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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40707?offset=120</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41920/liftoff-an-accurate-tool-that-maps-annotations-in-gff-or-gtf-between-assemblies</guid>
	<pubDate>Tue, 30 Jun 2020 21:40:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41920/liftoff-an-accurate-tool-that-maps-annotations-in-gff-or-gtf-between-assemblies</link>
	<title><![CDATA[Liftoff: an accurate tool that maps annotations in GFF or GTF between assemblies]]></title>
	<description><![CDATA[<p><span>&nbsp;Liftoff, an accurate tool that maps annotations in GFF or GTF between assemblies of the same, or closely-related species. Unlike current coordinate lift-over tools which require a pre-generated &ldquo;chain&rdquo; file as input, Liftoff is a standalone tool that takes two genome assemblies and a reference annotation as input and outputs an annotation of the target genome.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/agshumate/Liftoff" rel="nofollow">https://github.com/agshumate/Liftoff</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42155/clustergrammer-is-a-web-based-tool-for-visualizing-high-dimensional-data-as-an-interactive-and-shareable-hierarchically-clustered-heatmap</guid>
	<pubDate>Sun, 23 Aug 2020 19:30:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42155/clustergrammer-is-a-web-based-tool-for-visualizing-high-dimensional-data-as-an-interactive-and-shareable-hierarchically-clustered-heatmap</link>
	<title><![CDATA[Clustergrammer is a web-based tool for visualizing high-dimensional data as an interactive and shareable hierarchically clustered heatmap]]></title>
	<description><![CDATA[<p><span>Clustergrammer is a web-based tool for visualizing high-dimensional data (e.g. a matrix) as an interactive and shareable hierarchically clustered heatmap. Clustergrammer's front end (</span><a href="http://clustergrammer.readthedocs.io/clustergrammer_js.html#clustergrammer-js">Clustergrammer-JS</a><span>) is built using&nbsp;</span><a href="https://d3js.org/">D3.js</a><span>&nbsp;and its back-end (</span><a href="http://clustergrammer.readthedocs.io/clustergrammer_py.html#clustergrammer-py">Clustergrammer-PY</a><span>) is built using Python. Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several biology-specific features (e.g. enrichment analysis, see&nbsp;</span><a href="http://clustergrammer.readthedocs.io/biology_specific_features.html#biology-specific-features">Biology-Specific Features</a><span>) to facilitate the exploration of gene-level biological data.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/MaayanLab/clustergrammer" rel="nofollow">https://github.com/MaayanLab/clustergrammer</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43263/jumbodb-tool-for-de-bruijn-graph-construction</guid>
	<pubDate>Tue, 17 Aug 2021 13:33:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43263/jumbodb-tool-for-de-bruijn-graph-construction</link>
	<title><![CDATA[JumboDB: tool for de Bruijn graph construction]]></title>
	<description><![CDATA[<p><span>jumboDB tool for fast de Bruijn graph construction from long sequences (reads or genomes) with very low error rate. JumboDB is not a genome assembler by itself but rather a subroutine that translates a set of reads into compressed de Bruijn graph.</span></p>
<p><span>More at&nbsp;https://github.com/AntonBankevich/jumboDB</span></p><p>Address of the bookmark: <a href="https://github.com/AntonBankevich/jumboDB" rel="nofollow">https://github.com/AntonBankevich/jumboDB</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44483/baclife-an-automated-genome-mining-tool-for-identification-of-lifestyle-associated-genes</guid>
	<pubDate>Fri, 15 Mar 2024 04:59:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44483/baclife-an-automated-genome-mining-tool-for-identification-of-lifestyle-associated-genes</link>
	<title><![CDATA[bacLIFE: an automated genome mining tool for identification of lifestyle associated genes]]></title>
	<description><![CDATA[<p style="margin-top: 0px; margin-bottom: 16px; color: #1f2328; font-size: 16px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff;" dir="auto">bacLIFE is a streamlined computational workflow that annotates bacterial genomes and performs large-scale comparative genomics to predict bacterial lifestyles and to pinpoint candidate genes, denominated<span>&nbsp;</span><strong style="font-weight: var(--base-text-weight-semibold, 600);">lifestyle-associated genes (LAGs)</strong>, and biosynthetic gene clusters associated with each lifestyle detected. This whole process is divided into different modules:</p>
<ul style="margin-top: 0px; margin-bottom: 16px; color: #1f2328; font-size: 16px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff;" dir="auto">
<li><strong style="font-weight: var(--base-text-weight-semibold, 600);">Clustering module</strong><span>&nbsp;</span>Predicts, clusters and annotates the genes of every input genome</li>
<li style="margin-top: 0.25em;"><strong style="font-weight: var(--base-text-weight-semibold, 600);">Lifestyle prediction</strong><span>&nbsp;</span>Employs a machine learning model to forecast bacterial lifestyle or other specified metadata</li>
<li style="margin-top: 0.25em;"><strong style="font-weight: var(--base-text-weight-semibold, 600);">Analitical module (Shiny app)</strong><span>&nbsp;</span>Results from the previous modules are embedded in a user-friendly interface for comprehensive and interactive comparative genomics.</li>
</ul>
<p style="margin-top: 0px; margin-bottom: 16px; color: #1f2328; font-size: 16px; font-style: normal; font-weight: 400; text-align: start; background-color: #ffffff;" dir="auto">You can find the complete wiki here [<a href="https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki">https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki</a>]</p><p>Address of the bookmark: <a href="https://github.com/Carrion-lab/bacLIFE" rel="nofollow">https://github.com/Carrion-lab/bacLIFE</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44508/a-web-based-tool-for-sequence-alignment-statistics-and-innovative-visualization</guid>
	<pubDate>Thu, 04 Apr 2024 01:44:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44508/a-web-based-tool-for-sequence-alignment-statistics-and-innovative-visualization</link>
	<title><![CDATA[A web-based tool for sequence alignment statistics and innovative visualization]]></title>
	<description><![CDATA[<p>AlignStatPlot, a new R package and online tool that is well-documented and easy-to usefor MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analy-ses on sequencing data and generates new visualisation methods for MSA results. Whencompared to currently available tools, AlignStatPlot provides a robust ability to handle andvisualise diversity data, while the online version will save time and encourage researchersto focus on explaining their findings. It is a simple tool that can be used in conjunction withpopulation genetics software (PDF) AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data.</p><p>Address of the bookmark: <a href="https://bioinformatics.um6p.ma/AlignStatPlot/" rel="nofollow">https://bioinformatics.um6p.ma/AlignStatPlot/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44896/jaeger-an-accurate-and-fast-deep-learning-tool-to-detect-bacteriophage-sequences</guid>
	<pubDate>Sun, 31 Aug 2025 06:30:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44896/jaeger-an-accurate-and-fast-deep-learning-tool-to-detect-bacteriophage-sequences</link>
	<title><![CDATA[Jaeger : an accurate and fast deep-learning tool to detect bacteriophage sequences]]></title>
	<description><![CDATA[<p><span>Jaeger is a tool that utilizes homology-free machine learning to identify phage genome sequences that are hidden within metagenomes. It is capable of detecting both phages and prophages within metagenomic assemblies.</span></p><p>Address of the bookmark: <a href="https://github.com/MGXlab/Jaeger" rel="nofollow">https://github.com/MGXlab/Jaeger</a></p>]]></description>
	<dc:creator>LEGE</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/bookmarks/view/44622/variant-calling-resequencing-based-genome-inference</guid>
	<pubDate>Wed, 31 Jul 2024 02:02:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44622/variant-calling-resequencing-based-genome-inference</link>
	<title><![CDATA[Variant Calling Resequencing-Based Genome Inference]]></title>
	<description><![CDATA[<p>Variant Calling - Resequencing-Based Genome Inference</p>
<p>Erik Garrison<br>University of Tennessee Health Science Center<br>Workshop on Genomics - Česk&yacute; Krumlov<br>January 12, 2024</p>
<p>https://evomics.org/wp-content/uploads/2024/01/Variant-calling-Workshop-on-Genomics-2024-Cesky-Krumlov.pdf</p><p>Address of the bookmark: <a href="https://evomics.org/wp-content/uploads/2024/01/Variant-calling-Workshop-on-Genomics-2024-Cesky-Krumlov.pdf" rel="nofollow">https://evomics.org/wp-content/uploads/2024/01/Variant-calling-Workshop-on-Genomics-2024-Cesky-Krumlov.pdf</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39383/geck-trio-based-comparative-benchmarking-of-variant-calls</guid>
	<pubDate>Sun, 19 May 2019 20:54:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39383/geck-trio-based-comparative-benchmarking-of-variant-calls</link>
	<title><![CDATA[geck: trio-based comparative benchmarking of variant calls]]></title>
	<description><![CDATA[<p><span>Determine the accuracy of our model by comparing the precision and recall of GATK Unified Genotyper and Haplotype Caller on the high-confidence SNPs of the NIST Ashkenazim trio and the two independent Platinum Genome trios. We show that our method is able to estimate&nbsp;</span><em>differential</em><span>&nbsp;precision and recall between the two pipelines with&nbsp;</span><span>10<span>&minus;3</span></span><span>uncertainty.</span></p><p>Address of the bookmark: <a href="https://github.com/sbg/geck" rel="nofollow">https://github.com/sbg/geck</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43661/maftools</guid>
	<pubDate>Fri, 17 Dec 2021 03:18:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43661/maftools</link>
	<title><![CDATA[maftools]]></title>
	<description><![CDATA[<p>With advances in Cancer Genomics, <a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">Mutation Annotation Format</a> (MAF) is being widely accepted and used to store somatic variants detected. <a href="http://cancergenome.nih.gov">The Cancer Genome Atlas</a> Project has sequenced over 30 different cancers with sample size of each cancer type being over 200. <a href="https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files">Resulting data</a> consisting of somatic variants are stored in the form of <a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">Mutation Annotation Format</a>. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner from either TCGA sources or any in-house studies as long as the data is in MAF format.</p>
<p>https://www.bioconductor.org/packages/devel/bioc/vignettes/maftools/inst/doc/maftools.html</p><p>Address of the bookmark: <a href="https://github.com/PoisonAlien/maftools" rel="nofollow">https://github.com/PoisonAlien/maftools</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>

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