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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/27427?offset=900</link>
	<atom:link href="https://bioinformaticsonline.com/related/27427?offset=900" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22286/jrf-bioinformatics-pune-university</guid>
  <pubDate>Wed, 06 May 2015 06:21:09 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF Bioinformatics @ Pune University]]></title>
  <description><![CDATA[
<p>Pune University is inviting applications from indian citizens for recruiting following posts:</p>

<p>Vacancies:<br />Junior Research Fellow-04<br />Age Limits:<br />Candidates age should be not more than 28 years.<br />Qualification:<br />Candidates should possess M.Sc in Microbiology/ Marine microbiology/Marine Biotechnology/ Bioinformatics/Zoology or equivalent degree with minimum 60% marks.<br />Selection Process:<br />Shortlisted candidates will be called for interview.<br />How to Apply:<br />Eligible candidates may send their applications in the prescribed format along with CV in an envelope should be superscribed as the "Application for the post of JRF" to Head Department of Zoology, Savitribai Phule Pune University on or before 10-05-2015.<br />Last Date:<br />10-05-2015</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44491/cgviewjs-is-a-circular-genome-viewing-tool</guid>
	<pubDate>Wed, 27 Mar 2024 11:16:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44491/cgviewjs-is-a-circular-genome-viewing-tool</link>
	<title><![CDATA[CGView.js is a Circular Genome Viewing tool]]></title>
	<description><![CDATA[<p>CGView.js is a&nbsp;<span>C</span>ircular&nbsp;<span>G</span>enome&nbsp;<span>View</span>ing tool for visualizing and interacting with small genomes. This software is an adaptation of the Java program&nbsp;<a href="https://paulstothard.github.io/cgview/">CGView</a>.</p>
<div>
<p>CGView.js is the genome viewer of Proksee, an expert system for genome assembly, annotation and visualization.</p>
<a href="https://proksee.ca/"></a></div>
<h1 id="features">Features</h1>
<ul>
<li>
<p>Circular and linear views of genomes</p>
</li>
<li>
<p>Capable of drawing genomes up to 10 Mbp with 1000's of features and 100's contigs</p>
</li>
<li>
<p>Smooth zooming down to the sequence level</p>
</li>
<li>
<p>Easily generate features and plots directly form the sequence (e.g. ORFs, GC-content and GC-Skew)</p>
</li>
<li>
<p>Save high resolution PNG maps up to 8000x8000px</p>
</li>
<li>
<p>Fully documented API for interacting with CGView.js maps</p>
</li>
</ul><p>Address of the bookmark: <a href="https://js.cgview.ca/" rel="nofollow">https://js.cgview.ca/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/22352/affy-has-acquired-eureka-genomics-for-15m</guid>
	<pubDate>Wed, 20 May 2015 15:11:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/22352/affy-has-acquired-eureka-genomics-for-15m</link>
	<title><![CDATA[Affy has acquired Eureka Genomics for 15M $]]></title>
	<description><![CDATA[<p>Affymetrix Acquires Assets Of Eureka Genomics Corporation To Provide High Throughput And Economical Crop And Animal Genotyping</p><p>http://www.thestreet.com/story/13151062/1/affymetrix-acquires-assets-of-eureka-genomics-corporation-to-provide-high-throughput-and-economical-crop-and-animal-genotyping.html</p>]]></description>
	<dc:creator>Martin Jones</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44722/step-by-step-guide-to-running-genome-assembly</guid>
	<pubDate>Fri, 13 Dec 2024 11:35:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44722/step-by-step-guide-to-running-genome-assembly</link>
	<title><![CDATA[Step-by-Step Guide to Running Genome Assembly]]></title>
	<description><![CDATA[<p>Genome assembly is a critical process in bioinformatics, enabling the reconstruction of an organism's genome from short DNA sequence reads. Whether you&rsquo;re working on a new microbial genome or a complex eukaryotic organism, this guide will walk you through the steps of genome assembly using state-of-the-art tools and best practices.</p><h4><strong>What is Genome Assembly?</strong></h4><p>Genome assembly involves piecing together short DNA sequence reads generated by sequencing platforms (e.g., Illumina, PacBio, Oxford Nanopore) into longer, contiguous sequences called contigs. This can be performed as:</p><ul>
<li><strong>De Novo Assembly</strong>: Without a reference genome.</li>
<li><strong>Reference-Guided Assembly</strong>: Using a reference genome to guide the assembly process.</li>
</ul><h4><strong>Step 1: Preparing Your Data</strong></h4><p>Before starting the assembly, ensure that your raw sequencing data is high quality.</p><ol>
<li>
<p><strong>Input Data</strong></p>
<ul>
<li><strong>Short Reads</strong>: Illumina sequencing generates short, accurate reads ideal for scaffolding.</li>
<li><strong>Long Reads</strong>: PacBio and Nanopore sequencing provide long reads for resolving repetitive regions.</li>
</ul>
</li>
<li>
<p><strong>Quality Control (QC)</strong><br />Use tools like <strong>FastQC</strong> or <strong>MultiQC</strong> to assess the quality of your reads:</p>
<div>
<div dir="ltr"><code>fastqc reads.fastq multiqc . </code></div>
</div>
<p>Look for issues like low-quality bases, adapter contamination, or overrepresented sequences.</p>
</li>
<li>
<p><strong>Read Trimming and Filtering</strong><br />Trim low-quality bases and adapters using <strong>Trimmomatic</strong> or <strong>Cutadapt</strong>:</p>
<div>
<div dir="ltr"><code>trimmomatic PE reads_R1.fastq reads_R2.fastq trimmed_R1.fastq trimmed_R2.fastq \ ILLUMINACLIP:adapters.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:36 </code></div>
</div>
</li>
</ol><h4><strong>Step 2: Choosing an Assembly Strategy</strong></h4><p>Select an assembly strategy based on your data type:</p><ul>
<li>
<p><strong>Short-Read Assemblers</strong>:</p>
<ul>
<li>SPAdes: Popular for microbial genomes.</li>
<li>Velvet: Fast for smaller genomes.</li>
</ul>
</li>
<li>
<p><strong>Long-Read Assemblers</strong>:</p>
<ul>
<li>Canu: Ideal for long-read datasets.</li>
<li>Flye: Versatile for small and large genomes.</li>
</ul>
</li>
<li>
<p><strong>Hybrid Assemblers</strong>:</p>
<ul>
<li>MaSuRCA: Combines short and long reads.</li>
<li>Unicycler: Optimized for bacterial genomes.</li>
</ul>
</li>
</ul><h4><strong>Step 3: Running the Assembly</strong></h4><h5><strong>3.1. SPAdes (Short-Read Assembly)</strong></h5><p>SPAdes is an excellent choice for small genomes, such as bacteria.</p><div><div dir="ltr"><code>spades.py -1 trimmed_R1.fastq -2 trimmed_R2.fastq -o spades_output </code></div></div><p>The output includes assembled contigs (<code>contigs.fasta</code>) and scaffolds (<code>scaffolds.fasta</code>).</p><h5><strong>3.2. Canu (Long-Read Assembly)</strong></h5><p>Canu is designed for high-error long reads from PacBio or Nanopore.</p><div><div dir="ltr"><code>canu -p genome -d canu_output genomeSize=4.7m -nanopore-raw reads.fastq </code></div></div><p>The output will be in <code>canu_output/genome.contigs.fasta</code>.</p><h5><strong>3.3. Hybrid Assembly with Unicycler</strong></h5><p>Unicycler combines short and long reads for improved assemblies.</p><div><div dir="ltr"><code>unicycler -1 trimmed_R1.fastq -2 trimmed_R2.fastq -l long_reads.fastq -o unicycler_output </code></div></div><h4><strong>Step 4: Assessing Assembly Quality</strong></h4><p>After assembly, evaluate its quality using the following tools:</p><ol>
<li>
<p><strong>QUAST</strong><br />QUAST generates assembly statistics, such as N50, genome size, and GC content:</p>
<div>
<div dir="ltr"><code>quast contigs.fasta -o quast_output </code></div>
</div>
</li>
<li>
<p><strong>BUSCO</strong><br />BUSCO checks genome completeness by identifying conserved genes:</p>
<div>
<div dir="ltr"><code>busco -i contigs.fasta -o busco_output -l fungi_odb10 -m genome </code></div>
</div>
</li>
<li>
<p><strong>Assembly Graph Visualization</strong><br />Visualize assembly graphs with <strong>Bandage</strong>:</p>
<div>
<div dir="ltr"><code>Bandage load assembly_graph.gfa </code></div>
</div>
</li>
</ol><hr><h4><strong>Step 5: Post-Assembly Steps</strong></h4><ol>
<li>
<p><strong>Polishing</strong><br />Improve assembly accuracy using tools like <strong>Pilon</strong> (for short reads) or <strong>Racon</strong> (for long reads).</p>
<div>
<div dir="ltr"><code>racon long_reads.fasta mapped_reads.sam contigs.fasta &gt; polished_contigs.fasta </code></div>
</div>
</li>
<li>
<p><strong>Scaffolding</strong><br />Link contigs into scaffolds using tools like <strong>SSPACE</strong> or <strong>Opera-LG</strong> if required.</p>
</li>
<li>
<p><strong>Annotation</strong><br />Annotate the assembled genome using <strong>Prokka</strong> for prokaryotes or <strong>Maker</strong> for eukaryotes.</p>
<div>
<div dir="ltr"><code>prokka --outdir annotation_output --prefix genome contigs.fasta </code></div>
</div>
</li>
</ol><h4><strong>Step 6: Sharing and Archiving</strong></h4><ol>
<li>
<p><strong>Submit to Public Repositories</strong><br />Share your assembly in databases like <strong>NCBI GenBank</strong>, <strong>ENA</strong>, or <strong>DDBJ</strong>.</p>
</li>
<li>
<p><strong>Metadata Preparation</strong><br />Include detailed metadata for your submission, such as organism name, sequencing platform, and coverage.</p>
</li>
</ol><h4><strong>Best Practices</strong></h4><ul>
<li>Always perform quality checks at each stage to ensure data integrity.</li>
<li>Use multiple tools to cross-validate results when working with complex genomes.</li>
<li>Document parameters and software versions for reproducibility.</li>
</ul><h4><strong>Conclusion</strong></h4><p>Genome assembly is a powerful process that transforms raw sequencing data into a coherent representation of an organism&rsquo;s genome. By following this step-by-step guide, you can successfully assemble genomes and uncover valuable biological insights. Whether you&rsquo;re assembling a microbial genome or tackling the complexities of a eukaryotic genome, these tools and strategies will set you on the path to success.</p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/22402/alessandra-carbone-lab</guid>
  <pubDate>Tue, 26 May 2015 08:54:34 -0500</pubDate>
  <link></link>
  <title><![CDATA[Alessandra Carbone Lab]]></title>
  <description><![CDATA[
<p>Our group works on various problems connected with the functioning and evolution of biological systems. We use mathematical tools, coming from statistics and combinatorics, algorithmic tools and molecular physics tools to study basic principles of cellular functioning starting from genomic data. We run several projects in parallel, all aiming at understanding the basic principles of evolution and co-evolution of molecular structures in the cell. They are intimately linked to each other.</p>

<p>Our main research themes are:</p>

<p>Domain annotation and metagenomics <br />Transcriptomics and sequence analysis<br />Protein evolution and interactions<br />Protein conformational dynamics</p>

<p>More at http://www.lcqb.upmc.fr/AnalGenom/home.html</p>
]]></description>
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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>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/22414/x-shirley-liu-lab</guid>
  <pubDate>Tue, 26 May 2015 17:28:23 -0500</pubDate>
  <link></link>
  <title><![CDATA[X. Shirley Liu Lab]]></title>
  <description><![CDATA[
<p>The research in our laboratories are focused on the following three areas: </p>

<p>Bioinformatics<br />Cancer<br />Epigenetics</p>

<p>More at http://liulab.dfci.harvard.edu/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/3868/next-generation-sequencing-ngs-tutorials</guid>
	<pubDate>Sat, 24 Aug 2013 06:01:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/3868/next-generation-sequencing-ngs-tutorials</link>
	<title><![CDATA[Next Generation Sequencing (NGS) Tutorials]]></title>
	<description><![CDATA[<p>Institute of computational biomedicine, Cornell University provide an NGS workshop tutorial at&nbsp;<a href="http://chagall.med.cornell.edu/NGScourse/">http://chagall.med.cornell.edu/NGScourse/</a>&nbsp;</p>
<p>You can also add your favourite NGS educational material, or workshop tutorial by commenting on this bookmarks for user benefit.&nbsp;</p>
<p>Understanding the basics of genome sequencing:</p>
<p>Tutorial by Luke Jostins.</p>
<p>http://www.genetic-inference.co.uk/blog/2009/04/basics-sequencing-dna-part-1/</p>
<p>http://www.genetic-inference.co.uk/blog/2009/08/basics-sequencing-dna-part-2/</p>
<p>A window into third-generation sequencing</p>
<p>http://hmg.oxfordjournals.org/content/19/R2/R227.full.pdf</p>
<p>==============================================</p>
<p>NGS data analysis pipelines</p>
<ul>
<li><strong>Detecting and annotating genetic variations using the HugeSeq pipeline</strong>&nbsp; DOI: <a href="http://dx.doi.org/10.1038/nbt.2134">10.1038/nbt.2134</a></li>
<li><strong> NARWHAL, a primary analysis pipeline for NGS data</strong> <a href="http://bioinformatics.oxfordjournals.org/cgi/content/abstract/28/2/284?etoc">http://bioinformatics.oxfordjournals.org/cgi/content/abstract/28/2/284?etoc</a></li>
<li><strong>RseqFlow: Workflows for RNA-Seq data analysis</strong>&nbsp; DOI: <a href="http://dx.doi.org/10.1093/bioinformatics/btr441">10.1093/bioinformatics/btr441</a></li>
<li><strong>ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence</strong>&nbsp;&nbsp;<a href="http://dx.doi.org/10.1186/1471-2164-12-285">10.1186/1471-2164-12-285</a></li>
<li><strong>A framework for variation discovery and genotyping using next-generation DNA sequencing data</strong>&nbsp; PubMed: <a href="http://www.ncbi.nlm.nih.gov/pubmed/21478889">21478889</a></li>
<li><strong>SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects</strong>&nbsp; DOI: <a href="http://dx.doi.org/10.1186/1471-2105-12-134">10.1186/1471-2105-12-134</a> Abstract: <a href="http://www.biomedcentral.com/1471-2105/12/134/abstract">http://www.biomedcentral.com/1471-2105/12/134/abstract</a></li>
<li><strong>WEP: a high-performance analysis pipeline for whole-exome data&nbsp;</strong>http://www.biomedcentral.com/1471-2105/14/S7/S11</li>
<li><strong>DDBJ read annotation pipeline: a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data.&nbsp;</strong>http://www.ncbi.nlm.nih.gov/pubmed/23657089</li>
<li><strong>GATK: a Toolkit for Genome Analysis&nbsp;</strong>http://www.broadinstitute.org/gatk/</li>
<li><strong>Metagenomics</strong>:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsmetagenomics/</li>
<li><strong>RNASeq</strong>:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsrnaseq/</li>
<li><strong>Bioinformatics and Seq courses</strong>:&nbsp;http://www.isb-sib.ch/training/training-activities-schedule/archive-2013.html</li>
<li><strong>Variant Detection (Model organism) Advanced tutorial</strong> https://docs.google.com/document/pub?id=1CuKkKylVDb03tnN7RSWl5EUzleetn0ctjmvaidPKLxM</li>
<li><strong>Variant Detection Introductory tutorial</strong> https://docs.google.com/document/pub?id=1ZRzrjjOCvtAu3m-IKL-rbJ1f4On60dDL_IEwG7oejdI</li>
<li><strong>Microbial de novo Assembly for Illumina Data Introductory tutorial</strong> https://docs.google.com/document/pub?id=1N3AB9ptISUu4zULqe1kXpVF0BDyGb5f5yzxWSJd_WNM</li>
<li><strong>RNAseq Differential Gene Expression Introductory tutorial</strong> https://docs.google.com/document/pub?id=1KbTiBHtvHLfPRZ39AY3uriazrINA8TJzgjjwn1zPP7Y</li>
</ul>
<blockquote>
<p>" Please add your favourite NGS link below in comment section for the benefit of bioinformatics community ".&nbsp;</p>
</blockquote><p>Address of the bookmark: <a href="http://chagall.med.cornell.edu/NGScourse/" rel="nofollow">http://chagall.med.cornell.edu/NGScourse/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/22436/ra-bioinformatics-at-national-bureau-of-animal-genetic-resources</guid>
  <pubDate>Thu, 28 May 2015 19:25:33 -0500</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at NATIONAL BUREAU OF ANIMAL GENETIC RESOURCES]]></title>
  <description><![CDATA[
<p>NATIONAL BUREAU OF ANIMAL GENETIC RESOURCES</p>

<p>Near Basant Vihar G.T. Road Bypass P.O. Box No.129</p>

<p>Karnal - 132001 (Haryana)</p>

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

<p>A walk-in-Interview is proposed to be held at National Bureau of Animal Genetic Resources, Karnal (Haryana)-132001 at 10:30 AM on 10.06.2015 to select One Research Associate as per details given below:</p>

<p>1. One post of Research Associate under National Fellow project entitled “Genome data mining to unravel molecular basis of thermotolerance and adaptation to diverse environments in native cattle and buffaloes”.</p>

<p>The post duration is Upto 22.05.2016 or earlier &amp; Co-terminus with the project.</p>

<p>Essential Qualifications: Master’s degree (M.Sc. / M.V.Sc.) in Biotechnology/ Animal Genetics and Breeding/ Life Sciences/ Bioinformatics with 2 Years research experience in relevant subject or Ph.D in any of the above subjects.</p>

<p>Desirable: Working Experience in molecular biology, gene expression/ microarray data analysis, SNP genotyping and sequence data analysis, mammalian cell-culture handling etc.</p>

<p>Emolument: Rs. 23,000/- per month + HRA as per admissibility</p>

<p>Research Associate: ONE</p>

<p>Duration of engagement: Upto 22.05.2016 or earlier Co-terminus with the project</p>

<p>Age Limit:  40 years for Men  45 years for women as on date of interview</p>

<p>Note: Relaxation in age will be admissible for SC/ST &amp; OBC candidates as per Govt. of India /ICAR norms</p>

<p>1. The applicants must bring with them original documents and brief of research work done during post graduation along with a set of photocopy and latest two passport size photographs. 2. A panel of selected candidates will also be made which may be utilized for filling of positions of shorter durations in future if demand arises. 3. Experience certificate in original, if any 4. The above positions are purely on temporary basis and are coterminus with the project. No TA/DA will be paid to attend the interview. 5. Any other clarifications can be had on the date of interview. 6. The Director’s decision will be final and binding on all respects.</p>

<p>Advertisement: http://210.212.93.85/RAadvertisiment.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/23174/scaffolding-of-a-bacterial-genome-using-minion-nanopore-sequencing</guid>
	<pubDate>Tue, 07 Jul 2015 16:59:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/23174/scaffolding-of-a-bacterial-genome-using-minion-nanopore-sequencing</link>
	<title><![CDATA[Scaffolding of a bacterial genome using MinION nanopore sequencing]]></title>
	<description><![CDATA[<p><span>Second generation sequencing has revolutionized genomic studies. However, most genomes contain repeated DNA elements that are longer than the read lengths achievable with typical sequencers, so the genomic order of several generated contigs cannot be easily resolved. A new generation of sequencers offering substantially longer reads is emerging, notably the Pacific Biosciences (PacBio) RS II system and the MinION system, released in early 2014 by Oxford Nanopore Technologies through an early access program.</span></p><p>Address of the bookmark: <a href="http://www.nature.com/srep/2015/150707/srep11996/full/srep11996.html" rel="nofollow">http://www.nature.com/srep/2015/150707/srep11996/full/srep11996.html</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>

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