<?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/37636?offset=690</link>
	<atom:link href="https://bioinformaticsonline.com/related/37636?offset=690" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/44395/genomics-india-conference-2024</guid>
  <pubDate>Fri, 27 Oct 2023 05:48:11 -0500</pubDate>
  <link></link>
  <title><![CDATA[Genomics India Conference 2024 !]]></title>
  <description><![CDATA[
<p>Genomics India Conference is back and this time we are coming to Shiv<br />Nadar Intitution of Eminenece, Delhi NCR. GIC 2024 will be held from 1st<br />to 3rd of February 2024 and we are happy to send you an early invitation<br />for India's premier genomics conference.</p>

<p>GIC2024 focuses on "Advances In Genomics From AI-ML To Targeted<br />Therapies". GIC2024 encourages researchers to present original<br />contributions for poster presentations.</p>

<p>Note: Early bird registration closes on 1st December 2023.</p>

<p>Kindly, register at GIC 2024 Earlybird registartion</p>

<p>https://genomicsindia.co.in/</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/44679/rennison-lab</guid>
  <pubDate>Sat, 26 Oct 2024 15:10:32 -0500</pubDate>
  <link></link>
  <title><![CDATA[Rennison Lab !]]></title>
  <description><![CDATA[
<p>Welcome to the Rennison lab in the School of Biological Sciences at the University of California San Diego. We are a group interested in the evolution and maintenance of biodiversity. We study the processes related to biodiversity using methods from the fields of evolution, ecology, population genomics, and theory. </p>

<p>More at https://rennisonlab.com/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/13523/megadock-40</guid>
	<pubDate>Thu, 07 Aug 2014 18:08:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/13523/megadock-40</link>
	<title><![CDATA[MEGADOCK 4.0]]></title>
	<description><![CDATA[<p>An ultra&ndash;high-performance protein&ndash;protein docking software for heterogeneous supercomputers</p>
<p id="p-4"><strong>Summary:</strong> The application of protein&ndash;protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of over 97% strong scaling.</p>
<p id="p-5"><strong>Availability and Implementation:</strong> MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: <a href="http://www.bi.cs.titech.ac.jp/megadock">http://www.bi.cs.titech.ac.jp/megadock</a>.</p>
<p id="p-6"><strong>Contact:</strong> <a href="mailto:akiyama@cs.titech.ac.jp">akiyama@cs.titech.ac.jp</a></p><p>Address of the bookmark: <a href="http://bioinformatics.oxfordjournals.org/content/early/2014/08/06/bioinformatics.btu532.short" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/early/2014/08/06/bioinformatics.btu532.short</a></p>]]></description>
	<dc:creator>Suleman Khan</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/45309/ley-lab</guid>
  <pubDate>Fri, 11 Sep 2026 13:21:10 -0500</pubDate>
  <link></link>
  <title><![CDATA[Ley Lab !]]></title>
  <description><![CDATA[
<p>Research Program aims to elucidate how gut microbial species relate to genotypic differences between human individuals, to identify bacterial and archaeal species that share an evolutionary history with humans, and to determine the molecular basis of long-term host-microbial relationships. We approach these aims through a combination of population-level observations and laboratory-based molecular-level investigations. We link inter-microbial and microbial-host interactions at the molecular scale to patterns at population and evolutionary scales. </p>

<p>More at https://leylab.com/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/14024/grapher</guid>
	<pubDate>Thu, 14 Aug 2014 14:02:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/14024/grapher</link>
	<title><![CDATA[GrapheR !!!]]></title>
	<description><![CDATA[<p>What a wonderful gem <em>GrapheR</em> is.... Oh yes it is. <em>GrapheR</em> is a GUI for base graphics in R by http://www.maximeherve.com/. The package provides a graphical user interface for creating base charts in R. It is ideal for beginners in R, as the user interface is very clear and the code is written along side into a text file, allowing users to recreate the charts directly in the console. <br /><br />Adding and changing legends? Messing around with the plotting window settings? It is much easier/quicker with this GUI than reading the help file and trying to understand the various parameters.<br />Here is a little example using the iris data set.<br /><br />library(GrapheR)<br />data(iris)<br />run.GrapheR()<br /><br />This will bring up a window that helps me to create the chart and tweak the various parameters.</p><p><img src="http://4.bp.blogspot.com/-NbnCM1dPh3E/U9aW9YxJ9oI/AAAAAAAABgo/gEPzPhOpf2Y/s1600/GrapheR.png" alt="image" width="878" height="868" style="border: 0px; border: 0px;"><br /><br />Finally, I find the underlying R code in a file created by <em>GrapheR</em>. For more details read also the <a href="http://cran.r-project.org/web/packages/GrapheR/index.html" target="_blank">package vignette</a>, which is available in <a href="http://cran.r-project.org/web/packages/GrapheR/vignettes/manual_en.pdf" target="_blank">English</a>, <a href="http://cran.r-project.org/web/packages/GrapheR/vignettes/manual_fr.pdf" target="_blank">French</a> and <a href="http://cran.r-project.org/web/packages/GrapheR/vignettes/manual_de.pdf" target="_blank">German</a>!</p>]]></description>
	<dc:creator>John Parker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45372/uncovering-evolution%E2%80%99s-blueprint-how-ntsynt-is-changing-comparative-genomics</guid>
	<pubDate>Mon, 19 Oct 2026 03:08:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45372/uncovering-evolution%E2%80%99s-blueprint-how-ntsynt-is-changing-comparative-genomics</link>
	<title><![CDATA[Uncovering Evolution’s Blueprint: How ntSynt Is Changing Comparative Genomics]]></title>
	<description><![CDATA[<p>Over millions of years, evolution has shaped all life on Earth, leaving clues in the DNA of every organism. While different species look and act very differently, their genomes still hold signs of a shared evolutionary past. These connections show up not just in similar DNA sequences, but also in how genetic material is arranged on chromosomes. To study these patterns, scientists use a concept called synteny, which helps them track changes in genomes, chromosome rearrangements, and relationships between species. However, as genome sequencing technology has advanced and thousands of high-quality genomes are now available, finding conserved regions across many genomes has become a much tougher computational challenge.</p><p>To tackle this problem, researchers developed a new bioinformatics tool called ntSynt. It was first introduced in a 2025 study in BMC Biology titled &ldquo;ntSynt: multi-genome synteny detection using minimizer graph mappings&rdquo;. This tool (https://github.com/BirolLab/ntSynt) helps researcher find conserved genomic regions across several species without relying on traditional whole-genome alignments or gene annotations. Instead, ntSynt uses short DNA sequences called minimizers as landmarks. It finds minimizers shared by different genomes and maps their relationships in a graph, making it possible to spot large genomic regions that have kept a similar structure over time. The tool uses Bloom filters and graph-based mapping, which lets scientists analyze many genomes at once while saving memory and processing time.</p><p>The team tested ntSynt using both simulated data and real genomic sequences from groups like humans, chimpanzees, bonobos, gorillas, rodents, and bees. Their results showed that ntSynt can find conserved genomic regions and spot structural differences between species. In simulated tests, ntSynt identified syntenic blocks with over 97% accuracy. One standout example involved eleven bee genomes from the genus Andrena, which have a lot of variation in chromosome organization. Despite these differences, ntSynt found conserved regions covering about 85% of the smallest bee genome, completing the analysis in just 14.7 minutes and using less than 4 GB of memory. These findings highlight ntSynt&rsquo;s potential for large-scale comparative genomics, where efficiency is crucial as more genomes become available.</p><p>An interesting result came up when the researchers compared primate genomes. When they looked at regions conserved between humans and other great apes, ntSynt flagged what looked like a large deletion in one gorilla genome assembly. After checking with long-read sequencing data, they found that the missing section was likely a mistake in the assembly, not a real evolutionary change. This means ntSynt can help not only with studying evolutionary relationships but also with spotting possible errors in genome assemblies. By highlighting unexpected breaks in regions that are usually conserved, the tool helps scientists tell apart true evolutionary changes from technical mistakes in reference sequences.</p><p>The importance of ntSynt becomes even clearer when we look at global projects to sequence and study Earth&rsquo;s biodiversity. Projects like the Earth BioGenome Project aim to create genomic resources for many different organisms, opening up new ways to explore the history of life. But generating genomic data is just the beginning; making sense of these relationships requires powerful and scalable tools. ntSynt helps by letting researchers study chromosome evolution, conserved genome structures, and rearrangements across many species. While the tool has some limits, especially with detecting duplicated regions or analyzing very different genomes, its speed and scalability make it a valuable tool for modern comparative genomics.</p><p>The creation of ntSynt marks a significant advancement in our understanding of how genomes have evolved and become diverse over Earth's history. Since each conserved region in the genome is evidence of common ancestry and each rearrangement gives hinThe development of ntSynt is a big step forward in understanding how genomes have changed and diversified over time. Each conserved region in a genome points to shared ancestry, and each rearrangement offers clues about evolutionary events in different lineages. ntSynt helps scientists spot these patterns more easily, opening up new ways to study genome evolution across the tree of life. Evolution&rsquo;s story isn&rsquo;t just in the DNA sequence, but also in how those sequences are arranged and preserved over millions of years. Tools like ntSynt are helping scientists read this hidden story more clearly and reveal the deep genomic connections that link all life on Earth.</p><p>More at&nbsp;https://link.springer.com/article/10.1186/s12915-025-02455-w?</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/14186/pybedtools</guid>
	<pubDate>Wed, 20 Aug 2014 01:03:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/14186/pybedtools</link>
	<title><![CDATA[pybedtools]]></title>
	<description><![CDATA[<p>pybedtools is a Python wrapper for Aaron Quinlan's BEDtools programs (https://github.com/arq5x/bedtools), which are widely used for genomic interval manipulation or "genome algebra". pybedtools extends BEDTools by offering feature-level manipulations from with Python. See full online documentation, including installation instructions, at http://pythonhosted.org/pybedtools/.</p><p>More at http://pythonhosted.org/pybedtools/</p><p>A powerful toolset for genome arithmetic.http://code.google.com/p/bedtools/</p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/21150/webinar-on-an-integrated-rna-and-dna-approach-to-unravel-genetic-regulation-in-cancer</guid>
	<pubDate>Wed, 11 Feb 2015 04:59:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/21150/webinar-on-an-integrated-rna-and-dna-approach-to-unravel-genetic-regulation-in-cancer</link>
	<title><![CDATA[Webinar on 'An integrated RNA and DNA approach to unravel genetic regulation in cancer']]></title>
	<description><![CDATA[<div><p><strong>Webinar on 'An integrated RNA and DNA approach to unravel genetic regulation in cancer'</strong></p><p><strong>Abstract</strong></p><p>Whole exome DNA sequencing (WES) or whole genome DNA sequencing (WGS) allows detection of mutations and polymorphisms in all exonic and genomic regions, respectively, while messenger RNA sequencing (RNA-Seq) enables quantitative analysis of gene expression. Mutations in the genome result in diverse transcriptional aberrations that can be missed in a stand-alone WES/WGS analysis. An integration of DNA variant analysis and RNA-Seq analysis enables one to investigate the consequences of genomic changes in the RNA transcripts including germline and somatic changes, imprinting, RNA editing and allele specific expression (ASE). In this webinar, we will demonstrate this integrated approach using Strand NGS to identify high confidence mutations, RNA editing events and ASE in cancer.</p><p><strong>Webinar Details</strong></p><table width="100%" border="1" cellspacing="0" cellpadding="0">
<tbody>
<tr>
<td valign="top">
<p style="text-align: center;"><br /> <strong>Sessions</strong></p>
</td>
<td valign="top">
<p style="text-align: center;"><a href="http://www.strand-ngs.com/webinar_registration"><strong>San Francisco Time<br /> (PST)</strong></a></p>
</td>
<td valign="top">
<p style="text-align: center;"><a href="http://www.strand-ngs.com/webinar_registration"><strong>Tokyo Time<br /> (GMT+09:00)</strong></a></p>
</td>
<td valign="top">
<p style="text-align: center;"><a href="http://www.strand-ngs.com/webinar_registration"><strong>Berlin Time<br /> (GMT+01:00)</strong></a></p>
</td>
<td valign="top">
<p style="text-align: center;"><a href="http://www.strand-ngs.com/webinar_registration"><strong>Mumbai Time<br /> (GMT+05:30)</strong></a></p>
</td>
</tr>
<tr>
<td>
<p style="text-align: center;"><a href="http://www.strand-ngs.com/webinar_registration"><strong>Session 1</strong></a></p>
</td>
<td valign="top">
<p style="text-align: center;">25 Feb&nbsp;<br /> 12:30 AM</p>
</td>
<td>
<p style="text-align: center;">25 Feb&nbsp;<br /> 5:30 PM</p>
</td>
<td>
<p style="text-align: center;">25 Feb&nbsp;<br /> 9:30 AM</p>
</td>
<td>
<p style="text-align: center;">25 Feb&nbsp;<br /> 2:00 PM</p>
</td>
</tr>
<tr>
<td valign="top">
<p style="text-align: center;"><a href="http://www.strand-ngs.com/webinar_registration"><strong>Session 2</strong></a></p>
</td>
<td valign="top">
<p style="text-align: center;">25 Feb&nbsp;<br /> 9:00 AM</p>
</td>
<td>
<p style="text-align: center;">26 Feb<br /> 2:00 AM</p>
</td>
<td>
<p style="text-align: center;">25 Feb&nbsp;<br /> 6:00 PM</p>
</td>
<td>
<p style="text-align: center;">25 Feb&nbsp;<br /> 10:30 PM</p>
</td>
</tr>
</tbody>
</table><p><strong style="font-size: 12.8000001907349px;">Register here: </strong><a href="http://www.strand-ngs.com/webinar_registration">http://www.strand-ngs.com/webinar_registration</a></p><p><strong>About Speaker:</strong></p><p>Dr. Veena Hedatale, has a PhD in Plant Genetics from The Radboud University, Netherlands focused on meiosis and recombination. Her prior academic experience at Cornell University was on genetic mapping and gene transformation in Rice. She has worked with Monsanto, and contributed to data mining, database development as well as gene/promoter/pathway discovery for traits related to yield and stress in crop species. At Strand, Veena has worked on Pharmacogenomic analysis of targets and Gene family analysis projects. Currently, she is part of the Strand NGS Application Science team and is involved in the analysis of next generation sequencing data.</p><p>Please feel free to contact us 24/5, for availing free online training or if you have any questions.</p></div><div><p><strong style="font-size: 12.8000001907349px;">Email:</strong> sales@strandngs.com</p><p><strong>Phone (USA):</strong> 1-800-752-9122</p><p><strong>Phone (ROW):</strong> +1-650-353-5060</p><p>&nbsp;</p></div>]]></description>
	<dc:creator>Yeshodari</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34549/kraken-a-universal-genomic-coordinate-translator-for-comparative-genomics</guid>
	<pubDate>Thu, 07 Dec 2017 04:45:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34549/kraken-a-universal-genomic-coordinate-translator-for-comparative-genomics</link>
	<title><![CDATA[kraken: A universal genomic coordinate translator for comparative genomics]]></title>
	<description><![CDATA[<p><span>If you planning on conducting a study involving dozens of large genomes, then you do not have to run all pairwise synteny alignments .. simply try&nbsp;kraken: A universal genomic coordinate translator for comparative genomics</span></p><p>Address of the bookmark: <a href="https://github.com/nedaz/kraken" rel="nofollow">https://github.com/nedaz/kraken</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/14904/bioinformatics-jrfsrf-position-at-iari</guid>
  <pubDate>Thu, 04 Sep 2014 04:14:01 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics JRF/SRF position at IARI]]></title>
  <description><![CDATA[
<p>DIVISION OF NEMATOLOGY<br />INDIAN AGRICULTURAL RESEARCH INSTITUTE<br />NEW DELHI 110012<br />Applications are invited for the posts of one Junior<br />Research Fellow and one RA in the DBT funded project entitled “ Plant parasitic nematode genome informatics - insilico resource development”. The project is for a period of three years. </p>

<p>Essential qualifications for JRF<br />: M. Sc. in Bioinformatics with experience in Proteomics, genomics and structural biology. Knowledge of programming language, pearl and database – HTML, CSS,php and Java script.<br />Essential qualifications for Research Associate:<br />MSc/MTech in Bioinformatics with three years experience or Ph.D in Bioinformatics with experience in proteomics, genomics and structural biology. Knowledge of programming language, perl and database<br />– HTML, CSS, Java script. NGS sequence assembly and analysis and algorithm designing.<br />Age limit : 35 years maximum (5 year relaxation for SC/ST and women candidates)<br />Emoluments:<br />JRF: 16,000 + 30% HRA<br />.<br />Res Assoc: Rs22,000 + 30% HRA<br />The post is purely temporary in nature and is co-terminus with the project. The appointment would be initially for one year and may be extended further upon satisfactory performance.<br />Interested candidates<br />should send the duly filled application forms (format in the following page ) so as to reach on or before 20.9.2014 along with all the relevant documents.</p>

<p>More at http://www.iari.res.in/files/JRF_RA-03092014-20140903-135319.pdf</p>
]]></description>
</item>

</channel>
</rss>