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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44318?offset=80</link>
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	<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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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32948/simba-a-web-tool-for-managing-bacterial-genome-assembly-generated-by-ion-pgm-sequencing-technology</guid>
	<pubDate>Tue, 23 May 2017 05:28:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32948/simba-a-web-tool-for-managing-bacterial-genome-assembly-generated-by-ion-pgm-sequencing-technology</link>
	<title><![CDATA[SIMBA: a web tool for managing bacterial genome assembly generated by Ion PGM sequencing technology]]></title>
	<description><![CDATA[<p><span>SIMBA</span><span>, SImple Manager for Bacterial Assemblies, is a Web interface for managing assembly projects of bacterial genomes. SIMBA was created to assist bioinformaticians to assemble bacterial genomes sequenced with NextGeneration Sequencing (NGS) platforms quickly, easily and effectively. SIMBA also is open source tool, i.e., can be freely downloaded, shared and modified.</span></p>
<p>https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1344-7</p><p>Address of the bookmark: <a href="http://ufmg-simba.sourceforge.net/" rel="nofollow">http://ufmg-simba.sourceforge.net/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32187/chromhmm-chromatin-state-discovery-and-characterization</guid>
	<pubDate>Wed, 19 Apr 2017 04:06:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32187/chromhmm-chromatin-state-discovery-and-characterization</link>
	<title><![CDATA[ChromHMM: Chromatin state discovery and characterization]]></title>
	<description><![CDATA[<p><span>ChromHMM is software for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.&nbsp;</span><br><br></p>
<ul>
<li><a href="http://compbio.mit.edu/ChromHMM/ChromHMM.zip">ChromHMM software v1.12</a>&nbsp;(<a href="http://compbio.mit.edu/ChromHMM/versionlog.txt">version log</a>)</li>
<li><a href="http://compbio.mit.edu/ChromHMM/ChromHMM_manual.pdf">ChromHMM manual</a></li>
</ul><p>Address of the bookmark: <a href="http://compbio.mit.edu/ChromHMM/" rel="nofollow">http://compbio.mit.edu/ChromHMM/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31024/dagchainer-computing-chains-of-syntenic-genes-in-complete-genomes</guid>
	<pubDate>Fri, 17 Feb 2017 16:13:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31024/dagchainer-computing-chains-of-syntenic-genes-in-complete-genomes</link>
	<title><![CDATA[DAGchainer: Computing Chains of Syntenic Genes in Complete Genomes]]></title>
	<description><![CDATA[<p>The DAGchainer software computes chains of syntenic genes found within complete genome sequences. As input, DAGchainer accepts a list of gene pairs with sequence homology along with their genome coordinates. Using a scoring function which accounts for the distance between neighboring genes on each DNA molecule and the BLAST E-value score between homologs, maximally scoring chains of ordered gene pairs are computed and reported. This algorithm can be used to mine large evolutionary conserved regions of genomes between two organisms. Alternatively, by examining colinear sets of homologous genes found within a single genome, segmental genome duplications can be revealed.</p>
<p>This software distribution includes both the DAGchainer utility and a Java-based graphical interface that allows the inputs and outputs to be navigated and interrogated dynamically.</p><p>Address of the bookmark: <a href="http://dagchainer.sourceforge.net/" rel="nofollow">http://dagchainer.sourceforge.net/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33976/goldgenomes-online-database</guid>
	<pubDate>Wed, 26 Jul 2017 07:49:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33976/goldgenomes-online-database</link>
	<title><![CDATA[GOLD:Genomes Online Database]]></title>
	<description><![CDATA[<p><span>GOLD</span><span>:Genomes Online Database, is a World Wide Web resource for comprehensive access to information regarding genome and metagenome sequencing projects, and their associated metadata, around the world.</span></p>
<p>https://gold.jgi.doe.gov/</p><p>Address of the bookmark: <a href="https://gold.jgi.doe.gov/" rel="nofollow">https://gold.jgi.doe.gov/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/36870/understanding-liftover</guid>
	<pubDate>Wed, 06 Jun 2018 10:00:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/36870/understanding-liftover</link>
	<title><![CDATA[Understanding liftOver !]]></title>
	<description><![CDATA[<p>LiftOver is a necesary step to bring all genetical analysis to the same reference build. LiftOver can have three use cases:</p><p>(1) <a href="https://genome.sph.umich.edu/wiki/LiftOver#Lift_genome_positions">Convert genome position from one genome assembly to another genome assembly</a></p><p>In most scenarios, we have known genome positions in NCBI build 36 (UCSC hg 18) and hope to lift them over to NCBI build 37 (UCSC hg19).</p><p>(2) <a href="https://genome.sph.umich.edu/wiki/LiftOver#Lift_dbSNP_rs_numbers">Convert dbSNP rs number from one build to another</a></p><p>(3) <a href="https://genome.sph.umich.edu/wiki/LiftOver#Lift_Merlin.2FPLINK_format">Convert both genome position and dbSNP rs number over different versions</a></p><p>Run:</p><pre>liftOver input.bed hg18ToHg19.over.chain.gz output.bed unlifted.bed</pre><p>The outformat is as follow:</p><pre>Deleted in new:
    Sequence intersects no chains
Partially deleted in new:
    Sequence insufficiently intersects one chain
Split in new:
    Sequence insufficiently intersects multiple chains
Duplicated in new:
    Sequence sufficiently intersects multiple chains
Boundary problem:
    Missing start or end base in an exon</pre><p>For example:</p><p>If you liftOver <span>chr4:6497-6497 from <span>hg19 to GRch38 </span>and it return "deleted in new". </span></p><p>It means chr4:6497-6497 is part of a genomic contig on hg19 that is not anymore mapped on GRch38 because the new assembly is now better built without including this contig.</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33887/gview-a-java-application-for-viewing-and-examining-prokaryotic-genomes-in-a-circular-or-linear-context</guid>
	<pubDate>Fri, 14 Jul 2017 07:47:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33887/gview-a-java-application-for-viewing-and-examining-prokaryotic-genomes-in-a-circular-or-linear-context</link>
	<title><![CDATA[GView: A Java application for viewing and examining prokaryotic genomes in a circular or linear context]]></title>
	<description><![CDATA[<p>GView is a Java application for viewing and examining prokaryotic genomes in a circular or linear context. It accepts standard sequence file formats and an optional style specification file to generate customizable, publication quality genome maps in bitmap and scalable vector graphics formats. GView features an interactive pan-and-zoom interface, a command-line interface for incorporation in genome analysis pipelines, and a public Application Programming Interface for incorporation in other Java applications.</p>
<p><strong>Availability:</strong>&nbsp;GView is a freely available application licensed under the GNU Public License. The application, source code, documentation, file specifications, tutorials and image galleries are available at&nbsp;<a href="http://gview.ca/" target="pmc_ext">http://gview.ca</a></p>
<p><strong>Contact:</strong>&nbsp;<a href="mailto:dev@null">ac.cg.cpsa-cahp@raalesmod.nav.yrag</a></p>
<p>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34720/meraculous-haplotype-sensitive-assembly-of-highly-heterozygous-genomes</guid>
	<pubDate>Wed, 20 Dec 2017 18:59:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34720/meraculous-haplotype-sensitive-assembly-of-highly-heterozygous-genomes</link>
	<title><![CDATA[Meraculous: Haplotype-sensitive Assembly of Highly Heterozygous genomes.]]></title>
	<description><![CDATA[<p><span>Meraculous is a whole genome assembler for Next Generation Sequencing data geared for large genomes. It is a hybrid k-mer/read-based assembler that capitalizes on the high accuracy of Illumina sequence by eschewing an explicit error correction step which we argue to be redundant with the assembly process. Meraculous achieves high performance with large datasets by utilizing lightweight data structures and multi-threaded parallelization, allowing to assemble human-sized genomes on commodity clusters in under a day. The process pipeline implements a highly transparent and portable model of job control and monitoring where different assembly stages can be executed and re-executed separately or in unison on a wide variety of architectures.</span></p>
<p><span>https://jgi.doe.gov/data-and-tools/meraculous/</span></p>
<p><span>https://arxiv.org/ftp/arxiv/papers/1703/1703.09852.pdf</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/meraculous20/" rel="nofollow">https://sourceforge.net/projects/meraculous20/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35802/bioinformatics-tools-to-detect-horizontal-gene-transfer-hgt-in-genomes</guid>
	<pubDate>Fri, 02 Mar 2018 04:56:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35802/bioinformatics-tools-to-detect-horizontal-gene-transfer-hgt-in-genomes</link>
	<title><![CDATA[Bioinformatics tools to detect horizontal gene transfer (HGT) in genomes]]></title>
	<description><![CDATA[<p>Horizontal gene transfer (HGT), the &ldquo;non-sexual movement of genetic material between two organisms&rdquo; , is relatively common in prokaryotes&nbsp;and single-celled eukaryotes, but a number of factors combine to make it far rarer in multicellular eukaryotes. In order for a eukaryotic species to gain a gene by HGT, foreign DNA must enter the host nucleus, integrate into the genome, and in more complex organisms it must enter the sequestered germline in order to be transmitted to offspring. Once there, it must not experience strong negative selection, despite potential for genetic incompatibility with the host genome and mismatch between the niche of the donor and the host. Over the longer term, foreign DNA may become &ldquo;domesticated&rdquo; in the recipient genome and provide novel function.</p><p>Following are the popular tool to detect HGT in genomes:</p><p><a href="http://www.trex.uqam.ca/index.php?action=hgt&amp;project=trex">T-REX</a>&nbsp;/&nbsp;<a href="http://www.trex.uqam.ca/download/hgt-detection_3.22.zip">3.22</a></p><p>HGT detection /&nbsp;download &amp; compile</p><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/20525630">20525630</a></p><p>&nbsp;</p><p><a href="http://compbio.engr.uconn.edu/software/RANGER-DTL/">RANGER-DTL</a>&nbsp;/&nbsp;<a href="http://compbio.engr.uconn.edu/software/RANGER-DTL/Linux.zip">2.0</a></p><p>HGT detection /&nbsp;download binary</p><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/22689773">22689773</a></p><p>&nbsp;</p><p><a href="https://bioinfocs.rice.edu/phylonet">PhyloNet</a>&nbsp;/&nbsp;<a href="https://bioinfocs.rice.edu/sites/g/files/bxs266/f/kcfinder/files/PhyloNet_3.6.1.jar">3.6.1</a></p><p>HGT detection /&nbsp;download binary</p><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/18662388">18662388</a></p><p>&nbsp;</p><p><a href="https://www.cs.hmc.edu/~hadas/jane/index.html">Jane</a>&nbsp;/&nbsp;<a href="https://www.cs.hmc.edu/~hadas/jane/form.html">4.01</a></p><p>HGT detection /&nbsp;download binary (!license!)</p><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/20181081">20181081</a></p><p>&nbsp;</p><p><a href="http://www.tree-puzzle.de/">TREE-PUZZLE</a>&nbsp;/&nbsp;<a href="http://www.tree-puzzle.de/tree-puzzle-5.3.rc16-linux.tar.gz">5.3.rc16</a></p><p>HGT detection /&nbsp;download &amp; compile</p><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/11934758">11934758</a></p><p>&nbsp;</p><p><a href="http://www.sigmath.es.osaka-u.ac.jp/shimo-lab/prog/consel/">CONSEL</a>&nbsp;/&nbsp;<a href="http://www.sigmath.es.osaka-u.ac.jp/shimo-lab/prog/consel/pub/cnsls020.tgz">0.20</a></p><p>HGT detection /&nbsp;download</p><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/11751242">11751242</a></p><p>&nbsp;</p><p><a href="http://darkhorse.ucsd.edu/">DarkHorse</a>&nbsp;/&nbsp;<a href="http://darkhorse.ucsd.edu/DarkHorse-1.5_rev170.tar.gz">1.5 rev170</a></p><p>HGT detection /&nbsp;download &amp; install</p><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/17274820">17274820</a></p><p>&nbsp;</p><p><a href="https://github.com/DittmarLab/HGTector">HGTector</a>&nbsp;/&nbsp;<a href="https://github.com/DittmarLab/HGTector/archive/wgshgt.zip">0.2.1</a></p><p>HGT detection /&nbsp;git clone</p><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/25159222">25159222</a></p><p>&nbsp;</p><p><a href="http://www5.esu.edu/cpsc/bioinfo/software/EGID/">EGID</a>&nbsp;/&nbsp;<a href="http://www5.esu.edu/cpsc/bioinfo/software/EGID/EGID_1.0.tar.gz">1.0</a></p><p>HGT detection /&nbsp;download</p><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/22355228">22355228</a></p><p>&nbsp;</p><p><a href="http://exon.gatech.edu/GeneMark/">GeneMarkS</a>&nbsp;/&nbsp;<a href="http://exon.gatech.edu/GeneMark/license_download.cgi">4.30</a></p><p>HGT detection / download binary (!license!)</p><p><a href="https://www.ncbi.nlm.nih.gov/pubmed/9461475">9461475</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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