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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/35802?offset=60</link>
	<atom:link href="https://bioinformaticsonline.com/related/35802?offset=60" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44401/bioinformatics-tools-for-phylogeny</guid>
	<pubDate>Mon, 06 Nov 2023 03:09:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44401/bioinformatics-tools-for-phylogeny</link>
	<title><![CDATA[Bioinformatics Tools for Phylogeny !]]></title>
	<description><![CDATA[<p><span>Direct access to the individual tools available on this server.</span></p><table summary="list of individual tools">
<thead>
<tr><th>Multiple Alignment:</th><th>Phylogeny:</th><th>Tree viewers:</th><th>Utilities:</th></tr>
</thead>
<tbody>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=muscle">MUSCLE</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=phyml">PhyML</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=treedyn">TreeDyn</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=gblocks">Gblocks</a></td>
</tr>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=tcoffee">T-Coffee</a>&nbsp;/&nbsp;<a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=expresso">3DCoffee</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=tnt">TNT</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=drawgram">Drawgram</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=jalview">Jalview</a></td>
</tr>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=clustalw">ClustalW</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=bionj">BioNJ</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=drawtree">Drawtree</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=readseq">Readseq</a></td>
</tr>
<tr>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=probcons">ProbCons</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=mrbayes">MrBayes</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=atv">ATV (A Tree Viewer)</a></td>
<td><a href="http://phylogeny.lirmm.fr/phylo_cgi/data_converter.cgi">Built-in converter</a></td>
</tr>
</tbody>
</table>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44716/exploring-rna-sequence-analysis-tools-for-every-bioinformatician</guid>
	<pubDate>Fri, 13 Dec 2024 04:03:04 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44716/exploring-rna-sequence-analysis-tools-for-every-bioinformatician</link>
	<title><![CDATA[Exploring RNA Sequence Analysis: Tools for Every Bioinformatician]]></title>
	<description><![CDATA[<p>RNA sequence analysis has become an essential part of modern biological research. From RNA-seq pipelines to specialized tools for specific RNA types, here's a comprehensive guide to tools you can use to make sense of RNA data.</p><h4><strong>1. RNA-Seq Analysis Pipelines</strong></h4><p>RNA-seq is one of the most popular techniques for studying RNA. These tools streamline processing raw sequence data:</p><ul>
<li><strong>FASTQC</strong>: For quality control of raw RNA-seq reads.</li>
<li><strong>Trimmomatic</strong>: For trimming and filtering RNA-seq reads.</li>
<li><strong>HISAT2/STAR</strong>: High-performance aligners for RNA-seq reads.</li>
<li><strong>FeatureCounts</strong>: For quantifying gene expression.</li>
<li><strong>DESeq2/EdgeR</strong>: For differential expression analysis.</li>
</ul><h4><strong>2. Transcriptome Assembly and Annotation</strong></h4><p>For analyzing transcriptomes from non-model organisms or assembling novel transcripts:</p><ul>
<li><strong>Trinity</strong>: For de novo transcriptome assembly.</li>
<li><strong>StringTie</strong>: For transcript assembly and quantification from RNA-seq alignments.</li>
<li><strong>TransDecoder</strong>: To predict coding regions within assembled transcripts.</li>
<li><strong>TAU</strong>: Tools for annotating non-coding and coding RNAs.</li>
</ul><h4><strong>3. Exploring Non-Coding RNA (ncRNA)</strong></h4><p>Non-coding RNAs play critical regulatory roles. Dedicated tools for studying them include:</p><ul>
<li><strong>Infernal</strong>: For identifying ncRNA sequences based on covariance models.</li>
<li><strong>Rfam</strong>: Database and tools for ncRNA families.</li>
<li><strong>miRDeep</strong>: For identifying microRNAs in RNA-seq datasets.</li>
</ul><h4><strong>4. RNA Structure and Motif Analysis</strong></h4><p>Structural biology of RNA helps in understanding its function:</p><ul>
<li><strong>RNAfold (ViennaRNA)</strong>: Predicts secondary structures from RNA sequences.</li>
<li><strong>RNAstructure</strong>: Tools for RNA secondary structure prediction and analysis.</li>
<li><strong>MEME Suite</strong>: For identifying motifs in RNA sequences.</li>
<li><strong>IntaRNA</strong>: For RNA-RNA interaction prediction.</li>
</ul><h4><strong>5. RNA Editing and Modifications</strong></h4><p>Epitranscriptomics is a growing field focusing on RNA modifications:</p><ul>
<li><strong>REDItools</strong>: For RNA editing analysis.</li>
<li><strong>m6Aboost</strong>: For identifying m6A modifications in RNA.</li>
</ul><h4><strong>6. Long-Read RNA Sequencing Analysis</strong></h4><p>Long-read technologies like Nanopore and PacBio are transforming RNA research:</p><ul>
<li><strong>FLAIR</strong>: For isoform-level analysis of long-read RNA-seq data.</li>
<li><strong>NanoMod</strong>: For detecting modifications in RNA from Nanopore sequencing.</li>
</ul><h4><strong>7. RNA-Protein Interactions</strong></h4><p>To study RNA-protein interactions and complexes:</p><ul>
<li><strong>RBPmap</strong>: For identifying RNA-binding protein motifs.</li>
<li><strong>PARalyzer</strong>: For analyzing PAR-CLIP data.</li>
</ul><h4><strong>8. Functional Enrichment Analysis</strong></h4><p>Understanding biological functions and pathways from RNA-seq data:</p><ul>
<li><strong>getENRICH</strong>: A tool designed for pathway enrichment analysis of non-model organisms (hypergeometric P-value calculation with FDR correction).</li>
<li><strong>ClusterProfiler</strong>: For GO and KEGG pathway enrichment analysis.</li>
</ul><h4><strong>9. Visualization and Data Sharing</strong></h4><p>Presenting and sharing RNA sequence analysis results effectively:</p><ul>
<li><strong>IGV</strong>: Genome browser for visualizing RNA-seq alignments.</li>
<li><strong>Circos</strong>: Circular visualization of RNA-seq data.</li>
<li><strong>DashBio</strong>: A Python library for creating bioinformatics visualizations.</li>
</ul><h4><strong>Conclusion</strong></h4><p>The bioinformatics landscape for RNA sequence analysis is vast, with tools catering to specific needs. Whether you&rsquo;re studying coding RNAs, non-coding RNAs, or exploring RNA-protein interactions, the right tools can transform your data into biological insights.</p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/fun/view/4196/chemical-elements-of-bioinformatics</guid>
	<pubDate>Tue, 03 Sep 2013 16:35:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/fun/view/4196/chemical-elements-of-bioinformatics</link>
	<title><![CDATA[Chemical Elements of Bioinformatics]]></title>
	<description><![CDATA[<p>You must be familiar with periodic table and colour pattern, but this time you are going to amaze by new elements table by Eagle genomics. Just check it out and have fun :)</p><p><a href="http://elements.eaglegenomics.com/">http://elements.eaglegenomics.com/</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis</guid>
	<pubDate>Tue, 07 Nov 2017 05:33:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis</link>
	<title><![CDATA[Alignment-free sequence comparison tools available for next-generation sequencing data analysis]]></title>
	<description><![CDATA[<div><p><span>kallisto</span></p></div><div><p>Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)</p><p>Software (C++)</p><p><a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a></p><p>Sailfish</p><p>Estimation of isoform abundances from reference sequences and RNA-seq data (<em>k</em>-mer based)</p><p>Software (C++)</p><p><a href="http://www.cs.cmu.edu/~ckingsf/software/sailfish/">http://www.cs.cmu.edu/~ckingsf/software/sailfish/</a></p><p>Salmon</p><p>Quantification of the expression of transcripts using RNA-seq data (uses&nbsp;<em>k</em>-mers)</p><p><a href="https://combine-lab.github.io/salmon/">https://combine-lab.github.io/salmon/</a></p><p>RNA-Skim</p><p>RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses&nbsp;<em>sig</em>-mers, a special type of&nbsp;<em>k</em>-mers)</p><p>Software (C++)</p><p><a href="http://www.csbio.unc.edu/rs/">http://www.csbio.unc.edu/rs/</a></p><p>Variant calling</p><p>ChimeRScope</p><p>Fusion transcript prediction using gene&nbsp;<em>k</em>-mers profiles of the RNA-seq paired-end reads</p><p>Software (Java)</p><p><a href="https://github.com/ChimeRScope/ChimeRScope/wiki">https://github.com/ChimeRScope/ChimeRScope/wiki</a></p><p>FastGT</p><p>Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique&nbsp;<em>k</em>-mers</p><p>Software (C)</p><p><a href="https://github.com/bioinfo-ut/GenomeTester4/">https://github.com/bioinfo-ut/GenomeTester4/</a></p><p>Phy-Mer</p><p>Reference-independent mitochondrial haplogroup classifier from NGS data (<em>k</em>-mer based)</p><p>Software (Python)</p><p><a href="https://github.com/danielnavarrogomez/phy-mer">https://github.com/danielnavarrogomez/phy-mer</a></p><p>LAVA</p><p>Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (<em>k</em>-mer based)</p><p>Software (C)</p><p><a href="http://lava.csail.mit.edu/">http://lava.csail.mit.edu/</a></p><p>MICADo</p><p>Detection of mutations in targeted third-generation NGS data (can distinguish patients&rsquo; specific mutations; algorithm uses&nbsp;<em>k</em>-mers and is based on colored de Bruijn graphs)</p><p>Software (Python)</p><p><a href="http://github.com/cbib/MICADo">http://github.com/cbib/MICADo</a></p><p>General mapper</p><p>Minimap</p><p>Lightweight and fast read mapper and read overlap detector (uses the concept of &ldquo;minimazers&rdquo;, a special type of&nbsp;<em>k</em>-mers)</p><p>Software (C)</p><p><a href="https://github.com/lh3/minimap">https://github.com/lh3/minimap</a></p><p>Assembly</p><p>De novo genome assembly</p><p>MHAP</p><p>Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash</p><p>Software (Java)</p><p><a href="https://github.com/marbl/MHAP">https://github.com/marbl/MHAP</a></p><p>Miniasm</p><p>Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)</p><p>Software (C)</p><p><a href="https://github.com/lh3/miniasm">https://github.com/lh3/miniasm</a></p><p>LINKS</p><p>Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)</p><p>Software (Perl)</p><p><a href="https://github.com/warrenlr/LINKS/">https://github.com/warrenlr/LINKS/</a></p><p>Read clustering</p><p>afcluster</p><p>Clustering of reads from different genes and different species based on&nbsp;<em>k</em>-mer counts</p><p>Software (C++)</p><p><a href="https://github.com/luscinius/afcluster">https://github.com/luscinius/afcluster</a></p><p>QCluster</p><p>Clustering of reads with alignment-free measures (<em>k</em>-mer based) and quality values</p><p>Software (C++)</p><p><a href="http://www.dei.unipd.it/~ciompin/main/qcluster.html">http://www.dei.unipd.it/~ciompin/main/qcluster.html</a></p><p>Reads error correction</p><p>Lighter</p><p>Correction of sequencing errors in raw, whole genome sequencing reads (<em>k</em>-mer based)</p><p>Software (C++)</p><p><a href="https://github.com/mourisl/Lighter">https://github.com/mourisl/Lighter</a></p><p>QuorUM</p><p>Error corrector for Illumina reads using k-mers</p><p>Software (C++)</p><p><a href="https://github.com/gmarcais/Quorum">https://github.com/gmarcais/Quorum</a></p><p>Trowel</p><p>Software (C++)</p><p><a href="https://sourceforge.net/projects/trowel-ec/">https://sourceforge.net/projects/trowel-ec/</a></p><p>Metagenomics</p><p>Assembly-free phylogenomics</p><p>AAF</p><p>Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (<em>k</em>-mer based)</p><p>Software (Python)</p><p><a href="https://github.com/fanhuan/AAF">https://github.com/fanhuan/AAF</a></p><p>kSNP v3</p><p>Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on&nbsp;<em>k</em>-mer analysis)</p><p>Software (C)</p><p><a href="https://sourceforge.net/projects/ksnp/files/">https://sourceforge.net/projects/ksnp/files/</a></p><p>NGS-MC</p><p>Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2&nbsp;S&nbsp;under different Markov chain models (using&nbsp;<em>k</em>-words)</p><p>R package</p><p><a href="http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html">http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html</a></p><p>Species identification/taxonomic profiling</p><p>CLARK</p><p>Taxonomic classification of metagenomic reads to known bacterial genomes using&nbsp;<em>k</em>-mer search and LCA assignment</p><p>Software (C++)</p><p><a href="http://clark.cs.ucr.edu/">http://clark.cs.ucr.edu/</a></p><p>FOCUS</p><p>Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)</p><p>Web service Software (Python)</p><p><a href="http://edwards.sdsu.edu/FOCUS/">http://edwards.sdsu.edu/FOCUS/</a></p><p>GSM</p><p>Estimation of abundances of microbial genomes in metagenomic samples (<em>k</em>-mer based)</p><p>Software (Go)</p><p><a href="https://github.com/pdtrang/GSM">https://github.com/pdtrang/GSM</a></p><p>Mash</p><p>Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)</p><p>Software (C++)</p><p><a href="https://github.com/marbl/mash">https://github.com/marbl/mash</a></p><p>Kraken</p><p>Taxonomic assignment in metagenome analysis by exact&nbsp;<em>k</em>-mer search; LCA assignment of short reads based on a comprehensive sequence database</p><p>Software (C++)</p><p><a href="https://ccb.jhu.edu/software/kraken/">https://ccb.jhu.edu/software/kraken/</a></p><p>LMAT</p><p>Assignment of taxonomic labels to reads by&nbsp;<em>k</em>-mers searches in precomputed database</p><p>Software (C++/Python)</p><p><a href="https://sourceforge.net/projects/lmat/">https://sourceforge.net/projects/lmat/</a></p><p>stringMLST</p><p><em>k</em>-mer-based tool for MLST directly from the genome sequencing reads</p><p>Software (Python)</p><p><a href="http://jordan.biology.gatech.edu/page/software/stringMLST">http://jordan.biology.gatech.edu/page/software/stringMLST</a></p><p>Taxonomer</p><p><em>k</em>-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples</p><p>Web service</p><p><a href="http://taxonomer.iobio.io/">http://taxonomer.iobio.io/</a></p><p>Other</p><p>d2-tools</p><p>Word-based (<em>k</em>-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads</p><p>Software (Python/R)</p><p><a href="https://code.google.com/p/d2-tools/">https://code.google.com/p/d2-tools/</a></p><p>VirHostMatcher</p><p>Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)</p><p>Software (C++)</p><p><a href="https://github.com/jessieren/VirHostMatcher">https://github.com/jessieren/VirHostMatcher</a></p><p>MetaFast</p><p>Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray&ndash;Curtis dissimilarity measure</p><p>Software (Java)</p><p><a href="https://github.com/ctlab/metafast">https://github.com/ctlab/metafast</a></p></div>]]></description>
	<dc:creator>Abhimanyu Singh</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/36842/gap-filling-or-contigs-extensions-tools</guid>
	<pubDate>Fri, 01 Jun 2018 08:07:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36842/gap-filling-or-contigs-extensions-tools</link>
	<title><![CDATA[Gap filling or Contigs extensions tools !]]></title>
	<description><![CDATA[
<p>There are many tools to perform gap filling using Illumina short reads, for example "GapFiller: a de novo assembly approach to fill the gap within paired reads" or "Toward almost closed genomes with GapFiller". There are also some tools like GAPresolution that can help to perform local re-assemblies using 454 reads. We used GAPresolution but it is not a very good software, it is useful only in some specific situations.</p>

<p>Take a look at the PRICE software from the DeRisi lab. Its meant to do something very similar. http://derisilab.ucsf.edu/index.php?page=software</p>

<p>You could also look at SSPACE (http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/sspacev12/), ATLAS tools (http://www.hgsc.bcm.tmc.edu/content/bcm-hgsc-software), and SCARPA (http://compbio.cs.toronto.edu/hapsembler/scarpa.html).</p>

<p>See the PAGIT protocol: http://www.sanger.ac.uk/resources/software/pagit/ </p>

<p>In particular, take a look at the IMAGE tool: http://genomebiology.com/2010/11/4/R41 </p>

<p>Also SOAPdenovo has ha function for scaffolding. Not sure about ABYSS</p>

<p>Here there is a useful explanation of several tools.</p>

<p>https://bioinformaticsonline.com/search?q=scaffolding&amp;entity_type=object&amp;entity_subtype=bookmarks&amp;offset=0&amp;search_type=entities</p>

<p>I could be wrong, but the above answers to your hypothetical scenario appear to miss the point that you aren't interested in assembling the full genome, just the 100 kb part you're interested in. I suggest the following algorithm:</p>

<p>1. Start with the initial assembly C0 of the contigs you have identified as overlapping your region of interest, and the set S of reads those contigs contain. Let C = C0.</p>

<p>2. Repeat:<br />a. Identify paired-end reads (not in C) for which one or both ends align within, or extending, contigs in C.<br />b. Identify unpaired reads that align extending these new paired-end reads.<br />c. Construct a new assembly C' from C and the new reads identified in (a) and (b).<br />d. Trim C' so it does not extend more than 100 kb to either end of C0. Set C = C'.<br />e. Let S' denote the reads that contribute to C'. If S' does not contain any reads not present in S, stop. Otherwise, Set S = S'.</p>

<p>3. If you don't have a complete assembly of the region of interest, generate an STS for each end of each contig, probe a library for clones including these STSes, subclone these clones into a paired-end sequencing vector, and generate paired-end reads for this library; then try steps (1) and (2) again, adding these new sequencing reads to what you had before.</p>

<p>4. If your average sequencing depth for the region of interest exceeds 25 or so without filling all gaps, it is likely that the remaining gaps represent sequences that are not getting cloned in your sequencing vectors. Try different sequencing vectors.</p>
]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38743/molinspiration-broad-range-of-cheminformatics-software-tools-supporting-molecule-manipulation</guid>
	<pubDate>Sun, 20 Jan 2019 05:32:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38743/molinspiration-broad-range-of-cheminformatics-software-tools-supporting-molecule-manipulation</link>
	<title><![CDATA[molinspiration: broad range of cheminformatics software tools supporting molecule manipulation]]></title>
	<description><![CDATA[<p><span>Molinspiration offers&nbsp;</span><a href="https://www.molinspiration.com/products.html">broad range of cheminformatics software tools</a><span>&nbsp;supporting molecule manipulation and processing, including SMILES and SDfile conversion, normalization of molecules, generation of tautomers, molecule fragmentation, calculation of various molecular properties needed in QSAR, molecular modelling and drug design, high quality molecule depiction, molecular database tools supporting substructure and similarity searches. Our products support also fragment-based virtual screening, bioactivity prediction and data visualization. Molinspiration tools are written in Java, therefore can be used practically on any computer platform.</span></p><p>Address of the bookmark: <a href="https://www.molinspiration.com/" rel="nofollow">https://www.molinspiration.com/</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/40882/troyanskaya-lab</guid>
  <pubDate>Tue, 04 Feb 2020 06:40:36 -0600</pubDate>
  <link></link>
  <title><![CDATA[Troyanskaya Lab]]></title>
  <description><![CDATA[
<p>The goal of our research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods for systems-level pathway modeling through integrative analysis of genome-scale datasets. We apply these approaches in studying challenging biological problems, such as how pathways function in diverse cell types and how they change dynamically.</p>

<p>https://function.princeton.edu/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42166/software-for-genome-assembly</guid>
	<pubDate>Sun, 30 Aug 2020 09:51:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42166/software-for-genome-assembly</link>
	<title><![CDATA[Software for genome assembly !]]></title>
	<description><![CDATA[<p>List of bioinformatics tools/Software Website References for genome assembly:</p><p>1 Falcon&nbsp;https://github.com/PacificBiosciences/pb-assembly</p><p>2 Canu assembler http://canu.readthedocs.io/en/latest/index.html</p><p>3 Miniasm assembler https://github.com/lh3/miniasm</p><p>4 PBJelly scaffolding tool https://sourceforge.net/projects/pb-jelly/</p><p>5 ARCS scaffolding tool https://github.com/bcgsc/arcs</p><p>6 Redundans reduction and scaffolding tool https://github.com/Gabaldonlab/redundans</p><p>7 Arrow error correction https://github.com/PacificBiosciences/ GenomicConsensus</p><p>8 PILON error correction https://github.com/broadinstitute/pilon/wiki</p><p>9 BUSCO single copy gene markers http://busco.ezlab.org/</p><p>10 Bandage graph assembly viewer https://rrwick.github.io/Bandage/</p><p>11 Gepard dotter http://cube.univie.ac.at/gepard</p><p>12 MUMmer aligner and plotter http://mummer.sourceforge.net/</p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<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>

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