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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31566?offset=1120</link>
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	<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>
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	<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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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/23278/research-associate-project-fellow-biological-sciences-at-igib</guid>
  <pubDate>Sun, 12 Jul 2015 07:57:27 -0500</pubDate>
  <link></link>
  <title><![CDATA[Research Associate, Project Fellow (Biological Sciences) at IGIB]]></title>
  <description><![CDATA[
<p>Research Associate, Project Fellow (Biological Sciences)<br />Institute of Genomics &amp; Integrative Biology (IGIB) - New Delhi, Delhi<br />Pay Scale: Rs. 22,000/- + 30 % HRA per month<br />Educational Requirements: PhD in any branch of Biological Sciences with specialization in Bioinformatics with at least one research paper in Science Citation Indexed (SCI) journal<br />Desired Skills: Knowledge of molecular dynamics simulations<br />Details will be available at: http://www.igib.res.in/sites/default/files/24July2015.pdf</p>

<p>Project Fellow (Biological Sciences) Pay Scale: Rs. 16,000/- + 30 % HRA per month<br />Educational Requirements: M.Sc./B.Tech in life sciences/Biological sciences with at least 55 % marks<br />Experience Requirements: Research experience.<br />Details will be available at: http://www.igib.res.in/sites/default/files/24July2015.pdf</p>

<p>No of Post: 01<br />How To Apply: 1. Please fill up the proforma by clicking on the following link HR Online Form. 2. Candidate cannot apply for more than two posts. Last date of receiving application is 12-07-2015. No application would be entertained with “result awaited” status or after due date. List of shortlisted candidates will be put up on CSIR-IGIB website. No TA/DA will be paid to the candidates to attend the interview. The engagement shall be as per guidelines of CSIR/Funding agency. Candidates will have an option to give reply in Hindi. Note: The shortlisted candidates, have to report at 09:00 AM at Mall Road Campus, Delhi – 110007 on the day of interview along with any Photo ID card, (without photo ID card interview will not be conducted). 3 copies of updated signed C. V. (clearly mentioning Date of Birth and Highest Qualification with percentage), Dissertation (if any), PhD thesis (if any) and original certificates/Self attested photocopies for verification.<br />Detail of Interview: 24 July, 2015 at 10:30 AM<br />Age Limit: 28 Years</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45267/when-thousands-of-bacterial-genomes-become-one-giant-map</guid>
	<pubDate>Thu, 27 Aug 2026 10:56:47 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45267/when-thousands-of-bacterial-genomes-become-one-giant-map</link>
	<title><![CDATA[When Thousands of Bacterial Genomes Become One Giant Map]]></title>
	<description><![CDATA[<p>Imagine trying to understand a city by studying just one house.</p><p>You might learn a lot about that house&mdash;the rooms, the doors, the furniture&mdash;but you would miss the bigger story: the streets, the neighborhoods, and all the ways the city changes from one place to another.</p><p>Something similar happens when scientists study bacterial genomes one at a time.</p><p>Over the past decade, researchers have collected thousands of bacterial genomes. These genomes contain an enormous amount of information about how bacteria survive, adapt, and evolve. But comparing thousands of individual genomes can quickly become a computational maze.</p><p>That is where PANORAMA enters the story.</p><p>Developed by J&eacute;r&ocirc;me Arnoux and colleagues, PANORAMA is a computational tool designed to explore bacterial pangenomes&mdash;the complete collection of genetic possibilities found across a species or group of related organisms. Instead of looking at every genome as an isolated object, the approach represents their shared and variable genetic features as a graph.</p><p>Think of this graph as a giant subway map.</p><p>Some stations appear on almost every route. These represent genes that are highly conserved. Other stations appear only on certain routes, representing genes that some bacteria possess while others do not. By looking at the entire network, scientists can begin to see not just *what genes exist*, but how they are organized and how biological systems are distributed across populations.</p><p>The researchers put PANORAMA to the test using 941 genomes of Pseudomonas aeruginosa, a bacterium important in human health. They used the tool to investigate biological systems, including bacterial defense mechanisms against viruses known as bacteriophages. They then expanded the analysis to more than 6,000 genomes from four Enterobacteriaceae species.</p><p>The result is more than a faster way to process data.</p><p>PANORAMA allows researchers to ask a bigger question: What can an entire microbial species do, genetically speaking?</p><p>By comparing pangenomes, the researchers could identify systems shared between species as well as distinctive features. They could also find recurring genomic locations where genetic material is inserted&mdash;clues that may reveal common evolutionary processes.</p><p>And this is perhaps the most exciting part of the story.</p><p>Every bacterial genome is like a page in a huge evolutionary book. Until recently, reading thousands of those pages together was difficult. PANORAMA provides a way to turn those pages into a map, allowing scientists to see patterns that might disappear when each genome is studied separately.</p><p>The study, published in PLOS Computational Biology in July 2026, presents PANORAMA as a foundation for large-scale comparative pangenomics. The software and accompanying analysis resources are openly available, giving other researchers the opportunity to explore microbial diversity themselves.</p><p>So the story is not really about one bacterium or one genome.</p><p>It is about changing the way we look at life.</p><p>Instead of asking, &ldquo;What is inside this genome?&rdquo;, scientists can increasingly ask, &ldquo;What is the full genetic landscape of this species&mdash;and how did it become this way?&rdquo;</p><p>And sometimes, when you stop looking at one house and finally see the whole city, the most interesting discoveries are hiding in the streets between them.</p><p>Read more at&nbsp;https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013856</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/23378/ra-bioinformatics-at-bharathidasan-university</guid>
  <pubDate>Fri, 17 Jul 2015 19:40:45 -0500</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at Bharathidasan University]]></title>
  <description><![CDATA[
<p>Applications are invited from individuals who have high motivation to do research for the DBT sponsored project o n “Establishment of National Repository for Microalgae &amp; Cyanobacteria” funded by Department of Biotechnology, Govt. of India under the supervision of Dr. N. Thajuddin, Principal Investigator, Department of Microbiology, Bharathidasan University, Tiruchirappalli- 620 024.</p>

<p>1. Research Associate – 1 No.</p>

<p>Rs. 36,000/38,000/40,000 per month for I, II and III year + 20% HRA</p>

<p>Essential : Doctoral degree in relevant subject from recognized University/ Institutes</p>

<p>Desirable: Research experience in molecular biology and bioinformatics.</p>

<p>Interested candidates can send their complete CV in plain paper with a passport size photograph, with details of marks secured in all subjects from plus two stage (with proof, full postal address, sex, date of birth, community etc., along with additional qualification or experiences and two address of references whom could be contacted.</p>

<p>DEPARTMENT OF MICROBIOLOGY SCHOOL OF LIFE SCIENCES UNIVERSITY Dr. N. THAJUDDIN Professor &amp; Head Dean, Faculty of Science, Technology &amp; Engineering Tiruchirappalli – 620 024, India, Phone: +91 431 2407082; Mobile +91 098423 79719; E-mail: nthaju2002@yahoo.com</p>

<p>Application should reach the Principal Investigator on or before 5.8.2015 by Speed post/Couriers/Email (nthaju2002@yahoo.com), with subject printed as “Application for Research Associate /Technical Assistant /Lab attendant” in the envelop. Qualifying candidates will be short listed and communicated with date of interview. No TA and DA will be given for attending the interview. Address for Communication Dr.N.Thajuddin Principal Investigator Department of Microbiology Bharathidasan University Tiruchirappalli – 620 024, Tamil Nadu.</p>

<p>Advertisement: www.bdu.ac.in/adv/microbiology_advt.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45303/the-tree-that-learned-to-read-genomes</guid>
	<pubDate>Fri, 11 Sep 2026 00:51:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45303/the-tree-that-learned-to-read-genomes</link>
	<title><![CDATA[The Tree That Learned to Read Genomes]]></title>
	<description><![CDATA[<p>Imagine trying to reconstruct a family tree when many family photographs are missing, some names are unclear, and different relatives remember the past differently. This is similar to the challenge scientists face when trying to understand how organisms are related. Their family records are genomes, and the family tree is a phylogenetic tree. With modern sequencing technologies, scientists now have access to thousands of genomes, but comparing them can be difficult, especially when genomes are incomplete or poorly annotated.</p><p>A recent study by Webster and Chapman introduces Buscogeny (https://github.com/Jwebster89/Buscogeny), an open-source bioinformatics pipeline designed to make this process easier. The tool uses BUSCO (Benchmarking Universal Single-Copy Orthologs), which identifies conserved genes that are expected to occur as single copies in particular groups of organisms. These genes act like evolutionary landmarks, allowing researchers to compare genomes and investigate their relationships.</p><p>Buscogeny brings several steps of phylogenomic analysis into one workflow. It identifies BUSCO genes, extracts and aligns their sequences, removes unreliable regions, combines information from multiple genes, and uses the resulting data to construct a phylogenetic tree. It also provides quality-control information, helping researchers identify genomes that may be too incomplete for reliable analysis.</p><p>The authors demonstrate the usefulness of Buscogeny with bacterial and fungal genomes, including Alternaria. This is particularly valuable because fungal genomes can vary greatly in quality and annotation. Ultimately, Buscogeny helps transform massive amounts of genomic data into an understandable evolutionary story. Each DNA sequence becomes a clue, each conserved gene becomes a landmark, and together these clues reveal a picture of how life is connected.</p><p>More at&nbsp;https://link.springer.com/article/10.1007/s10123-025-00752-6?</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/23428/icgeb-bioinformatics-research-associate-vacancy</guid>
  <pubDate>Thu, 23 Jul 2015 19:45:16 -0500</pubDate>
  <link></link>
  <title><![CDATA[ICGEB Bioinformatics Research Associate Vacancy]]></title>
  <description><![CDATA[
<p>Junior Research Fellow (JRF) / Postdoc positions in Cell and Structural biology at ICGEB, New Delhi with Amit Sharma</p>

<p>Research positions are open starting 15th August 2015.</p>

<p>Projects are specifically for protein structure analysis. Projects also involve drug binding studies both computationally and experimentally.</p>

<p>CSIR/SPM/INSPIRE/DBT/UGC JRF/post-doc fellowships are essential for applications.</p>

<p>Email curriculum vitae to sb.icgeb@gmail.com by 14 August 2015</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/23578/srf-post-in-nehu-shillong</guid>
  <pubDate>Tue, 04 Aug 2015 03:17:05 -0500</pubDate>
  <link></link>
  <title><![CDATA[SRF post in NEHU, Shillong]]></title>
  <description><![CDATA[
<p>Bioinformatics Centre (DIC)<br />NORTH-EASTERN HILL UNIVERSITY<br />SHILLONG 793022</p>

<p>Applications with complete bio-data from candidates possessing the required qualifications are invited for the posts of JRF (1) and Project Assistant (1) in DBT, GOI-funded research project “Next Generation Sequencing (NGS)- based de novo assembly of expressed transcripts and genome information of Orchids in North-East India” in DBT’s Twinning programme for NE as per DBT sanction order and norms.</p>

<p>(i) JRF(1 no.):</p>

<p>Qualifications: M.Tech/M.Sc in Life Sciences/ Botany/ Zoology/ Biochemistry/ Biotechnology/ Bioinformatics;</p>

<p>Desirable: Aptitude for Bioinformatics and Computer Programming/ Next generation sequencing data analysis</p>

<p>(ii) Project Assistant (1 no.):</p>

<p>Qualifications: Graduation in Science,</p>

<p>Desirable: Experience of working in a Life Science/Plant Biotechnology lab. and familiarity with computers and field work viz. collection of samples.</p>

<p>The applications through email bicnehu@gmail.com or post must reach the undersigned within 15 days from the date of publication of this advertisement. The advertised posts are purely temporary for the duration of the project and subject to availability of the funds from DBT. The appointment does not confer any entitlement or right over the posts for absorption in the University service. Prof. Pramod Tandon, PI/Mr. Devendra Kumar Biswal (Co-PI) email: bicnehu@gmail.com</p>

<p>Advertisement: www.nehu.ac.in/Advertisements/BIC_AdvtPV_030815.pdf</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/23782/bioinformatics-openings-at-jnu</guid>
  <pubDate>Sun, 16 Aug 2015 01:03:11 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics openings at JNU]]></title>
  <description><![CDATA[
<p>School of Biotechnology<br />JAWAHARLAL NEHRU UNIVERSITY<br />NEW DELHI</p>

<p>Jawaharlal Nehru University has been granted funds by the Department of Biotechnology (DBT), Govt. of India to initiate an Inter-School programme in JNU to strengthen training and research called "DBT-Jawaharlal Nehru University, New Delhi-Interdisciplinary Life Science Programme for Advanced Research and education" with the broad project area entitled "From Molecules to Systems: Exploring biological space using chemical and synthetic biology" cutting across Physical, Chemical and Biological Sciences.</p>

<p>Applications are invited for the following purely temporary posts at various levels in the various Group projects for Research and Technical positions. The project is upto March 2017, but the appointments shall be initially for a period of one year, renewable every year depending on the performance of project staff:</p>

<p>Project Sub-Title: Synthetic Genomics: Making sense out of 'junk' DNA</p>

<p>Senior Research Fellow (SRF) - 01 Post</p>

<p>Qualifications: M.Sc in Bioinformatics, with minimum 2 year research experience, specialization in microRNA, molecular modelling, systems and/or Synthetic biology with publication in the relevant area would be desirable.</p>

<p>Investigator: Prof. Pawan Kumar Dhar</p>

<p>Fellowship: Rs. 14000+30% HRA p.m.</p>

<p>Project Sub-Title: "Structure, function, and dynamics of biomolecules and Molecular engineering"</p>

<p>Postdoctoral Fellow (PDF) - 01 Post</p>

<p>Essential Qualifications: PhD in Science(Bioinformatics/Computational Biology, Physics, Chemistry, Mathematics, &amp; Statistics), specialized in the area of Computational Biology/Structural Bioinformatics/ Quantum Chemistry/Molecular Dynamics &amp; Simulation /Systems Biology , reflected by thesis topics and or publication of papers during PhD and/or Post doctoral experience.</p>

<p>Desirable Qualifications: Computational methods and designing experience in the field of Structure based or ligand based drug design, Chemoinformatics, Programming capabilities as required for developing tools in the computational &amp; systems Biology.</p>

<p>Investigator: Prof. Indira Ghosh, SCIS</p>

<p>Salary: Rs. 22, 000 + 30% HRA</p>

<p>(Note: Revised emoluments shall be payable if Educational Qualifications or Eligibility Criteria as per DST OM No. A.20020/11/97-IFD dated 31-03-2010 are met by the research personnel)</p>

<p>The applications on plain paper indicating name, date of birth/age, address, essential / technical / professional qualifications, experience, research work, should reach the Programme Coordinator on or before 27th August 2015 at the following address:</p>

<p>The Envelop should be marked for the Post applied for. Any clarifications regarding projects may be sought from the respective project investigators as mentioned.</p>

<p>Address for correspondence:<br />Programme Coordinator<br />DBT-JNU BUILDER programme<br />School of Biotechnology<br />Jawaharlal Nehru University<br />New Delhi 110067</p>

<p>More at http://www.jnu.ac.in/career/currentjobs.htm</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool</guid>
	<pubDate>Thu, 02 Aug 2018 07:39:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool</link>
	<title><![CDATA[LSC :a long read error correction tool]]></title>
	<description><![CDATA[<h2>Getting Started</h2>
<p>These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.</p>
<ul>
<li>Read the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_requirements.asp">LSC_requirements</a>&nbsp;for running LSC.</li>
<li><a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_download.asp">Download</a>&nbsp;and set-up the LSC package.</li>
<li>Follow the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_tutorial.asp">tutorial</a>&nbsp;to see how LSC works on some example data.</li>
<li>Read the&nbsp;<a href="https://www.healthcare.uiowa.edu/labs/au/LSC/LSC_manual.asp">manual</a>&nbsp;if anything is unclear.</li>
<li>You're ready, Happy LSCing!</li>
</ul>
<h2>Latest publication</h2>
<p><span>Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong&nbsp;</span><br><strong>Improving PacBio Long Read Accuracy by Short Read Alignment&nbsp;</strong><span>[</span><a href="http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0046679">Manuscript</a><span>]&nbsp;</span><br><em>PLoS ONE</em><span>&nbsp;2012. 7(10): e46679. doi:10.1371/journal.pone.0046679</span></p><p>Address of the bookmark: <a href="https://www.healthcare.uiowa.edu/labs/au/LSC/" rel="nofollow">https://www.healthcare.uiowa.edu/labs/au/LSC/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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