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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42559?offset=410</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38166/pygenometracks-standalone-program-and-library-to-plot-beautiful-genome-browser-tracks</guid>
	<pubDate>Fri, 09 Nov 2018 12:34:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38166/pygenometracks-standalone-program-and-library-to-plot-beautiful-genome-browser-tracks</link>
	<title><![CDATA[pyGenomeTracks: Standalone program and library to plot beautiful genome browser tracks]]></title>
	<description><![CDATA[<p>pyGenomeTracks aims to produce high-quality genome browser tracks that are highly customizable. Currently, it is possible to plot:</p>
<ul>
<li>bigwig</li>
<li>bed (many options)</li>
<li>bedgraph</li>
<li>links (represented as arcs)</li>
<li>Hi-C matrices (if&nbsp;<a href="http://hicexplorer.readthedocs.io/">HiCExplorer</a>&nbsp;is installed)</li>
</ul><p>Address of the bookmark: <a href="https://github.com/deeptools/pyGenomeTracks" rel="nofollow">https://github.com/deeptools/pyGenomeTracks</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41831/merqury-reference-free-quality-and-phasing-assessment-for-genome-assemblies</guid>
	<pubDate>Sat, 06 Jun 2020 05:38:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41831/merqury-reference-free-quality-and-phasing-assessment-for-genome-assemblies</link>
	<title><![CDATA[Merqury: reference-free quality and phasing assessment for genome assemblies]]></title>
	<description><![CDATA[<p><span>Often, genome assembly projects have illumina whole genome sequencing reads available for the assembled individual. The k-mer spectrum of this read set can be used for independently evaluating assembly quality without the need of a high quality reference. Merqury provides a set of tools for this purpose.</span></p>
<p><span><a href="https://github.com/marbl/meryl">https://github.com/marbl/meryl</a></span></p><p>Address of the bookmark: <a href="https://github.com/marbl/merqury" rel="nofollow">https://github.com/marbl/merqury</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</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/39903/integrative-meta-assembly-pipeline-imap-chromosome-level-genome-assembler-combining-multiple-de-novo-assemblies</guid>
	<pubDate>Sat, 31 Aug 2019 11:30:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39903/integrative-meta-assembly-pipeline-imap-chromosome-level-genome-assembler-combining-multiple-de-novo-assemblies</link>
	<title><![CDATA[Integrative Meta-Assembly Pipeline (IMAP): Chromosome-level genome assembler combining multiple de novo assemblies]]></title>
	<description><![CDATA[<p><span>Chromosome-level genome assembler combining multiple de novo assemblies</span></p>
<p><span><a href="https://github.com/jkimlab/IMAP">https://github.com/jkimlab/IMAP</a></span></p><p>Address of the bookmark: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221858" rel="nofollow">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221858</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/40881/liu-lab</guid>
  <pubDate>Tue, 04 Feb 2020 06:27:02 -0600</pubDate>
  <link></link>
  <title><![CDATA[Liu Lab]]></title>
  <description><![CDATA[
<p>Shirley is a computational biologist with expertise in cancer epigenetics. Her research focuses on algorithm development and integrative mining from big data generated on microarrays, massively parallel sequencing, and other high throughput techniques to model the specificity and function of transcription factors, chromatin regulators and lncRNAs in tumor development, progression, drug response and resistance.</p>

<p>https://liulab-dfci.github.io/software/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41464/phytozome-v121-plant-science-community-hub-for-accessing-palnts-genomic-data</guid>
	<pubDate>Tue, 17 Mar 2020 07:30:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41464/phytozome-v121-plant-science-community-hub-for-accessing-palnts-genomic-data</link>
	<title><![CDATA[Phytozome  v12.1: plant science community hub for accessing palnts genomic data]]></title>
	<description><![CDATA[<p>Phytozome, the Plant Comparative Genomics portal of the Department of Energy's Joint Genome Institute, provides JGI users and the broader plant science community a hub for accessing, visualizing and analyzing JGI-sequenced plant genomes, as well as selected genomes and datasets that have been sequenced elsewhere. As of release v12.1.6, Phytozome hosts 93 assembled and annotated genomes, from 82 Viridiplantae species. More than half of these genomes have been sequenced, assembled and/or annotated with JGI Plant Science program resources. By integrating this large collection of plant genomes into a single resource and performing comprehensive and uniform annotation and analyses, Phytozome facilitates accurate and insightful comparative genomics studies.</p><p>Address of the bookmark: <a href="https://phytozome.jgi.doe.gov/pz/portal.html" rel="nofollow">https://phytozome.jgi.doe.gov/pz/portal.html</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/42900/svardal-lab</guid>
  <pubDate>Sat, 20 Feb 2021 10:01:19 -0600</pubDate>
  <link></link>
  <title><![CDATA[Svardal lab]]></title>
  <description><![CDATA[
<p>In the Svardal lab they are interested how the astonishing natural diversity we see on earth came into being, by which forces it formed and how it is changing today. Hence, they are trying to understand the process of evolution, with mathematical models and through the analysis of genome sequencing data.</p>

<p>Genomes, and in particular differences between them, are a crucial source of information to understand evolution and biology in general. They provide a record of the evolutionary past of populations, their relatedness patterns, their demography, and their adaptations.</p>

<p>More at https://www.uantwerpen.be/en/staff/hannes-svardal/svardal-lab/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43112/calling-variants-in-non-diploid-systems</guid>
	<pubDate>Sat, 26 Jun 2021 15:37:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43112/calling-variants-in-non-diploid-systems</link>
	<title><![CDATA[Calling variants in non-diploid systems]]></title>
	<description><![CDATA[<p><span>The main challenge associated with non-diploid variant calling is the difficulty in distinguishing between the sequencing noise (abundant in all NGS platforms) and true low frequency variants. Some of the early attempts to do this well have been accomplished on human mitochondrial&nbsp;</span><span>DNA</span><span>&nbsp;although the same approaches will work equally good on viral and bacterial genomes (</span><a href="https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html#Rebolledo-Jaramillo2014">Rebolledo-Jaramillo&nbsp;<em>et al.</em>&nbsp;2014</a><span>,&nbsp;</span><a href="https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html#Li2015">Li&nbsp;<em>et al.</em>&nbsp;2015</a><span>).</span></p><p>Address of the bookmark: <a href="https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html" rel="nofollow">https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43620/ncbi-datasets-cli-quickstart-command-line-tools</guid>
	<pubDate>Tue, 07 Dec 2021 02:51:26 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43620/ncbi-datasets-cli-quickstart-command-line-tools</link>
	<title><![CDATA[ncbi-datasets-cli -- Quickstart: command line tools !]]></title>
	<description><![CDATA[<p><span>Install and use the NCBI Datasets command line tools</span></p>
<p>The NCBI Datasets datasets command line tools are&nbsp;<a href="https://www.ncbi.nlm.nih.gov/datasets/docs/v1/reference-docs/command-line/datasets/">datasets</a>&nbsp;and&nbsp;<a href="https://www.ncbi.nlm.nih.gov/datasets/docs/v1/reference-docs/command-line/dataformat/">dataformat</a>&nbsp;.</p>
<p>Use&nbsp;<span>datasets</span>&nbsp;to download biological sequence data across all domains of life from NCBI.</p>
<p>Use&nbsp;<span>dataformat</span>&nbsp;to convert metadata from&nbsp;<a href="https://jsonlines.org/" target="_blank">JSON Lines</a>&nbsp;format to other formats.</p>
<p><strong>Conda download:</strong></p>
<p>https://anaconda.org/conda-forge/ncbi-datasets-cli</p>
<p><strong>Buld Download</strong></p>
<p>&nbsp;https://www.ncbi.nlm.nih.gov/datasets/builder/?tax_id=29979</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/datasets/docs/v1/quickstarts/command-line-tools/" rel="nofollow">https://www.ncbi.nlm.nih.gov/datasets/docs/v1/quickstarts/command-line-tools/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data</guid>
	<pubDate>Fri, 25 Feb 2022 04:42:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data</link>
	<title><![CDATA[Smudgeplot: Inference of ploidy and heterozygosity structure using whole genome sequencing data]]></title>
	<description><![CDATA[<p dir="auto">This tool extracts heterozygous kmer pairs from kmer count databases and performs gymnastics with them. We are able to disentangle genome structure by comparing the sum of kmer pair coverages (CovA + CovB) to their relative coverage (CovB / (CovA + CovB)). Such an approach also allows us to analyze obscure genomes with duplications, various ploidy levels, etc.</p>
<p dir="auto">Smudgeplots are computed from raw or even better from trimmed reads and show the haplotype structure using heterozygous kmer pairs. For example:</p>
<p dir="auto"><a href="https://user-images.githubusercontent.com/8181573/45959760-f1032d00-c01a-11e8-8576-ff0512c33da9.png" target="_blank"><img src="https://user-images.githubusercontent.com/8181573/45959760-f1032d00-c01a-11e8-8576-ff0512c33da9.png" alt="smudgeexample" style="border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/KamilSJaron/smudgeplot" rel="nofollow">https://github.com/KamilSJaron/smudgeplot</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>

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