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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40701?offset=90</link>
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	<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>
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<ul>
<li><a href="https://software.broadinstitute.org/gatk/">Genome Analysis Toolkit</a></li>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35619/tallymer-method-to-compute-k-mer-frequencies-and-its-application-to-annotate-large-repetitive-plant-genomes</guid>
	<pubDate>Thu, 15 Feb 2018 10:21:02 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35619/tallymer-method-to-compute-k-mer-frequencies-and-its-application-to-annotate-large-repetitive-plant-genomes</link>
	<title><![CDATA[Tallymer: method to compute K-mer frequencies and its application to annotate large repetitive plant genomes]]></title>
	<description><![CDATA[<p>Tallymer is based on enhanced suffix arrays. This gives a much larger flexibility concerning the choice of the&nbsp;<span>k</span>-mer size. Tallymer can process large data sizes of several billion bases. We used it in a variety of applications to study the genomes of maize and other plant species. In particular, Tallymer was used to index a set whole genome shotgun sequences from maize (B73) (total size 10<sup>9</sup>&nbsp;bp).&nbsp;<br>Tallymer was effective in a variety of applications to aid genome annotation in maize, despite limitations imposed by the relatively low coverage of sequence available.</p>
<p>A manual can be found&nbsp;<a href="https://www.zbh.uni-hamburg.de/fileadmin/gi/tallymer/tallymer.pdf" target="_blank" title="tallymer.pdf (111 KB)">here</a>.</p><p>Address of the bookmark: <a href="https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html" rel="nofollow">https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40525/heatmaply-popular-graphical-method-for-visualizing-high-dimensional-data</guid>
	<pubDate>Sat, 11 Jan 2020 07:34:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40525/heatmaply-popular-graphical-method-for-visualizing-high-dimensional-data</link>
	<title><![CDATA[heatmaply: popular graphical method for visualizing high-dimensional data]]></title>
	<description><![CDATA[<p>This work is based on ggplot2 and plotly.js engine. It produces similar heatmaps as d3heatmap, with the advantage of speed (plotly.js is able to handle larger size matrix), and the ability to zoom from the dendrogram.</p>
<p>heatmaply also provides an interface based around the&nbsp;<a href="https://cran.r-project.org/package=plotly">plotly R package</a>. This interface can be used by choosing&nbsp;<code>plot_method = "plotly"</code>&nbsp;instead of the default&nbsp;<code>plot_method = "ggplot"</code>. This interface can provide smaller objects and faster rendering to disk in many cases and provides otherwise almost identical features.</p>
<p>Documentation for this package is also available as a&nbsp;<a href="https://cran.r-project.org/package=pkgdown">pkgdown</a>&nbsp;site:&nbsp;<a href="http://talgalili.github.io/heatmaply/">http://talgalili.github.io/heatmaply/</a></p><p>Address of the bookmark: <a href="http://talgalili.github.io/heatmaply/articles/heatmaply.html" rel="nofollow">http://talgalili.github.io/heatmaply/articles/heatmaply.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</guid>
	<pubDate>Wed, 22 Jun 2016 05:37:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse</link>
	<title><![CDATA[DarkHorse]]></title>
	<description><![CDATA[<p><em>DarkHorse</em>&nbsp;is a bioinformatic method for rapid, automated identification and ranking of phylogenetically atypical proteins on a genome-wide basis. It works by selecting potential ortholog matches from a reference database of amino acid sequences, then using these matches to calculate a lineage probability index (LPI) score for each genome protein.</p>
<p>LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scale<em>de novo</em>&nbsp;predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.</p>
<p>One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.</p>
<p><em>DarkHorse</em>&nbsp;is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the&nbsp;<a href="http://darkhorse.ucsd.edu/#references">references</a>&nbsp;cited below for a complete explanation of parameter selection and result interpretation. A brief&nbsp;<a href="http://darkhorse.ucsd.edu/tutorial.html">tutorial</a>&nbsp;page is also available on-line.</p><p>Address of the bookmark: <a href="http://darkhorse.ucsd.edu/download.html" rel="nofollow">http://darkhorse.ucsd.edu/download.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42530/shovill-assemble-bacterial-isolate-genomes-from-illumina-paired-end-reads</guid>
	<pubDate>Sat, 02 Jan 2021 07:05:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42530/shovill-assemble-bacterial-isolate-genomes-from-illumina-paired-end-reads</link>
	<title><![CDATA[shovill: Assemble bacterial isolate genomes from Illumina paired-end reads]]></title>
	<description><![CDATA[<p><span>Shovill is a pipeline which uses SPAdes at its core, but alters the steps before and after the primary assembly step to get similar results in less time. Shovill also supports other assemblers like SKESA, Velvet and Megahit, so you can take advantage of the pre- and post-processing the Shovill provides with those too.</span></p><p>Address of the bookmark: <a href="https://github.com/tseemann/shovill" rel="nofollow">https://github.com/tseemann/shovill</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34528/cope-an-accurate-k-mer-based-pair-end-reads-connection-tool-to-facilitate-genome-assembly</guid>
	<pubDate>Wed, 06 Dec 2017 02:08:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34528/cope-an-accurate-k-mer-based-pair-end-reads-connection-tool-to-facilitate-genome-assembly</link>
	<title><![CDATA[COPE: an accurate k-mer-based pair-end reads connection tool to facilitate genome assembly]]></title>
	<description><![CDATA[<p><span>An efficient tool called Connecting Overlapped Pair-End (COPE) reads, to connect overlapping pair-end reads using k-mer frequencies. We evaluated our tool on 30&times; simulated pair-end reads from Arabidopsis thaliana with 1% base error. COPE connected over 99% of reads with 98.8% accuracy, which is, respectively, 10 and 2% higher than the recently published tool FLASH. When COPE is applied to real reads for genome assembly, the resulting contigs are found to have fewer errors and give a 14-fold improvement in the N50 measurement when compared with the contigs produced using unconnected reads.</span></p><p>Address of the bookmark: <a href="ftp://ftp.genomics.org.cn/pub/cope" rel="nofollow">ftp://ftp.genomics.org.cn/pub/cope</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35899/reference-free-prediction-of-rearrangement-breakpoint-reads</guid>
	<pubDate>Thu, 08 Mar 2018 05:05:25 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35899/reference-free-prediction-of-rearrangement-breakpoint-reads</link>
	<title><![CDATA[Reference-free prediction of rearrangement breakpoint reads]]></title>
	<description><![CDATA[<p><span>lideSort-BPR (&nbsp;</span><span>b</span><span>&nbsp;reak&nbsp;</span><span>p</span><span>&nbsp;oint&nbsp;</span><span>r</span><span>&nbsp;eads) is based on a fast algorithm for all-against-all comparisons of short reads and theoretical analyses of the number of neighboring reads. When applied to a dataset with a sequencing depth of 100&times;, it finds &sim;88% of the breakpoints correctly with no false-positive reads. Moreover, evaluation on a real prostate cancer dataset shows that the proposed method predicts more fusion transcripts correctly than previous approaches, and yet produces fewer false-positive reads. To our knowledge, this is the first method to detect breakpoint reads without using a reference genome.</span></p>
<p><span>https://github.com/ewijaya/slidesort-bpr</span></p><p>Address of the bookmark: <a href="https://code.google.com/archive/p/slidesort-bpr/" rel="nofollow">https://code.google.com/archive/p/slidesort-bpr/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36880/jvarkit-java-utilities-for-bioinformatics</guid>
	<pubDate>Fri, 08 Jun 2018 09:31:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36880/jvarkit-java-utilities-for-bioinformatics</link>
	<title><![CDATA[Jvarkit : Java utilities for Bioinformatics]]></title>
	<description><![CDATA[Collection of Java tool kits for bioinformatics works:

Jvarkit : Java utilities for Bioinformatics<p>Address of the bookmark: <a href="http://lindenb.github.io/jvarkit/" rel="nofollow">http://lindenb.github.io/jvarkit/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36800/genomemapper-simultaneous-alignment-of-short-reads-against-multiple-genomes</guid>
	<pubDate>Fri, 25 May 2018 09:29:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36800/genomemapper-simultaneous-alignment-of-short-reads-against-multiple-genomes</link>
	<title><![CDATA[GenomeMapper: Simultaneous alignment of short reads against multiple genomes]]></title>
	<description><![CDATA[GenomeMapper is a short read mapping tool designed for accurate read alignments. It quickly aligns millions of reads either with ungapped or gapped alignments. It can be used to align against multiple genomes simulanteously or against a single reference. If you are unsure which one is the appropriate GenomeMapper, you might want to use the latter

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2768987/<p>Address of the bookmark: <a href="http://1001genomes.org/software/genomemapper.html" rel="nofollow">http://1001genomes.org/software/genomemapper.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36918/p-rna-scaffolder-a-fast-and-accurate-genome-scaffolder-using-paired-end-rna-sequencing-reads</guid>
	<pubDate>Tue, 12 Jun 2018 08:14:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36918/p-rna-scaffolder-a-fast-and-accurate-genome-scaffolder-using-paired-end-rna-sequencing-reads</link>
	<title><![CDATA[P_RNA_scaffolder: a fast and accurate genome scaffolder using paired-end RNA-sequencing reads]]></title>
	<description><![CDATA[P_RNA_scaffolder, a fast and accurate tool using paired-end RNA-sequencing reads to scaffold genomes. This tool aims to improve the completeness of both protein-coding and non-coding genes. After this tool was applied to scaffolding human contigs, the structures of both protein-coding genes and circular RNAs were almost completely recovered and equivalent to those in a complete genome, especially for long proteins and long circular RNAs.<p>Address of the bookmark: <a href="http://www.fishbrowser.org/software/P_RNA_scaffolder/" rel="nofollow">http://www.fishbrowser.org/software/P_RNA_scaffolder/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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