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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/27438?offset=660</link>
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	<description><![CDATA[]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43661/maftools</guid>
	<pubDate>Fri, 17 Dec 2021 03:18:28 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43661/maftools</link>
	<title><![CDATA[maftools]]></title>
	<description><![CDATA[<p>With advances in Cancer Genomics, <a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">Mutation Annotation Format</a> (MAF) is being widely accepted and used to store somatic variants detected. <a href="http://cancergenome.nih.gov">The Cancer Genome Atlas</a> Project has sequenced over 30 different cancers with sample size of each cancer type being over 200. <a href="https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files">Resulting data</a> consisting of somatic variants are stored in the form of <a href="https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/">Mutation Annotation Format</a>. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner from either TCGA sources or any in-house studies as long as the data is in MAF format.</p>
<p>https://www.bioconductor.org/packages/devel/bioc/vignettes/maftools/inst/doc/maftools.html</p><p>Address of the bookmark: <a href="https://github.com/PoisonAlien/maftools" rel="nofollow">https://github.com/PoisonAlien/maftools</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/11000/professorassociate-professor-assistant-professor-at-chettinad-academy-of-research-and-education</guid>
  <pubDate>Sat, 24 May 2014 00:00:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[Professor/Associate Professor/ Assistant Professor at Chettinad Academy of Research and Education]]></title>
  <description><![CDATA[
<p>OPEN FACULTY POSITION</p>

<p>Chettinad Academy of Research and Education (CARE) invites applications from eligible and translational research-oriented candidates to the posts of Professor/Associate Professor/ Assistant Professor  Computational Biology, Bioinformatics, and Pharmaceutical Chemistry.</p>

<p>Emoluments: As per UGC norms (Adequate Compensation for Postdoctoral/Teaching experience)</p>

<p>Candidates fulfilling the eligibility criteria as per the UGC norms can send their full CV with copies of certificates and reference letters to the following address by post or by e-mail on or before 31st May 2014</p>

<p>The Registrar,<br />Chettinad Academy of Research and Education,<br />Chettinad Health City<br />Kelambakkam, Chennai 603 103<br />Tamil Nadu<br />T +91 (0)44 4741 1000<br />F +91 (0)44 4741 1011<br />Email: jobs @chettinadhealthcity.com</p>

<p>Advertisement: http://182.73.176.163/chc/ads2014.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43770/chromeister-an-ultra-fast-heuristic-approach-to-detect-conserved-signals-in-extremely-large-pairwise-genome-comparisons</guid>
	<pubDate>Thu, 03 Feb 2022 04:01:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43770/chromeister-an-ultra-fast-heuristic-approach-to-detect-conserved-signals-in-extremely-large-pairwise-genome-comparisons</link>
	<title><![CDATA[chromeister: An ultra fast, heuristic approach to detect conserved signals in extremely large pairwise genome comparisons.]]></title>
	<description><![CDATA[<p>chromeister: An ultra fast, heuristic approach to detect conserved signals in extremely large pairwise genome comparisons.</p>
<p dir="auto">USAGE:</p>
<ul dir="auto">
<li>-query: sequence A in fasta format</li>
<li>-db: sequence B in fasta format</li>
<li>-out: output matrix</li>
<li>-kmer Integer: k&gt;1 (default 32) Use 32 for chromosomes and genomes and 16 for small bacteria</li>
<li>-diffuse Integer: z&gt;0 (default 4) Use 4 for everything - if using large plant genomes you can try using 1</li>
<li>-dimension Size of the output matrix and plot. Integer: d&gt;0 (default 1000) Use 1000 for everything that is not full genome size, where 2000 is recommended</li>
</ul><p>Address of the bookmark: <a href="https://github.com/estebanpw/chromeister" rel="nofollow">https://github.com/estebanpw/chromeister</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/11107/the-minerva-research-group-for-bioinformatics</guid>
  <pubDate>Tue, 27 May 2014 15:48:14 -0500</pubDate>
  <link></link>
  <title><![CDATA[The Minerva Research Group for Bioinformatics]]></title>
  <description><![CDATA[
<p>The focus of the bioinformatics group is to use computational approaches to gain an insight into genome evolution in primates.</p>

<p>http://www.eva.mpg.de/genetics/bioinformatics/overview.html?Fsize=0%2C%20%40%2F%27</p>

<p>Kelso Group<br />Department of Evolutionary Genetics<br />Max Planck Institute for Evolutionary Anthropology<br />Deutscher Platz 6<br />04103 Leipzig<br />Germany<br />Phone: +49 341 3550 500</p>

<p>Job: <br />http://www.eva.mpg.de/genetics/bioinformatics/jobs.html?Fsize=0%2C%2B%40</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43846/the-complete-sequence-of-a-human-genome</guid>
	<pubDate>Thu, 31 Mar 2022 23:58:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43846/the-complete-sequence-of-a-human-genome</link>
	<title><![CDATA[The complete sequence of a human genome]]></title>
	<description><![CDATA[<p><span>The completed regions include all centromeric satellite arrays, recent segmental duplications, and the short arms of all five acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies.</span></p><p>Address of the bookmark: <a href="https://www.science.org/doi/10.1126/science.abj6987" rel="nofollow">https://www.science.org/doi/10.1126/science.abj6987</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/11582/monitor-running-jobs-on-linux-server</guid>
	<pubDate>Fri, 06 Jun 2014 16:18:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/11582/monitor-running-jobs-on-linux-server</link>
	<title><![CDATA[Monitor running jobs on Linux server]]></title>
	<description><![CDATA[<p>You as a bioinformatican run lots of program on your servers. Sometime the shared server is also used by your colleague. If server is busy you sometime need to check the running programs and want to monitor the running programs as well. The "top" command will come in handy when you need to find out if things are still running, how long they&rsquo;ve been running, or how much memory is being used.<br /><br />&lsquo;top&rsquo; is very simple to run: type<br /><br />%% top<br /><br />You&rsquo;ll get a screen that looks like this, and is updated regularly:<br /><br /><img src="http://bioinformaticsonline.com/mod/photo/top.png" width="659" height="582" alt="image" style="border: 0px;"><br />Simple, right? Heh.<br /><br />First! Note that you can use &lsquo;q&rsquo; or &lsquo;CTRL-C&rsquo; to exit from &lsquo;top&rsquo;.<br /><br />Now let&rsquo;s read and understand at each line independently.<br /><br />The first line:<br /><br />top - 23:00:48 up 39 days,&nbsp; 2 user,&nbsp; load average: 0.00, 0.00, 0.00<br /><br />The first line tells you the current time, how long the machine has been up, how many users are logged in, and the short/medium/long-term compute load on the machine. If you run something for a long time, you&rsquo;ll see these numbers go up. Right now, the machine is basically just sitting there, so these are all close to 0.<br /><br />The second line:</p><p>Tasks:&nbsp; 239 total,&nbsp;&nbsp; 1 running,&nbsp; 238 sleeping,&nbsp;&nbsp; 0 stopped,&nbsp;&nbsp; 0 zombie<br /><br />This line tells you how many processes are running. If you are using laptops machines it&rsquo;s not so interesting because you really are the only one using this machine.<br /><br />Cpu(s):&nbsp; 0.0%us,&nbsp; 0.0%sy,&nbsp; 0.0%ni,100.0%id,&nbsp; 0.0%wa,&nbsp; 0.0%hi,&nbsp; 0.0%si,&nbsp; 0.0%st<br /><br />This line contains the CPU load. The first two numbers are how busy the system is doing computation (&ldquo;us&rdquo; stands for &ldquo;user&rdquo;) and how busy the system is doing system-y things like accessing disks or network (&ldquo;sy&rdquo; stands for &ldquo;system&rdquo;). We&rsquo;ll talk more about this later.<br /><br />Mem:&nbsp;&nbsp; 49457320k total,&nbsp;&nbsp;&nbsp; 3492174k used,&nbsp; 14535596k free,&nbsp;&nbsp;&nbsp; 1435148k buffers<br /><br />This should be easy to understand &ndash; how much memory you&rsquo;re using! <br /><br />Swap:&nbsp;&nbsp; 539356k total,&nbsp;&nbsp; 28332k used,&nbsp;&nbsp; 836562k free,&nbsp;&nbsp;&nbsp; 29862014k cached<br /><br />Swap is just on-disk memory that can be used to &ldquo;swap&rdquo; out programs from main memory. Again, we&rsquo;ll talk about this later.:<br /><br />PID USER&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PR&nbsp; NI&nbsp; VIRT&nbsp; RES&nbsp; SHR S %CPU %MEM&nbsp;&nbsp;&nbsp; TIME+&nbsp; COMMAND<br />&nbsp; 1 root&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 39 &nbsp; 19&nbsp; 0&nbsp; 0&nbsp; 0 S&nbsp; 0.0&nbsp; 0.0&nbsp;&nbsp; 246:57.22 kipmi0<br />&nbsp; 2 root&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; RT&nbsp;&nbsp; 0&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp;&nbsp;&nbsp; 0&nbsp;&nbsp;&nbsp; 0 S&nbsp; 0.0&nbsp; 0.0&nbsp;&nbsp; 0:00.00 migration/0<br /><br />And... finally! What&rsquo;s actually running! The two most important numbers are the %CPU and %MEM towards the right, as well as the COMMAND. This tells you how compute- and memory-intensive your program is. Right now, nothing&rsquo;s running so the numbers aren&rsquo;t very interesting, but just wait until we run something...</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44783/when-chromosomes-shift-understanding-chromosome-rearrangement-and-human-disease</guid>
	<pubDate>Fri, 11 Apr 2025 01:07:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44783/when-chromosomes-shift-understanding-chromosome-rearrangement-and-human-disease</link>
	<title><![CDATA[When Chromosomes Shift: Understanding Chromosome Rearrangement and Human Disease]]></title>
	<description><![CDATA[<p>In the vast and complex world of genetics, our chromosomes are like carefully arranged bookshelves &mdash; each holding critical information that defines who we are. But what happens when those books are shuffled, inverted, or swapped? The answer lies in a phenomenon known as <strong>chromosome rearrangement</strong>, a powerful force behind many human diseases, from developmental disorders to cancer.</p><h2>What Are Chromosome Rearrangements?</h2><p><strong>Chromosome rearrangements</strong> are structural changes that alter the normal configuration of chromosomes. These changes can involve large segments of DNA &mdash; from thousands to millions of base pairs &mdash; and can occur <strong>spontaneously</strong>, be <strong>inherited</strong>, or result from <strong>exposure to mutagens</strong> (like radiation or chemicals).</p><h3>Common Types of Rearrangements:</h3><ol>
<li>
<p><strong>Deletions</strong> &ndash; Loss of a chromosome segment</p>
</li>
<li>
<p><strong>Duplications</strong> &ndash; Repetition of a segment</p>
</li>
<li>
<p><strong>Inversions</strong> &ndash; A segment breaks off, flips, and reattaches</p>
</li>
<li>
<p><strong>Translocations</strong> &ndash; Segments exchange places between non-homologous chromosomes</p>
</li>
<li>
<p><strong>Insertions</strong> &ndash; A segment is inserted into another part of the genome</p>
</li>
</ol><p>These changes can disrupt genes directly or affect gene regulation, leading to disease.</p><h2>How Do Chromosome Rearrangements Cause Disease?</h2><p>The impact of a rearrangement depends on <strong>which genes are involved</strong>, <strong>how much DNA is affected</strong>, and <strong>when the rearrangement occurs</strong> (in development vs. adulthood). Here are some key mechanisms:</p><ul>
<li>
<p><strong>Gene disruption</strong>: Breaking a gene can lead to loss of function or the creation of a non-functional protein.</p>
</li>
<li>
<p><strong>Gene fusion</strong>: Joining parts of two genes may form a novel hybrid gene with new functions (common in cancer).</p>
</li>
<li>
<p><strong>Dosage effects</strong>: Extra or missing gene copies can disturb the balance of gene expression.</p>
</li>
<li>
<p><strong>Position effects</strong>: Moving a gene to a new regulatory environment may silence or over-activate it.</p>
</li>
</ul><h2>Chromosome Rearrangements in Human Disease</h2><h3>1. <strong>Developmental Disorders</strong></h3><ul>
<li>
<p><strong>Cri-du-chat syndrome</strong>: Caused by a deletion on chromosome 5p. Affected infants often have a high-pitched cry and intellectual disability.</p>
</li>
<li>
<p><strong>Williams syndrome</strong>: Results from a microdeletion on chromosome 7q, affecting genes related to cardiovascular and cognitive function.</p>
</li>
</ul><h3>2. <strong>Cancer</strong></h3><p>Cancer is perhaps the most striking example of disease caused by chromosome rearrangements.</p><ul>
<li>
<p><strong>Chronic Myeloid Leukemia (CML)</strong>: Caused by a translocation between chromosomes 9 and 22, forming the <em>Philadelphia chromosome</em>. This creates the <strong>BCR-ABL fusion gene</strong>, which drives uncontrolled cell growth.</p>
</li>
<li>
<p><strong>Burkitt lymphoma</strong>: Involves translocation of the <strong>MYC</strong> gene, leading to excessive cell division.</p>
</li>
<li>
<p><strong>Ewing sarcoma</strong>: A fusion of EWSR1 and FLI1 genes through translocation promotes tumor development.</p>
</li>
</ul><h3>3. <strong>Infertility and Miscarriages</strong></h3><p>Balanced rearrangements (like inversions or translocations) in carriers may not cause disease directly but can result in:</p><ul>
<li>
<p><strong>Recurrent miscarriages</strong></p>
</li>
<li>
<p><strong>Infertility</strong></p>
</li>
<li>
<p><strong>Birth defects in offspring</strong></p>
</li>
</ul><h2>Detecting Rearrangements</h2><p>Thanks to modern genomics, chromosome rearrangements can now be detected with high precision using:</p><ul>
<li>
<p><strong>Karyotyping</strong> &ndash; Classic method for detecting large rearrangements</p>
</li>
<li>
<p><strong>FISH (Fluorescence In Situ Hybridization)</strong> &ndash; Uses fluorescent probes to target specific DNA sequences</p>
</li>
<li>
<p><strong>Array CGH (Comparative Genomic Hybridization)</strong> &ndash; Detects copy number changes across the genome</p>
</li>
<li>
<p><strong>Whole Genome Sequencing (WGS)</strong> &ndash; Identifies even small or complex rearrangements at base-pair resolution</p>
</li>
</ul><h2>Looking Forward: The Future of Chromosome Medicine</h2><p>Understanding chromosome rearrangements is now central to:</p><ul>
<li>
<p><strong>Personalized medicine</strong></p>
</li>
<li>
<p><strong>Genetic counseling</strong></p>
</li>
<li>
<p><strong>Targeted therapies</strong>, especially in cancer (e.g., tyrosine kinase inhibitors for BCR-ABL fusion)</p>
</li>
</ul><p>With the rise of long-read sequencing and single-cell genomics, even previously &ldquo;invisible&rdquo; rearrangements are being uncovered, offering new insights into both rare diseases and common conditions.</p><h2>Final Thoughts</h2><p>Chromosome rearrangements remind us that genetics isn't just about which genes we have &mdash; but where they are, how they're arranged, and when they're active. As our tools grow sharper, so does our ability to diagnose, understand, and treat diseases rooted in genomic architecture.</p><p>In a way, the genome is like a book not just defined by its words, but also by how the chapters are ordered. Rearranging them can create a new story &mdash; sometimes harmful, sometimes insightful &mdash; and understanding these changes is key to writing a healthier future.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/11441/assistant-professor-in-bioinformatics-at-dr-d-y-patil-biotechnology-bioinformatics-institute</guid>
  <pubDate>Tue, 03 Jun 2014 19:54:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[Assistant Professor 	in Bioinformatics at Dr. D. Y. Patil Biotechnology &amp; Bioinformatics Institute]]></title>
  <description><![CDATA[
<p>Dr. D. Y. Patil Biotechnology &amp; Bioinformatics Institute <br />Tathawade, Pune 411033.</p>

<p>Assistant Professor 	in Bioinformatics </p>

<p>Essential :<br />First Class Master’s Degree in the appropriate branch of Life Sciences / Technology (Tech.)<br />OR<br />Ph.D in Life Sciences or in the respective subject area of specialization<br />OR<br />Good Academic record with at least 55% marks (or an equivalent grade) at the Master’s Degree level, in the relevant subject or an equivalent degree from an Indian / Foreign University.<br />Besides fulfilling the above qualifications, candidates should have cleared the eligibility test (NET) for lecturers conducted by the UGC, CSIR or similar test accredited by the UGC and as per the requirements of UGC guidelines.</p>

<p>Desirable :<br />Teaching, research industrial and/or professional experience in a reputed organization. <br />Papers presented at Conferences and/or in refereed journals</p>

<p>Note : Application are invited in prescribed form Click here for Application Form<br />Kindly send your applications to “Registrar, Dr. D. Y. Patil Vidyapeeth, Pune, Sant Tukaram Nagar, Pimpri, Pune – 411018., Maharashtra, India.” should reach in the University office within 15 days from the publication.</p>

<p>More Info: http://www.dpu.edu.in/BiotechResearchPositions.aspx</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/4183/320000-viruses-in-mammals-yet-to-sequenced-in-future</guid>
	<pubDate>Tue, 03 Sep 2013 08:35:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/4183/320000-viruses-in-mammals-yet-to-sequenced-in-future</link>
	<title><![CDATA[320000 viruses in mammals yet to sequenced in future!!!]]></title>
	<description><![CDATA[<p>With current biological technique improvements, finally it is now possible to look at millions of unknown viruses at genomic level and understand the mechanism. According to available data, close to 70 per cent of emerging viral diseases such as HIV/AIDS, West Nile, Ebola, SARS, and influenza, are zoonoses - infections of animals that cross into humans.</p><p>To address the challenges of describing and estimating virodiversity, a team of investigators from Center for Infection and Immunity (CII) and EcoHealth Alliance began in jungles of Bangladesh - home to the flying fox.</p><p>Reference:</p><p><a href="http://economictimes.indiatimes.com/news/news-by-industry/et-cetera/mammals-harbour-at-least-320000-new-viruses/articleshow/22253268.cms">http://economictimes.indiatimes.com/news/news-by-industry/et-cetera/mammals-harbour-at-least-320000-new-viruses/articleshow/22253268.cms</a></p><p><a href="http://www.bbc.co.uk/news/science-environment-23932400">http://www.bbc.co.uk/news/science-environment-23932400</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/11603/ncbi-webinar</guid>
	<pubDate>Sun, 08 Jun 2014 02:47:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/11603/ncbi-webinar</link>
	<title><![CDATA[NCBI Webinar]]></title>
	<description><![CDATA[<p>In less than two weeks, NCBI will offer a webinar entitled "Introducing 3 NCBI Resources to Navigate Testing for Disease Linked Variants: MedGen, GTR and ClinVar". This webinar will delve into the lifecycle of genetic testing and teach attendees how to navigate the NIH Genetic Testing Registry, ClinVar, and MedGen resources. These resources can be used to prepare for clinical cases, access detailed information about orderable genetic tests, interpret test results, and more.</p><p>More at https://attendee.gotowebinar.com/register/8452228815737989634</p>]]></description>
	<dc:creator>Jit</dc:creator>
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