7468756d625f6d67726173742e706e67" width="210" height="167" alt="image" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/marbl/Krona/wiki" rel="nofollow">https://github.com/marbl/Krona/wiki</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40703/%CF%80-cyc-a-reference-free-snp-discovery-application-using-parallel-graph-search</guid>
	<pubDate>Tue, 28 Jan 2020 03:34:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40703/%CF%80-cyc-a-reference-free-snp-discovery-application-using-parallel-graph-search</link>
	<title><![CDATA[Π-cyc: A Reference-free SNP Discovery Application using Parallel Graph Search]]></title>
	<description><![CDATA[<p>Reference free SNP search for comparative population genomics: multiple samples run simultanously. **experimental phase, compiles and runs with OpenMPI-1.8.8 with Intel Compiler only</p>
<p><span>Cycles enumeration (aka Bubbles) as part of de novo de bruijn graphs assembly using colours can be unpractical for large error prone genomes which makes the assembly process produce an excessive number of false positive cycles.&nbsp; Our solution is to search the graph in multicores shared memory parallel mode using graph decomposition then use filtering method to generate good quality SNPs.</span></p>
<p><a href="https://arxiv.org/abs/1809.06700">https://arxiv.org/abs/1809.06700</a></p>
<p><a href="https://github.com/redayounsi/2KP2P">https://github.com/redayounsi/2KP2P</a></p>
<blockquote>
<p>/2kp2omp/bin/main_2kp2_K63_C2 -i fastq_files.txt -o fungus_bub.fasta -r stat_fungus.txt -c cov_fungus_hash.txt -k 63 -h 20 -b 100 -g 600 -l 100 -f 16 -t 5.0 -x 1 -v 0 -p 1 -y 1 -u 1</p>
<p>&nbsp;</p>
</blockquote><p>Address of the bookmark: <a href="https://github.com/redayounsi/2KP2P" rel="nofollow">https://github.com/redayounsi/2KP2P</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/40711/vg-variation-graph-data-structures-interchange-formats-alignment-genotyping-and-variant-calling-methods</guid>
	<pubDate>Tue, 28 Jan 2020 03:53:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40711/vg-variation-graph-data-structures-interchange-formats-alignment-genotyping-and-variant-calling-methods</link>
	<title><![CDATA[VG: variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods]]></title>
	<description><![CDATA[<p><em>Variation graphs</em>&nbsp;provide a succinct encoding of the sequences of many genomes. A variation graph (in particular as implemented in vg) is composed of:</p>
<ul>
<li><em>nodes</em>, which are labeled by sequences and ids</li>
<li><em>edges</em>, which connect two nodes via either of their respective ends</li>
<li><em>paths</em>, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges</li>
</ul><p>Address of the bookmark: <a href="https://github.com/vgteam/vg" rel="nofollow">https://github.com/vgteam/vg</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>