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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40604?offset=330</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44375/phyloherb-a-high%E2%80%90throughput-phylogenomic-pipeline-for-processing-genome-skimming-data</guid>
	<pubDate>Wed, 06 Sep 2023 00:14:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44375/phyloherb-a-high%E2%80%90throughput-phylogenomic-pipeline-for-processing-genome-skimming-data</link>
	<title><![CDATA[PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome skimming data]]></title>
	<description><![CDATA[<p dir="auto"><span>Phylo</span>genomic Analysis Pipeline for&nbsp;<span>Herb</span>arium Specimens</p>
<p dir="auto"><span>What is PhyloHerb</span>: PhyloHerb is a wrapper program to process&nbsp;<span>genome skimming</span>&nbsp;data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include&nbsp;<span>low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming</span>.</p>
<p dir="auto"><span>License</span>: GNU General Public License</p>
<p dir="auto"><span>Citation</span>:</p>
<ul dir="auto">
<li>Cai, Liming, Hongrui Zhang, and Charles C. Davis. 2022. PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome‐skimming data. Applications in Plant Sciences 10(3): 1&ndash;9.&nbsp;<a href="https://doi.org/10.1002/aps3.11475">https://doi.org/10.1002/aps3.11475</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/lmcai/PhyloHerb/" rel="nofollow">https://github.com/lmcai/PhyloHerb/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44597/imagine-in-silico-metagenomics-pipeline</guid>
	<pubDate>Sat, 06 Jul 2024 04:32:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44597/imagine-in-silico-metagenomics-pipeline</link>
	<title><![CDATA[iMAGine - in silico MetAGenomics pipeline]]></title>
	<description><![CDATA[<p dir="auto"><span>iMAGine</span>&nbsp;is a metagenomic workflow which includes filtering, assembling, and binning.</p>
<p dir="auto">This workflow includes the following tools which are needed to be installed in the system.</p>
<ol dir="auto">
<li><a href="https://github.com/OpenGene/fastp">fastp</a></li>
<li><a href="https://github.com/ablab/spades">spades assembler</a></li>
<li><a href="https://github.com/ablab/quast">QUAST</a></li>
<li><a href="https://github.com/lh3/bwa">bwa</a></li>
<li><a href="https://github.com/samtools/samtools">samtools</a></li>
<li><a href="https://bitbucket.org/berkeleylab/metabat/src/master/">metabat2</a></li>
<li><a href="https://github.com/Ecogenomics/CheckM">CheckM</a></li>
</ol><p>Address of the bookmark: <a href="https://github.com/avishekdutta14/iMAGine" rel="nofollow">https://github.com/avishekdutta14/iMAGine</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads</guid>
	<pubDate>Mon, 11 Jun 2018 05:14:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads</link>
	<title><![CDATA[npScarf: real-time scaffolder using SPAdes contigs and Nanopore sequencing reads]]></title>
	<description><![CDATA[npScarf (jsa.np.npscarf) is a program that connect contigs from a draft genomes to generate sequences that are closer to finish. These pipelines can run on a single laptop for microbial datasets. In real-time mode, it can be integrated with simple structural analyses such as gene ordering, plasmid forming.<p>Address of the bookmark: <a href="http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html" rel="nofollow">http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools</guid>
	<pubDate>Tue, 16 Jul 2013 14:30:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools</link>
	<title><![CDATA[List of popular bioinformatics software/tools]]></title>
	<description><![CDATA[<p><a href="http://samtools.sourceforge.net/swlist.shtml">I</a>n current genome era, our day to day work is to handle the huge geneome sequences, expression data, several other datasets. This link provide a comprehensive list of commonly used sofware/tools.</p><p>Address of the bookmark: <a href="http://samtools.sourceforge.net/swlist.shtml" rel="nofollow">http://samtools.sourceforge.net/swlist.shtml</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26852/awesome-bioinformatics-pipelines</guid>
	<pubDate>Wed, 30 Mar 2016 21:50:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26852/awesome-bioinformatics-pipelines</link>
	<title><![CDATA[Awesome bioinformatics pipelines !]]></title>
	<description><![CDATA[<p><span>A curated list of awesome pipeline toolkits ...</span></p>
<p><span>https://github.com/pditommaso/awesome-pipeline</span></p><p>Address of the bookmark: <a href="https://github.com/pditommaso/awesome-pipeline" rel="nofollow">https://github.com/pditommaso/awesome-pipeline</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</guid>
	<pubDate>Wed, 22 Jun 2016 05:37:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</link>
	<title><![CDATA[DarkHorse]]></title>
	<description><![CDATA[<p><em>DarkHorse</em>&nbsp;is a bioinformatic method for rapid, automated identification and ranking of phylogenetically atypical proteins on a genome-wide basis. It works by selecting potential ortholog matches from a reference database of amino acid sequences, then using these matches to calculate a lineage probability index (LPI) score for each genome protein.</p>
<p>LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scale<em>de novo</em>&nbsp;predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.</p>
<p>One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.</p>
<p><em>DarkHorse</em>&nbsp;is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the&nbsp;<a href="http://darkhorse.ucsd.edu/#references">references</a>&nbsp;cited below for a complete explanation of parameter selection and result interpretation. A brief&nbsp;<a href="http://darkhorse.ucsd.edu/tutorial.html">tutorial</a>&nbsp;page is also available on-line.</p><p>Address of the bookmark: <a href="http://darkhorse.ucsd.edu/download.html" rel="nofollow">http://darkhorse.ucsd.edu/download.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30701/harvest</guid>
	<pubDate>Tue, 31 Jan 2017 10:57:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30701/harvest</link>
	<title><![CDATA[Harvest]]></title>
	<description><![CDATA[<p>Harvest is a suite of core-genome alignment and visualization tools for quickly analyzing thousands of intraspecific microbial genomes, including variant calls, recombination detection, and phylogenetic trees.</p>
<p><a href="http://harvest.readthedocs.io/en/latest/_images/screen.png"><img src="http://harvest.readthedocs.io/en/latest/_images/screen.png" alt="_images/screen.png" style="border: 0px;"></a><span></span></p>
<p><strong>Tools</strong></p>
<ul>
<li><a href="http://harvest.readthedocs.io/en/latest/content/parsnp.html">Parsnp</a>&nbsp;- Core-genome alignment and analysis</li>
<li><a href="http://harvest.readthedocs.io/en/latest/content/gingr.html">Gingr</a>&nbsp;- Interactive visualization of alignments, trees and variants</li>
<li><a href="http://harvest.readthedocs.io/en/latest/content/harvest-tools.html">HarvestTools</a>&nbsp;- Archiving and postprocessing</li>
</ul>
<p><strong>Citation</strong></p>
<blockquote>
<div>Treangen TJ, Ondov BD, Koren S, Phillippy AM. The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biology, 15 (11), 1-15 [<a href="http://www.biomedcentral.com/content/pdf/s13059-014-0524-x.pdf">PDF</a>]</div>
</blockquote><p>Address of the bookmark: <a href="http://harvest.readthedocs.io/en/latest/index.html" rel="nofollow">http://harvest.readthedocs.io/en/latest/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<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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<item>
	<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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping</guid>
	<pubDate>Tue, 08 May 2018 04:15:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping</link>
	<title><![CDATA[MITObim - mitochondrial baiting and iterative mapping]]></title>
	<description><![CDATA[<p>This document contains instructions on how to use the MITObim pipeline described in Hahn et al. 2013. The full article can be found&nbsp;<a href="http://nar.oxfordjournals.org/content/41/13/e129" title="MITObim full article at NAR">here</a>. Kindly cite the article if you are using MITObim in your work. The pipeline was originally developed for&nbsp;<span>Illumina</span>&nbsp;data, but thanks to the versatility of the MIRA assembler, MITObim supports in principle also data from the&nbsp;<span>Iontorrent</span>,&nbsp;<span>454</span>&nbsp;and&nbsp;<span>PacBio</span>&nbsp;sequencing platforms.</p>
<p>Below you can find a few basic tutorials for how to run MITObim and I encorage you to give them a try with the testdata that comes with this Repo, just to make sure everything is running smoothly on your system. It'll only take a few minutes and will potentially safe you a lot of time down the line.</p>
<p>I provide further examples&nbsp;<a href="https://github.com/chrishah/MITObim/tree/master/examples">here</a>&nbsp;as Jupyter notebooks. Get in touch if you feel like sharing your particular MITObim solution and I'd be happy to put it up here, too!</p><p>Address of the bookmark: <a href="https://github.com/chrishah/MITObim" rel="nofollow">https://github.com/chrishah/MITObim</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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