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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42633?offset=470</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44775/genomic-architecture-surrounding-the-fusion-site-of-human-chromosome-2</guid>
	<pubDate>Tue, 04 Mar 2025 12:26:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44775/genomic-architecture-surrounding-the-fusion-site-of-human-chromosome-2</link>
	<title><![CDATA[Genomic architecture surrounding the fusion site of human chromosome 2]]></title>
	<description><![CDATA[<p>The article <strong>"Genomic Structure and Evolution of the Ancestral Chromosome Fusion Site in 2q13&ndash;2q14.1 and Paralogous Regions on Other Human Chromosomes (https://pmc.ncbi.nlm.nih.gov/articles/PMC187548/)"</strong> explores the genomic architecture surrounding the fusion site of human chromosome 2. This fusion event is a key evolutionary marker distinguishing humans from other great apes, as humans have 46 chromosomes while chimpanzees, gorillas, and orangutans possess 48. The fusion occurred through an end-to-end joining of two ancestral chromosomes, which remain separate in nonhuman primates.</p><h3><strong>Key Findings:</strong></h3><ol>
<li>
<p><strong>Chromosomal Fusion and Its Molecular Signature:</strong></p>
<ul>
<li>The fusion site is located at <strong>2q13&ndash;2q14.1</strong> and is characterized by <strong>degenerate telomeric sequences</strong> appearing interstitially, indicating the historical head-to-head joining of ancestral chromosomes.</li>
<li>Despite being a signature of a past fusion event, these telomeric repeats are no longer functional and have undergone sequence degradation over time.</li>
</ul>
</li>
<li>
<p><strong>Extensive Duplications in the Surrounding Genomic Region:</strong></p>
<ul>
<li>The study identifies <strong>large-scale segmental duplications</strong> flanking the fusion site, with several of these regions duplicated and scattered across multiple chromosomes.</li>
<li>These duplications are predominantly located in <strong>subtelomeric and pericentromeric regions</strong>, suggesting their role in genomic instability and chromosomal evolution.</li>
</ul>
</li>
<li>
<p><strong>Paralogous Regions and Their Evolutionary Relationships:</strong></p>
<ul>
<li>A <strong>168-kilobase (kb) segment</strong> near the fusion site has <strong>98%&ndash;99% sequence identity</strong> with three regions on <strong>chromosome 9 (9pter, 9p11.2, and 9q13)</strong>.</li>
<li>Another <strong>67-kb region distal to the fusion site</strong> shows a high degree of homology to sequences in <strong>chromosome 22qter</strong>.</li>
<li>Additionally, a <strong>100-kb segment</strong> exhibits <strong>96% sequence identity</strong> with a region in <strong>chromosome 2q11.2</strong>.</li>
</ul>
</li>
<li>
<p><strong>Comparative Genomics and Evolutionary Implications:</strong></p>
<ul>
<li>By comparing the duplicated sequences and their arrangement in primates, the researchers traced the order of duplication events leading to their present distribution.</li>
<li>The presence of specific repetitive elements within these duplicated segments serves as <strong>evolutionary markers</strong> that help infer their historical rearrangements.</li>
<li>Some of these <strong>duplicated regions are associated with chromosomal inversion breakpoints</strong>, potentially contributing to evolutionary changes in primates.</li>
<li>Recurrent <strong>structural rearrangements</strong> in these regions have been linked to human chromosomal disorders.</li>
</ul>
</li>
</ol><h3><strong>Conclusions and Implications:</strong></h3><ul>
<li>The findings provide valuable insights into <strong>the structural evolution of human chromosome 2</strong>, which played a crucial role in human speciation.</li>
<li>Understanding these <strong>segmental duplications</strong> and their evolutionary trajectories sheds light on <strong>genomic instability</strong>, which may contribute to <strong>human genetic diseases</strong>.</li>
<li>The study highlights how large-scale chromosomal rearrangements, such as fusion and duplication, have influenced the <strong>evolutionary divergence of humans</strong> from other primates.</li>
</ul><p>This research advances our understanding of <strong>human genome evolution</strong> and offers a foundation for studying the effects of <strong>structural variants in genetic disorders</strong>.</p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
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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>
</item>
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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>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/3030/illuminating-next-generation-sequencing-data-with-go</guid>
	<pubDate>Fri, 23 Aug 2013 07:13:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/3030/illuminating-next-generation-sequencing-data-with-go</link>
	<title><![CDATA[Illuminating next generation sequencing data with Go]]></title>
	<description><![CDATA[<p>Another good lecture for Illumina sequencing data analysis from&nbsp;</p>
<p>Dan Kortschak,&nbsp;Bioinformatics Group,&nbsp;School of Molecular and Biomedical Science ,The University of Adelaide</p><p>Address of the bookmark: <a href="http://talks.biogo.googlecode.com/git/illumination/illumination.pdf" rel="nofollow">http://talks.biogo.googlecode.com/git/illumination/illumination.pdf</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/38061/illumina-to-acquire-pacific-biosciences-for-approximately-12-billion</guid>
	<pubDate>Fri, 02 Nov 2018 09:57:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/38061/illumina-to-acquire-pacific-biosciences-for-approximately-12-billion</link>
	<title><![CDATA[Illumina to Acquire Pacific Biosciences for Approximately $1.2 Billion !]]></title>
	<description><![CDATA[<p>Illumina and Pacific Biosciences announced they have signed an agreement for Illumina to acquire Pacific Biosciences at a price of $8.00 per Pacific Biosciences share in an all-cash transaction.<br /><br />The agreement has been approved by the board of directors of Illumina and Pacific Biosciences. The acquisition complements Illumina sequencing solutions with accurate long-read sequencing capabilities to answer a set of complex genomic questions. While Illumina's accurate and economic short-read sequencing platforms address the majority of sequencing applications optimally, select applications, such as de novo sequencing and sequencing of highly homologous regions of genomes, are better addressed with accurate long-reads.</p><p>Reference https://www.pacb.com/press_releases/illumina-to-acquire-pacific-biosciences-for-approximately-1-2-billion-broadening-access-to-long-read-sequencing-and-accelerating-scientific-discovery/</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4191/high-density-sheep-snp-genotyping-chip-released</guid>
	<pubDate>Tue, 03 Sep 2013 13:58:04 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4191/high-density-sheep-snp-genotyping-chip-released</link>
	<title><![CDATA[High Density Sheep SNP Genotyping Chip released!!!]]></title>
	<description><![CDATA[<p>If you are working on Sheep genomics then there is a good news for you. FarmIQ in conjunction with Illumina and the International Sheep Genomics Consortium (ISGC) are today announcing completion of the &ldquo;Ovine Infinium&reg; HD SNP BeadChip&rdquo;,&nbsp;a high definition SNP chip for ship genome. The OvineSNP50 BeadChip features over 54,241 evenly spaced probes that target SNPs, offering more than sufficient SNP density for genome-wide association studies and other applications such as genome-wide selection, determination of genetic merit, identification of quantitative trait loci, and comparative genetic studies.</p><p>The BeadChip was developed in collaboration with leading ovine researchers from AgResearch, Baylor UCSC, CSIRO, and the USDA as part of the International Sheep Genomics Consortium. It features over 54,241 evenly spaced probes that target single nucleotide polymorphisms (SNPs). More than 18,000 of these markers were discovered through sequencing reduced representation libraries with the Illumina Genome Analyzer IIx. A set of 600 SNPs were identified by BAC end sequencing and validated with Illumina GoldenGate Genotyping Assays over 403 animals from 23 breeds. The remaining SNPs were derived from the draft ovine genome.</p><p>Read more @</p><p><a href="http://res.illumina.com/documents/products/datasheets/datasheet_ovinesnp50.pdf">http://res.illumina.com/documents/products/datasheets/datasheet_ovinesnp50.pdf</a><a href="http://www.scoop.co.nz/stories/SC1309/S00004/high-density-snp-genotyping-chip-for-the-sheep-genome.htm"><br /></a></p><p><a href="http://www.illumina.com/products/ovinesnp50_dna_analysis_kit.ilmn">http://www.illumina.com/products/ovinesnp50_dna_analysis_kit.ilmn</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34702/run-miniasm-assembler-on-nanopore-reads</guid>
	<pubDate>Mon, 18 Dec 2017 04:07:50 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34702/run-miniasm-assembler-on-nanopore-reads</link>
	<title><![CDATA[Run miniasm assembler on nanopore reads !]]></title>
	<description><![CDATA[<p>Miniasm is a very fast OLC-based&nbsp;<em>de novo</em>&nbsp;assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by&nbsp;<a href="https://github.com/lh3/minimap">minimap</a>) as input and outputs an assembly graph in the&nbsp;<a href="https://github.com/pmelsted/GFA-spec/blob/master/GFA-spec.md">GFA</a>&nbsp;format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final&nbsp;<a href="http://wgs-assembler.sourceforge.net/wiki/index.php/Celera_Assembler_Terminology">unitig</a>&nbsp;sequences. Thus the per-base error rate is similar to the raw input reads.</p><p>Find the detail of the reads repeats:</p><blockquote><p>fq2fa ONT_A.fastq ONT_A.fasta&nbsp;<br /><br />minimap2 -xava-ont ONT_A.fasta ONT_A.fasta -t10 -X &gt; AONT.paf&nbsp;<br /><br />awk '{if($1==$6){print}}' AONT.paf &gt; AONTself.paf&nbsp;<br /><br />awk '$5=="-"' AONTself.paf | awk '{print $1}'| sort|uniq &gt; invertedrepeat.list</p></blockquote><p>Generated a few palindrome and repeats plots (highlighting only repeats largest than 10, 20 and 30 kb)</p><blockquote><p>minidot -f 5 -m 30000 AONTself.paf &gt; AONTself30000.eps&nbsp;<br />sed 's/_template_pass_FAH31515//' AONTself30000.eps &gt; AONTself30000final.eps&nbsp;<br /><br />minidot -f 5 -m 20000 AONTself.paf &gt; AONTself20000.eps&nbsp;<br />sed 's/_template_pass_FAH31515//' AONTself20000.eps &gt; AONTself20000final.eps&nbsp;<br /><br />minidot -f 5 -m 10000 AONTself.paf &gt; AONTself10000.eps&nbsp;<br />sed 's/_template_pass_FAH31515//' AONTself10000.eps &gt; AONTself10000final.eps&nbsp;</p></blockquote><p>Assemble with miniasm:</p><blockquote><p>miniasm -f ONT_A.fasta AONT.paf &gt; AONT.gfa&nbsp;</p><p>grep '^S' AONT.gfa |awk '{print "&gt;"$2"\n"$3}' &gt; AONT_miniasm.fasta&nbsp;<br /><br />minimap2 -xasm10 AONT_miniasm.fasta AONT_miniasm.fasta -t1 -X &gt; AONT_miniasm.paf&nbsp;<br /><br />awk '{if($1==$6){print}}' AONT_miniasm.paf &gt; AONT_miniasm_self.paf&nbsp;<br /><br />minidot -f 5 -m 10000 AONT_miniasm_self.paf &gt; AONT_miniasm_self10000.eps&nbsp;</p></blockquote><p>Njoy the assembly !</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36630/frequent-paired-end-reads-pe-2x100-mapping-command-lines</guid>
	<pubDate>Tue, 15 May 2018 08:59:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36630/frequent-paired-end-reads-pe-2x100-mapping-command-lines</link>
	<title><![CDATA[Frequent Paired-end reads (PE 2x100) mapping command lines]]></title>
	<description><![CDATA[
<p>bowtie2 -x hs37m -X 650 -q -1 r1.fq -2 r2.fq -S r12.bowtie2.sam  </p>

<p>bwa aln hs37m.fa r1.fq &gt; r1.sai &amp;&amp; bwa aln hs37m.fa r2.fq &gt; r2.sai \  <br />    &amp;&amp; bwa sampe hs37m r1.sai r2.sai r1.fq r2.fq &gt; r12.bwa.sam  </p>

<p>bwa bwasw ../index/bwa/hs37m.fa r12.fq &gt; r12.bwasw.sam  </p>

<p>gsnap -A sam -d hs37m r1.fq r2.fq &gt; r12.gsnap.sam  </p>

<p>novoalign -r Random -o SAM -f r1.fq r2.fq -i 500 50 -d hs37m-k14s3.novo &gt; r12.novo.sam  </p>

<p>smalt map -f samsoft -i 650 -o r12.smalt-k20s13.sam hs37m-k20s13 r1.fq r2.fq  </p>

<p>stampy.py -g hs37m -h hs37m -o r12.stampy.sam -M r1.fq,r2.fq  </p>

<p>soap -D hs37m.fa.index -a r1.fq -b r2.fq -l 32 -g 3 -u dummy -2 dummy -o r12.soap</p>
]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36800/genomemapper-simultaneous-alignment-of-short-reads-against-multiple-genomes</guid>
	<pubDate>Fri, 25 May 2018 09:29:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36800/genomemapper-simultaneous-alignment-of-short-reads-against-multiple-genomes</link>
	<title><![CDATA[GenomeMapper: Simultaneous alignment of short reads against multiple genomes]]></title>
	<description><![CDATA[GenomeMapper is a short read mapping tool designed for accurate read alignments. It quickly aligns millions of reads either with ungapped or gapped alignments. It can be used to align against multiple genomes simulanteously or against a single reference. If you are unsure which one is the appropriate GenomeMapper, you might want to use the latter

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2768987/<p>Address of the bookmark: <a href="http://1001genomes.org/software/genomemapper.html" rel="nofollow">http://1001genomes.org/software/genomemapper.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37241/remilo-reference-assisted-misassembly-detection-algorithm-using-short-and-long-reads</guid>
	<pubDate>Fri, 06 Jul 2018 04:27:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37241/remilo-reference-assisted-misassembly-detection-algorithm-using-short-and-long-reads</link>
	<title><![CDATA[ReMILO: reference assisted misassembly detection algorithm using short and long reads.]]></title>
	<description><![CDATA[ReMILO, a reference assisted misassembly detection algorithm that uses both short reads and PacBio SMRT long reads. ReMILO aligns the initial short reads to both the contigs and reference genome, and then constructs a novel data structure called red-black multipositional de Bruijn graph to detect misassemblies. In addition, ReMILO also aligns the contigs to long reads and find their differences from the long reads to detect more misassemblies.<p>Address of the bookmark: <a href="https://github.com/songc001/remilo" rel="nofollow">https://github.com/songc001/remilo</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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