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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43846?offset=410</link>
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	<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/40715/mutatrix-a-population-genome-simulator-which-generates-simulated-genomes</guid>
	<pubDate>Tue, 28 Jan 2020 04:06:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40715/mutatrix-a-population-genome-simulator-which-generates-simulated-genomes</link>
	<title><![CDATA[mutatrix: a population genome simulator which generates simulated genomes.]]></title>
	<description><![CDATA[<p><span>genome simulation across a population with zeta-distributed allele frequency, snps, insertions, deletions, and multi-nucleotide polymorphisms</span></p>
<p><span>More at&nbsp;<a href="https://github.com/ekg/mutatrix">https://github.com/ekg/mutatrix</a></span></p>
<pre>./mutatrix -S sample -P test/ -p 2 -n 10 reference.fasta</pre><p>Address of the bookmark: <a href="https://github.com/ekg/mutatrix" rel="nofollow">https://github.com/ekg/mutatrix</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41459/jcvipython-utility-libraries-on-genome-assembly-annotation-and-comparative-genomics</guid>
	<pubDate>Tue, 17 Mar 2020 06:19:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41459/jcvipython-utility-libraries-on-genome-assembly-annotation-and-comparative-genomics</link>
	<title><![CDATA[JCVI:Python utility libraries on genome assembly, annotation and comparative genomics]]></title>
	<description><![CDATA[<p>Collection of Python libraries to parse bioinformatics files, or perform computation related to assembly, annotation, and comparative genomics.</p>
<p>https://github.com/tanghaibao/jcvi</p>
<p>More at https://github.com/tanghaibao/jcvi/wiki</p><p>Address of the bookmark: <a href="https://github.com/tanghaibao/jcvi" rel="nofollow">https://github.com/tanghaibao/jcvi</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</guid>
	<pubDate>Mon, 04 May 2020 02:04:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads</link>
	<title><![CDATA[HASLR: a hybrid assembler which uses both second and third generation sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples. Availability. HASLR is an open source tool available at https://github.com/vpc-ccg/haslr.</span></p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/haslr" rel="nofollow">https://github.com/vpc-ccg/haslr</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43057/hapsolo-an-optimization-approach-for-removing-secondary-haplotigs-during-diploid-genome-assembly-and-scaffolding</guid>
	<pubDate>Sat, 08 May 2021 21:25:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43057/hapsolo-an-optimization-approach-for-removing-secondary-haplotigs-during-diploid-genome-assembly-and-scaffolding</link>
	<title><![CDATA[HapSolo: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding]]></title>
	<description><![CDATA[<p><span>HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/esolares/HapSolo" rel="nofollow">https://github.com/esolares/HapSolo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/43293/josefa-gonzalez-lab</guid>
  <pubDate>Thu, 19 Aug 2021 08:52:56 -0500</pubDate>
  <link></link>
  <title><![CDATA[Josefa González Lab]]></title>
  <description><![CDATA[
<p>Lab focus on understanding how organisms adapt to their environments. They combine omics approaches with detailed molecular and phenotypic analyses to get a comprehensive picture of adaptation. Our aim at being internationally recognized as a leading lab in the field of environmental adaptation.<br />Lab share our passion for science with the general public by leading outreach projects aimed at increasing science awareness.</p>

<p>More at https://www.biologiaevolutiva.org/gonzalez_lab/</p>
]]></description>
</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/44311/jbrowse-2-a-modular-genome-browser-with-views-of-synteny-and-structural-variation</guid>
	<pubDate>Tue, 25 Apr 2023 20:58:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44311/jbrowse-2-a-modular-genome-browser-with-views-of-synteny-and-structural-variation</link>
	<title><![CDATA[JBrowse 2: a modular genome browser with views of synteny and structural variation]]></title>
	<description><![CDATA[<ul dir="auto">
<li>igvjs - a create-react-app with igv package from npm installed. the igv.js is instrumented to output "DONE" to the console when finished, and to have an increased fetchSizeLimit (which is otherwise git in CRAM longread tests)</li>
<li>jb2-web - stock instance of jbrowse-web v1.7.5</li>
<li>jb1 - stock instance of jbrowse 1 v1.16.11</li>
<li>jb2 embedded - a create-react-app with @jbrowse/react-linear-genome-view</li>
</ul><p>Address of the bookmark: <a href="https://github.com/GMOD/jb2profile" rel="nofollow">https://github.com/GMOD/jb2profile</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44377/mitochondrial-genome-assembly-tools</guid>
	<pubDate>Wed, 06 Sep 2023 00:37:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44377/mitochondrial-genome-assembly-tools</link>
	<title><![CDATA[Mitochondrial genome assembly tools !]]></title>
	<description><![CDATA[<p>Mitochondrial genome assembly tools are specialized software and algorithms designed to accurately reconstruct the mitochondrial genome (mitogenome) from sequencing data, typically obtained through techniques like next-generation sequencing (NGS). The mitochondrial genome is relatively small compared to the nuclear genome, making it an ideal target for assembly. Here are some commonly used mitochondrial genome assembly tools:</p><p><strong>MitoFinder:</strong> Mitofinder is a pipeline to assemble mitochondrial genomes and annotate mitochondrial genes from trimmed read sequencing data.</p><p><strong>MitoHiFi:</strong> a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads</p><p>MITObim: MITObim is a tool specifically developed for the iterative assembly of mitochondrial genomes. It starts with a reference mitogenome and iteratively refines the assembly using the read data.</p><p><strong>MITOS:</strong> MITOS is a web-based platform that provides a pipeline for annotating mitochondrial genomes. It integrates multiple software tools for assembly, annotation, and visualization of mitogenomes.</p><p><strong>MIRA:</strong> MIRA (Mimicking Intelligent Read Assembly) is a versatile genome assembly tool that can be used for mitochondrial genome assembly. It supports various sequencing technologies and allows for reference-based or de novo assembly.</p><p><strong>NOVOPlasty:</strong> NOVOPlasty is a user-friendly tool designed for de novo assembly of organelle genomes, including mitochondria. It utilizes a seed-and-extend algorithm and is suitable for both short-read and long-read data.</p><p><strong>MITOS2:</strong> MITOS2 is an updated version of the MITOS pipeline, which automates the annotation of mitochondrial genomes. It provides improved accuracy and additional features for mitochondrial genome analysis.</p><p><strong>GetOrganelle:</strong> While primarily designed for chloroplast genome assembly, GetOrganelle can also be used for mitochondrial genome assembly. It is particularly useful for dealing with high-throughput sequencing data.</p><p><strong>SPAdes:</strong> SPAdes (St. Petersburg genome assembler) is a versatile genome assembly tool that can be employed for mitochondrial genome assembly, especially when dealing with complex datasets that may contain nuclear mitochondrial DNA sequences (numts).</p><p><strong>IDBA-UD:</strong> IDBA-UD (Iterative De Bruijn Graph De Novo Assembler) is another de novo assembly tool that can be used for mitochondrial genome assembly, especially in cases with relatively low coverage.</p><p><strong>Velvet:</strong> Velvet is a de novo assembly tool that can be applied to mitochondrial genome assembly, especially when working with short-read data.</p><p>When selecting a mitochondrial genome assembly tool, it's important to consider the specific characteristics of your sequencing data, such as read length and coverage, as well as the complexity of the mitochondrial genome. Additionally, some tools are better suited for specific organisms or research objectives, so choosing the right tool will depend on your particular project requirements.</p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

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