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	<link>https://bioinformaticsonline.com/related/36897?offset=370</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45267/when-thousands-of-bacterial-genomes-become-one-giant-map</guid>
	<pubDate>Thu, 27 Aug 2026 10:56:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45267/when-thousands-of-bacterial-genomes-become-one-giant-map</link>
	<title><![CDATA[When Thousands of Bacterial Genomes Become One Giant Map]]></title>
	<description><![CDATA[<p>Imagine trying to understand a city by studying just one house.</p><p>You might learn a lot about that house&mdash;the rooms, the doors, the furniture&mdash;but you would miss the bigger story: the streets, the neighborhoods, and all the ways the city changes from one place to another.</p><p>Something similar happens when scientists study bacterial genomes one at a time.</p><p>Over the past decade, researchers have collected thousands of bacterial genomes. These genomes contain an enormous amount of information about how bacteria survive, adapt, and evolve. But comparing thousands of individual genomes can quickly become a computational maze.</p><p>That is where PANORAMA enters the story.</p><p>Developed by J&eacute;r&ocirc;me Arnoux and colleagues, PANORAMA is a computational tool designed to explore bacterial pangenomes&mdash;the complete collection of genetic possibilities found across a species or group of related organisms. Instead of looking at every genome as an isolated object, the approach represents their shared and variable genetic features as a graph.</p><p>Think of this graph as a giant subway map.</p><p>Some stations appear on almost every route. These represent genes that are highly conserved. Other stations appear only on certain routes, representing genes that some bacteria possess while others do not. By looking at the entire network, scientists can begin to see not just *what genes exist*, but how they are organized and how biological systems are distributed across populations.</p><p>The researchers put PANORAMA to the test using 941 genomes of Pseudomonas aeruginosa, a bacterium important in human health. They used the tool to investigate biological systems, including bacterial defense mechanisms against viruses known as bacteriophages. They then expanded the analysis to more than 6,000 genomes from four Enterobacteriaceae species.</p><p>The result is more than a faster way to process data.</p><p>PANORAMA allows researchers to ask a bigger question: What can an entire microbial species do, genetically speaking?</p><p>By comparing pangenomes, the researchers could identify systems shared between species as well as distinctive features. They could also find recurring genomic locations where genetic material is inserted&mdash;clues that may reveal common evolutionary processes.</p><p>And this is perhaps the most exciting part of the story.</p><p>Every bacterial genome is like a page in a huge evolutionary book. Until recently, reading thousands of those pages together was difficult. PANORAMA provides a way to turn those pages into a map, allowing scientists to see patterns that might disappear when each genome is studied separately.</p><p>The study, published in PLOS Computational Biology in July 2026, presents PANORAMA as a foundation for large-scale comparative pangenomics. The software and accompanying analysis resources are openly available, giving other researchers the opportunity to explore microbial diversity themselves.</p><p>So the story is not really about one bacterium or one genome.</p><p>It is about changing the way we look at life.</p><p>Instead of asking, &ldquo;What is inside this genome?&rdquo;, scientists can increasingly ask, &ldquo;What is the full genetic landscape of this species&mdash;and how did it become this way?&rdquo;</p><p>And sometimes, when you stop looking at one house and finally see the whole city, the most interesting discoveries are hiding in the streets between them.</p><p>Read more at&nbsp;https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013856</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45303/the-tree-that-learned-to-read-genomes</guid>
	<pubDate>Fri, 11 Sep 2026 00:51:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45303/the-tree-that-learned-to-read-genomes</link>
	<title><![CDATA[The Tree That Learned to Read Genomes]]></title>
	<description><![CDATA[<p>Imagine trying to reconstruct a family tree when many family photographs are missing, some names are unclear, and different relatives remember the past differently. This is similar to the challenge scientists face when trying to understand how organisms are related. Their family records are genomes, and the family tree is a phylogenetic tree. With modern sequencing technologies, scientists now have access to thousands of genomes, but comparing them can be difficult, especially when genomes are incomplete or poorly annotated.</p><p>A recent study by Webster and Chapman introduces Buscogeny (https://github.com/Jwebster89/Buscogeny), an open-source bioinformatics pipeline designed to make this process easier. The tool uses BUSCO (Benchmarking Universal Single-Copy Orthologs), which identifies conserved genes that are expected to occur as single copies in particular groups of organisms. These genes act like evolutionary landmarks, allowing researchers to compare genomes and investigate their relationships.</p><p>Buscogeny brings several steps of phylogenomic analysis into one workflow. It identifies BUSCO genes, extracts and aligns their sequences, removes unreliable regions, combines information from multiple genes, and uses the resulting data to construct a phylogenetic tree. It also provides quality-control information, helping researchers identify genomes that may be too incomplete for reliable analysis.</p><p>The authors demonstrate the usefulness of Buscogeny with bacterial and fungal genomes, including Alternaria. This is particularly valuable because fungal genomes can vary greatly in quality and annotation. Ultimately, Buscogeny helps transform massive amounts of genomic data into an understandable evolutionary story. Each DNA sequence becomes a clue, each conserved gene becomes a landmark, and together these clues reveal a picture of how life is connected.</p><p>More at&nbsp;https://link.springer.com/article/10.1007/s10123-025-00752-6?</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36403/ngmlr-long-read-mapper-designed-to-align-pacbio-or-oxford-nanopore</guid>
	<pubDate>Wed, 25 Apr 2018 07:30:54 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36403/ngmlr-long-read-mapper-designed-to-align-pacbio-or-oxford-nanopore</link>
	<title><![CDATA[NGMLR: long-read mapper designed to align PacBio or Oxford Nanopore]]></title>
	<description><![CDATA[<p><span>CoNvex Gap-cost alignMents for Long Reads (ngmlr) is a long-read mapper designed to sensitively align PacBilo or Oxford Nanopore to (large) reference genomes. It was designed to quickly and correctly align the reads, including those spanning (complex) structural variations. Ngmlr uses an SV aware k-mer search to find approximate mapping locations for a read and then a banded Smith-Waterman alignment algorithm to compute the final alignment. Ngmlr uses a convex gap cost model that penalizes gap extensions for longer gaps less than for shorter ones to compute precise alignments. The gap model allows ngmlr to account for both the sequencing error and real genomic variations at the same time and makes it especially effective at more precisely identifying the position of breakpoints stemming from structural variations. The k-mer search helps to detect and split reads that cannot be aligned linearly, enabling ngmlr to reliably align reads to a wide range of different structural variations including nested SVs (e.g. inversions flanked by deletions).</span></p><p>Address of the bookmark: <a href="https://github.com/philres/ngmlr" rel="nofollow">https://github.com/philres/ngmlr</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38535/nanopack-visualizing-and-processing-long-read-sequencing-data</guid>
	<pubDate>Tue, 25 Dec 2018 21:20:50 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38535/nanopack-visualizing-and-processing-long-read-sequencing-data</link>
	<title><![CDATA[NanoPack: visualizing and processing long-read sequencing data]]></title>
	<description><![CDATA[The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.<p>Address of the bookmark: <a href="https://github.com/wdecoster/nanopack" rel="nofollow">https://github.com/wdecoster/nanopack</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26391/radka-reifova-lab</guid>
  <pubDate>Mon, 15 Feb 2016 06:00:48 -0600</pubDate>
  <link></link>
  <title><![CDATA[Radka Reifová Lab]]></title>
  <description><![CDATA[
<p>We are generally interested in the mechanisms of species origin from a molecular and ecological perspective. Particularly, we are interested in the role of sex chromosomes in speciation. Most of our research is done on birds and mammals. Currently, we focus our research on two hybridizing song birds, the Common nightingale (Luscinia megarhynchos) and the Thrush Nightingale (L. luscinia). Combining population genomic and ecological approaches we try to elucidate the genetic architecture of reproductive isolation and understand the role of interspecific competition and song convergence in the evolution of reproductive isolation between the species. </p>

<p>More at http://web.natur.cuni.cz/~radkas/index.php?page=research</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</guid>
	<pubDate>Mon, 27 Jun 2016 11:01:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments</link>
	<title><![CDATA[Kraken: ultrafast metagenomic sequence classification using exact alignments]]></title>
	<description><![CDATA[<p>Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of <em>k</em>-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at <a href="http://ccb.jhu.edu/software/kraken/" target="pmc_ext">http://ccb.jhu.edu/software/kraken/</a>.</p>
<p>Krona</p>
<p>https://sourceforge.net/p/krona/home/krona/</p><p>Address of the bookmark: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/" rel="nofollow">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42313/crbhits-from-conditional-reciprocal-best-hits-to-codon-alignments-and-kaks-in-r</guid>
	<pubDate>Wed, 11 Nov 2020 23:06:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42313/crbhits-from-conditional-reciprocal-best-hits-to-codon-alignments-and-kaks-in-r</link>
	<title><![CDATA[CRBHits: From Conditional Reciprocal Best Hits to Codon Alignments and Ka/Ks in R]]></title>
	<description><![CDATA[<p>CRBHits is a coding sequence (CDS) analysis pipeline in R (R Core Team, 2019). It reimplements the Conditional Reciprocal Best Hit (CRBH) algorithm crb-blast and covers all necessary steps from sequence similarity searches, codon alignments to Ka/Ks calculations and synteny. The new R package targets ecology, population and evolutionary biologists working in the field of comparative genomics.</p><p>Address of the bookmark: <a href="https://gitlab.gwdg.de/mpievolbio-it/crbhits" rel="nofollow">https://gitlab.gwdg.de/mpievolbio-it/crbhits</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36758/pbalign-maps-pacbio-reads-to-reference-sequences-and-saves-alignments-to-a-bam-file</guid>
	<pubDate>Thu, 24 May 2018 10:06:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36758/pbalign-maps-pacbio-reads-to-reference-sequences-and-saves-alignments-to-a-bam-file</link>
	<title><![CDATA[pbalign: maps PacBio reads to reference sequences and saves alignments to a BAM file]]></title>
	<description><![CDATA[pbalign aligns PacBio reads to reference sequences, filters aligned reads according to user-specific filtering criteria, and converts the output to either the SAM format or PacBio Compare HDF5 (e.g., .cmp.h5) format. The output Compare HDF5 file will be compatible with Quiver if --forQuiver option is specified.<p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/pbalign" rel="nofollow">https://github.com/PacificBiosciences/pbalign</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39039/dotplotly-generate-an-interactive-dot-plot-from-mummer-or-minimap-alignments</guid>
	<pubDate>Thu, 21 Feb 2019 10:22:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39039/dotplotly-generate-an-interactive-dot-plot-from-mummer-or-minimap-alignments</link>
	<title><![CDATA[dotPlotly: Generate an interactive dot plot from mummer or minimap alignments]]></title>
	<description><![CDATA[<p>Create an interactive dot plot from mummer output OR PAF format</p>
<p>R script that makes a plotly interactive and/or static (png/pdf) dot plot.</p>
<p><a href="https://tom-poorten.shinyapps.io/dotplotly_shiny/">Shiny app available for testing</a></p><p>Address of the bookmark: <a href="https://github.com/tpoorten/dotPlotly" rel="nofollow">https://github.com/tpoorten/dotPlotly</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32862/gam-ngs-genomic-assemblies-merger-for-next-generation-sequencing</guid>
	<pubDate>Fri, 19 May 2017 07:44:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32862/gam-ngs-genomic-assemblies-merger-for-next-generation-sequencing</link>
	<title><![CDATA[GAM-NGS: genomic assemblies merger for next generation sequencing]]></title>
	<description><![CDATA[<p><span>GAM-NGS is a tool able to merge two or more assemblies in order to improve contiguity and correctness. It can be used on all NGS-based assembly projects and it shows its full potential with multi-library Illumina-based projects. With more than 20 available assemblers it is hard to select the best tool. In this context we propose a tool that improves assemblies (and, as a by-product, perhaps even assemblers) by merging them and selecting the generating that is most likely to be correct.</span></p><p>Address of the bookmark: <a href="https://github.com/vice87/gam-ngs" rel="nofollow">https://github.com/vice87/gam-ngs</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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