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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/28997?offset=220</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34569/ksnp30-snp-detection-and-phylogenetic-analysis-of-genomes-without-genome-alignment-or-reference-genome</guid>
	<pubDate>Fri, 08 Dec 2017 16:48:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34569/ksnp30-snp-detection-and-phylogenetic-analysis-of-genomes-without-genome-alignment-or-reference-genome</link>
	<title><![CDATA[kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome]]></title>
	<description><![CDATA[<p><span>Sept. 20, 2017 Version 3.1 released. Major upgrade. Version 3.1 fixes the problems with SNP annotation that arose when NCBI discontinued use of GI numbers. Please read carefully the Preface (page 3) and the File of annotated genomes section (pages 9-10) in the version 3.1 User Guide. Thanks to Tom Slezak for revsing the get_genbank_file3 script and to Tod Stuber (USDA) for testing version 3.1 even though he doesn't need the annotation feature. All users are encouraged to upgrade to version 3.1.&nbsp;<br></span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/ksnp/files/" rel="nofollow">https://sourceforge.net/projects/ksnp/files/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36730/bprna-large-scale-automated-annotation-and-analysis-of-rna-secondary-structure</guid>
	<pubDate>Wed, 23 May 2018 03:24:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36730/bprna-large-scale-automated-annotation-and-analysis-of-rna-secondary-structure</link>
	<title><![CDATA[bpRNA: large-scale automated annotation and analysis of RNA secondary structure]]></title>
	<description><![CDATA[<p>bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature.</p>
<p>The bpRNA code is written in perl and requires the Graph perl module. Several additional scripts for analysis are included. The source code is available at http://github.com/hendrixlab/bpRNA.</p><p>Address of the bookmark: <a href="http://github.com/hendrixlab/bpRNA" rel="nofollow">http://github.com/hendrixlab/bpRNA</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/35125/eugene-v-koonin-lab</guid>
  <pubDate>Tue, 09 Jan 2018 05:01:15 -0600</pubDate>
  <link></link>
  <title><![CDATA[Eugene V. Koonin Lab]]></title>
  <description><![CDATA[
<p>Interested in understanding the evolution of life. To obtain glimpses of such understanding, we employ existing and new methods of computational biology to perform research in several major areas.</p>

<p>https://www.ncbi.nlm.nih.gov/research/groups/koonin/</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/42559/sample-bandage-input-file-for-visual-analysis</guid>
	<pubDate>Wed, 06 Jan 2021 03:51:50 -0600</pubDate>
	<link>https://bioinformaticsonline.com/file/view/42559/sample-bandage-input-file-for-visual-analysis</link>
	<title><![CDATA[Sample bandage input file for visual analysis]]></title>
	<description><![CDATA[<p>Sample bandage input file for visual analysis ...</p>]]></description>
	<dc:creator>Jit</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/42559" length="112199" type="text/plain" />
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/40248/industrial-training-in-computer-aided-drug-designing-cadd</guid>
	<pubDate>Wed, 13 Nov 2019 05:00:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/40248/industrial-training-in-computer-aided-drug-designing-cadd</link>
	<title><![CDATA[Industrial Training in Computer Aided Drug Designing (CADD)]]></title>
	<description><![CDATA[<p>Learn More about&nbsp; Computer Aided Drug Designing (CADD)!!!</p><p>#rasalsi #rasa In our Industrial program you will get Knowledge on RNA Seq, CHIP Seq,</p><h2 style="text-align: center;"><strong>Batch Starts From 18<sup>th</sup> November 2019</strong></h2><p>#hurryup #registernow #enquirenow The primary goal of the industrial training program is to provide students with necessary skills making with employable. RASA LSI trains students with the enhanced skills required for them to excel in jobs in biotechnology, pharmaceuticals, BioIT and related industry sectors. At Rasa you will&nbsp; learn from industry leaders&nbsp;how to apply these skills in life science &amp; have a command over software developing process &nbsp;by using various methodologies. For Registration visit us on: https://www.rasalsi.com/index.php/front-page/industrial-training/</p>]]></description>
	<dc:creator>RASA Life Sciences</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41041/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-mfd</guid>
  <pubDate>Sat, 15 Feb 2020 06:13:35 -0600</pubDate>
  <link></link>
  <title><![CDATA[Post Doc Computational Biology, Bioinformatics - Network Biology &amp; Data Science, NGS (m/f/d)]]></title>
  <description><![CDATA[
<p>https://www.jobvector.de/jobs-stellenangebote/biologie-life-sciences/forschung-entwicklung/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-129867.html?suid=e522e9793b41817e52ac58d6963b94e2519920df</p>

<p>Requirements<br />Doctoral degree in Bioinformatics, Computational Biology, (Bio)physics/-mathematics, Biochemistry/Biology or similar with strong quantitative and numeric focus<br />Ability to numerically process complex and large data sets<br />Good programming skills (R/Bioconductor and/or Python preferred, Linux is a plus)<br />Experience in analyzing next-generation sequencing data sets using network biology<br />Scientific publication record in applied bioinformatics<br />Familiarity with single cell NGS analyses and other –omics techniques is a plus, but not essential</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26375/ncbi-remap</guid>
	<pubDate>Thu, 11 Feb 2016 11:02:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26375/ncbi-remap</link>
	<title><![CDATA[NCBI Remap]]></title>
	<description><![CDATA[<p><span><span><strong>NCBI Remap</strong>. This tool is conceptually similar to liftOver in that in manages conversions between a pair of genome assemblies but it uses different methods to achieve these mappings. It is also available through a simple <a href="http://www.ncbi.nlm.nih.gov/genome/tools/remap">web interface</a> or you can use the <a href="http://www.ncbi.nlm.nih.gov/genome/tools/remap/docs/api">API for NCBI Remap</a>.</span></span></p>
<p><span><span>More at http://www.ncbi.nlm.nih.gov/genome/tools/remap</span></span></p>
<p><span><span>API http://www.ncbi.nlm.nih.gov/genome/tools/remap/docs/api</span></span></p><p>Address of the bookmark: <a href="http://www.ncbi.nlm.nih.gov/genome/tools/remap" rel="nofollow">http://www.ncbi.nlm.nih.gov/genome/tools/remap</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33221/genome-annotation-transfer-utility-gatu</guid>
	<pubDate>Mon, 29 May 2017 05:54:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33221/genome-annotation-transfer-utility-gatu</link>
	<title><![CDATA[Genome Annotation Transfer Utility (GATU)]]></title>
	<description><![CDATA[<p>Genome Annotation Transfer Utility (GATU) was designed to facilitate quick, efficient annotation of similar genomes using genomes that have already been annotated. For example, whenever a new strain of SARS coronavirus is sequenced, it is possible, using GATU, to automatically annotate the new strain using a previously-annotated strain of SARS CoV. This saves researchers from tedious manual annotation of these sequences.</p>
<p>The program utilizes tBLASTn and BLASTn algorithms to map genes from the reference genome (the annotated strain) to the new sequence (the unannotated strain). The goal is to annotate the majority of the new genome&rsquo;s genes in a single step. ORFs present in the target genome and absent from the reference genome are also identified; these ORFs can be further analyzed using BLAST, VGO and BBB. Afterwards, they can either be accepted for/rejected from annotation. GATU can handle multiple-exon genes as well as mature peptides. Although it was designed for use with viral genomes, GATU can also be used to help annotate larger genomes (ie. bacterial genomes).</p>
<p>The output is saved in GenBank, XML, or EMBL file format.</p><p>Address of the bookmark: <a href="https://virology.uvic.ca/help/tool-help/help-books/genome-annotation-transfer-utility-gatu-documentation/" rel="nofollow">https://virology.uvic.ca/help/tool-help/help-books/genome-annotation-transfer-utility-gatu-documentation/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38515/genome-annotation-using-maker-tutorial</guid>
	<pubDate>Thu, 20 Dec 2018 17:39:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38515/genome-annotation-using-maker-tutorial</link>
	<title><![CDATA[Genome Annotation using MAKER tutorial !]]></title>
	<description><![CDATA[<p><a href="http://www.yandell-lab.org/software/maker.html">MAKER</a><span>&nbsp;is a great tool for annotating a reference genome using empirical and&nbsp;</span><em>ab initio</em><span>gene predictions.&nbsp;</span><a href="http://gmod.org/wiki/Main_Page">GMOD</a><span>, the umbrella organization that includes MAKER, has some nice tutorials online for running MAKER. However, these were quite simplified examples and it took a bit of effort to wrap my head completely around everything. Here I will describe a&nbsp;</span><em>de novo</em><span>&nbsp;genome annotation for&nbsp;</span><em>Boa constrictor</em><span>&nbsp;in detail, so that there is a record and that it is easy to use this as a guide to annotate any genome.</span></p><p>Address of the bookmark: <a href="https://www.biostars.org/p/261203/" rel="nofollow">https://www.biostars.org/p/261203/</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</guid>
	<pubDate>Sun, 12 Apr 2020 10:02:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</link>
	<title><![CDATA[WEGO : simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results]]></title>
	<description><![CDATA[<p><span>WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. As the GO vocabulary became more and more popular, WEGO was widely adopted and used in many researches. Therefore we have updated WEGO 2.0 in 2018. Here are some changes we&rsquo;ve made:</span><br><span>1. The limit of input file numbers was cancelled. Now the users could upload as many files as they want with one operation.</span><br><span>2. We have added the reference data of 9 species for users selection.</span><br><span>3. Besides the traditional WEGO histogram, WEGO 2.0 outputs an additional type of bar graph showing GO terms with significant gene number differences.</span></p><p>Address of the bookmark: <a href="http://wego.genomics.org.cn/" rel="nofollow">http://wego.genomics.org.cn/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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