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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/28290?offset=310</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/40204/iitm-tokyo-tech-joint-symposium</guid>
	<pubDate>Thu, 24 Oct 2019 10:30:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/40204/iitm-tokyo-tech-joint-symposium</link>
	<title><![CDATA[IITM-Tokyo Tech Joint Symposium]]></title>
	<description><![CDATA[<p>The IITM-Tokyo Tech Joint Symposium is a biannual international symposium held in Indian Institute of Technology Madras (IITM), India in collaboration with Tokyo Institute of Technology (Tokyo-Tech), Japan. During the symposium, experts in various domains of Bioinformatics gather from India and Japan under one roof to discuss and present their works. This provides an unique opportunity to the researchers and students to learn the frontiers and interact with eminent scientists in Bioinformatics. The 5th IITM - Tokyo Tech Joint Symposium titled "Current trends in Bioinformatics: Big data analysis, machine learning and drug design", will be held on 6th - 7th March 2020 in IITM, Chennai, India.</p><p>The symposium will focus on topics in the below mentioned areas.</p><p>Topics: Algorithms for biomolecular sequences / structures Bioinformatics databases and tools Protein function Structure based drug design Machine learning Deep learning Large scale data analysis Big Data NGS Analysis Protein interactions/network Molecular modelling/docking/screening Biomolecular structure and function More</p><p>Info: https://web.iitm.ac.in/bioinfo2/symposium2020/home</p>]]></description>
	<dc:creator>Jit</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43084/frequently-used-bioinformatics-tools-for-viral-genome-analysis</guid>
	<pubDate>Wed, 23 Jun 2021 07:40:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43084/frequently-used-bioinformatics-tools-for-viral-genome-analysis</link>
	<title><![CDATA[Frequently used bioinformatics tools for viral genome analysis !]]></title>
	<description><![CDATA[<p><strong>IVA: accurate de novo assembly of RNA virus genomes.</strong><br /> Hunt M, Gall A, Ong SH, Brener J, Ferns B, Goulder P, Nastouli E, Keane JA, Kellam P, Otto TD.<br /> Bioinformatics. 2015 Jul 15;31(14):2374-6. doi: <a href="http://bioinformatics.oxfordjournals.org/content/31/14/2374.long">10.1093/bioinformatics/btv120</a>. Epub 2015 Feb 28.</p><p><a href="http://www.nature.com/nmeth/journal/v9/n1/full/nmeth.1814.html"><strong>Adapter sequences</strong></a>:<br /> <strong>Optimal enzymes for amplifying sequencing libraries.</strong><br /> Quail, M. a et al. Nat. Methods 9, 10-1 (2012).</p><p><a href="http://genome.cshlp.org/content/early/2012/01/12/gr.131383.111"><strong>GAGE</strong></a>:<br /> <strong>GAGE: A critical evaluation of genome assemblies and assembly algorithms.</strong><br /> Salzberg, S. L. et al. Genome Res. 22, 557-67 (2012).</p><p><a href="http://www.biomedcentral.com/1471-2105/14/160"><strong>KMC</strong></a>:<br /> <strong>Disk-based k-mer counting on a PC.</strong><br /> Deorowicz, S., Debudaj-Grabysz, A. &amp; Grabowski, S. BMC Bioinformatics 14, 160 (2013).</p><p><a href="http://genomebiology.com/2014/15/3/R46"><strong>Kraken</strong></a>:<br /> <strong>Kraken: ultrafast metagenomic sequence classification using exact alignments.</strong><br /> Wood, D. E. &amp; Salzberg, S. L. Genome Biol. 15, R46 (2014).</p><p><a href="http://genomebiology.com/2004/5/2/r12"><strong>MUMmer</strong></a>:<br /> <strong>Versatile and open software for comparing large genomes.</strong><br /> Kurtz, S. et al. Genome Biol. 5, R12 (2004).</p><p><strong>R</strong>:<br /> <strong>R: A language and environment for statistical computing.</strong><br /> R Core Team (2013). R Foundation for Statistical Computing, Vienna, Austria. URL <a href="http://www.R-project.org/">http://www.R-project.org/</a>.</p><p><a href="http://nar.oxfordjournals.org/content/39/9/e57"><strong>RATT</strong></a>:<br /> <strong>RATT: Rapid Annotation Transfer Tool.</strong><br /> Otto, T. D., Dillon, G. P., Degrave, W. S. &amp; Berriman, M. Nucleic Acids Res. 39, e57 (2011).</p><p><a href="http://bioinformatics.oxfordjournals.org/content/25/16/2078.abstract"><strong>SAMtools</strong></a>:<br /> <strong>The Sequence Alignment/Map format and SAMtools.</strong><br /> Li, H. et al. Bioinformatics 25, 2078-9 (2009).</p><p><a href="http://bioinformatics.oxfordjournals.org/content/early/2014/04/12/bioinformatics.btu170"><strong>Trimmomatic</strong></a>:<br /> <strong>Trimmomatic: A flexible trimmer for Illumina Sequence Data.</strong><br /> Bolger, A. M., Lohse, M. &amp; Usadel, B. Bioinformatics 1-7 (2014).</p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44672/libraries-or-management-tools-for-high-throughput-sequencing-data</guid>
	<pubDate>Fri, 04 Oct 2024 02:45:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44672/libraries-or-management-tools-for-high-throughput-sequencing-data</link>
	<title><![CDATA[Libraries or management tools for high throughput sequencing data]]></title>
	<description><![CDATA[<ul>
<li><a href="http://gatb.inria.fr/"><span>GATB</span></a>&nbsp;Library.&nbsp;The&nbsp;<span>Genome Analysis Toolbox with de-Bruijn graph.&nbsp;</span>A large part of tools developed by the GenScale team are based on this library.<br />These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (<em>e.g.</em>&nbsp;metagenomes). Among them are (the full is available here:&nbsp;<a href="https://gatb.inria.fr/software/">https://gatb.inria.fr/software/</a>):</li>
<li><a href="https://github.com/morispi/LRez"><span>LRez</span></a>: C++ Library and toolkit for the barcode-based management and indexation of linked-read datasets.</li>
</ul><h2>Variant calling and/or genotyping</h2><ul>
<li><a href="https://gatb.inria.fr/software/discosnp/" title="DiscoSNP">DiscoSNP++ and&nbsp;discoSnpRAD</a>: Reference-free small variant discovery (SNPs and indels)</li>
<li><a href="https://gatb.inria.fr/software/mind-the-gap/" title="MindTheGap">MindTheGap</a>: Detection and assembly of large insertion variants</li>
<li><a href="https://gatb.inria.fr/software/takeabreak/" title="TakeABreak">TakeABreak</a>:&nbsp;reference-free inversion discovery tool</li>
<li><a href="https://github.com/llecompte/SVJedi">SVJedi</a>: Structural Variant genotyper with long read data</li>
<li><a href="https://github.com/SandraLouise/SVJedi-graph">SVJedi-graph</a>: Structural Variant genotyper with long read data using a variation graph</li>
</ul><h2>Sequence assembly</h2><ul>
<li><a href="https://github.com/cguyomar/MinYS">MinYS</a>: reference-guided genome assembly in metagenomics data</li>
<li><a href="https://github.com/anne-gcd/MTG-Link">MTG-link</a>: local assembly tool for linked-read data</li>
<li><a href="https://gatb.inria.fr/software/minia/" title="Minia">Minia</a>: De novo short read assembler</li>
<li><a href="https://gatb.inria.fr/de-novo-genome-assembly/">de-novo pipeline</a>:&nbsp;<em>de-novo</em>&nbsp;assembly pipeline (error correction / contigs / scaffolding) for genomes and meta-genomes</li>
<li><a href="https://gatb.inria.fr/software/mapsembler/" title="Mapsembler2">Mapsembler2</a>: Targeted assembly (not maintained)</li>
</ul><h2>Managing k-mers &amp; indexation</h2><ul>
<li><a href="https://github.com/lrobidou/findere">findere</a>:&nbsp;simple strategy for speeding up queries and for reducing false positive calls from any Approximate Membership Query data structure.
<ul>
<li><a href="https://github.com/lrobidou/fimpera">fimpera</a>&nbsp;extends findere adding the abundance information.</li>
</ul>
</li>
<li><a href="https://github.com/tlemane/kmtricks">kmtricks</a>:&nbsp;modular tool suite for counting kmers, and constructing Bloom filters or kmer matrices, for large collections of sequencing data.</li>
<li><a href="https://github.com/tlemane/kmindex">kmindex&nbsp;</a>is a tool for indexing and querying sequencing samples. It is built on top of kmtricks.</li>
<li><a href="https://github.com/pierrepeterlongo/back_to_sequences">back to sequences</a>: Find sequences (reads, unitigs, genes) related to a set of kmers in large datasets, in a matter of seconds.</li>
<li><a href="https://github.com/vicLeva/bqf">Backpack Quotient Filter</a>:&nbsp;k-mer indexing data structure with abundance</li>
<li><a href="http://github.com/GATB/rconnector">short read connector</a>:&nbsp;Detect similar reads from potentially large read set</li>
<li><a href="https://gatb.inria.fr/software/dsk/" title="DSK">DSK</a>:&nbsp;Count K-mer in sequences</li>
</ul><h2>Pangenome graph manipulation</h2><ul>
<li><a href="https://github.com/Tharos-ux/pancat">Pancat</a>: Pangenome Comparison and Analysis Toolkit</li>
<li><a href="https://pypi.org/project/gfagraphs/">GFAGraphs</a>: a Python library to handle pangenome graph files in GFA format.</li>
</ul><h2>Comparative metagenomics with k-mers</h2><ul>
<li><a href="https://github.com/GATB/simka">Simka and SimkaMin</a>:&nbsp;Comparative metagenomics for large-scale datasets</li>
<li><a href="https://team.inria.fr/genscale/high-throughput-sequence-analysis/compreads-metagenomic-data-analysis/">Comparead &amp; Commet</a>:&nbsp;comparison of metagenomic datasets</li>
</ul><h2>Species and bacterial strains identification</h2><ul>
<li><a href="https://github.com/gsiekaniec/ORI">ORI</a>: software using long nanopore reads to identify bacteria present in a sample at the strain level</li>
<li><a href="https://github.com/kevsilva/StrainFLAIR">StrainFLAIR</a>:&nbsp;STRAIN-level proFiLing using vArIation gRaph</li>
</ul><h2>General-purpose sequencing data manipulation</h2><ul>
<li><a href="https://team.inria.fr/genscale/ngs-software/gassst/">GASSST</a>:&nbsp;long read mapper</li>
<li><a href="https://gatb.inria.fr/software/leon/" title="Leon">Leon</a>: short read compressor (now included in GATB-core)</li>
<li><a href="https://gatb.inria.fr/software/bloocoo/" title="Bloocoo">Bloocoo</a>:&nbsp;short read corrector</li>
<li><a href="https://github.com/GATB/bcalm">BCALM</a>:&nbsp;Construct compacted de Bruijn graphs (unitigs)</li>
</ul><h2>&nbsp;Protein Structure</h2><ul>
<li><a href="https://team.inria.fr/genscale/protein-structure/a-purva-contact-map-overlap-solver/">A_Purva</a>:&nbsp;Contact Map Overlap solver</li>
<li><a href="https://team.inria.fr/genscale/protein-structure/md-jeep-distance-geomtry-solver/">MD-Jeep</a>:&nbsp;Distance Geometry solver</li>
<li><a href="https://team.inria.fr/genscale/csa-comparative-structural-alignment/">CSA</a>:&nbsp;Comparative Structural Alignment</li>
</ul><h2>Workflow</h2><ul>
<li><a href="https://team.inria.fr/genscale/workflows/slicee/">SLICEE</a>:&nbsp;parallel execution of bioinformatics workflows</li>
</ul><h3>Comparative Genomics</h3><ul>
<li><a href="https://team.inria.fr/genscale/comparative-genomics/cassis/">CASSIS</a>:&nbsp;detection of rearrangement breakpoints</li>
<li><a href="https://team.inria.fr/genscale/high-throughput-sequence-analysis/plast-intensive-sequence-comparison/">PLAST</a>:&nbsp;intensive bank-to-bank sequence comparison</li>
<li><a href="https://github.com/stephanierobin/DrjBreakpointFinder">DRJBreakpointFinder</a>: detection and precise localization of excision sites in proviral segments</li>
</ul>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/7913/the-genome-factory</guid>
	<pubDate>Thu, 16 Jan 2014 02:09:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/7913/the-genome-factory</link>
	<title><![CDATA[The genome factory !!!]]></title>
	<description><![CDATA[<p>Illumina, Inc. announced Tuesday that its new HiSeq X Ten Sequencing System has broken the &ldquo;sound barrier&rdquo; of human genomics by enabling the $1,000 genome. &ldquo;This platform includes dramatic technology breakthroughs that enable researchers to undertake studies of unprecedented scale by providing the throughput to sequence tens of thousands of human whole genomes in a single year in a single lab,&rdquo; Illumina stated.</p><p>Initial customers for the HiSeq X Ten System, which will ship in Q1 2014, include Macrogen, based in Seoul, South Korea and its CLIA laboratory in Rockville, Maryland, the Broad Institute in Cambridge, Massachusetts, and the Garvan Institute of Medical Research in Sydney, Australia.</p><p>&ldquo;For the first time, it looks like it will be possible to deliver the $1,000 genome, which is tremendously exciting,&rdquo; said Eric Lander, founding director of the Broad Institute and a professor of biology at MIT. &ldquo;The HiSeq X Ten should give us the ability to analyze complete genomic information from huge sample populations. Over the next few years, we have an opportunity to learn as much about the genetics of human disease as we have learned in the history of medicine.&rdquo;</p><p>&ldquo;The HiSeq X Ten is an ideal platform for scientists and institutions focused on the discovery of genotypic variation to enable a deeper understanding of human biology and genetic disease,&rdquo; Illumina stated. &ldquo;It can sequence tens of thousands of samples annually with high-quality, high-coverage sequencing, delivering a comprehensive catalog of human variation within and outside coding regions.&rdquo;</p><p>HiSeq X Ten utilizes a number of advanced design features to generate massive throughput. Patterned flow cells, which contain billions of nanowells at fixed locations, combined with a new clustering chemistry deliver a significant increase in data density (6 billion clusters per run). Using state-of-the art optics and faster chemistry, HiSeq X Ten can process sequencing flow cells more quickly than ever before &mdash; generating a 10x increase in daily throughput when compared to current HiSeq 2500 performance.</p><p>The HiSeq X Ten is sold as a set of 10 or more ultra-high throughput sequencing systems, each generating up to 1.8 terabases (Tb) of sequencing data in less than three days or up to 600 gigabases (Gb) per day, per system, providing the throughput to sequence tens of thousands of high-quality, high-coverage genomes per year. Illumina says the $1,000 includes typical instrument depreciation, DNA extraction, library preparation, and estimated labor.</p>]]></description>
	<dc:creator>Madhvan Reddy</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</guid>
	<pubDate>Fri, 24 Jan 2020 04:09:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40598/mitoz-a-toolkit-for-animal-mitochondrial-genome-assembly-annotation-and-visualization</link>
	<title><![CDATA[MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization]]></title>
	<description><![CDATA[<p><span>MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from&nbsp;</span><code>https://github.com/linzhi2013/MitoZ</code><span>.</span></p>
<p><span><a href="https://academic.oup.com/nar/article/47/11/e63/5377471">https://academic.oup.com/nar/article/47/11/e63/5377471</a></span></p><p>Address of the bookmark: <a href="https://github.com/linzhi2013/MitoZ" rel="nofollow">https://github.com/linzhi2013/MitoZ</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43364/ragtag-a-collection-of-software-tools-for-scaffolding-and-improving-modern-genome-assemblies</guid>
	<pubDate>Sat, 11 Sep 2021 00:28:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43364/ragtag-a-collection-of-software-tools-for-scaffolding-and-improving-modern-genome-assemblies</link>
	<title><![CDATA[RagTag: a collection of software tools for scaffolding and improving modern genome assemblies]]></title>
	<description><![CDATA[<p>RagTag is a collection of software tools for scaffolding and improving modern genome assemblies. Tasks include:</p>
<ul>
<li>Homology-based misassembly&nbsp;<a href="https://github.com/malonge/RagTag/wiki/correct">correction</a></li>
<li>Homology-based assembly&nbsp;<a href="https://github.com/malonge/RagTag/wiki/scaffold">scaffolding</a>&nbsp;and&nbsp;<a href="https://github.com/malonge/RagTag/wiki/patch">patching</a></li>
<li>Scaffold&nbsp;<a href="https://github.com/malonge/RagTag/wiki/merge">merging</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/malonge/RagTag" rel="nofollow">https://github.com/malonge/RagTag</a></p>]]></description>
	<dc:creator>Jit</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/pages/view/11399/next-generation-sequencing-in-r-or-bioconductor-environment</guid>
	<pubDate>Mon, 02 Jun 2014 18:03:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/11399/next-generation-sequencing-in-r-or-bioconductor-environment</link>
	<title><![CDATA[Next generation sequencing in R or bioconductor environment]]></title>
	<description><![CDATA[<p>There are many R software and bioconductor packages for NGS data analysis, some of them are as follows</p><h3><a name="TOC-Biostrings" id="TOC-Biostrings"></a>Biostrings</h3><p>The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R. It contains many speed and memory effective string containers, string matching algorithms, and other utilities, for fast manipulation of large sets of biological sequences. The objects and functions provided by Biostrings form the basis for many other sequence analysis packages. <a href="http://bioconductor.org/packages/release/bioc/html/Biostrings.html">Documentation</a></p><div><div style="text-align: left;"><div style="color: #000000;"><h4><a name="TOC-IRanges-Overview" id="TOC-IRanges-Overview"></a>IRanges Overview</h4><p>IRanges provides the low-level infrastructure and containers for handling sets of integer ranges within Bioconductor's BioC-Seq domain. Its classes and methods provide support for many more high-level packages like GenomicRanges, ShortRead, Rsamtools, etc. <a href="http://bioconductor.org/packages/release/bioc/html/IRanges.html">Documentation</a></p><div style="text-align: right;"><div style="text-align: left;"><h4><a name="TOC-GenomicRanges-Overview" id="TOC-GenomicRanges-Overview"></a>GenomicRanges Overview</h4><p>The <em>GenomicRanges</em> package serves as the foundation for representing genomic locations within the Bioconductor project. It is built upon the <em>IRanges</em> infrastructure and defines three major data containers - <em>GRanges, GRangesList</em> and <em>GappedAlignments</em> - which are supporting other important BioC-Seq packages including <em>ShortRead, Rsamtools, rtracklayer, GenomicFeatures</em> and <em>BSgenome</em>.&nbsp; Compared to the IRanges container, the GRanges/<em>GRangesList</em> classes are more flexible and extensible to store additional information about sequence ranges, such as chromosome identifiers (sequence space), strand information and annotation data. <a href="http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html">Documentation</a></p></div></div></div></div><h3><a name="TOC-Motif-Discovery" id="TOC-Motif-Discovery"></a>Motif Discovery</h3><h4><a name="TOC-cosmo" id="TOC-cosmo"></a>cosmo</h4><p>The cosmo package allows to search a set of unaligned DNA sequences for a shared motif that may function as transcription factor binding site. The algorithm extends the popular motif discovery tool MEME (Bailey and Elkan, 1995) in that it allows the search to be supervised by specifying a set of constraints that the motif to be discovered must satisfy. <a href="http://bioconductor.org/packages/release/bioc/html/cosmo.html">Documentation</a></p></div><div>
<p><span></span><span></span></p>
<div style="color: #0000ff;"><h4><a name="TOC-BCRANK" id="TOC-BCRANK"></a>BCRANK</h4><p>BCRANK is a method that takes a ranked list of genomic regions as input and outputs short DNA sequences that are overrepresented in some part of the list. The algorithm was developed for detecting transcription factor (TF) binding sites in a large number of enriched regions from high-throughput ChIP-chip or ChIP-seq experiments, but it can be applied to any ranked list of DNA sequences. Documentation</p>
<p><a href="http://bioconductor.org/packages/release/bioc/html/BCRANK.html"></a></p>
<p>rGADEM: <a href="http://bioconductor.org/packages/devel/bioc/html/rGADEM.html">Documentation</a></p><p>MotIV: <a href="http://bioconductor.org/packages/devel/bioc/html/MotIV.html">Documentation</a></p></div><h3><a name="TOC-ShortRead" id="TOC-ShortRead"></a>ShortRead</h3><p>The ShortRead package provides input, quality control, filtering, parsing, and manipulation functionality for short read sequences produced by high throughput sequencing technologies. While support is provided for many sequencing technologies, this package is primairly focused on Solexa/Illumina reads. <a href="http://bioconductor.org/packages/release/bioc/html/ShortRead.html">Documentation</a></p><h3><a name="TOC-Rsamtools" id="TOC-Rsamtools"></a>Rsamtools</h3><p>Rsamtools provides functions for parsing and inspecting samtools BAM formatted binary alignment data. SAM/BAM is quickly becoming a universal standard alignment format, and is now supported by a wide variety of alignment tools. <a href="http://bioconductor.org/help/bioc-views/2.7/bioc/html/Rsamtools.html">Documentation</a></p>
<p><a href="http://samtools.sourceforge.net/">Samtools Website</a><br /> <a href="http://bio-bwa.sourceforge.net/">BWA (Burrows-Wheeler Alignment) Website</a><br /><span style="color: #0000ff;"></span></p>
<div style="color: #000000;">&nbsp;</div></div><div>
<p><span style="color: #000000;">Additional tools for SNP analysis:&nbsp;</span></p>
<p><a href="http://bioconductor.org/help/bioc-views/release/bioc/html/snpMatrix.html">snpMatrix</a></p><h3><a name="TOC-BSgenome" id="TOC-BSgenome"></a>BSgenome</h3><p>BSgenome provides an object oriented infrastructure for interacting with a Biostring based genome sequence. BSgenome packages exist for many common genomes, and can be created to represent custom genomes. See the "How to forge a BSgenome data package" Vignette for instructions to create a new BSgenome package if a prebuilt package does not exist for your organism. <a href="http://bioconductor.org/packages/release/bioc/html/BSgenome.html">Documentation</a></p><h3><a name="TOC-rtracklayer" id="TOC-rtracklayer"></a>rtracklayer</h3><p>rtracklayer provides an interface for exporting annotation feature data to various genome browsers and file formats (such as GFF). See the Small RNA Profiling exercise for an example of using rtracklayer to visualize alignment coverage. <a href="http://bioconductor.org/packages/release/bioc/html/rtracklayer.html">Documentation</a></p><h3><a name="TOC-biomaRt" id="TOC-biomaRt"></a>biomaRt</h3><p>The biomaRt package, provides an interface to a growing collection of databases implementing the BioMart software suite (http:// www.biomart.org). The package enables online retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas. This data is retrieved automatically via the Internet, so it's recommended that you cache the data locally, or check versions if your code will be adversely affected by updates to these data. <a href="http://bioconductor.org/packages/release/bioc/html/biomaRt.html">Documentation</a></p><h3><a name="TOC-ChIP-Seq-Analysis-Packages" id="TOC-ChIP-Seq-Analysis-Packages"></a>ChIP-Seq Analysis Packages</h3><p>Bioconductor provides various packages for analyzing and visualizing ChIP-Seq data. Only a small selection of these packages is introduced here. Additional useful introductions to this topic are: <a href="http://www.bioconductor.org/workshops/2009/SeattleJan09/ChIP-seq/">BioC ChIP-seq Case Study</a> and BioC <a href="http://www.bioconductor.org/help/course-materials/2009/SeattleNov09/ChIP-seq/">ChIP-Seq</a>.</p><h4><a name="TOC-chipseq" id="TOC-chipseq"></a>chipseq</h4><p>The chipseq package combines a variety of HT-Seq packages to a pipeline for ChIP-Seq data analysis. <a href="http://bioconductor.org/packages/release/bioc/html/chipseq.html">Documentation</a></p><h4><a name="TOC-BayesPeak" id="TOC-BayesPeak"></a>BayesPeak</h4><p>BayesPeak is a peak calling package for identifying DNA binding sites of proteins in ChIP-Seq experiments. Its algorithm uses hidden Markov models (HMM) and Bayesian statistical methods. The following sample code introduces the identification of peaks with the BayesPeak package as well as the incorporation of read coverage information obtained by the chipseq package. <a href="http://bioconductor.org/packages/release/bioc/html/BayesPeak.html">Documentation</a> [ <a href="http://www.biomedcentral.com/1471-2105/10/299">Publication</a> ]</p><h4><a name="TOC-PICS" id="TOC-PICS"></a>PICS</h4><p>The PICS package applies probabilistic inference to aligned-read ChIP-Seq data in order to identify regions bound by transcription factors. PICS identifies enriched regions by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. The following sample code uses the test data set from the above BayesPeak package in order to compare the results from both methods by identifying their consensus peak set. <a href="http://www.bioconductor.org/packages/release/bioc/html/PICS.html">Documentation</a> [ <a href="http://www.hubmed.org/display.cgi?uids=20528864">Publication</a> ]</p><h4><a name="TOC-ChIPpeakAnno" id="TOC-ChIPpeakAnno"></a>ChIPpeakAnno</h4><p>The ChIPpeakAnno package provides. batch annotation of the peaks identified from either ChIP-seq or ChIP-chip experiments. It includes functions to retrieve the sequences around peaks, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. The package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. <a href="http://bioconductor.org/packages/release/bioc/html/ChIPpeakAnno.html">Documentation</a></p><h4><a name="TOC-Additional-ChIP-Seq-Packages" id="TOC-Additional-ChIP-Seq-Packages"></a>Additional ChIP-Seq Packages</h4><p>DiffBind: <a href="http://www.bioconductor.org/packages/release/bioc/html/DiffBind.html">Documentation</a></p><p>MOSAICS: <a href="http://bioconductor.org/packages/devel/bioc/html/mosaics.html">Documentation</a></p><p>iSeq: <a href="http://bioconductor.org/packages/release/bioc/html/iSeq.html">Documentation</a></p><p>ChIPseqR: <a href="http://bioconductor.org/packages/release/bioc/html/ChIPseqR.html">Documentation</a></p><p>ChiPsim: <a href="http://bioconductor.org/packages/release/bioc/html/ChIPsim.html">Documentation</a></p><p>CSAR: <a href="http://www.bioconductor.org/packages/devel/bioc/html/CSAR.html">Documentation</a></p><p>ChIP-Seq Pipeline: <a href="http://www.bioconductor.org/packages/release/bioc/html/PICS.html">PICS</a>, rGADEM and MotIV (<a href="http://www.rglab.org/pics-and-bioconductor/">developer web site</a>)</p><p>SPP: <a href="http://compbio.med.harvard.edu/Supplements/ChIP-seq/">ChIP-seq processing pipeline</a></p><p><a href="http://compbio.med.harvard.edu/Supplements/ChIP-seq/tutorial.html">SPP Tutorial</a></p><p><a href="http://liulab.dfci.harvard.edu/MACS/index.html">MACS</a></p><p><a href="http://gmdd.shgmo.org/Computational-Biology/ChIP-Seq/download/SIPeS">SIPeS</a></p><h3><a name="TOC-RNA-Seq-Analysis" id="TOC-RNA-Seq-Analysis"></a>RNA-Seq Analysis</h3><h4><a name="TOC-Counting-Reads-that-Overlap-with-Annotation-Ranges-" id="TOC-Counting-Reads-that-Overlap-with-Annotation-Ranges-"></a>Counting Reads that Overlap with Annotation Ranges&nbsp;</h4><p>The GenomicRanges package provides support for importing into R short read alignment data in BAM format (via Rsamtools) and associating them with genomic feature ranges, such as exons or genes. This way one can quantify the number of reads aligning to annotated genomic regions. The package defines general purpose containers for storing genomic intervals as well as more specialized containers for storing alignments against a reference genome. The two main functions for read counting provided by this infrastructure are <span>countOverlaps <span style="color: #000000;"><span>and</span></span> summarizeOverlaps</span>. For their proper usage, it is important to read the corresponding <a href="http://www.bioconductor.org/packages/devel/bioc/vignettes/GenomicRanges/inst/doc/summarizeOverlaps.pdf">PDF manual</a>. <a href="http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html">Documentation</a></p><h4><a name="TOC-Differential-Gene-Expression-Analysis-with-DESeq" id="TOC-Differential-Gene-Expression-Analysis-with-DESeq"></a>Differential Gene Expression Analysis with DESeq</h4><p>The DESeq package contains functions to call differentially expressed genes (DEGs) in count tables based on a model using the negative binomial distribution. It expects as input a data frame with the raw read counts per region/gene of interest (rows) for each test sample (columns).&nbsp; Such a count table can be imported into R or generated from BAM alignment files using the <span>countOverlaps</span> function as introduced above. <a href="http://www.bioconductor.org/packages/release/bioc/html/DESeq.html">Documentation</a></p><h4><a name="TOC-Differential-Gene-Expression-Analysis-with-edgeR" id="TOC-Differential-Gene-Expression-Analysis-with-edgeR"></a>Differential Gene Expression Analysis with edgeR</h4><p>The edgeR package uses empirical Bayes estimation and exact tests based on the negative binomial distribution to call differentially expressed genes (DEGs) in count data.&nbsp;</p>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/edgeR.html">Documentation</a></p>
<p><span style="color: #000000;">A variety of additional R packages are available for normalizing RNA-Seq read count data and identifying differentially expressed genes (DEG): <br /> </span></p><p><a href="http://bioconductor.org/packages/devel/bioc/html/easyRNASeq.html">easyRNASeq</a> (simplifies read counting per genome feature)</p><p><a href="http://www.bioconductor.org/packages/release/bioc/html/DEXSeq.html">DEXSeq</a> (Inference of differential exon usage);&nbsp;<a href="http://www.bioconductor.org/packages/release/data/experiment/html/parathyroidSE.html">parathyroidSE</a> explains how to generate exon read counts in R</p><p><a href="http://bioconductor.org/packages/release/bioc/html/DEGseq.html">DEGseq</a></p><p><a href="http://www.bioconductor.org/packages/release/bioc/html/baySeq.html">baySeq</a> (also see: <a href="http://www.bioconductor.org/packages/release/bioc/html/segmentSeq.html">segmentSeq</a>)</p><p><a href="http://bioconductor.org/packages/release/bioc/html/Genominator.html">Genominator</a> (<a href="http://www.hubmed.org/display.cgi?uids=20167110">Bullard et al. 2010</a>)</p><div style="text-align: right;"><div style="text-align: left;"><h4><a name="TOC-Detection-of-Alternative-Splice-Junctions" id="TOC-Detection-of-Alternative-Splice-Junctions"></a>Detection of Alternative Splice Junctions</h4>
<p><span style="color: #000000;">Another utility of RNA-Seq experiments is the analysis of splice junctions. The following software suggestions provide this utility:</span></p>
<p><a href="http://woldlab.caltech.edu/rnaseq/">ERANGE<br /> </a><a href="http://tophat.cbcb.umd.edu/">TopHat</a></p><p><a href="http://biogibbs.stanford.edu/%7Ekinfai/SpliceMap/">SpliceMap</a></p><p><a href="http://solidsoftwaretools.com/gf/project/splitseek/">SplitSeek</a></p><h3><a name="TOC-DNA-Methylation-Data-Analysis" id="TOC-DNA-Methylation-Data-Analysis"></a>DNA-Methylation Data Analysis</h3><div><ul>
<li><span style="font-size: 10pt;"><a href="http://www.bioconductor.org/help/course-materials/2012/BiocEurope2012/mattia_pelizzola_methylPipe.pdf">methylPipe</a></span></li>
<li><span style="font-size: 10pt;"><a href="http://www.bioconductor.org/packages/devel/bioc/html/bsseq.html">bsseq</a></span></li>
<li><a href="http://www.bioconductor.org/packages/devel/bioc/html/BiSeq.html">BiSeq</a></li>
<li>Much more under <a href="http://www.bioconductor.org/packages/devel/BiocViews.html#___DNAMethylation">BiocViews</a></li>
</ul></div></div></div><h3><a name="TOC-HT-Seq-Data-Visualization" id="TOC-HT-Seq-Data-Visualization"></a>HT-Seq Data Visualization</h3>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/ggbio.html">ggbio</a>: ggplot2 extension for genomics data (<a href="http://tengfei.github.com/ggbio/">online manual</a>) <a href="http://www.bioconductor.org/packages/devel/bioc/html/Gviz.html">Gviz</a>:&nbsp;Plotting data and annotation information along genomic coordinates <a href="http://bioconductor.org/packages/release/bioc/html/HilbertVis.html">HilbertVis</a>: Hilbert genome plots</p>
<p><a href="http://bioconductor.org/packages/release/bioc/html/GenomeGraphs.html">GenomeGraphs</a>: Plotting genomic information from Ensembl</p><p><a href="http://www.hubmed.org/display.cgi?uids=18507856">TileQC</a>: Flow Cell Quality Visualization</p><p><a href="http://bioconductor.org/packages/release/bioc/html/rtracklayer.html">rtracklayer</a>: R interface to genome browsers</p><p><a href="http://genoplotr.r-forge.r-project.org/">genoPlotR</a>: Plotting maps of genes and genomes</p><p><a href="http://bioconductor.org/packages/release/bioc/html/Genominator.html">Genominator</a>: Tools for storing, accessing, analyzing and visualizing genomic data.</p><p>&nbsp;</p><p>To install all packages</p><blockquote><p>source("http://bioconductor.org/biocLite.R")<br />biocLite()<br />biocLite(c("ShortRead", "Biostrings", "IRanges", "BSgenome", "rtracklayer", "biomaRt", "chipseq", "ChIPpeakAnno", "Rsamtools", "BayesPeak", "PICS", "GenomicRanges", "DESeq", "edgeR", "leeBamViews", "GenomicFeatures", "BSgenome.Celegans.UCSC.ce2"))</p></blockquote></div>]]></description>
	<dc:creator>John Parker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/11611/ten-recommendations-for-creating-usable-bioinformatics-command-line-software</guid>
	<pubDate>Sun, 08 Jun 2014 10:06:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/11611/ten-recommendations-for-creating-usable-bioinformatics-command-line-software</link>
	<title><![CDATA[Ten recommendations for creating usable bioinformatics command line software]]></title>
	<description><![CDATA[<p><span>Bioinformatics software varies greatly in quality. In terms of usability, the command line interface is the first experience a user will have of a tool. Unfortunately, this is often also the last time a tool will be used. Here I present ten recommendations for command line software author&rsquo;s tools to follow, which I believe would greatly improve the uptake and usability of their products, waste less user&rsquo;s time, and improve the quality of scientific analyses.</span></p><p>Address of the bookmark: <a href="http://www.gigasciencejournal.com/content/2/1/15?utm_content=buffer25ee0&amp;utm_medium=social&amp;utm_source=twitter.com&amp;utm_campaign=buffer" rel="nofollow">http://www.gigasciencejournal.com/content/2/1/15?utm_content=buffer25ee0&amp;utm_medium=social&amp;utm_source=twitter.com&amp;utm_campaign=buffer</a></p>]]></description>
	<dc:creator>RAJESH DETROJA</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/12206/bioinformatics-algorithms-tutorials</guid>
	<pubDate>Tue, 24 Jun 2014 00:10:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/12206/bioinformatics-algorithms-tutorials</link>
	<title><![CDATA[Bioinformatics algorithms tutorials]]></title>
	<description><![CDATA[<p>Useful bioinformatics tutorial, such as</p>
<p>De Bruijn Graphs for NGS Assembly<br>Algorithms for PacBio Reads<br>Software and Hardware Concepts for Bioinformatics<br>Finding us in Homolog.us (Search Algorithms)<br>NGS Genome and RNAseq Assembly - a Hands on Primer<br>Introduction to PERL, Python, R and C/C++ for Bioinformatics</p><p>Address of the bookmark: <a href="http://www.homolog.us/Tutorials/" rel="nofollow">http://www.homolog.us/Tutorials/</a></p>]]></description>
	<dc:creator>John Parker</dc:creator>
</item>

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