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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44489?offset=330</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/42626/spades-team-announce-new-version-spades-v315</guid>
	<pubDate>Fri, 15 Jan 2021 10:24:27 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/42626/spades-team-announce-new-version-spades-v315</link>
	<title><![CDATA[SPADes team announce new version SPADes v3.15]]></title>
	<description><![CDATA[<p>New SPAdes 3.15.0.0. announced by the SPADes team This release includes such new features as:&nbsp;<br />- CoronaSPAdes pipeline for the assembly of transcriptomic and metatranscriptomic data of full-length coronaviridae genomes;&nbsp;<br />- Meta-Viral and RNA-Viral pipelines for metagenomic and metatranscriptomic data defining viral genomes;&nbsp;<br />-New trusted contiguous use algorithm;&nbsp;<br />-Switched to the memory allocator mimalloc;&nbsp;<br />- PlasmidSPAdes and bgcSPAdes are now provided as an input assembly graph;&nbsp;<br />- Important improvements and corrections to the metaplasmid pipeline;&nbsp;<br />- Multiple performance improvements in procedures for simplification and repeat resolving.&nbsp;<br />Please, consider updating.</p><p>Check out more at&nbsp;https://cab.spbu.ru/software/spades/</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/2778/mom-knows-best</guid>
	<pubDate>Thu, 22 Aug 2013 13:30:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/2778/mom-knows-best</link>
	<title><![CDATA[Mom Knows Best]]></title>
	<description><![CDATA[<p>We always get instructions to wash our hands, sterilize kitchen stuff and bla bla. However, recent findings suggest something else. Perhaps we can't survive, or will face lots of problems if bacteria&rsquo;s colonies are absent in mother womb. Please find the detail sources of microbial transmission in humans from mother to child in recently published PLOS paper.</p><p><a href="http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001631">http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001631</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28164/greengenes-database</guid>
	<pubDate>Wed, 29 Jun 2016 10:03:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28164/greengenes-database</link>
	<title><![CDATA[Greengenes database]]></title>
	<description><![CDATA[<p>The greengenes web application provides access to the 2011 version of the greengenes 16S rRNA gene sequence alignment for browsing, blasting, probing, and downloading. The data and tools presented by greengenes can assist the researcher in choosing phylogenetically specific probes, interpreting microarray results, and aligning/annotating novel sequences. If you are an ARB user, you can use greengenes to keep your own local database current.</p><p>Address of the bookmark: <a href="http://greengenes.lbl.gov/cgi-bin/nph-index.cgi" rel="nofollow">http://greengenes.lbl.gov/cgi-bin/nph-index.cgi</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38447/kegg-mapper-%E2%80%93-reconstruct-pathway</guid>
	<pubDate>Wed, 12 Dec 2018 09:14:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38447/kegg-mapper-%E2%80%93-reconstruct-pathway</link>
	<title><![CDATA[KEGG Mapper – Reconstruct Pathway]]></title>
	<description><![CDATA[<p><strong>Reconstruct Pathway</strong><span>&nbsp;is a KEGG PATHWAY mapping tool that assists genome and metagenome annotations. The input data is a single gene list (for a single organism) or multiple gene lists (for multiple organisms) annotated with KEGG Orthology (KO) identifiers or K numbers. Each line of the gene list contains the user-defined gene identifier followed by, if any, the assigned K number. The mapping is performed through the K numbers against the KEGG reference pathways.&nbsp;</span></p><p>Address of the bookmark: <a href="https://www.kegg.jp/kegg/tool/map_pathway.html" rel="nofollow">https://www.kegg.jp/kegg/tool/map_pathway.html</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44731/exploring-bacterial-comparative-genomics-a-bioinformatics-approach</guid>
	<pubDate>Sat, 14 Dec 2024 12:31:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44731/exploring-bacterial-comparative-genomics-a-bioinformatics-approach</link>
	<title><![CDATA[Exploring Bacterial Comparative Genomics: A Bioinformatics Approach]]></title>
	<description><![CDATA[<p>In the world of microbiology, bacteria have long fascinated scientists for their diversity, adaptability, and crucial roles in ecosystems and human health. Comparative genomics&mdash;a field that involves analyzing and comparing the genomes of different organisms&mdash;has revolutionized our understanding of bacterial evolution, adaptation, and pathogenicity. By leveraging bioinformatics tools and techniques, researchers can uncover genomic insights that were once hidden. This blog delves into the principles, methodologies, and applications of bacterial comparative genomics from a bioinformatics perspective.</p><h4><strong>What is Bacterial Comparative Genomics?</strong></h4><p>Comparative genomics involves the systematic comparison of genomes across different bacterial species or strains. This approach allows scientists to:</p><ul>
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<p>Identify conserved and unique genes.</p>
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<p>Explore genetic determinants of pathogenicity.</p>
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<p>Understand bacterial evolution and phylogenetics.</p>
</li>
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<p>Investigate horizontal gene transfer and its role in antibiotic resistance.</p>
</li>
</ul><p>Bioinformatics is central to these analyses, enabling the processing and interpretation of large-scale genomic data.</p><h4><strong>Key Steps in Bacterial Comparative Genomics</strong></h4><ol>
<li>
<p><strong>Genome Sequencing and Assembly</strong>: The process begins with obtaining high-quality bacterial genome sequences. Advances in next-generation sequencing (NGS) technologies have made it faster and more affordable to sequence bacterial genomes. Tools such as SPAdes and Velvet are commonly used for genome assembly.</p>
</li>
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<p><strong>Genome Annotation</strong>: Annotating a genome involves identifying genes, regulatory elements, and other genomic features. Automated tools like Prokka and RAST provide functional annotations, allowing researchers to predict the roles of genes and proteins.</p>
</li>
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<p><strong>Genome Alignment</strong>: Aligning genomes is crucial for identifying conserved regions, single-nucleotide polymorphisms (SNPs), and structural variations. Tools like Mauve and progressiveMauve are commonly employed for whole-genome alignments.</p>
</li>
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<p><strong>Comparative Analyses</strong>:</p>
<ul>
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<p><strong>Core and Pan-genome Analysis</strong>: The core genome consists of genes shared across all strains of a species, while the pan-genome includes all genes found in any strain. Software like Roary and BPGA can perform core and pan-genome analyses.</p>
</li>
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<p><strong>Phylogenetic Analysis</strong>: Comparative genomics often involves reconstructing evolutionary relationships. Tools such as MEGA and IQ-TREE facilitate phylogenetic tree construction based on genomic data.</p>
</li>
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<p><strong>Functional Enrichment Analysis</strong>: To understand the biological significance of unique or shared genes, functional enrichment analysis using databases like GO (Gene Ontology) and KEGG is essential.</p>
</li>
</ul>
</li>
</ol><div>&nbsp;<strong style="font-size: 1em;">Recommended Bioinformatics Tools for Comparative Genomics</strong></div><p>Here are some additional bioinformatics tools that can aid bacterial comparative genomics:</p><ul>
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<p><strong>OrthoFinder</strong>: For accurate ortholog identification across multiple genomes.</p>
</li>
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<p><strong>PanOCT</strong>: Specifically designed for pan-genome clustering and annotation.</p>
</li>
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<p><strong>FASTANI</strong>: A tool for calculating Average Nucleotide Identity (ANI) for microbial genome comparisons.</p>
</li>
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<p><strong>CIRCOS</strong>: For visually comparing genomic data through circular genome plots.</p>
</li>
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<p><strong>Galaxy Platform</strong>: A user-friendly web-based platform offering numerous genomic analysis tools.</p>
</li>
<li>
<p><strong>BLAST</strong>: Essential for sequence alignment and similarity searches.</p>
</li>
<li>
<p><strong>PhyloSift</strong>: Focused on phylogenetic analysis of microbial genomes using marker genes.</p>
</li>
</ul><p>These tools, in combination with the methods discussed, provide a robust framework for conducting comprehensive comparative genomic studies.</p><h4><strong>Applications of Bacterial Comparative Genomics</strong></h4><ol>
<li>
<p><strong>Understanding Pathogenicity</strong>: Comparative genomics helps identify virulence factors that distinguish pathogenic strains from non-pathogenic relatives. For instance, comparing genomes of <em>Escherichia coli</em> strains has revealed key genetic determinants of pathogenicity in enterohemorrhagic strains.</p>
</li>
<li>
<p><strong>Antibiotic Resistance Research</strong>: The spread of antibiotic resistance genes through horizontal gene transfer is a major global concern. Comparative analyses can trace the origins and dissemination of resistance genes, aiding in the development of countermeasures.</p>
</li>
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<p><strong>Microbial Ecology and Evolution</strong>: By studying genomic variations, researchers can understand how bacteria adapt to different environments. This is particularly relevant for extremophiles and symbiotic bacteria.</p>
</li>
<li>
<p><strong>Vaccine Development</strong>: Identifying conserved antigens across pathogenic strains is critical for vaccine design. Comparative genomics has been instrumental in developing vaccines against pathogens like <em>Neisseria meningitidis</em>.</p>
</li>
<li>
<p><strong>Biotechnology Applications</strong>: Comparative studies can uncover unique metabolic pathways in bacteria, paving the way for applications in bioremediation, synthetic biology, and industrial microbiology.</p>
</li>
</ol><h4><strong>Challenges in Bacterial Comparative Genomics</strong></h4><p>While the field has made significant strides, several challenges remain:</p><ul>
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<p><strong>Data Overload</strong>: The rapid growth of sequencing data requires robust computational infrastructure and efficient algorithms.</p>
</li>
<li>
<p><strong>Genome Plasticity</strong>: High rates of horizontal gene transfer and genome rearrangements in bacteria complicate comparative analyses.</p>
</li>
<li>
<p><strong>Annotation Accuracy</strong>: Automated annotation tools are not infallible, and manual curation is often needed for high-confidence results.</p>
</li>
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<p><strong>Interpreting Non-Coding Regions</strong>: Understanding the functional significance of non-coding genomic regions remains a challenge.</p>
</li>
</ul><h4><strong>Future Directions</strong></h4><p>The integration of bacterial comparative genomics with other &lsquo;omics&rsquo; approaches&mdash;such as transcriptomics, proteomics, and metabolomics&mdash;promises a more comprehensive understanding of bacterial biology. Additionally, advancements in machine learning and artificial intelligence are likely to further enhance bioinformatics analyses, enabling the prediction of complex phenotypes from genomic data.</p><h4><strong>Conclusion</strong></h4><p>Bacterial comparative genomics, driven by bioinformatics, continues to unravel the complexities of bacterial life. From combating antibiotic resistance to uncovering the secrets of microbial evolution, this interdisciplinary field holds immense potential for addressing pressing challenges in microbiology and beyond. As technology advances, so too will our ability to harness the power of comparative genomics for scientific and societal benefit.</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45262/gipsy2-detect-hidden-islands-inside-bacteria</guid>
	<pubDate>Mon, 24 Aug 2026 18:15:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45262/gipsy2-detect-hidden-islands-inside-bacteria</link>
	<title><![CDATA[GIPSy2 Detect Hidden Islands Inside Bacteria]]></title>
	<description><![CDATA[<p>Imagine a tiny bacterium living in a crowded environment.</p><p>It looks simple, but inside its microscopic body is a huge library of DNA. Most of this DNA has been passed down from its parents. But sometimes, bacteria can pick up new pieces of DNA from other bacteria.</p><p>These new pieces can give them useful abilities &mdash; such as surviving antibiotics, causing disease, or adapting to a new environment.</p><p>Scientists call some of these special DNA regions genomic islands.</p><p>But there is a problem: How do we find these hidden islands?</p><p>That is where GIPSy2 comes in.</p><p>Researchers developed GIPSy2, a computer tool that acts like a detective. It scans bacterial DNA and looks for unusual patterns that might show where a genomic island is hiding.</p><p>Instead of looking at just one clue, GIPSy2 uses several methods to investigate the DNA. It can also study many bacterial genomes, making it useful when researchers have large amounts of genetic data.</p><p>In their study, the researchers tested GIPSy2 on different bacteria. The tool was able to identify genomic regions connected with important features such as antibiotic resistance, disease-causing abilities, metabolism, and relationships with other organisms.</p><p>Think of it like searching for hidden islands on a huge map. The DNA is the ocean, and genomic islands are the small pieces of land that may contain valuable information.</p><p>GIPSy2 does not give scientists the final answer by itself. Its predictions still need to be carefully checked and, when possible, confirmed through experiments.</p><p>But it gives researchers a faster way to explore bacterial genomes and discover clues about how bacteria change and survive.</p><p>And that is why this small piece of software could help scientists better understand the invisible world of bacteria &mdash; one hidden genomic island at a time.</p><p><span>GIPSy2 is currently available on Zenodo repository at </span><a href="https://zenodo.org/doi/10.5281/zenodo.10222587">https://zenodo.org/doi/10.5281/zenodo.10222587</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/38551/gupta-lab</guid>
  <pubDate>Sat, 29 Dec 2018 13:18:31 -0600</pubDate>
  <link></link>
  <title><![CDATA[Gupta Lab]]></title>
  <description><![CDATA[
<p>Work include (i) understanding the evolutionary relationships among different prokaryotic and eukaryotic organisms; (ii) Understanding the cellular functions of these lineage-specific signature proteins as well as lineage-specific conserved inserts and deletions in important housekeeping proteins by genetic and biochemical studies; (iii) Development of novel diagnostic methods (PCR based and immunological) for identification of different groups of organisms based upon these signature proteins and conserved indels; (iv) The use of these lineage-specific probes with predicitive ability to identify/explore the presence of different groups of organisms in metagenomic sequences from various environments.</p>

<p>https://fhs.mcmaster.ca/gupta-lab/index.html</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4590/tigers-genome-sequenced</guid>
	<pubDate>Tue, 17 Sep 2013 16:48:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4590/tigers-genome-sequenced</link>
	<title><![CDATA[Tigers genome sequenced]]></title>
	<description><![CDATA[<p>Fifteen scientists led by Dr Jong Bhak of Genome Research Foundation, South Korea, decoded as many as 3 billion nucleotides (organic molecules that form the basic building blocks of nucleic acids, such as DNA). They identified 20,000 genes related to various functions of the tiger.&nbsp;</p><p>The biggest and perhaps most fearsome of the world's big cats, the tiger, shares 95.6 percent of its DNA with humans' cute and furry companions, domestic cats.</p><p>The new research showed that big cats have genetic mutations that enabled them to be carnivores. The team also identified mutations that allow snow leopards to thrive at high altitudes.</p><p>Reference:</p><p><a href="http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690">http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690</a></p><p><a href="http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms">http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms</a></p><p>Paper:</p><p><a href="http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html">http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program</guid>
	<pubDate>Mon, 27 Nov 2017 10:18:20 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program</link>
	<title><![CDATA[Opera: An optimal genome scaffolding program]]></title>
	<description><![CDATA[<p><span>Opera (Optimal Paired-End Read Assembler) is a sequence assembly program (</span><a href="http://en.wikipedia.org/wiki/Sequence_assembly" target="_blank">http://en.wikipedia.org/wiki/Sequence_assembly&nbsp;<img src="https://a.fsdn.com/con/img/icons/external_asset.png" alt="image" style="border: 0px;"></a><span>). It uses information from paired-end or long reads to optimally order and orient contigs assembled from shotgun-sequencing reads.</span><br><br><span>An updated version called OPERA-LG has been re-engineered with features for the assembly of large and complex genomes.</span><br><br><span>Song Gao, Denis Bertrand, Burton K. H. Chia and Niranjan Nagarajan. OPERA-LG: efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees. Genome Biology, May 2016, doi: 10.1186/s13059-016-0951-y.</span><br><br><span>Song Gao, Wing-Kin Sung, Niranjan Nagarajan. Opera: reconstructing optimal genomic scaffolds with high-throughput paired-end sequences. Journal of Computational Biology, Sept. 2011, doi:10.1089/cmb.2011.0170.</span></p>
<p><span>https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0951-y</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/operasf/" rel="nofollow">https://sourceforge.net/projects/operasf/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34488/scripts-for-the-analysis-of-hgt-in-genome-sequence-data</guid>
	<pubDate>Wed, 29 Nov 2017 16:44:10 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34488/scripts-for-the-analysis-of-hgt-in-genome-sequence-data</link>
	<title><![CDATA[Scripts for the analysis of HGT in genome sequence data.]]></title>
	<description><![CDATA[<p><span>Scripts for the analysis of HGT in genome sequence data</span></p><p>Address of the bookmark: <a href="https://github.com/reubwn/hgt" rel="nofollow">https://github.com/reubwn/hgt</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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