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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/17176?offset=980</link>
	<atom:link href="https://bioinformaticsonline.com/related/17176?offset=980" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44352/bioinformatics-tools-for-genome-assembly</guid>
	<pubDate>Mon, 24 Jul 2023 07:04:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44352/bioinformatics-tools-for-genome-assembly</link>
	<title><![CDATA[Bioinformatics tools for genome assembly !]]></title>
	<description><![CDATA[<p>There are numerous genome assembly tools available, each with its strengths and weaknesses. Here is a list of some widely used genome assembly tools as of my last update in September 2021:</p><ol>
<li>
<p><span>SPAdes:</span> An assembler specifically designed for single-cell and multi-cell bacterial genomes, as well as small eukaryotic genomes.</p>
</li>
<li>
<p><span>ABySS:</span> A parallelized assembler for large genomes that uses de Bruijn graphs.</p>
</li>
<li>
<p><span>Velvet:</span> Another de Bruijn graph-based assembler optimized for short-read sequencing data.</p>
</li>
<li>
<p><span>SOAPdenovo:</span> A de Bruijn graph-based assembler designed for short reads, widely used for assembling large and complex genomes.</p>
</li>
<li>
<p><span>MaSuRCA:</span> A hybrid assembler that combines data from multiple sequencing technologies, such as Illumina and PacBio.</p>
</li>
<li>
<p><span>Canu:</span> A long-read assembler optimized for PacBio and Oxford Nanopore sequencing data.</p>
</li>
<li>
<p><span>Flye:</span> A long-read assembler suitable for bacterial and small eukaryotic genomes.</p>
</li>
<li>
<p><span>SMARTdenovo:</span> An assembler designed for long reads, particularly suited for PacBio data.</p>
</li>
<li>
<p><span>SPAdes Long Read (SPAdesLR):</span> An extension of SPAdes for long-read data, such as those from PacBio or Nanopore.</p>
</li>
<li>
<p><span>Minia:</span> An assembler optimized for low memory consumption, suitable for small and medium-sized genomes.</p>
</li>
<li>
<p><span>Unicycler:</span> A hybrid assembler that combines short and long reads for circular bacterial genome assembly.</p>
</li>
<li>
<p><span>wtdbg2:</span> A de Bruijn graph assembler for long reads, efficient for very large genomes.</p>
</li>
<li>
<p><span>Shasta:</span> A long-read assembler that uses the Overlap-Layout-Consensus approach, suitable for PacBio and Nanopore data.</p>
</li>
<li>
<p><span>Sparc:</span> An assembler designed to handle noisy long reads from Nanopore sequencing.</p>
</li>
<li>
<p><span>CANA:</span> An assembler for metagenomic data, particularly for complex and diverse microbial communities.</p>
</li>
<li>
<p><span>Ra</span> Assembler: A metagenome assembler for long reads, designed for highly complex metagenomic samples.</p>
</li>
</ol><p>Please note that the field of bioinformatics is constantly evolving, and new assembly tools may have emerged since my last update. Additionally, the performance of these tools can vary depending on the characteristics of the sequencing data and the genome being assembled. When selecting an assembly tool, consider the specific requirements of your project, the available data types, and the computational resources at your disposal. Always refer to the respective tool's documentation and publications for the most up-to-date information and recommendations.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4959/evolution-and-cancer</guid>
	<pubDate>Fri, 27 Sep 2013 11:28:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4959/evolution-and-cancer</link>
	<title><![CDATA[Evolution and Cancer]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/j3uKOcNwYBw" frameborder="0" allowfullscreen></iframe>Air date:  Wednesday, January 04, 2012, 3:00:00 PM
Time displayed is Eastern Time, Washington DC Local  
 
Category:  Wednesday Afternoon Lectures  
Description:  There is a broad consensus that cancer is the result of somatic cells having serially gained, by a series of mutations, the ability to grow independently, to recruit resources from the circulation and the stroma, to invade local tissues, and to found anatomically distant metastases, ultimately killing the host. From the point of view of the cancer-causing somatic cell population, this is evolution driven by mutation and selection. Genomics has resulted in a parallel consensus that the central functions of all eukaryotes are highly conserved, not only at the level of individual protein functions, but also complex biological pathways and systems. These ideas motivated a comparison between results of molecular genetic studies of experimental evolution in yeast and the molecular genetic phenomena associated with tumorigenesis and tumor progression. We find some very striking similarities, including recurring genomic rearrangements, alterations of the regulation of specific growth-promoting genes, population-genetic features that affect the fitness trajectories of growth rate variants in evolving populations, and physiological and metabolic similarities derived from the conservation of the basic plan of growth and cell multiplication among all eukaryotes. It is hoped that some of the insights from yeast will aid the interpretation of sequence changes found in tumors, especially in the urgent necessity to distinguish 'driver' from 'passenger' mutations." 

David Botstein's fundamental contributions to modern genetics include the development of genetic methods for understanding biological functions and the discovery of the functions of many yeast and bacterial genes. In 1980, Botstein and three colleagues proposed a method for mapping human genes that laid the groundwork for the Human Genome Project. The basic principle of the mapping scheme was to develop, by recombinant DNA techniques, random single-copy DNA probes capable of detecting DNA sequence polymorphisms when hybridized to restriction digests, or specific fragments, of an individual's DNA. The method was used in subsequent years to identify several human disease genes, such as Huntington's and BRCA1. Variations of this method enabled the sequencing phase of the Human Genome Project. 

In the 1990s Botstein, having moved to Stanford University School of Medicine, collaborated with Patrick O. Brown of Stanford in exploiting DNA microarrays to study genome-wide gene expression patterns in yeast and in human cancers. This required developing a new statistical method and graphical interface, widely used today to interpret genomic data. Botstein also has helped to create, with Michael Ashburner and Gerald Rubin, a bioinformatics initiative to unify the representation of gene and gene product attributes across all species, called Gene Ontology. He graduated from Harvard College and earned his doctorate from the University of Michigan. He worked at Massachusetts Institute of Technology from 1967 to 1988; served as vice president for science at Genentech from 1988 to 1990; chaired the Department of Genetics at the Stanford University School of Medicine from 1990 to 2003; and joined the Princeton University faculty in 2003. He has sat on numerous editorial boards and was the founding editor of Molecular Biology of the Cell. Among recent major awards, Bostein won the Peter Gruber Foundation Prize in Genetics in 2003, the Apple Science Innovator Award in 2008, and the Albany Medical Center Prize in 2010. 

The NIH Wednesday Afternoon Lecture Series includes weekly scientific talks by some of the top researchers in the biomedical sciences worldwide. 

For more information, visit: The NIH Director's Wednesday Afternoon Lecture Series  
Author:  Dr. David Botstein, Princeton University  
Runtime:  00:59:58  

Permanent link:  http://videocast.nih.gov/launch.asp?17046]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44930/bioinformatics-the-bridge-between-curiosity-and-discovery</guid>
	<pubDate>Mon, 24 Nov 2025 05:16:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44930/bioinformatics-the-bridge-between-curiosity-and-discovery</link>
	<title><![CDATA[Bioinformatics: The Bridge Between Curiosity and Discovery]]></title>
	<description><![CDATA[<p>In the sprawling universe of modern science, bioinformatics stands as one of the most transformative and empowering fields of our time. It is where biology meets computation, where data becomes meaning, and where curiosity becomes discovery. If you&rsquo;ve stepped into this world&mdash;or are considering it&mdash;here&rsquo;s your reminder: you&rsquo;re part of a revolution.</p><p><strong>Why Bioinformatics Matters More Than Ever</strong></p><p>Every day, our world generates massive amounts of biological data&mdash;from genome sequences to microbiome profiles to real-time pathogen surveillance. Hidden within these datasets are the answers to some of the greatest challenges humanity faces: emerging diseases, antimicrobial resistance, environmental stress, genetic disorders, sustainable agriculture, and more.</p><p>Bioinformatics isn&rsquo;t just a skill.<br />It&rsquo;s the language of the future of biology.</p><p>By mastering it, you give yourself the power to:</p><p>Decode genomes and understand life at its most fundamental level</p><p>Identify patterns no microscope could ever reveal</p><p>Predict disease outbreaks before they occur</p><p>Accelerate drug discovery with computational precision</p><p>Contribute to open-source tools that empower scientists worldwide</p><p>You don&rsquo;t just follow science&mdash;you drive it.</p><p><strong>Every Expert Was Once a Beginner</strong></p><p>Many newcomers feel intimidated. Command-line interfaces. R scripts. Python packages. Next-generation sequencing data. Complex machine learning models.</p><p>But here&rsquo;s the truth: every bioinformatician started exactly where you are now&mdash;curious, unsure, but excited.</p><p>No one writes perfect code on day one.</p><p>No one understands genomics pipelines immediately.</p><p>What makes you a bioinformatician is not perfection, but perseverance.</p><p>When your script throws a cryptic error&hellip;<br />When your data refuses to format&hellip;<br />When your pipeline runs for 6 hours only to crash&hellip;</p><p>Remember: this is part of the journey.<br />Every error teaches you. Every retry strengthens you. Every breakthrough energizes you.</p><p>Bioinformatics Is Not Just a Career&mdash;It&rsquo;s a Mindset</p><p>It&rsquo;s the mindset of:</p><p>Problem-solving.</p><p>Continuous learning.</p><p>Turning chaos into clarity.</p><p>Seeing what others can&rsquo;t.</p><p>Bioinformaticians are detectives of biological complexity. You sit at the intersection of innovation, using tools that can shape public health, medicine, agriculture, and ecology. Few fields give you such direct impact on the world.</p><p><strong>Your Contribution Matters</strong></p><p>As you work on your script, pipeline, genome, or model, remember:</p><p>Somewhere, your analysis might contribute to:</p><p>A new therapy</p><p>A faster diagnostic test</p><p>A better understanding of a pathogen</p><p>A more resilient crop</p><p>An open-source dataset that helps thousands</p><p>A discovery that rewrites textbooks</p><p>Your code may be small, but its ripple effect is powerful.</p><p>The Future Is Bioinformatics&mdash;And You Are Part of It</p><p>The world is shifting. Wet labs are integrating AI. Hospitals rely on genomic insights. Farmers use gene-level predictions. Governments monitor disease in real time. Students launch pipelines that become global tools.</p><p>This is a golden era&mdash;and you are not late.<br />You are exactly where you need to be.</p><p>Keep Pushing. Keep Learning. Keep Discovering.</p><p>Bioinformatics is a journey filled with challenges, but also with unmatched rewards.</p><p>So the next time you feel stuck, frustrated, or overwhelmed, remember:<br />You&rsquo;re building the science of tomorrow.</p><p>Be proud. Stay curious. Keep going.<br />Your work matters more than you think.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/4107/natasa-przulj-lab</guid>
  <pubDate>Fri, 30 Aug 2013 06:29:17 -0500</pubDate>
  <link></link>
  <title><![CDATA[Nataša Pržulj Lab]]></title>
  <description><![CDATA[
<p>Nataša Pržulj Lab's research involves applications of graph theory, mathematical modeling, and computational techniques to solving large-scale problems in computational and systems biology.They are interested in computational and theoretical solutions to practical problems in many areas of systems biology, planar cell polarity, proteomics, cancer informatics, and drug discovery and design.</p>

<p>More at http://www.doc.ic.ac.uk/~natasha/index.html</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/24984/ra-bioinformatics-at-nii</guid>
  <pubDate>Thu, 22 Oct 2015 01:56:26 -0500</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at NII]]></title>
  <description><![CDATA[
<p>NATIONAL INSTITUTE OF IMMUNOLOGY</p>

<p>NEW DELHI-110067</p>

<p>Applications are invited for the position of Research Associate (RA) for the following time-bound sponsored project as per the details given below:</p>

<p>1. BTIS project entitled, “National Infrastructural Facility in the Area of Immunology” funded by DBT</p>

<p>Research Associate (One Position only)</p>

<p>Dr. Debasisa Mohanty Staff Scientist-VI deb@nii.res.in</p>

<p>Educational Qualifications: Ph.D in Bioinformatics or Biological Sciences or Biotechnology with research experience and publication record in indexed peer reviewed journals in the area of bioinformatics or computational biology.</p>

<p>Emoluments: The selected candidates will draw consolidated emoluments as per Institute Rules, depending upon qualifications &amp; experience Research Associate: Rs. 36,000/- per month plus 30% HRA</p>

<p>Job description &amp; Desired Knowledge: The candidate should be well versed in Programming in PERL/C++, HTML, CGI, web sever and portal development, computational analysis of protein structure &amp; function, molecular dynamics simulations and use of high performance computing systems.</p>

<p>General Terms &amp; Conditions:-</p>

<p>1. The candidates selected for the above posts will be on contract for one year or duration of the project whichever is shorter, at a time.</p>

<p>2. No hostel/ housing facility will be provided.</p>

<p>3. Applicants may clearly mention the category they belong to i.e. SC/ST/OBC/PH and attach documentary proof of the same.</p>

<p>4. No TA/DA will be paid for attending the interview, if called for.</p>

<p>5. Apart from sending application in the prescribed format given below, candidates should send complete Curriculum Vitae along with the names of three referees. Curriculum Vitae should contain details of the experimental expertise and list of publications. 6. Canvassing in any form will be a disqualification.</p>

<p>HOW TO APPLY Interested candidates may apply directly, STRICTLY IN THE PRESCRIBED FORMAT GIVEN BELOW, through e-mail, to the Investigator of the project, clearly indicating the name of the project along with their complete C.V., Email ID, fax numbers, telephone numbers. Only Short listed candidates will be called for interview and they required to submit attested copies of all their certificates and a Demand Draft of Rs 100/- drawn on Canara Bank or Indian Bank payable at Delhi/New Delhi in favour of the Director, NII (SC/ST/PH and Women candidates are exempted from payment of fees) subject to submission of documentary proof), at the time of interview. (E-MAIL APPLICATIONS SHOULD MENTION BTIS-RA 2015 IN THE SUBJECT LINE)</p>

<p>LAST DATE OF RECEIPT OF APPLICATIONS: 29th October, 2015</p>

<p>Advertisement:</p>

<p>www1.nii.res.in/sites/default/files/projectappointments-Dr.Mohanty-29oct2015.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35619/tallymer-method-to-compute-k-mer-frequencies-and-its-application-to-annotate-large-repetitive-plant-genomes</guid>
	<pubDate>Thu, 15 Feb 2018 10:21:02 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35619/tallymer-method-to-compute-k-mer-frequencies-and-its-application-to-annotate-large-repetitive-plant-genomes</link>
	<title><![CDATA[Tallymer: method to compute K-mer frequencies and its application to annotate large repetitive plant genomes]]></title>
	<description><![CDATA[<p>Tallymer is based on enhanced suffix arrays. This gives a much larger flexibility concerning the choice of the&nbsp;<span>k</span>-mer size. Tallymer can process large data sizes of several billion bases. We used it in a variety of applications to study the genomes of maize and other plant species. In particular, Tallymer was used to index a set whole genome shotgun sequences from maize (B73) (total size 10<sup>9</sup>&nbsp;bp).&nbsp;<br>Tallymer was effective in a variety of applications to aid genome annotation in maize, despite limitations imposed by the relatively low coverage of sequence available.</p>
<p>A manual can be found&nbsp;<a href="https://www.zbh.uni-hamburg.de/fileadmin/gi/tallymer/tallymer.pdf" target="_blank" title="tallymer.pdf (111 KB)">here</a>.</p><p>Address of the bookmark: <a href="https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html" rel="nofollow">https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44709/a-step-by-step-guide-to-running-blast-offline</guid>
	<pubDate>Sat, 07 Dec 2024 22:32:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44709/a-step-by-step-guide-to-running-blast-offline</link>
	<title><![CDATA[A Step-by-Step Guide to Running BLAST Offline]]></title>
	<description><![CDATA[<p>BLAST (Basic Local Alignment Search Tool) is a powerful algorithm used to compare nucleotide or protein sequences to sequence databases, identifying regions of similarity. Running BLAST offline provides more control, ensures data security, and allows customization for specific research needs. Here&rsquo;s a detailed guide to set up and run BLAST locally on your system.</p><hr><h3>Step 1: <strong>Install BLAST</strong></h3><ol>
<li>
<p><strong>Download BLAST</strong>:</p>
<ul>
<li>Visit the <a href="https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/">NCBI BLAST+ download page</a> to download the appropriate version for your operating system (Windows, macOS, or Linux).</li>
</ul>
</li>
<li>
<p><strong>Install BLAST</strong>:</p>
<ul>
<li>Extract the downloaded archive. For Linux/Mac, use:
<pre><code>tar -xvzf ncbi-blast-*.tar.gz
cd ncbi-blast-*
</code></pre>
</li>
<li>Add the BLAST binary folder to your system PATH for easier access:
<pre><code>export PATH=$PATH:/path/to/ncbi-blast-*/bin
</code></pre>
</li>
</ul>
</li>
<li>
<p><strong>Verify Installation</strong>:<br /> Run the following command to ensure BLAST is installed correctly:</p>
<pre><code>blastn -version
</code></pre>
</li>
</ol><hr><h3>Step 2: <strong>Prepare a Local Database</strong></h3><p>To run BLAST offline, you&rsquo;ll need a sequence database.</p><ol>
<li>
<p><strong>Download a Pre-Built Database (Optional)</strong>:</p>
<ul>
<li>NCBI provides ready-to-use databases such as <code>nt</code>, <code>nr</code>, and <code>Swiss-Prot</code>. Use the <code>update_blastdb.pl</code> script (bundled with BLAST) to download these:
<pre><code>update_blastdb.pl --decompress nt
</code></pre>
</li>
</ul>
</li>
<li>
<p><strong>Create a Custom Database</strong>:<br /> If you have specific sequences to use as a database:</p>
<ul>
<li>Prepare a FASTA file containing the sequences.</li>
<li>Use <code>makeblastdb</code> to create a database:
<pre><code>makeblastdb -in your_sequences.fasta -dbtype [nucl|prot] -out custom_db
</code></pre>
Replace <code>[nucl|prot]</code> with <code>nucl</code> for nucleotide sequences or <code>prot</code> for protein sequences.</li>
</ul>
</li>
</ol><hr><h3>Step 3: <strong>Prepare the Query Sequence</strong></h3><ul>
<li>Save your query sequence(s) in FASTA format.</li>
<li>Ensure the file is properly formatted, with a header line starting with <code>&gt;</code> followed by the sequence name, and the sequence on subsequent lines.</li>
</ul><p>Example:</p><pre><code>&gt;query_sequence
ATGCGTAGCTAGCGTAGCTAGCTAGCTA
</code></pre><hr><h3>Step 4: <strong>Run BLAST</strong></h3><ol>
<li>
<p><strong>Choose the Appropriate BLAST Tool</strong>:<br /> Depending on your data type:</p>
<ul>
<li><strong>blastn</strong>: For nucleotide-nucleotide searches.</li>
<li><strong>blastp</strong>: For protein-protein searches.</li>
<li><strong>blastx</strong>: Translates nucleotide sequences into proteins and compares them to a protein database.</li>
<li><strong>tblastn</strong>: Compares protein sequences to a nucleotide database.</li>
<li><strong>tblastx</strong>: Translates both nucleotide query and database sequences.</li>
</ul>
</li>
<li>
<p><strong>Run the Command</strong>:<br /> Example command for <code>blastn</code>:</p>
<pre><code>blastn -query query.fasta -db custom_db -out results.txt -outfmt 6 -evalue 1e-5
</code></pre>
<p><strong>Explanation of Parameters</strong>:</p>
<ul>
<li><code>-query</code>: Specifies the query file.</li>
<li><code>-db</code>: Points to the local database.</li>
<li><code>-out</code>: Output file name.</li>
<li><code>-outfmt</code>: Output format (e.g., 6 for tabular format).</li>
<li><code>-evalue</code>: E-value cutoff for significance.</li>
</ul>
</li>
</ol><hr><h3>Step 5: <strong>Interpret Results</strong></h3><ol>
<li>
<p><strong>Output Formats</strong>:</p>
<ul>
<li><strong>Default (outfmt 0)</strong>: Human-readable format.</li>
<li><strong>Tabular (outfmt 6)</strong>: Includes fields like query ID, subject ID, percent identity, alignment length, etc.</li>
</ul>
</li>
<li>
<p><strong>Analyze Results</strong>:<br /> Use tools like <code>grep</code>, Python, or R to parse and filter results for downstream analysis.</p>
</li>
</ol><hr><h3>Step 6: <strong>Optimize Performance</strong></h3><p>For large datasets, BLAST can be resource-intensive. To improve performance:</p><ol>
<li>
<p><strong>Multithreading</strong>:<br /> Use the <code>-num_threads</code> option to leverage multiple CPU cores:</p>
<pre><code>blastn -query query.fasta -db custom_db -out results.txt -num_threads 4
</code></pre>
</li>
<li>
<p><strong>Database Subsetting</strong>:<br /> Split large databases into smaller chunks for faster searches.</p>
</li>
<li>
<p><strong>Adjust Parameters</strong>:</p>
<ul>
<li>Lower the <code>-evalue</code> threshold for stricter matches.</li>
<li>Use <code>-max_target_seqs</code> to limit the number of results per query.</li>
</ul>
</li>
</ol><hr><h3>Step 7: <strong>Update Databases (Optional)</strong></h3><p>If using NCBI databases, regularly update them to ensure the inclusion of the latest sequences:</p><pre><code>update_blastdb.pl --decompress nt
</code></pre><hr><h3>Conclusion</h3><p>Running BLAST offline is a straightforward process that offers flexibility and security for bioinformaticians working with sensitive data. By following this guide, you can harness the power of BLAST to analyze sequences efficiently and gain valuable biological insights.</p><p>For advanced use cases, explore BLAST&rsquo;s customization options, such as custom scoring matrices, filtering, and iterative searches with tools like PSI-BLAST. Happy BLASTing!</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37502/alignqc-a-tool-for-assessing-an-alignment-and-generating-reports-that-are-easy-to-share</guid>
	<pubDate>Tue, 07 Aug 2018 04:41:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37502/alignqc-a-tool-for-assessing-an-alignment-and-generating-reports-that-are-easy-to-share</link>
	<title><![CDATA[AlignQC: A tool for assessing an alignment, and generating reports that are easy to share]]></title>
	<description><![CDATA[<p><span>Long read alignment analysis. Generate a reports on sequence alignments for mappability vs read sizes, error patterns, annotations and rarefraction curve analysis. The most basic analysis only requires a BAM file, and outputs a web browser compatible xhtml to visualize/share/store/extract analysis results.</span></p>
<p>https://f1000research.com/articles/6-100/</p>
<p>https://github.com/jason-weirather/AlignQC</p><p>Address of the bookmark: <a href="https://www.healthcare.uiowa.edu/labs/au/AlignQC/" rel="nofollow">https://www.healthcare.uiowa.edu/labs/au/AlignQC/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/7288/critical-to-discoveries-in-bioinformatics</guid>
	<pubDate>Mon, 16 Dec 2013 17:13:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/7288/critical-to-discoveries-in-bioinformatics</link>
	<title><![CDATA[Critical to discoveries in bioinformatics]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/MnKvMP8CeSQ" frameborder="0" allowfullscreen></iframe>EMBL-EBI distributes datasets worldwide using the Janet network. This biological data enables the discovery of new drugs, new diagnostics and increasingly new agro-chemicals.  Their work, which includes the 1000-genome project, has generated petabytes of data and this growth is showing no signs of abating.  On-demand bandwidth over Janet will therefore be critical to their ongoing work.]]></description>
	
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/7483/research-associate-indian-institute-of-spices-research</guid>
  <pubDate>Wed, 25 Dec 2013 12:34:43 -0600</pubDate>
  <link></link>
  <title><![CDATA[Research Associate @ INDIAN INSTITUTE OF SPICES RESEARCH]]></title>
  <description><![CDATA[
<p>INDIAN INSTITUTE OF SPICES RESEARCH<br />(Indian Council of Agricultural Research)<br />Marikunnu P.O., Kozhikode – 673 012, Kerala</p>

<p>WALK -IN- TEST CUM INTERVIEW</p>

<p>Walk- in- Test cum Interview (based on test) for the selection of Research Associate (Bioinformatics) &amp; Bioinformatic Trainees under the scheme ‘Distributed Information Sub Centre- DISC’ will be held at this Institute as per details indicated below.</p>

<p>Research Associate</p>

<p>Date of Interview : 21 -01-2014 at 10.00 A.M</p>

<p>Qualifications : a) Essential: Doctorate degree in Bioinformatics or Biotechnology/Life Sciences/Biochemistry with expertise in  Bioinformatics as evidenced by publications.</p>

<p>OR</p>

<p>Three years research experience after MVSc/MPharm/ME/MTech with Bioinformatics  Specialization.</p>

<p>b Desirable: Experience in handling NGS data  Programming skills in Python/Bioperl</p>

<p>Emoluments : Rs:22000/- per month + HRA (higher pay upto Rs.24000/- can be paid  depending on the qualifications and experience.</p>

<p>Upper age limit : 40 years for Men &amp; 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)</p>

<p>Duration of Project : Till the closure of the project.</p>

<p>General Terms and conditions</p>

<p>1. The above positions are purely on temporary basis and is co-terminus with the closure of the project. There is no provision of re-employment after termination of project. The selected candidate will not have any right for claiming pay scale or absorption against any regular post being vacant on a later date at this Institute.<br />2 . No TA/DA will be paid for attending the Interview.<br />3. Canvassing in any form will lead to cancellation of candidate.<br />4. The decision of Director, IISR would be final and binding in all aspects.<br />5. Candidates will not be permitted to enter the Examination Hall after 10.00 A.M.<br />6. Candidates who secure the minimum marks prescribed by the Institute in written test  only will be eligible for calling for the interview. The number of candidates to be  called for the interview will be decided by the Director of the Institute.<br />7 Those who do not possess original Degree/PG certificate or Provisional certificate will not be allowed to attend the Test/Interview.</p>

<p>Note: All relevant certificates (in original) and bio data<br />No objection certificate in case he/she is employed elsewhere and experience certificate in original (if any) need to be produced at the time of interview.<br />Location of IISR Kozhikode Main Campus - Pallithazham bus stop between Moozhikkal East and Chelavoor on the NH 212 ”Kozhikode - Kollegal” Road.</p>

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