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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43683?offset=40</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39674/simka-and-simkamin-are-comparative-metagenomics-method-dedicated-to-ngs-datasets</guid>
	<pubDate>Sat, 06 Jul 2019 13:56:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39674/simka-and-simkamin-are-comparative-metagenomics-method-dedicated-to-ngs-datasets</link>
	<title><![CDATA[Simka and SimkaMin are comparative metagenomics method dedicated to NGS datasets]]></title>
	<description><![CDATA[<p>Simka is a de novo comparative metagenomics tool. Simka represents each dataset as a k-mer spectrum and compute several classical ecological distances between them.</p>
<p>Developper:&nbsp;<a href="http://people.rennes.inria.fr/Gaetan.Benoit/">Ga&euml;tan Benoit</a>, PhD, former member of the&nbsp;<a href="http://team.inria.fr/genscale/">Genscale</a>&nbsp;team at Inria.</p>
<p>Contact: claire dot lemaitre at inria dot fr</p>
<p><span>Simka and SimkaMin are comparative metagenomics method dedicated to NGS datasets.&nbsp;</span><span></span><span><a href="https://gatb.inria.fr/software/simka/">https://gatb.inria.fr/software/simka/</a></span></p><p>Address of the bookmark: <a href="https://github.com/GATB/simka" rel="nofollow">https://github.com/GATB/simka</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40948/bio7-an-integrated-development-environment-for-ecological-modeling-scientific-image-analysis-and-statistical-analysis</guid>
	<pubDate>Fri, 07 Feb 2020 23:32:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40948/bio7-an-integrated-development-environment-for-ecological-modeling-scientific-image-analysis-and-statistical-analysis</link>
	<title><![CDATA[Bio7: an integrated development environment for ecological modeling, scientific image analysis and statistical analysis]]></title>
	<description><![CDATA[<p><span>The application Bio7 is an integrated development environment for ecological modeling, scientific image analysis and statistical analysis. The application itself is based on an RCP-Eclipse-Environment (Rich-Client-Platform) which offers a huge flexibility in configuration and extensibility because of its plug-in structure and the possibility of customization.</span></p>
<p><a href="https://bio7.org/about/">https://bio7.org/about/</a></p><p>Address of the bookmark: <a href="https://bio7.org/home-2/" rel="nofollow">https://bio7.org/home-2/</a></p>]]></description>
	<dc:creator>Nidhi Rajput</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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40862/alien-index-identify-potential-contaminants-or-horizontally-transferred-genes-in-transcriptomes</guid>
	<pubDate>Sun, 02 Feb 2020 13:51:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40862/alien-index-identify-potential-contaminants-or-horizontally-transferred-genes-in-transcriptomes</link>
	<title><![CDATA[alien_index : identify potential contaminants or horizontally transferred genes in transcriptomes.]]></title>
	<description><![CDATA[<p><span>identify potential contaminants or horizontally transferred genes in transcriptomes.</span></p>
<p><span><span>algorithm is based on alogorithm described in the following: Gladyshev, Eugene A., Matthew Meselson, and Irina R. Arkhipova. "Massive horizontal gene transfer in bdelloid rotifers." science 320.5880 (2008): 1210-1213.</span></span></p>
<p><a href="https://github.com/josephryan/alien_index">alien_index</a></p><p>Address of the bookmark: <a href="https://github.com/josephryan/alien_index" rel="nofollow">https://github.com/josephryan/alien_index</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43011/deg-50-a-database-of-essential-genes-in-both-prokaryotes-and-eukaryotes</guid>
	<pubDate>Tue, 30 Mar 2021 11:47:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43011/deg-50-a-database-of-essential-genes-in-both-prokaryotes-and-eukaryotes</link>
	<title><![CDATA[DEG 5.0: a database of essential genes in both prokaryotes and eukaryotes]]></title>
	<description><![CDATA[<p><span>Essential genes are those indispensable for the survival of an organism, and their functions are therefore considered a foundation of life. Determination of a minimal gene set needed to sustain a life form, a fundamental question in biology, plays a key role in the emerging field, synthetic biology. </span></p>
<p><span></span><span>DEG is freely available at the website&nbsp;</span><a href="http://tubic.tju.edu.cn/deg" target="_blank">http://tubic.tju.edu.cn/deg</a><span>&nbsp;or&nbsp;</span><a href="http://www.essentialgene.org/" target="_blank">http://www.essentialgene.org</a><span>.</span></p><p>Address of the bookmark: <a href="http://www.essentialgene.org/" rel="nofollow">http://www.essentialgene.org/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44479/doubletrouble-identify-duplicated-genes-from-whole-genome-protein-sequences-and-classify</guid>
	<pubDate>Tue, 05 Mar 2024 00:23:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44479/doubletrouble-identify-duplicated-genes-from-whole-genome-protein-sequences-and-classify</link>
	<title><![CDATA[doubletrouble: identify duplicated genes from whole-genome protein sequences and classify]]></title>
	<description><![CDATA[<p><span>doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a simpler classification scheme, duplicates can also be classified into SD- and SSD-derived (small-scale duplication) gene pairs. Besides classifying gene pairs, users can also classify genes, so that each gene is assigned a unique mode of duplication. Users can also calculate substitution rates per substitution site (i.e., Ka and Ks) from duplicate pairs, find peaks in Ks distributions with Gaussian Mixture Models (GMMs), and classify gene pairs into age groups based on Ks peaks.</span></p><p>Address of the bookmark: <a href="https://bioconductor.org/packages/release/bioc/html/doubletrouble.html" rel="nofollow">https://bioconductor.org/packages/release/bioc/html/doubletrouble.html</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4408/fourth-branch-of-life</guid>
	<pubDate>Mon, 09 Sep 2013 21:48:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4408/fourth-branch-of-life</link>
	<title><![CDATA[Fourth Branch of Life]]></title>
	<description><![CDATA[<p>Scientist have found the biggest viruses known, pandoraviruses which opened up entirely /completely... new questions questions and raise objections to in science. It even suggesting a fourth domain of life.</p><p>The new visrus are about one micron&mdash;a thousandth of a millimeter&mdash;in length, the newfound genus Pandoravirus dwarfs other viruses, which range in size from about 50 nanometers up to 100 nanometers. A genus is a taxonomic ranking between species and family.</p><p>Find&nbsp; more at @ http://www.nature.com/scitable/blog/viruses101/newly_found_pandoraviruses_hint_at</p><p>http://news.nationalgeographic.co.uk/news/2013/07/130718-viruses-pandoraviruses-science-biology-evolution/</p><p>&nbsp;</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27850/clusterprofiler</guid>
	<pubDate>Thu, 16 Jun 2016 18:57:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27850/clusterprofiler</link>
	<title><![CDATA[clusterProfiler]]></title>
	<description><![CDATA[<p>statistical analysis and visulization of functional profiles for genes and gene clusters<br><br>Bioconductor version: Release (3.3)<br><br>This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters.<br><br>Author: Guangchuang Yu &lt;guangchuangyu at gmail.com&gt; with contributions from Li-Gen Wang and Giovanni Dall'Olio.<br><br>Maintainer: Guangchuang Yu &lt;guangchuangyu at gmail.com&gt;<br><br>Citation (from within R, enter citation("clusterProfiler")):<br><br>Yu G, Wang L, Han Y and He Q (2012). &ldquo;clusterProfiler: an R package for comparing biological themes among gene clusters.&rdquo; OMICS: A Journal of Integrative Biology, 16(5), pp. 284-287.<br>Installation<br><br>To install this package, start R and enter:<br><br>## try http:// if https:// URLs are not supported<br>source("https://bioconductor.org/biocLite.R")<br>biocLite("clusterProfiler")</p>
<p>https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html</p><p>Address of the bookmark: <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html" rel="nofollow">https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29620/hybpiper</guid>
	<pubDate>Fri, 04 Nov 2016 05:02:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29620/hybpiper</link>
	<title><![CDATA[HybPiper]]></title>
	<description><![CDATA[<p>HybPiper was designed for targeted sequence capture, in which DNA sequencing libraries are enriched for gene regions of interest, especially for phylogenetics. HybPiper is a suite of Python scripts that wrap and connect bioinformatics tools in order to extract target sequences from high-throughput DNA sequencing reads.</p>
<p>Targeted bait capture is a technique for sequencing many loci simultaneously based on bait sequences. HybPiper pipeline starts with high-throughput sequencing reads (for example from Illumina MiSeq), and assigns them to target genes using BLASTx or BWA. The reads are distributed to separate directories, where they are assembled separately using SPAdes. The main output is a FASTA file of the (in frame) CDS portion of the sample for each target region, and a separate file with the translated protein sequence.</p>
<p>HybPiper also includes post-processing scripts, run after the main pipeline, to also extract the intronic regions flanking each exon, investigate putative paralogs, and calculate sequencing depth. For more information,&nbsp;<a href="https://github.com/mossmatters/HybPiper/wiki/">please see our wiki</a>.</p>
<p>HybPiper is run separately for each sample (single or paired-end sequence reads). When HybPiper generates sequence files from the reads, it does so in a standardized directory hierarchy. Many of the post-processing scripts rely on this directory hierarchy, so do not modify it after running the initial pipeline. It is a good idea to run the pipeline for each sample from the same directory. You will end up with one directory per run of HybPiper, and some of the later scripts take advantage of this predictable directory structure.</p><p>Address of the bookmark: <a href="https://github.com/mossmatters/HybPiper" rel="nofollow">https://github.com/mossmatters/HybPiper</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40505/decostar-reconstructing-the-ancestral-organization-of-genes-or-genomes-using-reconciled-phylogenies</guid>
	<pubDate>Fri, 03 Jan 2020 13:28:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40505/decostar-reconstructing-the-ancestral-organization-of-genes-or-genomes-using-reconciled-phylogenies</link>
	<title><![CDATA[DeCoSTAR: Reconstructing the Ancestral Organization of Genes or Genomes Using Reconciled Phylogenies]]></title>
	<description><![CDATA[<p>DeCoSTAR computes adjacency evolutionary scenarios using a scoring scheme based on a weighted sum of adjacency gains and breakages. Solutions, both optimal and near-optimal, are sampled according to the Boltzmann&ndash;Gibbs distribution centered around parsimonious solutions, and statistical supports on ancestral and extant adjacencies are provided. DeCoSTAR supports the features of previously contributed tools that reconstruct ancestral adjacencies, namely DeCo, DeCoLT, ART-DeCo, and DeClone. In a few minutes, DeCoSTAR can reconstruct the evolutionary history of domains inside genes, of gene fusion and fission events, or of gene order along chromosomes, for large data sets including dozens of whole genomes from all kingdoms of life.</p><p>Address of the bookmark: <a href="https://github.com/YoannAnselmetti/DeCoSTAR_pipeline" rel="nofollow">https://github.com/YoannAnselmetti/DeCoSTAR_pipeline</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>

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