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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38659?offset=20</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</guid>
	<pubDate>Fri, 13 Aug 2021 07:44:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r</link>
	<title><![CDATA[QuasR: Quantification and annotation of short reads in R]]></title>
	<description><![CDATA[<p>The <em><a href="https://bioconductor.org/packages/3.14/QuasR">QuasR</a></em> package (short for <em>Qu</em>antify and <em>a</em>nnotate <em>s</em>hort reads in <em>R</em>) integrates the functionality of several <strong>R</strong> packages (such as <em><a href="https://bioconductor.org/packages/3.14/IRanges">IRanges</a></em> <span>(Lawrence et al. 2013)</span> and <em><a href="https://bioconductor.org/packages/3.14/Rsamtools">Rsamtools</a></em>) and external software (e.g.&nbsp;<code>bowtie</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rbowtie">Rbowtie</a></em> package, and <code>HISAT2</code>, through the <em><a href="https://bioconductor.org/packages/3.14/Rhisat2">Rhisat2</a></em> package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single <strong>R</strong> script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.</p><p>Address of the bookmark: <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html" rel="nofollow">https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33219/dbcan-a-web-server-and-database-for-automated-carbohydrate-active-enzyme-annotation</guid>
	<pubDate>Mon, 29 May 2017 05:39:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33219/dbcan-a-web-server-and-database-for-automated-carbohydrate-active-enzyme-annotation</link>
	<title><![CDATA[dbCAN: a web server and DataBase for automated Carbohydrate-active enzyme ANnotation]]></title>
	<description><![CDATA[<p><a href="http://csbl.bmb.uga.edu/dbCAN/index.php">dbCAN</a>&nbsp;is a web server and&nbsp;<span style="text-decoration: underline;">D</span>ata<span style="text-decoration: underline;">B</span>ase for&nbsp;<a href="http://csbl.bmb.uga.edu/dbCAN/annotate.php"><strong>automated&nbsp;<span style="text-decoration: underline;">C</span>arbohydrate-active enzyme&nbsp;<span style="text-decoration: underline;">AN</span>notation</strong></a>, funded by the&nbsp;<a href="http://bioenergycenter.org/">BioEnergy Science Center of the DOE</a>. Similar resources on the web include&nbsp;<a href="http://www.cazy.org/" target="_blank">CAZy database</a>&nbsp;and&nbsp;<a href="http://cricket.ornl.gov/cgi-bin/cat.cgi" target="_blank">CAT</a>. All data in dbCAN are generated based on the family classification from&nbsp;<a href="http://www.cazy.org/" target="_blank">CAZy database</a>&nbsp;while it has the following&nbsp;<strong><span style="text-decoration: underline;">unique features</span></strong>&nbsp;compared with CAZy database and CAT:</p>
<ul>
<li>dbCAN provides the capability of&nbsp;<a href="http://csbl.bmb.uga.edu/dbCAN/annotate.php">automated and comprehensive CAZyme annotation</a>&nbsp;of a given genome submitted by the user;</li>
<li>dbCAN provides an explicitly defined&nbsp;<span style="text-decoration: underline;">signature domain</span>&nbsp;for each and every CAZyme family along with its location in all the relevant full-length CAZyme proteins in all sequenced&nbsp;<a href="http://csbl.bmb.uga.edu/dbCAN/genome.php">genomes</a>;</li>
<li>dbCAN provides the most complete set of&nbsp;<span style="text-decoration: underline;">metagenomic CAZyme</span>&nbsp;genes published so far and represents the first step towards discovering novel CAZyme catalysts in metagenomes;</li>
<li>dbCAN provides a&nbsp;<span style="text-decoration: underline;">subfamily classification</span>&nbsp;of the existing CAZyme families based on sequence similarities;</li>
<li>dbCAN make all pre-computed data freely available to the public, including sequence alignments,&nbsp;<a href="http://csbl.bmb.uga.edu/dbCAN/download/">hidden markov models (HMMs)</a>&nbsp;and phylogenies of the signature domain regions in each and every CAZyme family and subfamily.</li>
</ul>
<p><a href="http://csbl.bmb.uga.edu/dbCAN/help.php">dbCAN</a>&nbsp;is updated regularly when&nbsp;<a href="http://www.cazy.org/" target="_blank">CAZy database</a>&nbsp;created new families based on latest literature.</p><p>Address of the bookmark: <a href="http://csbl.bmb.uga.edu/dbCAN/index.php" rel="nofollow">http://csbl.bmb.uga.edu/dbCAN/index.php</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26499/katju-lab</guid>
  <pubDate>Fri, 26 Feb 2016 03:25:32 -0600</pubDate>
  <link></link>
  <title><![CDATA[Katju Lab]]></title>
  <description><![CDATA[
<p>TheLab seek to understand the genetic factors contributing to genomic variation and phenotypic diversity.  To this end, we employ molecular and bioinformatic tools to study evolutionary processes at the level of populations, both experimental and natural, and genomes.  Our research interests encompass a wide range of topics, including the evolution of organellar and nuclear genomes, gene duplication and the origin of novel function, and the fitness and phenotypic consequences of mutation in evolution. For details regards ongoing projects, please see the Research page.</p>

<p>http://katjulab.com/research.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39236/causel-an-epigenome-and-genome-editing-pipeline-for-establishing-function-of-noncoding-gwas-variants</guid>
	<pubDate>Tue, 09 Apr 2019 07:23:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39236/causel-an-epigenome-and-genome-editing-pipeline-for-establishing-function-of-noncoding-gwas-variants</link>
	<title><![CDATA[CAUSEL: an epigenome- and genome-editing pipeline for establishing function of noncoding GWAS variants]]></title>
	<description><![CDATA[<p><span>Validated a widely accessible approach that can be used to establish functional causality for noncoding sequence variants identified by GWASs.</span></p>
<p><a href="https://www.nature.com/articles/nm.3975">https://www.nature.com/articles/nm.3975</a></p><p>Address of the bookmark: <a href="https://www.nature.com/articles/nm.3975" rel="nofollow">https://www.nature.com/articles/nm.3975</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38501/fgenesh-program-for-predicting-multiple-genes-in-genomic-dna-sequences</guid>
	<pubDate>Thu, 20 Dec 2018 11:55:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38501/fgenesh-program-for-predicting-multiple-genes-in-genomic-dna-sequences</link>
	<title><![CDATA[FGENESH - Program for predicting multiple genes in genomic DNA sequences]]></title>
	<description><![CDATA[<p>FGENESH is the fastest (50-100 times faster than GenScan) and most accurate gene finder available - see the figure and the table below. In recent rice genome sequencing projects, it was cited "the most successful (gene finding) program (Yu&nbsp;<em>et al</em>. (2002) Science 296:79) and was used to produce 87% of all high-evidence predicted genes (Goff&nbsp;<em>et al</em>. (2002) Science 296:79).</p><p>Address of the bookmark: <a href="http://www.softberry.com/berry.phtml?topic=fgenesh&amp;group=help&amp;subgroup=gfind" rel="nofollow">http://www.softberry.com/berry.phtml?topic=fgenesh&amp;group=help&amp;subgroup=gfind</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42923/flanker</guid>
	<pubDate>Sat, 27 Feb 2021 22:04:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42923/flanker</link>
	<title><![CDATA[Flanker]]></title>
	<description><![CDATA[<p><span>Flanker, a Python package which performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of&nbsp;<span>mobile genetic elements (</span>MGEs) without prior knowledge of their structure.&nbsp;<span>Flanker can be flexibly parameterised to finetune outputs by characterising upstream and downstream regions separately and investigating variable lengths of flanking sequence.</span></span></p>
<p><span><img src="https://github.com/wtmatlock/flanker/raw/main/docs/frontpage.png" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/wtmatlock/flanker" rel="nofollow">https://github.com/wtmatlock/flanker</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43683/genview-a-phylogeny-based-comparative-genomics-software-to-analyze-the-genetic-environment-of-genes</guid>
	<pubDate>Tue, 28 Dec 2021 01:49:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43683/genview-a-phylogeny-based-comparative-genomics-software-to-analyze-the-genetic-environment-of-genes</link>
	<title><![CDATA[GEnView: A phylogeny based comparative genomics software to analyze the genetic environment of genes]]></title>
	<description><![CDATA[<p><span>A phylogeny based comparative genomics software to analyze the genetic environment of genes. The user can select one or several taxa and provide one or several reference protein(s). Genomes and plasmids (based on user choice) will be downloaded from the NCBI Assembly/NR database and searched for the respective gene. Alternatively, custom genomes can be provided. User selected stretches (20kbp by default) of the genes genetic environment are extracted, annotated and aligned between all genomes. The sequences are then visualized, enabling comparison of synteny and gene content.</span></p>
<p><span>More at&nbsp;https://pubmed.ncbi.nlm.nih.gov/34951622/</span></p><p>Address of the bookmark: <a href="https://github.com/EbmeyerSt/GEnView" rel="nofollow">https://github.com/EbmeyerSt/GEnView</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/1471/24-mb-genome-size-for-worlds-biggest-virus</guid>
	<pubDate>Thu, 08 Aug 2013 10:05:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/1471/24-mb-genome-size-for-worlds-biggest-virus</link>
	<title><![CDATA[2.4 Mb Genome Size for World's Biggest Virus]]></title>
	<description><![CDATA[<p>The genome size of new discovered Pandoraviruses have roughly twice the size of the record-holding Megavirus genomic code. Interestingly only 6 percent of its genes resembled the genes other organisms. It is assume that it may come from a different origin.</p><p>For detail : http://www.sciencemag.org/content/341/6143/281</p><p>http://www.npr.org/blogs/health/2013/07/18/203298244/worlds-biggest-virus-may-have-ancient-roots</p><p>&nbsp;</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27427/rcircos-an-r-package-for-circos-2d-track-plots</guid>
	<pubDate>Fri, 20 May 2016 11:01:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27427/rcircos-an-r-package-for-circos-2d-track-plots</link>
	<title><![CDATA[RCircos: an R package for Circos 2D track plots]]></title>
	<description><![CDATA[<p>RCircos package provides a simple and flexible way to make Circos 2D track plots with R and could be easily integrated into other R data processing and graphic manipulation pipelines for presenting large-scale multi-sample genomic research data. It can also serve as a base tool to generate complex Circos images.</p>
<p>More at https://bitbucket.org/henryhzhang/rcircos/src</p><p>Address of the bookmark: <a href="https://bitbucket.org/henryhzhang/rcircos/src" rel="nofollow">https://bitbucket.org/henryhzhang/rcircos/src</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>

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