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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42313?offset=30</link>
	<atom:link href="https://bioinformaticsonline.com/related/42313?offset=30" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<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>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44362/biostats-book</guid>
	<pubDate>Mon, 14 Aug 2023 03:11:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44362/biostats-book</link>
	<title><![CDATA[Biostats book !]]></title>
	<description><![CDATA[<p>https://practical-stats-med-r.netlify.app/</p><p>Address of the bookmark: <a href="https://practical-stats-med-r.netlify.app/" rel="nofollow">https://practical-stats-med-r.netlify.app/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</guid>
	<pubDate>Mon, 27 Jun 2016 11:01:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</link>
	<title><![CDATA[Kraken: ultrafast metagenomic sequence classification using exact alignments]]></title>
	<description><![CDATA[<p>Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of <em>k</em>-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at <a href="http://ccb.jhu.edu/software/kraken/" target="pmc_ext">http://ccb.jhu.edu/software/kraken/</a>.</p>
<p>Krona</p>
<p>https://sourceforge.net/p/krona/home/krona/</p><p>Address of the bookmark: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/" rel="nofollow">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34482/ribbon-visualizing-complex-genome-alignments-and-structural-variation</guid>
	<pubDate>Wed, 29 Nov 2017 07:40:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34482/ribbon-visualizing-complex-genome-alignments-and-structural-variation</link>
	<title><![CDATA[Ribbon: Visualizing complex genome alignments and structural variation:]]></title>
	<description><![CDATA[<p>Ribbon can be used for long reads, short reads, paired-end reads, and assembly/genome alignments. Instructions for each data format are available by clicking on "instructions" in each tab on the right.</p>
<p>Local installation:</p>
<p>You can install Ribbon locally from Github by following the instructions here:&nbsp;<a href="https://github.com/MariaNattestad/ribbon" target="_blank">https://github.com/MariaNattestad/Ribbon</a></p><p>Address of the bookmark: <a href="http://genomeribbon.com/" rel="nofollow">http://genomeribbon.com/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36897/gmcloser-closing-gaps-in-assemblies-accurately-with-a-likelihood-based-selection-of-contig-or-long-read-alignments</guid>
	<pubDate>Mon, 11 Jun 2018 05:43:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36897/gmcloser-closing-gaps-in-assemblies-accurately-with-a-likelihood-based-selection-of-contig-or-long-read-alignments</link>
	<title><![CDATA[GMcloser: closing gaps in assemblies accurately with a likelihood-based selection of contig or long-read alignments]]></title>
	<description><![CDATA[GMcloser uses likelihood-based classifiers calculated from the alignment statistics between scaffolds, contigs and paired-end reads to correctly assign contigs or long reads to gap regions of scaffolds, thereby achieving accurate and efficient gap closure. We demonstrate with sequencing data from various organisms that the gap-closing accuracy of GMcloser is 3–100-fold higher than those of other available tools, with similar efficiency.

https://academic.oup.com/bioinformatics/article/31/23/3733/209212<p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article/31/23/3733/209212" rel="nofollow">https://academic.oup.com/bioinformatics/article/31/23/3733/209212</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39039/dotplotly-generate-an-interactive-dot-plot-from-mummer-or-minimap-alignments</guid>
	<pubDate>Thu, 21 Feb 2019 10:22:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39039/dotplotly-generate-an-interactive-dot-plot-from-mummer-or-minimap-alignments</link>
	<title><![CDATA[dotPlotly: Generate an interactive dot plot from mummer or minimap alignments]]></title>
	<description><![CDATA[<p>Create an interactive dot plot from mummer output OR PAF format</p>
<p>R script that makes a plotly interactive and/or static (png/pdf) dot plot.</p>
<p><a href="https://tom-poorten.shinyapps.io/dotplotly_shiny/">Shiny app available for testing</a></p><p>Address of the bookmark: <a href="https://github.com/tpoorten/dotPlotly" rel="nofollow">https://github.com/tpoorten/dotPlotly</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41222/best-practices-for-variant-calling-with-the-gatk</guid>
	<pubDate>Sat, 22 Feb 2020 03:07:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41222/best-practices-for-variant-calling-with-the-gatk</link>
	<title><![CDATA[Best Practices for Variant Calling with the GATK]]></title>
	<description><![CDATA[<p>The presentations below were filmed during the March 2015 GATK Workshop, part of the BroadE Workshop series. At the time of this workshop, the current version of Broad&rsquo;s Genome Analysis Toolkit (GATK) was version 3.3.</p>
<div>
<div>
<div>
<div>
<div>
<div>
<div>
<div>
<div>
<ul>
<li><a href="https://software.broadinstitute.org/gatk/">Genome Analysis Toolkit</a></li>
</ul>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<table>
<tbody style="vertical-align: top;">
<tr>
<td>03/19/15</td>
<td>Introduction to High-Throughput Sequencing data formats and methods</td>
<td>Joel Thibault</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeY3g1M1ZjVjFrZ2s/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6696">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Introduction to the GATK</td>
<td>Geraldine Van der Auwera</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeVEJ1Z1pXUF9Ib3M/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6707">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Mapping, processing, and duplicate marking with Picard tools</td>
<td>Matt Sooknah</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeaGVrbE1GVV9SQkE/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6706">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Mapping and processing RNAseq</td>
<td>Ami Levy-Moonshine</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeLUkwUm5vTGl4bG8/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6705">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Indel realignment</td>
<td>Mark Fleharty</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeLTFzNndsNDBuVms/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6704">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Base quality score recalibration</td>
<td>David Roazen</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeZk1rMXpTYmZzTXc/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6703">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Introduction to variant discovery: calling cohorts</td>
<td>Louis Bergelson</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeQUFYUFRmM1hhRUE/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6702">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Variant calling and joint genotyping</td>
<td>Sheila Chandran</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeYzVTUGs0bjM3M1E/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6701">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Variant quality score recalibration</td>
<td>Bertrand Haas</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeSEpwRkNVQm4wdkE/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6700">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Introduction to working with variants</td>
<td>Yossi Farjoun</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWec0NqUTN2WTRuWWs/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6699">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Genotype refinement</td>
<td>Laura Gauthier</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeMzFldVF5SUp4dWM/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6698">Video</a></td>
</tr>
<tr>
<td>03/19/15</td>
<td>Annotation and variant evaluation</td>
<td>David Benjamin</td>
<td><a href="https://docs.google.com/file/d/0B2dK2q40HDWeWi1YMm42bWdpRE0/preview" target="_blank">PDF</a></td>
<td><a href="https://www.broadinstitute.org/node/6697">Video</a></td>
</tr>
</tbody>
</table><p>Address of the bookmark: <a href="https://www.broadinstitute.org/partnerships/education/broade/best-practices-variant-calling-gatk-1" rel="nofollow">https://www.broadinstitute.org/partnerships/education/broade/best-practices-variant-calling-gatk-1</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/40239/analyzing-codon-usage</guid>
	<pubDate>Thu, 07 Nov 2019 08:31:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/file/view/40239/analyzing-codon-usage</link>
	<title><![CDATA[analyzing codon usage]]></title>
	<description><![CDATA[<div title="Page 1"><div><div><p><span style="font-size: 12.000000pt; font-weight: bold;">perl script analyzing codon usage in an input sequence to evaluate how efficiently it will be expressed in </span><span style="font-size: 12.000000pt; font-weight: bold; font-style: italic;">Anopheles gambiae</span><span style="font-size: 12.000000pt; font-weight: bold;">. </span><span style="font-size: 12.000000pt;">The input codon usage table derived from highly‐expressed </span><span style="font-size: 12.000000pt; font-style: italic;">A. gambiae </span><span style="font-size: 12.000000pt;">genes is appended below. </span></p></div></div></div>]]></description>
	<dc:creator>Neel</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/40239" length="197184" type="application/pdf" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/1178/r-package-for-visualising-go-enrichment</guid>
	<pubDate>Mon, 22 Jul 2013 12:25:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/1178/r-package-for-visualising-go-enrichment</link>
	<title><![CDATA[R package for visualising GO enrichment]]></title>
	<description><![CDATA[<p>An R package that visualizes the GO enrichment results as word clouds and arranges them together with figures of experimental data. This allows us to draw informative summary plots for analyses such as differential expression or clustering, where for each gene list we display its behaviour in the experiment alongside with its GO annotations.</p><p>Links @ http://raivokolde.github.io/GOsummaries/</p><p>Lab @ http://biit.cs.ut.ee/about/main</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4433/upcoming-r-webinar</guid>
	<pubDate>Wed, 11 Sep 2013 10:30:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4433/upcoming-r-webinar</link>
	<title><![CDATA[Upcoming R Webinar]]></title>
	<description><![CDATA[<p>This webinar will describe an R based approach to considerably speed GWAS computation time on a notebook book computer.</p><p>More http://www.extension.org/pages/68354/upcoming-webinar:-fast-semi-parallel-linear-and-logistic-regression-for-genome-wide-association-studi#.UjCL9azyPqV</p><p>Register @ https://www1.gotomeeting.com/register/237810425</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>

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