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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26617?offset=150</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4093/ibm-research-computational-biology-center</guid>
	<pubDate>Thu, 29 Aug 2013 08:43:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4093/ibm-research-computational-biology-center</link>
	<title><![CDATA[IBM Research Computational Biology Center]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/lr2bB_2g_Uc" frameborder="0" allowfullscreen></iframe>The IBM Computational Biology Center embraces activities at Yorktown Heights, with strong affiliations with activities at Almaden and other IBM Research Centers. Computational Biology (CompBio) including bioinformatics is the study of how computer systems can manage, analyze, and simulate the complex structures and processes inherent in living systems. CompBio Research at IBM spans pattern recognition in sequences, structures and processes, the studying of systems ranging from single protein molecules through to complex molecular interactions, and the data analysis, interpretation and reverse-engineering of complex disease-lifestyle-genomic interactions for fuller biological understanding. "CompBio" has a flavor of its own independant of its parents, biology and computer science. Nonetheless, CompBio is inherently a multi- disciplinary field with important applications in biology, chemical physics, materials science, agriculture, chemistry and ultimately nanotechnology.]]></description>
	
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4655/mathivanan-lab</guid>
  <pubDate>Fri, 20 Sep 2013 13:09:38 -0500</pubDate>
  <link></link>
  <title><![CDATA[Mathivanan Lab]]></title>
  <description><![CDATA[
<p>The major research interests are in exploring the role of extracellular matrix components (soluble secreted proteins and membrane vesicles) in cancer and intercellular communication. The lab integrates proteomic, genomic and bioinformatics methodologies to explore cancer cells. </p>

<p>More at http://www.mathivananlab.org/index.html</p>

<p>http://scholar.google.com/citations?user=U6PyEdYAAAAJ&amp;hl=en</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/43292/bioinformatics-scientist-production-bioinformatics-south-san-francisco-ca</guid>
  <pubDate>Thu, 19 Aug 2021 08:45:24 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics Scientist, Production Bioinformatics @ South San Francisco, CA]]></title>
  <description><![CDATA[
<p>wist is looking for a Bioinformatics Scientist to join our Production Bioinformatics Team. You will work alongside research scientists, software engineers and data scientists to further deliver on our mission to expand access to best-in-class synthetic biology and next-generation sequencing applications. You will be developing and engineering tools to better evaluate and build hardened, production quality pipelines, optimize data quality, and automate lab and bioinformatics processes. Our ideal candidate is an organized problem solver with a background in developing and building novel production-quality bioinformatics tools and packages. Equally excellent communication skills and a proven ability to work independently are required.</p>

<p>More at https://boards.greenhouse.io/twistbioscience/jobs/3135495?gh_src=9ecc0b941us</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5209/anders-krogh-lab</guid>
  <pubDate>Mon, 30 Sep 2013 19:07:40 -0500</pubDate>
  <link></link>
  <title><![CDATA[Anders Krogh Lab]]></title>
  <description><![CDATA[
<p>In a lot of my work in bioinformatics, I have been using hidden Markov models (HMMs). As a postdoc with David Haussler at UCSC we developed the so-called profile HMMs (refs). Since then I have applied HMMs to membrane proteins (refs) and gene identification (refs) and have worked on methods for such things as discriminative estimation of HMMs (refs) and alternative decoding algorithms etc. (refs).</p>

<p>Now my main interests are in gene regulation, where we work on promoter analysis; non-coding RNA, where miRNAs and structure prediction are the main areas; and protein structure, where the group is working on methods for structure prediction from sequence. To read more about these topics, please see the research pages. </p>

<p>Lab page @ http://wiki.binf.ku.dk/User:Krogh</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/5187/bioinformatics-algorithms-part-1-with-pavel-pevzner-phillip-e-c-compeau</guid>
	<pubDate>Mon, 30 Sep 2013 11:34:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/5187/bioinformatics-algorithms-part-1-with-pavel-pevzner-phillip-e-c-compeau</link>
	<title><![CDATA[Bioinformatics Algorithms (Part 1)  with Pavel  Pevzner, Phillip E. C. Compeau,]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/t5t_nfzdzEg" frameborder="0" allowfullscreen></iframe><p>The course Bioinformatics Algorithms (Part 1) by Pavel Pevzner, Phillip E. C. Compeau, and Nikolay Vyahhi from University of California, San Diego will be offered free of charge to everyone on the Coursera platform. Sign up at http://www.coursera.org/course/bioinformatics.</p>]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44518/virus-bioinformatics-tools</guid>
	<pubDate>Wed, 24 Apr 2024 06:19:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44518/virus-bioinformatics-tools</link>
	<title><![CDATA[Virus Bioinformatics Tools]]></title>
	<description><![CDATA[<p><span>Bioinformatics tools play a crucial role in studying viruses, enabling researchers to analyze their genetic makeup, structure, function, and evolution. Here are some commonly used bioinformatics tools for virus research</span></p>
<p>https://evirusbioinfc.notion.site/18e21bc49827484b8a2f84463cb40b8d?v=92e7eb6703be4720abf17a901bc9a947</p><p>Address of the bookmark: <a href="https://evirusbioinfc.notion.site/18e21bc49827484b8a2f84463cb40b8d?v=92e7eb6703be4720abf17a901bc9a947" rel="nofollow">https://evirusbioinfc.notion.site/18e21bc49827484b8a2f84463cb40b8d?v=92e7eb6703be4720abf17a901bc9a947</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5422/shendure-lab</guid>
  <pubDate>Wed, 09 Oct 2013 14:21:58 -0500</pubDate>
  <link></link>
  <title><![CDATA[Shendure Lab]]></title>
  <description><![CDATA[
<p>The Shendure Lab is part of the Department of Genome Sciences at the University of Washington (Seattle, WA). The mission of the lab is to develop and apply new technologies in genomics and molecular biology. Most projects in the lab exploit new DNA sequencing technologies (Shendure et al., Nature Reviews Genetics 2004; Shendure &amp; Ji, Nature Biotechnology 2008; Shendure &amp; Lieberman Aiden, Nature Biotechnology 2012), and generally fall into one of six areas: 1) next-generation human genetics; 2) genome contiguity &amp; completeness; 3) massively parallel functional analysis; 4) molecular tagging; 5) synthetic biology; 6) translational genomics. Our interests in each of these areas are outlined briefly below, and a full list of publications is available via PubMed. http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&amp;term=shendure<br />More http://krishna.gs.washington.edu/research.html</p>

<p>Lab page @ http://krishna.gs.washington.edu/index.html</p>
]]></description>
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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>
<li>
<p>Identify conserved and unique genes.</p>
</li>
<li>
<p>Explore genetic determinants of pathogenicity.</p>
</li>
<li>
<p>Understand bacterial evolution and phylogenetics.</p>
</li>
<li>
<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>
<li>
<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>
<li>
<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>
<li>
<p><strong>Comparative Analyses</strong>:</p>
<ul>
<li>
<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>
<li>
<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>
<li>
<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>
<li>
<p><strong>OrthoFinder</strong>: For accurate ortholog identification across multiple genomes.</p>
</li>
<li>
<p><strong>PanOCT</strong>: Specifically designed for pan-genome clustering and annotation.</p>
</li>
<li>
<p><strong>FASTANI</strong>: A tool for calculating Average Nucleotide Identity (ANI) for microbial genome comparisons.</p>
</li>
<li>
<p><strong>CIRCOS</strong>: For visually comparing genomic data through circular genome plots.</p>
</li>
<li>
<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>
<li>
<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>
<li>
<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>
<li>
<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>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5661/shankar-lab</guid>
  <pubDate>Wed, 16 Oct 2013 07:02:22 -0500</pubDate>
  <link></link>
  <title><![CDATA[Shankar Lab]]></title>
  <description><![CDATA[
<p>Research Interest:</p>

<p>(A) Regulatory System Analysis with respect to microRNAs</p>

<p>(B) Computational Epigenomics &amp; Regulomics:</p>

<p>(C) Computational issues with Next Generation Sequencing:</p>

<p>Department of Biotechnology, <br />Institute of Himalyan Bioresources Technology<br />CSIR, Palampur(Himachal Pradesh), India.<br />Email: ravishihbt.res.in; ravish9gmail.com</p>

<p>More @ http://scbb.ihbt.res.in/SCBB_dept/Lab_Member.php</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5747/dbbrowser-attwood-lab</guid>
  <pubDate>Fri, 18 Oct 2013 10:48:19 -0500</pubDate>
  <link></link>
  <title><![CDATA[DbBrowser: Attwood Lab]]></title>
  <description><![CDATA[
<p>DbBrowser: Attwood Lab research concerns protein sequence analysis, primarily using the method of protein 'fingerprinting'. DbBrowser: Attwood Lab maintain a diagnostic fingerprint database (PRINTS), one of the founding partner of InterPro. We also design software to display sequence and structural data in visually-striking ways (e.g., Ambrosia, CINEMA); DbBrowser: Attwood Lab are building re-usable software components to create semantically integrated bioinformatics applications through UTOPIA, including a 'smart' PDF reader that links bioinformatics databases and tools directly with scientific articles (Utopia Documents); and have developed a number of tools for automatic annotation and text mining (e.g., MINOTAUR, PRECIS, METIS). </p>

<p>More @ http://www.bioinf.manchester.ac.uk/dbbrowser/index.php</p>
]]></description>
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