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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/19633?offset=190</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32190/dbg2olcefficient-assembly-of-large-genomes-using-long-erroneous-reads-of-the-third-generation-sequencing-technologies</guid>
	<pubDate>Wed, 19 Apr 2017 10:09:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32190/dbg2olcefficient-assembly-of-large-genomes-using-long-erroneous-reads-of-the-third-generation-sequencing-technologies</link>
	<title><![CDATA[DBG2OLC:Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies]]></title>
	<description><![CDATA[<p>DBG2OLC:Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies</p>
<p>Our work is published in Scientific Reports:</p>
<p>Ye, C. et al. DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies. Sci. Rep. 6, 31900; doi: 10.1038/srep31900 (2016).</p>
<p><a href="http://www.nature.com/articles/srep31900">http://www.nature.com/articles/srep31900</a></p>
<p>The manual can be downloaded from:</p>
<p><a href="https://github.com/yechengxi/DBG2OLC/raw/master/Manual.docx">https://github.com/yechengxi/DBG2OLC/raw/master/Manual.docx</a></p>
<p>To use precompiled versions,please go to:</p>
<p><a href="https://github.com/yechengxi/DBG2OLC/tree/master/compiled">https://github.com/yechengxi/DBG2OLC/tree/master/compiled</a></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/yechengxi/DBG2OLC" rel="nofollow">https://github.com/yechengxi/DBG2OLC</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/6420/studentship-and-traineeship-university-of-madras</guid>
  <pubDate>Sat, 16 Nov 2013 19:27:40 -0600</pubDate>
  <link></link>
  <title><![CDATA[STUDENTSHIP and TRAINEESHIP @ University of Madras]]></title>
  <description><![CDATA[
<p>Bioinformatics Infrastructure Facility<br />University of Madras<br />Chennai 600 025</p>

<p>Applications are invited for the STUDENTSHIP and TRAINEESHIP vacancies to carry out project/research work in the DBT - Bioinformatics Infrastructure Facility with consolidated stipend of Rs.5,000/- per month.</p>

<p>Essential Qualification</p>

<p>Student Trainee: Those who have completed M.Sc., Bioinformatics/Biophysics/Life sciences or Pursuing M.Tech., Bioinformatics/Biotechnology</p>

<p>Duration : 3-4 Months</p>

<p>Student Trainee: Those who are pursuing M.Sc Bioinformatics/Biophysics/ Life sciences/others</p>

<p>Duration : 2-3 Months</p>

<p>Mail your CV on or before 25th November 2013 to shirai2011@gmail.com and hard copy to "Dr. D. Velmurugan, Professor &amp; Head, CAS in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600 025". Also, the applicants are requested to attend the interview on 29th November, 2013 at 11 A.M.</p>

<p>Advertisement:</p>

<p>www.unom.ac.in/uploads/announcements/bifadvertisement_20131114080003_23240.pdf</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32379/enrichr-a-comprehensive-gene-set-enrichment-analysis</guid>
	<pubDate>Thu, 27 Apr 2017 05:42:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32379/enrichr-a-comprehensive-gene-set-enrichment-analysis</link>
	<title><![CDATA[Enrichr: a comprehensive gene set enrichment analysis]]></title>
	<description><![CDATA[<p><span>Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at:&nbsp;</span><a href="http://amp.pharm.mssm.edu/Enrichr" target="">http://amp.pharm.mssm.edu/Enrichr</a><span>.</span></p>
<p>https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw377</p><p>Address of the bookmark: <a href="http://amp.pharm.mssm.edu/Enrichr/" rel="nofollow">http://amp.pharm.mssm.edu/Enrichr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32481/sspace</guid>
	<pubDate>Fri, 05 May 2017 05:42:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32481/sspace</link>
	<title><![CDATA[SSPACE]]></title>
	<description><![CDATA[<p>SSPACE standard is a stand-alone program for scaffolding pre-assembled contigs using NGS paired-read data. It is unique in offering the possibility to manually control the scaffolding process. By using the distance information of paired-end and/or matepair data, SSPACE is able to assess the order, distance and orientation of your contigs and combine them into scaffolds. Currently we offer this as a command-line tool in Perl. The input data is given by pre-assembled contig sequences (FASTA) and NGS paired-read data (Illumina/454/Solid FASTA or FASTQ). The final scaffolds are provided in FASTA format.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://www.baseclear.com/genomics/bioinformatics/basetools/SSPACE" rel="nofollow">https://www.baseclear.com/genomics/bioinformatics/basetools/SSPACE</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32709/cabog-celera-assembler-with-best-overlap-graph</guid>
	<pubDate>Mon, 15 May 2017 05:04:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32709/cabog-celera-assembler-with-best-overlap-graph</link>
	<title><![CDATA[CABOG: Celera Assembler with Best Overlap Graph]]></title>
	<description><![CDATA[<p>CABOG (Celera Assembler with Best Overlap Graph) is scientific software for&nbsp;<a href="http://bioinformatics.oxfordjournals.org/content/24/24/2818.abstract">DNA research</a>. CABOG has been a critical component of many genome sequencing projects. CABOG operates on small genomes such as bacterial as well as large genomes such as mammalian. CABOG is an extension of the Celera Assembler software that was originally developed at&nbsp;<a href="http://www.celera.com/">Celera</a>&nbsp;for the 2001 publication of the first draft human genome sequence. The software was released to the public domain in 2004. Its open source&nbsp;<a href="http://wgs-assembler.sf.net/">repository</a>&nbsp;on Source Forge is an internet resource for scientists around the world.&nbsp;</p>
<p>CABOG is one of many software programs called genome assemblers. These programs exist to overcome the fundamental limitation of all sequencing machines, namely, that they read out very few DNA letters at a time. These programs reconstruct genomes that are billions of letters long from the hundreds of letters per read that modern sequencers provide. What these programs do is often described as a scaled up version of a family solving a jigsaw puzzle.</p>
<p>The CABOG software was the first to accomplish many scientific goals. It was the first to assemble the genome of a multicellular organism (<em>Drosophila melanogaster</em>, 2000). It was the first to assemble both parental haplotypes of one human genome (J. Craig Venter, 2007). It was the first to assemble environmental sequence from the oceans (Sargasso Sea in 2004 and Global Ocean Sampling in 2007). It was first to combine reads from first-generation Sanger sequencing machines and second-generation pyrosequencing machines (Marine microbes, 2006). Today, CABOG is one of the leading assembly programs for data sets that include paired end data from the Roche 454 line of sequencing machines.</p><p>Address of the bookmark: <a href="http://www.jcvi.org/cms/research/projects/cabog/overview/" rel="nofollow">http://www.jcvi.org/cms/research/projects/cabog/overview/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/1295/five-points-for-bioinformatics-softwaretools</guid>
	<pubDate>Mon, 05 Aug 2013 04:12:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/1295/five-points-for-bioinformatics-softwaretools</link>
	<title><![CDATA[Five points for bioinformatics software/tools]]></title>
	<description><![CDATA[<p><span>In the bioinformatics sector we mostly spend time on computational analysis of huge amounts of data and try to make sense of it, biologically. But, most of the newbie bioinformaticians are faced with dilemma when they receive biological sequence data for the first time. They mostly found confusing over open source, user friendly GUI, and commercial bioinformatics software. Don&rsquo;t be surprise this is true and also not an easy task to decide, because analytical step is the most crucial part and believe to be the biggest bottleneck in publishing paper in high impact journals. Through this blog I would like to address the pros and cons of both kind of software/tools and try to assist (Hmmm not really, It looks convince) you to make decision on your software selections.</span></p><p><span><img src="http://bioinformaticsonline.com/mod/photo/five.jpg" alt="image" style="border: 0px;"></span></p><p><span>The most common newbie questions are:</span><span></span></p><p><span>Should I try to use these free open source programs? &nbsp;Why are we not trying GUI software for computational analysis? Should I use commercial bioinformatics programs/software?&rdquo;</span><span><br /></span><span><br />1. Let&rsquo;s be open</span><span></span></p><p><span>We generally think free and cheap are useless. But this concept is not applicable when we discuss open source software. Mostly, the bioinformatics software is developed by highly competitive biological programmers who believe in open sharing of knowledge. They come under Open Bioinformatics Foundation or O|B|F which is a non-profit, volunteer run organization focused on supporting open source programming in bioinformatics. The best part about open source tools/software is that they&rsquo;re free to download the source code and read exactly what the program does. If you are so inclined, you can view all of the parts of the program and see the logical flow of the pipeline. In addition, open source makes an excellent learning tool for any beginning bioinformatician. Moreover, you can modify existing open source programs to deal with cutting-edge problems or to customize your pipeline.</span><span>&nbsp;</span><span>Apart from your computational and analysis work, most of the reviewer also prefers the open source based results so that they can validate the results if validation required.</span></p><p><span>2. Code headache</span><span></span></p><p><span>As a bioinformatician you are supposed to know the basics of programming languages, and if you are not good at it, then please learn it as soon as possible because you are not a bio-analyst but biological programmers. The<span>&nbsp;</span>open source programs usually lack dedicated service and support teams (often because they were the product of an overworked doc/postdoc!) so you are responsible for troubleshooting your own errors most of the time.<span>&nbsp;</span>We commonly receive the HELP email to support and assist to setup the pipeline; you can also find this kind of request on any QA forum. I personally believe this coding horror brings the biggest downside of open-source programs; where you need some programming skills in order to implement the program in your pipeline. But, if you are not able to fix the pipeline and modify the open source code according to your requirements them you should re-think on your bioinformatician name tag!!!</span><span></span></p><p><span>3. Dive into the codes</span><span></span></p><p><span>Some of the biologist turn bioinformatician says &ldquo;if you can do the same thing with commercial software then why to get migraine with weird codes&rdquo;, well this statement looks to me that guys are keen to learn swimming but still don&rsquo;t like to get wet. If you are still using paid software and doing your work by customer support and clicking some of the well-designed GUI button then perhaps you are not interested in learning and trying new and challenging bioinformatics works. You are missing the basic flavour of bioinformatics. Let&rsquo;s dive into the coding world, I am sure your will enjoy it. I recommend your to swim freely in code&rsquo;s sea, and enjoy the journey; do not merely watch it from the outside. &nbsp;</span></p><p><span>4. Paid does not mean better</span><span></span></p><p><span>The bioinformatics company which are specializes in bioinformatics solutions develop well designed/packed, user friendly software by using a large number of specialised scientist, programmers and support staff. They also provide good services to accomplice your biological analysis work. This means that if you hit a &lsquo;snag&rsquo; with your data, help is likely only a phone call away! These companies price their products competitively against the cost of a dedicated bioinformatician. You may be able to afford the program, but not the additional staff! Additionally, most of the functionality that you need in your analysis is already coded into the program. Need to plot a graph? Just click this button right here. It is that easy.</span><span>&nbsp;</span><span>But, as a bioinformatician this is not generally well encouraged approach in biological analysis work, because the software is not available to everyone and your data can&rsquo;t be validated. Moreover, there is very less chances that anyone will repeat your work or love to do similar kind of research (because not all the labs in the world are rich like yours).</span></p><p><span>5. Take a caution<br /><br />In biological analysis work, in which you deal GB/TB of data are having maximum chances of getting errors, so please be careful and always cross check your data before coming to any conclusion. Even an error in two line code can alter your entire analysis and display weird results. Some of the scientist blindly believes on commercial software, which is entirely wrong. Using proprietary tools does not absolve you of the need to actually read and research the type of analysis that you are doing. This is particularly true in the case of genome assembly and annotation.</span></p><p><span><br />At the end, I would like to tell only one think that open source solutions allows you to do more cutting edge analysis than the commercial tools. So let&rsquo;s go for it.</span></p><p>Disclaimer:</p><p>This is my personal view. I have nothing to do with any company or open source community.&nbsp;The views expressed on these pages are mine alone and not those of my current/past employers. I do reserve the right to remove comments left by spammers or off-topic comments.</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/1469/prime-minister%E2%80%99s-100k-genome-project</guid>
	<pubDate>Thu, 08 Aug 2013 09:40:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/1469/prime-minister%E2%80%99s-100k-genome-project</link>
	<title><![CDATA[Prime Minister’s 100k Genome Project]]></title>
	<description><![CDATA[<p>Genomics Ebgland is destined to sequence 100,000 patients over the next five year in England.&nbsp; A landmark project by british government.</p><p>Genomics England will play a key role in building on the UK&rsquo;s long track record as leader in medical science advances to push the boundaries by unlocking the power of DNA data. The UK will become the first ever country to introduce this technology in its mainstream health system &ndash; leading the global race for better tests, better drugs and above all better, more personalised care.</p><p>http://www.genomicsengland.co.uk/100k-genome-project/</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/view/2021</guid>
	<pubDate>Mon, 12 Aug 2013 09:27:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/view/2021</link>
	<title><![CDATA[What are the difference between BioRuby and BioGem?]]></title>
	<description><![CDATA[<p>I came across two diferent but matching term BioRuby and BioGem. What are the difference between these two term? If both are using same Ruby language for development then why did they develope two different biological packages.</p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4090/computational-biology-in-the-21st-century-making-sense-out-of-massive-data</guid>
	<pubDate>Thu, 29 Aug 2013 08:32:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4090/computational-biology-in-the-21st-century-making-sense-out-of-massive-data</link>
	<title><![CDATA[Computational Biology in the 21st Century: Making Sense out of Massive Data]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/I99UiA_vaJQ" frameborder="0" allowfullscreen></iframe>Computational Biology in the 21st Century: Making Sense out of Massive Data    
    
Air date:  Wednesday, February 01, 2012, 3:00:00 PM
Category:  Wednesday Afternoon Lectures  
 
Description:  The last two decades have seen an exponential increase in genomic and biomedical data, which will soon outstrip advances in computing power to perform current methods of analysis. Extracting new science from these massive datasets will require not only faster computers; it will require smarter algorithms. We show how ideas from cutting-edge algorithms, including spectral graph theory and modern data structures, can be used to attack challenges in sequencing, medical genomics and biological networks. 

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

Author:  Dr. Bonnie Berger  
Runtime:  00:58:06  
Permanent link:  http://videocast.nih.gov/launch.asp?17563]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2334/binc-bioinformatics-national-certification-website-address</guid>
	<pubDate>Wed, 14 Aug 2013 09:40:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2334/binc-bioinformatics-national-certification-website-address</link>
	<title><![CDATA[BINC (BioInformatics National Certification) Website address]]></title>
	<description><![CDATA[<p><span>BINC (BioInformatics National Certification) is an initiative of Department of Biotechnology(DBT), Government Of India in coordination with Bioinformatics Center, University of Pune. The objective of the examination is to recognize trained manpower in the area of Bioinformatics. Currently, various Indian universities, Government and private institutions are involved in imparting courses in Bioinformatics in India.</span></p>
<p>Foreign nationals intending to have certification are eligible to appear for BINC examination.<br>Minimum qualification includes a degree from a recognized university/institute in the areas listed in FAQ.<br>Formal training in the area of Bioinformatics is not a prerequisite.<br>Note that the foreign students will only be certified by DBT and are not eligible for the cash award as well as junior research fellowship.</p><p>Address of the bookmark: <a href="http://binc.scisjnu.ernet.in/" rel="nofollow">http://binc.scisjnu.ernet.in/</a></p>]]></description>
	<dc:creator>Kamalakshi Mukherjee</dc:creator>
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