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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36514?offset=110</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34685/tools-for-bacterial-whole-genome-annotation</guid>
	<pubDate>Sat, 16 Dec 2017 17:37:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34685/tools-for-bacterial-whole-genome-annotation</link>
	<title><![CDATA[Tools for bacterial whole genome annotation]]></title>
	<description><![CDATA[<p><a href="http://rast.nmpdr.org/">RAST</a>&nbsp;&ndash;&nbsp;Web tool (upload contigs), uses the subsystems in the SEED database and&nbsp;provides detailed annotation and pathway analysis. Takes several hours per genome but I think this is the best way to get a high quality annotation (if you have only a few genomes to annotate).</p><p><a href="http://www.vicbioinformatics.com/software.prokka.shtml">Prokka</a>&nbsp;&ndash;&nbsp;Standalone command line tool, takes just a few minutes per genome.&nbsp;This is the best way to get good quality annotation in a flash, which is particularly useful if you have loads of genomes or need to annotate a pangenome or metagenome. Note however that the quality of functional information is not as good as RAST, and you&nbsp;will need several extra steps if you want to do&nbsp;functional profiling and pathway analysis of your genome(s)&hellip; which is in-built in RAST.</p><p>NCBI Prokaryotic Genome Annotation Pipeline is designed to annotate bacterial and archaeal genomes (chromosomes and plasmids).</p><p>Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.</p><p><a href="https://www.ncbi.nlm.nih.gov/genome/annotation_prok/">PGAP</a>: NCBI has developed an automatic prokaryotic genome annotation pipeline that combines&nbsp;<em>ab initio</em>&nbsp;gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP;&nbsp;<a href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=pubmed&amp;dopt=Abstract&amp;list_uids=18416670">see Pubmed Article</a>) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume.&nbsp; NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.</p><p><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC453985">BEACON</a> (automated tool for Bacterial GEnome Annotation ComparisON), a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at:&nbsp;<a href="http://www.cbrc.kaust.edu.sa/BEACON/" target="pmc_ext">http://www.cbrc.kaust.edu.sa/BEACON/</a>.</p><p><a href="http://www.kegg.jp/blastkoala/">BlastKOLA</a>: Assigns K numbers to the user's sequence data by BLAST searches, respectively, against a nonredundant set of KEGG GENES. KOALA (KEGG Orthology And Links Annotation) is KEGG's internal annotation tool for K number assignment of KEGG GENES using SSEARCH computation. Annotate Sequence in KEGG Mapper and Pathogen Checker in KEGG Pathogen are special interfaces to this server and can be executed in an interactive mode. BlastKOALA is suitable for annotating fully sequenced genomes.</p><p><a href="http://www.sanger.ac.uk/science/tools/pagit">PAGIT</a>: Provides a toolkit for improving the quality of genome assemblies created via an assembly software. PAGIT compiled four tools: (i) ABACAS which classifies and orientates contigs and estimates the sizes of gaps between them; (ii) IMAGE uses paired-end reads to extend contigs and close gaps within the scaffolds; (iii) ICORN for identifying and correcting small errors in consensus sequences and; (iv) RATT for help annotation. The software was mainly created to analyze parasite genomes of up to about 300 Mb.</p><p><a href="http://www.yandell-lab.org/software/maker.html">MAKER: </a>A portable and easily configurable genome annotation pipeline. MAKER allows smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. It identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values. MAKER's inputs are minimal and its ouputs can be directly loaded into a Generic Model Organism Database (GMOD). They can also be viewed in the Apollo genome browser; this feature of MAKER provides an easy means to annotate, view and edit individual contigs and BACs without the overhead of a database. MAKER is available for download and can be tested online via the MAKER Web Annotation Service (MWAS).</p><p><a href="https://www.sciencedirect.com/science/article/pii/S0167701215001207">MyPro</a> is a software pipeline for high-quality prokaryotic genome assembly and annotation. It was validated on 18 oral streptococcal strains to produce submission-ready, annotated draft genomes. MyPro installed as a virtual machine and supported by updated databases will enable biologists to perform quality prokaryotic genome assembly and annotation with ease.</p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36384/binding-site-prediction-in-protein</guid>
	<pubDate>Wed, 25 Apr 2018 04:35:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36384/binding-site-prediction-in-protein</link>
	<title><![CDATA[Binding Site Prediction in Protein !]]></title>
	<description><![CDATA[<p><span>The interaction between proteins and other molecules is fundamental to all biological functions. In this section we include tools that can assist in prediction of interaction sites on protein surface and tools for predicting the structure of the intermolecular complex formed between two or more molecules (docking).</span></p><h4>Pockets Identification</h4><p><a href="http://sts.bioengr.uic.edu/castp/" target="_blank">CASTp</a></p><div style="text-align: justify;">Automatic Identification of pockets and cavities in proteins structure, and quantitation of their volumes using Delaunay triangulation. Available also as PyMOL plugin</div><p><a href="http://www.bioinformatics.leeds.ac.uk/pocketfinder/" target="_blank">Pocket-Finder</a></p><div style="text-align: justify;">Automatic identification of pockets and cavities in proteins structure, and quantitation of their volumes.</div><p><a href="http://gecco.org.chemie.uni-frankfurt.de/pocketpicker/index.html" target="_blank">PocketPicker</a></p><div style="text-align: justify;">Grid-based technique for the analysis of protein pockets. PocketPicker available as a plugin for&nbsp;<a href="https://bip.weizmann.ac.il/toolbox/structure/pymol.htm">PyMOL</a></div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;"><h4>Binding Site Prediction</h4>
<p><a href="http://consurf.tau.ac.il/" target="_blank">ConSurf</a></p>
</div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;">Identification of functional regions in proteins by surface-mapping of phylogenetic information</div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;"><a href="http://www-cryst.bioc.cam.ac.uk/~crescendo/crescendo.php" target="_blank">CRESCENDO</a></div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;">Identification protein interaction sites. It uses sequence conservation patterns in homologous proteins to distinguish between residues that are conserved due to structural restraints from those due to functional restraints.&nbsp;&nbsp;</div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;"><strong>Ligand Binding Sites</strong></div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;"><a href="http://www.sbg.bio.ic.ac.uk/~3dligandsite/" target="_blank">3DLigandSite</a></div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;">The server utilizes protein-structure prediction to provide structural models of the binding site. Ligands bound to structures are superimposed onto the model and use to predict the binding site.</div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;">F<a href="http://cssb.biology.gatech.edu/skolnick/files/FINDSITE/" target="_blank">INDSITE</a></div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;">A threading-based method for ligand-binding site prediction and functional annotation based on binding-site similarity across superimposed groups of threading templates.</div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;">
<p><a href="http://scoppi.biotec.tu-dresden.de/pocket/" target="_blank">LIGSITE<sup>csc</sup></a></p>
<div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;">Prediction of binding site by pocket identification using the Connolly surface and degree of conservation</div>
<p><a href="http://metapocket.eml.org/" target="_blank"></a></p>
</div><div style="text-align: justify;">&nbsp;</div><div style="text-align: justify;"><a href="http://metapocket.eml.org/" target="_blank">metaPocket</a>A meta server for ligand-binding site prediction. metaPocket use&nbsp;<a href="https://bip.weizmann.ac.il/toolbox/structure/binding.htm#ligsite">LIGSITE<sup>csc</sup></a>,&nbsp;<a href="https://bip.weizmann.ac.il/toolbox/structure/binding.htm#pass">PASS</a>,&nbsp;<a href="https://bip.weizmann.ac.il/toolbox/structure/binding.htm#qsite">Q-SiteFinder</a>&nbsp;and&nbsp;<a href="http://www.biochem.ucl.ac.uk/~roman/surfnet/surfnet.html" target="_blank">SURFNET</a></div>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36510/scallop-reference-based-transcriptome-assembler-for-rna-seq</guid>
	<pubDate>Tue, 08 May 2018 04:23:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36510/scallop-reference-based-transcriptome-assembler-for-rna-seq</link>
	<title><![CDATA[Scallop: reference-based transcriptome assembler for RNA-seq]]></title>
	<description><![CDATA[<p>Scallop is an accurate reference-based transcript assembler. Scallop features its high accuracy in assembling multi-exon transcripts as well as lowly expressed transcripts. Scallop achieves this improvement through a novel algorithm that can be proved preserving all phasing paths from reads and paired-end reads, while also achieves both transcripts parsimony and coverage deviation minimization.</p>
<p>Scallop paper has been published at&nbsp;<a href="https://www.nature.com/articles/nbt.4020"><span>Nature Biotechnology</span></a>. The datasets and scripts used in this paper to compare the performance of Scallop and other assemblers are available at&nbsp;<a href="https://github.com/Kingsford-Group/scalloptest"><span>scalloptest</span></a>.</p>
<p>Please also checkout the&nbsp;<span>podcast</span>&nbsp;about Scallop (thanks&nbsp;<a href="https://ro-che.info/">Roman Cheplyaka</a>&nbsp;for the interview). It is available at both&nbsp;<a href="https://bioinformatics.chat/scallop">the bioinformatics chat</a>&nbsp;and&nbsp;<a href="https://itunes.apple.com/us/podcast/the-bioinformatics-chat/id1227281398">iTunes</a>.</p>
<p>&nbsp;</p>
<p>https://github.com/Kingsford-Group/scallop</p><p>Address of the bookmark: <a href="https://github.com/Kingsford-Group/scallop" rel="nofollow">https://github.com/Kingsford-Group/scallop</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40604/gapfinisher-a-reliable-gap-filling-pipeline-for-sspace-longread-scaffolder-output</guid>
	<pubDate>Fri, 24 Jan 2020 06:04:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40604/gapfinisher-a-reliable-gap-filling-pipeline-for-sspace-longread-scaffolder-output</link>
	<title><![CDATA[gapFinisher: A reliable gap filling pipeline for SSPACE-LongRead scaffolder output]]></title>
	<description><![CDATA[<p><span>gapFinisher is based on the controlled use of a previously published gap filling tool FGAP and works on all standard Linux/UNIX command lines. They compare the performance of gapFinisher against two other published gap filling tools PBJelly and GMcloser. </span></p>
<p><span>gapFinisher can fill gaps in draft genomes quickly and reliably.</span></p><p>Address of the bookmark: <a href="https://github.com/kammoji/gapFinisher" rel="nofollow">https://github.com/kammoji/gapFinisher</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43997/tools-for-rna-classification</guid>
	<pubDate>Tue, 08 Nov 2022 03:39:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43997/tools-for-rna-classification</link>
	<title><![CDATA[Tools for RNA classification]]></title>
	<description><![CDATA[<p><span>barrnap</span>&nbsp;-&nbsp;<a href="https://github.com/tseemann/barrnap" target="_blank">https://github.com/tseemann/barrnap</a></p><p><span>CPAT</span>&nbsp;-&nbsp;<a href="https://github.com/liguowang/cpat" target="_blank">https://github.com/liguowang/cpat</a>,&nbsp;<a href="http://lilab.research.bcm.edu/" target="_blank">http://lilab.research.bcm.edu/</a>&nbsp;(web server)</p><p><span>CPC2</span>&nbsp;-&nbsp;<a href="https://github.com/gao-lab/CPC2_standalone" target="_blank">https://github.com/gao-lab/CPC2_standalone</a>,&nbsp;<a href="http://cpc2.gao-lab.org/" target="_blank">http://cpc2.gao-lab.org/</a>&nbsp;(web server)</p><p><span>Infernal</span>&nbsp;-&nbsp;<a href="http://eddylab.org/infernal/" target="_blank">http://eddylab.org/infernal/</a>,&nbsp;<a href="https://github.com/EddyRivasLab/infernal" target="_blank">https://github.com/EddyRivasLab/infernal</a></p><p><span>NCBI RefSeq</span>&nbsp;-&nbsp;<a href="https://www.ncbi.nlm.nih.gov/refseq/" target="_blank">https://www.ncbi.nlm.nih.gov/refseq/</a></p><p><span>Rfam</span>&nbsp;-&nbsp;<a href="http://rfam.xfam.org/" target="_blank">http://rfam.xfam.org/</a>,&nbsp;<a href="https://docs.rfam.org/en/latest/index.html" target="_blank">https://docs.rfam.org/en/latest/index.html</a></p><p><span>SILVA</span>&nbsp;-&nbsp;<a href="https://www.arb-silva.de/" target="_blank">https://www.arb-silva.de/</a></p><p><span>RNAmmer</span>&nbsp;-&nbsp;<a href="http://www.cbs.dtu.dk/services/RNAmmer/" target="_blank">http://www.cbs.dtu.dk/services/RNAmmer/</a>&nbsp;(web server, standalone download link)</p>]]></description>
	<dc:creator>Abhi</dc:creator>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44718/mycology-research-resources-for-bioinformaticians-unlocking-the-fungal-kingdom</guid>
	<pubDate>Fri, 13 Dec 2024 11:21:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44718/mycology-research-resources-for-bioinformaticians-unlocking-the-fungal-kingdom</link>
	<title><![CDATA[Mycology Research Resources for Bioinformaticians: Unlocking the Fungal Kingdom]]></title>
	<description><![CDATA[<p>Mycology, the study of fungi, is a field that bridges ecology, medicine, and biotechnology. With advancements in bioinformatics, researchers now have unprecedented opportunities to explore the fungal kingdom at molecular, genetic, and ecological levels. From understanding pathogenic fungi to harnessing fungal enzymes for industrial applications, the potential is vast.</p><p>To fully leverage these opportunities, bioinformaticians require specialized tools and databases. This blog highlights essential resources for mycology research, focusing on databases, tools, and platforms tailored for fungal biology.</p><h4><strong>1. Fungal Databases</strong></h4><h5><strong>1.1. MycoCosm</strong></h5><p><strong>Website</strong>: <a target="_new">MycoCosm</a><br />Developed by the DOE Joint Genome Institute, MycoCosm is a comprehensive portal for fungal genomics. It offers genomic and transcriptomic data for a wide range of fungi, including saprobes, pathogens, and symbionts.</p><ul>
<li><strong>Key Features</strong>: Genome browsers, comparative genomics tools, and functional annotations.</li>
<li><strong>Best For</strong>: Large-scale studies on fungal evolution and ecology.</li>
</ul><h5><strong>1.2. FungiDB</strong></h5><p><strong>Website</strong>: <a href="https://fungidb.org/" target="_new">FungiDB</a><br />FungiDB is an integrated genomic resource for fungal pathogens and non-pathogens. It provides access to genome sequences, transcriptomic data, and functional annotations.</p><ul>
<li><strong>Key Features</strong>: Advanced search options, BLAST, and pathway analysis tools.</li>
<li><strong>Best For</strong>: Studying fungal pathogenesis and host-pathogen interactions.</li>
</ul><h5><strong>1.3. Index Fungorum</strong></h5><p><strong>Website</strong>: <a href="http://www.indexfungorum.org/" target="_new">Index Fungorum</a><br />This nomenclatural database provides information on the scientific names of fungi. It&rsquo;s an essential resource for taxonomists and researchers focused on fungal biodiversity.</p><ul>
<li><strong>Key Features</strong>: Taxonomic hierarchy and synonymy tracking.</li>
<li><strong>Best For</strong>: Identifying and classifying fungal species.</li>
</ul><h5><strong>1.4. UNITE</strong></h5><p><strong>Website</strong>: <a target="_new">UNITE</a><br />UNITE is a specialized database for fungal ITS (Internal Transcribed Spacer) sequences, often used in fungal identification and phylogenetics.</p><ul>
<li><strong>Key Features</strong>: Curated reference datasets and community annotations.</li>
<li><strong>Best For</strong>: Environmental mycology and microbial ecology studies.</li>
</ul><h4><strong>2. Analytical Tools</strong></h4><h5><strong>2.1. Funannotate</strong></h5><p><strong>Repository</strong>: <a href="https://github.com/nextgenusfs/funannotate" target="_new">GitHub - Funannotate</a><br />Funannotate is a genome annotation tool designed for fungi. It supports tasks like gene prediction, functional annotation, and orthology analysis.</p><ul>
<li><strong>Best For</strong>: Annotating newly sequenced fungal genomes.</li>
</ul><h5><strong>2.2. BUSCO (Benchmarking Universal Single-Copy Orthologs)</strong></h5><p><strong>Website</strong>: <a target="_new">BUSCO</a><br />BUSCO evaluates genome assembly and annotation completeness using orthologs. It includes a fungal-specific dataset.</p><ul>
<li><strong>Best For</strong>: Assessing the quality of fungal genome assemblies.</li>
</ul><h5><strong>2.3. Pathogen-Host Interactions Database (PHI-base)</strong></h5><p><strong>Website</strong>: <a href="http://www.phi-base.org/" target="_new">PHI-base</a><br />PHI-base is a manually curated resource containing information on pathogen-host interactions, including fungal pathogens.</p><ul>
<li><strong>Best For</strong>: Exploring virulence factors and host-pathogen relationships.</li>
</ul><h4><strong>3. Visualization Platforms</strong></h4><h5><strong>3.1. Cytoscape</strong></h5><p><strong>Website</strong>: <a href="https://cytoscape.org/" target="_new">Cytoscape</a><br />A powerful tool for visualizing molecular interaction networks, Cytoscape can be used to study protein-protein interactions, gene networks, and metabolic pathways in fungi.</p><ul>
<li><strong>Best For</strong>: Network biology and functional genomics.</li>
</ul><h5><strong>3.2. iTOL (Interactive Tree of Life)</strong></h5><p><strong>Website</strong>: <a target="_new">iTOL</a><br />iTOL is an interactive tool for visualizing phylogenetic trees.</p><ul>
<li><strong>Best For</strong>: Displaying fungal phylogenies and comparing evolutionary relationships.</li>
</ul><h4><strong>4. Community Resources</strong></h4><h5><strong>4.1. Mycological Society of America (MSA)</strong></h5><p><strong>Website</strong>: <a href="https://msafungi.org/" target="_new">MSA</a><br />The MSA promotes fungal research and provides access to resources, conferences, and publications.</p><ul>
<li><strong>Best For</strong>: Networking with fungal researchers and accessing recent studies.</li>
</ul><h5><strong>4.2. OpenFungi</strong></h5><p><strong>Website</strong>: <a href="https://openfungi.org/" target="_new">OpenFungi</a><br />OpenFungi is an open-source initiative providing fungal genomic and transcriptomic datasets for research and education.</p><ul>
<li><strong>Best For</strong>: Sharing and accessing public fungal datasets.</li>
</ul><h4><strong>5. Genomics Workflows</strong></h4><h5><strong>5.1. Galaxy</strong></h5><p><strong>Website</strong>: <a href="https://usegalaxy.org/" target="_new">Galaxy Project</a><br />Galaxy offers a web-based platform for reproducible bioinformatics workflows, including tools for fungal genome and transcriptome analysis.</p><ul>
<li><strong>Best For</strong>: User-friendly analysis pipelines without requiring coding skills.</li>
</ul><h5><strong>5.2. Snakemake</strong></h5><p><strong>Repository</strong>: <a target="_new">Snakemake</a><br />A flexible pipeline management tool that supports fungal data processing and analysis.</p><ul>
<li><strong>Best For</strong>: Custom workflows for large-scale fungal datasets.</li>
</ul><h4><strong>Conclusion</strong></h4><p>Fungal research is a rapidly growing field with vast implications for medicine, agriculture, and industry. For bioinformaticians, the availability of specialized resources&mdash;databases, tools, and community platforms&mdash;opens doors to innovative discoveries. Whether you are investigating fungal genomics, studying host-pathogen interactions, or exploring fungal biodiversity, the resources outlined above will empower your research journey.</p><p>Dive into these resources and help unravel the mysteries of the fungal kingdom!</p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35395/comprehensive-list-of-visualization-tools-for-biological-pathways</guid>
	<pubDate>Tue, 30 Jan 2018 06:01:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35395/comprehensive-list-of-visualization-tools-for-biological-pathways</link>
	<title><![CDATA[Comprehensive list of visualization tools for biological pathways]]></title>
	<description><![CDATA[<p>The study of biological pathways is a key to understand the different processes inside a cell: proteins exert their function not in isolation but in a tightly controlled network of interactions and reactions. Activation of a pathway typically leads to a change of state in the cell. Pathways come in different flavors, depending on their functions in the cell &ndash; the three main types are metabolic pathways, gene regulatory pathways, and signaling pathways. These biological pathways and networks are not only an appropriate approach to visualize molecular reactions. They have also become one leading method in -omics data analysis and visualization.</p><p><img src="https://photos-1.dropbox.com/t/2/AABemz29qAuSTqSzr5mEsQE7JIMxZlU1CBy0E5n0yUVYbA/12/85115969/png/32x32/1/_/1/2/pathway.png/EOfXoUIYrJ8CIAcoBw/01qsT2eykyPvSH-rNpy3cqioDzZPc4i-xULG3BEZvCk?preserve_transparency=1&amp;size=1280x960&amp;size_mode=3" width="800" height="533" alt="image" style="border: 0px;"></p><p>Following are the comprehensive list of visualization tools for biological pathways:</p><p>BiNA</p><p>Drawings of metabolic networks supporting hiding of cofactors and drawing of chemical structures</p><p>http://bina.unipax.info/</p><p>BioTapestry</p><p>Interactive tool for building, visualizing and sharing gene regulatory network models over the web</p><p>http://www.biotapestry.org/</p><p>Caleydo</p><p>Visual analysis framework targeted at biomolecular data. Visualization of interdependencies between multiple datasets</p><p>http://www.caleydo.org/</p><p>CellDesigner</p><p>A modeling tool for biochemical networks</p><p>http://www.celldesigner.org/</p><p>Edinburgh Pathway Editor</p><p>Edit and draw pathway diagrams</p><p>http://epe.sourceforge.net/SourceForge/EPE.html</p><p>GenMAPP</p><p>Visualization of gene expression and other genomic data on maps representing biological pathways and groupings of genes</p><p>http://www.genmapp.org/</p><p>Ingenuity IPA</p><p>Data integration platform and manually annotated pathways</p><p>http://tinyurl.com/IngenuityPath</p><p>JDesigner</p><p>Graphical modeling environment for biochemical reaction networks</p><p>http://jdesigner.sourceforge.net/Site/JDesigner.html</p><p>KaPPA View</p><p>Plant pathways</p><p>http://kpv.kazusa.or.jp/</p><p>KEGG Atlas</p><p>Interactive Kyoto Encyclopedia of Genes and Genomes pathways</p><p>http://www.genome.jp/kegg/</p><p>Omix&nbsp;</p><p>Visualizing multi-omics data in metabolic networks</p><p>https://www.omix-visualization.com</p><p>PathVisio&nbsp;</p><p>Biological pathway analysis software that allows drawing, editing and analysis of biological pathways</p><p>http://www.pathvisio.org/</p><p>VitaPad&nbsp;</p><p>Application to visualize biological pathways and map experimental data to them</p><p>http://tinyurl.com/vitapad/</p><p>Web tools for pathways</p><p>ArrayXPath&nbsp;</p><p>Mapping and visualizing microarray gene-expression data and integrated biological pathway resources using SVG</p><p>http://tinyurl.com/ArrayXPath/</p><p>GEPAT&nbsp;</p><p>Integrated analysis of transcriptome data in genomic, proteomic and metabolic contexts</p><p>http://gepat.sourceforge.net/</p><p>iPath&nbsp;</p><p>Web-based tool for the visualization, analysis and customization of pathway maps</p><p>http://pathways.embl.de/</p><p>Kegg-Based Viewer&nbsp;</p><p>KEGG-based pathway visualization tool for complex high-throughput data</p><p>http://www.g-language.org/data/marray/</p><p>MapMan&nbsp;</p><p>User-driven tool that displays large datasets onto diagrams of metabolic pathways or other processes</p><p>http://mapman.gabipd.org/web/guest/mapman</p><p>MetPA&nbsp;</p><p>Analysis and visualization of metabolomic data within the biological context of metabolic pathways</p><p>http://metpa.metabolomics.ca</p><p>Omics Viewer&nbsp;</p><p>Data mapping on BioCyc pathways (collection of 5500 pathway/genome databases)</p><p>http://www.biocyc.org/</p><p>Pathway Explorer</p><p>Interactive Java drawing tool for the construction of biological pathway diagrams in a visual way and the annotation of the components and interactions between them</p><p>http://genome.tugraz.at/pathwayexplorer/pathwayexplorer_description.shtml</p><p>Pathway projector&nbsp;</p><p>Zoomable pathway browser using KEGG atlas and Google Maps API</p><p>http://www.g-language.org/PathwayProjector/</p><p>PATIKA&nbsp;</p><p>Integrated environment composed of a central database and a visual editor, built around an extensive ontology and an integration framework</p><p>http://www.cs.bilkent.edu.tr/~patikaweb/</p><p>Reactome SkyPainter&nbsp;</p><p>Visualization of over-represented pathways and reactions from gene lists</p><p>http://www.reactome.org/skypainter-2</p><p>WikiPathways</p><p>Wiki-based, open, public platform dedicated to the curation of biological pathways by and for the scientific community</p><p>http://www.wikipathways.org/</p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35057/ectools-long-read-correction-and-other-correction-tools</guid>
	<pubDate>Fri, 05 Jan 2018 04:02:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35057/ectools-long-read-correction-and-other-correction-tools</link>
	<title><![CDATA[ECTOOLS: Long Read Correction and other Correction tools]]></title>
	<description><![CDATA[<p>Long Read Correction and other Correction tools</p>
<p>This package is a loose collection of scripts. To run the correction<br>routine see the section below. Descriptions of the other scripts<br>are at the bottom of this file.</p>
<p>Contact: gurtowsk@cshl.edu</p>
<p>In short, the correction algorithm takes as input the unitigs from a short read assembly and uses them to correct long read data. More background information for the algorithm can be found:<br>http://schatzlab.cshl.edu/presentations/2013-06-18.PBUserMeeting.pdf</p><p>Address of the bookmark: <a href="https://github.com/jgurtowski/ectools" rel="nofollow">https://github.com/jgurtowski/ectools</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36516/metassembler-merging-and-optimizing-de-novo-genome-assemblies</guid>
	<pubDate>Tue, 08 May 2018 04:52:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36516/metassembler-merging-and-optimizing-de-novo-genome-assemblies</link>
	<title><![CDATA[Metassembler: merging and optimizing de novo genome assemblies]]></title>
	<description><![CDATA[<p><span>Metassembler combines multiple whole genome de novo assemblies into a combined consensus assembly using the best segments of the individual assemblies.</span></p>
<p><span><span>Genome assembly projects typically run multiple algorithms in an attempt to find the single best assembly, although those assemblies often have complementary, if untapped, strengths and weaknesses. We present our metassembler algorithm that merges multiple assemblies of a genome into a single superior sequence.&nbsp;</span></span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/metassembler/?source=directory" rel="nofollow">https://sourceforge.net/projects/metassembler/?source=directory</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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