<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41493?offset=530</link>
	<atom:link href="https://bioinformaticsonline.com/related/41493?offset=530" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38515/genome-annotation-using-maker-tutorial</guid>
	<pubDate>Thu, 20 Dec 2018 17:39:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38515/genome-annotation-using-maker-tutorial</link>
	<title><![CDATA[Genome Annotation using MAKER tutorial !]]></title>
	<description><![CDATA[<p><a href="http://www.yandell-lab.org/software/maker.html">MAKER</a><span>&nbsp;is a great tool for annotating a reference genome using empirical and&nbsp;</span><em>ab initio</em><span>gene predictions.&nbsp;</span><a href="http://gmod.org/wiki/Main_Page">GMOD</a><span>, the umbrella organization that includes MAKER, has some nice tutorials online for running MAKER. However, these were quite simplified examples and it took a bit of effort to wrap my head completely around everything. Here I will describe a&nbsp;</span><em>de novo</em><span>&nbsp;genome annotation for&nbsp;</span><em>Boa constrictor</em><span>&nbsp;in detail, so that there is a record and that it is easy to use this as a guide to annotate any genome.</span></p><p>Address of the bookmark: <a href="https://www.biostars.org/p/261203/" rel="nofollow">https://www.biostars.org/p/261203/</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39250/darwin-wga-a-co-processor-provides-increased-sensitivity-in-whole-genome-alignments-with-high-speedup</guid>
	<pubDate>Sat, 13 Apr 2019 08:55:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39250/darwin-wga-a-co-processor-provides-increased-sensitivity-in-whole-genome-alignments-with-high-speedup</link>
	<title><![CDATA[Darwin-WGA: A Co-processor Provides Increased Sensitivity in Whole Genome Alignments with High Speedup]]></title>
	<description><![CDATA[<p>Darwin-WGA, is the first hardware accelerator for whole genome alignment and accelerates the gapped filtering stage. Darwin-WGA also employs GACT-X, a novel algorithm used in the extension stage to align arbitrarily long genome sequences using a small on-chip memory, that provides better quality alignments at 2&times; improvement in memory and speed over the previously published GACT algorithm. Implemented on an FPGA, Darwin-WGA provides up to 24&times; improvement (performance/$) in WGA over iso-sensitive software.</p>
<p><a href="https://stanford.edu/~yatisht/pubs/darwin-wga.pdf">https://stanford.edu/~yatisht/pubs/darwin-wga.pdf</a></p><p>Address of the bookmark: <a href="https://github.com/gsneha26/Darwin-WGA" rel="nofollow">https://github.com/gsneha26/Darwin-WGA</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/40609/genome-informatics-section-lab</guid>
  <pubDate>Sat, 25 Jan 2020 06:38:23 -0600</pubDate>
  <link></link>
  <title><![CDATA[Genome Informatics Section Lab !]]></title>
  <description><![CDATA[
<p>Our section develops and applies computational methods for the analysis of massive genomics datasets, focusing on the challenges of genome sequencing and comparative genomics. We aim to improve such foundational processes and translate emerging genomic technologies into practice.</p>

<p>The Genome Informatics Section is hiring! Come join our outstanding team at the NIH’s National Human Genome Research Institute and contribute to the development of new reference genomes and computational methods for DNA sequencing and analysis. Both postdoc and PhD students positions are available. More information and application instructions follow below.</p>

<p>More at https://genomeinformatics.github.io/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43060/simons-genome-diversity-project</guid>
	<pubDate>Sat, 08 May 2021 21:55:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43060/simons-genome-diversity-project</link>
	<title><![CDATA[Simons Genome Diversity Project]]></title>
	<description><![CDATA[<p><em>Complete genome sequences from more than one hundred diverse human populations</em></p>
<p>All genomes in the dataset were sequenced to at least 30x coverage using Illumina technology. The sequencing reads were mapped and genotyped using a customized procedure that was optimized for population genetic analysis. The researchers eliminated bias of alleles toward matching the human genome reference sequence, and determined genotypes on a single-sample basis to avoid preferential calling of genotypes from populations that had more individuals represented.</p><p>Address of the bookmark: <a href="https://www.simonsfoundation.org/simons-genome-diversity-project/" rel="nofollow">https://www.simonsfoundation.org/simons-genome-diversity-project/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43658/uniquekmer-generate-unique-kmers-for-every-contig-in-a-fasta-file</guid>
	<pubDate>Fri, 17 Dec 2021 00:08:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43658/uniquekmer-generate-unique-kmers-for-every-contig-in-a-fasta-file</link>
	<title><![CDATA[UniqueKmer: Generate unique KMERs for every contig in a FASTA file]]></title>
	<description><![CDATA[<p dir="auto">Generate unique k-mers for every contig in a FASTA file.</p>
<p dir="auto">Unique k-mer is consisted of k-mer keys (i.e. ATCGATCCTTAAGG) that are only presented in one contig, but not presented in any other contigs (for both forward and reverse strands).</p>
<p dir="auto">This tool accepts the input of a FASTA file consisting of many contigs, and extract unique k-mers for each contig.</p>
<p dir="auto">The output unique k-mer file and Genome file can be used for fastv:&nbsp;<a href="https://github.com/OpenGene/fastv">https://github.com/OpenGene/fastv</a>, which is an ultra-fast tool to identify and visualize microbial sequences from sequencing data.</p>
<p>https://github.com/OpenGene/UniqueKMER</p><p>Address of the bookmark: <a href="https://github.com/OpenGene/UniqueKMER" rel="nofollow">https://github.com/OpenGene/UniqueKMER</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44537/the-atcc-genome-portal</guid>
	<pubDate>Wed, 15 May 2024 14:24:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44537/the-atcc-genome-portal</link>
	<title><![CDATA[The ATCC Genome Portal]]></title>
	<description><![CDATA[<p><span>The ATCC Genome Portal (AGP,&nbsp;</span><a href="https://genomes.atcc.org/">https://genomes.atcc.org/</a><span>) is a database of authenticated genomes for bacteria, fungi, protists, and viruses held in ATCC&rsquo;s biorepository. It now includes 3,938 assemblies (253% increase) produced under ISO 9000 by ATCC. Here, we present new features and content added to the AGP for the research community.</span></p><p>Address of the bookmark: <a href="https://genomes.atcc.org/" rel="nofollow">https://genomes.atcc.org/</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44503/entire-human-genome-sequencing</guid>
	<pubDate>Tue, 02 Apr 2024 01:19:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44503/entire-human-genome-sequencing</link>
	<title><![CDATA[Entire Human Genome Sequencing !]]></title>
	<description><![CDATA[<p>Cost-effective whole human genome sequencing has revolutionized the landscape of genetic research and personalized medicine by making comprehensive genetic analysis accessible to a wider population. Through advancements in sequencing technologies, such as next-generation sequencing (NGS), costs have significantly decreased, enabling researchers and healthcare providers to analyze an individual's complete genetic makeup with greater efficiency and affordability. This has profound implications for disease diagnosis, prognosis, and treatment, as it allows for the identification of genetic predispositions and the customization of healthcare interventions based on an individual's unique genetic profile. Moreover, as the cost continues to decline, the potential for population-scale genomic studies and large-scale screening programs becomes increasingly feasible, promising to further enhance our understanding of human genetics and improve healthcare outcomes on a global scale.</p><p>Here are few companies:</p><p>https://mynucleus.com/</p><p>https://myome.com/</p><p>https://nebula.org/whole-genome-sequencing-dna-test/</p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies</guid>
	<pubDate>Sat, 20 Sep 2025 09:34:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies</link>
	<title><![CDATA[HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies]]></title>
	<description><![CDATA[<p dir="auto"><code>HiTE</code>&nbsp;is a Python software that uses a dynamic boundary adjustment approach to detect and annotate full-length Transposable Elements in Genome Assemblies. In comparison to other tools, HiTE demonstrates superior performance in detecting a greater number of full-length TEs.</p>
<div dir="auto">
<h2 dir="auto">panHiTE</h2>
<a href="https://github.com/CSU-KangHu/HiTE#panhite"></a></div>
<p dir="auto">We have developed panHiTE, a comprehensive and accurate pipeline for TE detection in large-scale population genomes. It has been successfully applied to hundreds of plant population genomes, demonstrating its effectiveness and scalability.</p>
<p dir="auto">For detailed instructions, please refer to the&nbsp;<a href="https://github.com/CSU-KangHu/HiTE/wiki/panHiTE-tutorial">panHiTE tutorial</a>.</p><p>Address of the bookmark: <a href="https://github.com/CSU-KangHu/HiTE" rel="nofollow">https://github.com/CSU-KangHu/HiTE</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/861/fiona-brinkman-laboratory</guid>
  <pubDate>Sun, 14 Jul 2013 12:46:31 -0500</pubDate>
  <link></link>
  <title><![CDATA[Fiona Brinkman Laboratory]]></title>
  <description><![CDATA[
<p>Infectious disease control needs to be made more “sustainable”. We need to reduce selective pressure on pathogens to evolve antibiotic resistance. We need to control infectious disease outbreaks and associated immune disorders with a better understanding of the genetic,  environmental and social factors that impact disease spread and severity.</p>

<p>Research Area</p>

<p>Investigating the role in disease of both the microbe and its host (i.e immune system failure), using genomics and systems biology-based approaches<br />Using genomics and network analysis to characterize disease outbreaks and their environmental/social/genetic causes, and<br />Identifying new anti-infective and immune modulating therapies/biomarkers.</p>

<p>Link @ http://www.brinkman.mbb.sfu.ca/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/879/bioprogramming</guid>
	<pubDate>Sun, 14 Jul 2013 16:29:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/879/bioprogramming</link>
	<title><![CDATA[BioProgramming]]></title>
	<description><![CDATA[<p>The completion of the first human genome drafts was just a start of the modern DNA sequencing era which resulted in further invention, improved development toward new advanced strategies of high-throughput DNA sequencing, so called the &ldquo;high-throughput next generation sequencing&rdquo; (HT-NGS). The decreasing genome sequencing cost and desire to explore and understand biological machanism at genomic level, speed up the genomic sequencing projects. In the fast growing HT-NGS technologies, the main challenge is to cope with the analysis of vast production of sequencing database through advanced bioinformatics tools. In oder to develope sotware/tools bioinformatician/ biological programmers need to expertise in any one one the programming language. However, sometime one language are not enough to handle all sort of biological needs, which compel us to learn new biologically suitable language to handle ever growing genome or protein sequences.</p><p>The next step after reading genetic code is writing a script to analyse and explore the hidden information. This tutorial is aimed to introduce you new biological programming languages with their packages/libraries, and assist in your scripting work.</p><p>Navigate the sub-section of this page [ see right hand side of the page for it ]</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>

</channel>
</rss>