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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/34552?offset=100</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29144/fermi</guid>
	<pubDate>Fri, 09 Sep 2016 05:37:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29144/fermi</link>
	<title><![CDATA[FERMI]]></title>
	<description><![CDATA[<p><span>Fermi is a de novo assembler with a particular focus on assembling Illumina&nbsp;</span><span>short sequence reads from a mammal-sized genome. In addition to the role of a&nbsp;</span><span>typical assembler, fermi also aims to preserve heterozygotes which are often&nbsp;</span><span>collapsed by other assemblers. Its ultimate goal is to find a minimal set of</span><br><span>unitigs to represent all the information in raw reads.</span><br><br><span>Fermi follows the overlap-layout-consensus paradigm and uses the FM-DNA-index&nbsp;</span><span>(FMD-index) as the key data structure. It is inspired by the string graph&nbsp;</span><span>assembler (Simpson and Durbin, 2010 and 2012) and has a similar workflow.</span><br><br><span>As a typical de novo assembler, fermi tends to produce contigs with slightly&nbsp;</span><span>longer N50. However, the major weakness of fermi is the high misassembly rate.&nbsp;</span><span>Although fermi provides a tool to fix misassemblies by using paired-end reads&nbsp;</span><span>to achieve an accuracy comparable to other assemblers, this is not a favorable&nbsp;</span><span>solution.</span><br><br><span>Fermi is designed to be used on a multi-core Linux machine with large shared&nbsp;</span><span>memory. The easiest way to run fermi is to use the run-fermi.pl script. It&nbsp;</span><span>generates a Makefile. The actual assembly is done by invoking make. Premature&nbsp;</span><span>assembly processes can be resumed. Here is an example:</span><br><br><span>run-fermi.pl -dAPe ./fermi -p NA12878 -t16 -f18 reads*.fq.gz &gt; NA12878.mak</span><br><span>make -f NA12878.mak -j16</span></p><p>Address of the bookmark: <a href="https://github.com/lh3/fermi" rel="nofollow">https://github.com/lh3/fermi</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29263/srf-bioinformatics-at-bose-institute-india</guid>
  <pubDate>Thu, 29 Sep 2016 09:39:13 -0500</pubDate>
  <link></link>
  <title><![CDATA[SRF Bioinformatics at Bose Institute, India]]></title>
  <description><![CDATA[
<p>Advt. No. S/DPB(CB)/17/2016-17 <br />Junior Research Assistant/ Senior Research Assistant  Job vacancies in Bose Institute on temporary basis<br />Project Title : “Genome wide transcriptome analysis to identity MYMIV-stress related genomic resources of blackgram”<br />Junior Research Assistant <br />Qualification : Good academic record in B.Sc. and M.Sc. in Botany or Biotechnology from reputed Universities. Must have good practical hand, this should be certified by two Senior/reputed PG teachers on a scale of 10. <br />Desirable : Preference will be given to those who already have some research experience (for at least 2 months). <br />Consolidated Pay : Rs. 16,400/- p.m. <br />Age Limit : Below 28 years (relaxable in case of SC/ST/OBC/Women candidates only as per rules).<br />Senior Research Assistant <br />Qualification : B.Sc. in Biotechnology and M.Sc. in Biotechnology or Bioinformatics. Hands on training in Bioinformatics. At least two Research Publications with Bioinformatics components. <br />Desirable : Preference will be given to the candidates who have previous experience in Next Gen Sequencing data analysis of genomic/transcriptome/miRNA. <br />Consolidated Pay : Rs. 18,700/- p.m.  <br />Age Limit : Below 30 years (relaxable in case of SC/ST/OBC/Women candidates only as per rules).</p>

<p>http://www.boseinst.ernet.in/advertisement.html</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29276/murasaki</guid>
	<pubDate>Fri, 30 Sep 2016 10:22:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29276/murasaki</link>
	<title><![CDATA[Murasaki]]></title>
	<description><![CDATA[<p>Murasaki is an anchor alignment program that is</p>
<ul style="margin-left: 16px;">
<li>exteremely fast (17 CPU hours for whole Human x Mouse genome (with 40 nodes: 35 wall minutes), or 8 mammals in 21 CPU hours (42 wall minutes))</li>
<li>scalable (Arbitrarily parallelizable across multiple nodes using MPI)</li>
<li>memory efficient. (Even a single node with 16GB of ram can handle over 1Gbp of sequence)</li>
<li>unlimited by pattern length or selection</li>
<li>repeat tolerant</li>
</ul>
<p><img src="http://murasaki.dna.bio.keio.ac.jp/9mammals-small.png" width="500" height="375" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="http://murasaki.dna.bio.keio.ac.jp/wiki/index.php?Murasaki" rel="nofollow">http://murasaki.dna.bio.keio.ac.jp/wiki/index.php?Murasaki</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29305/miro-mirna-omics</guid>
	<pubDate>Tue, 04 Oct 2016 14:50:48 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29305/miro-mirna-omics</link>
	<title><![CDATA[MIRO : miRNA omics]]></title>
	<description><![CDATA[<p><span>The MIRO (the miRNA omics) pipeline is a flexible and powerful tool for the analysis of miRNA (or more generall short RNA) expression using short-read deep sequencing data. In its present implementation MIRO is especially adapted for the analysis of reads generated with the Illumina sequencing platform. MIRO allows to preprocess the Solexa-reads, map them flexibly to several reference genomes using one of four different mappers, create differential gene (miRNA) expression profiles and cluster reads using one of several algorithm. MIRO output is furthermore compatible with software such as genome browsers and miRDeep.</span></p><p>Address of the bookmark: <a href="http://seq.crg.es/download/software/Miro/" rel="nofollow">http://seq.crg.es/download/software/Miro/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29410/entrez-direct-e-utilities-on-the-unix-command-line</guid>
	<pubDate>Wed, 19 Oct 2016 08:06:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29410/entrez-direct-e-utilities-on-the-unix-command-line</link>
	<title><![CDATA[Entrez Direct: E-utilities on the UNIX Command Line]]></title>
	<description><![CDATA[<p>Entrez Direct (EDirect) is an advanced method for accessing the NCBI's suite of interconnected databases (publication, sequence, structure, gene, variation, expression, etc.) from a UNIX terminal window. Functions take search terms from command-line arguments. Individual operations are combined to build multi-step queries. Record retrieval and formatting normally complete the process.</p>
<p>EDirect also provides an argument-driven function that simplifies the extraction of data from document summaries or other results that are returned in structured XML format. This can eliminate the need for writing custom software to answer ad hoc questions. Queries can move seamlessly between EDirect commands and UNIX utilities or scripts to perform actions that cannot be accomplished entirely within Entrez.</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/books/NBK179288/" rel="nofollow">https://www.ncbi.nlm.nih.gov/books/NBK179288/</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29500/genomescope-open-source-web-tool-to-rapidly-estimate-the-overall-characteristics-of-a-genome-including-genome-size-heterozygosity-rate-and-repeat-content-from-unprocessed-short-reads</guid>
	<pubDate>Fri, 21 Oct 2016 05:46:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29500/genomescope-open-source-web-tool-to-rapidly-estimate-the-overall-characteristics-of-a-genome-including-genome-size-heterozygosity-rate-and-repeat-content-from-unprocessed-short-reads</link>
	<title><![CDATA[GenomeScope: open-source web tool to rapidly estimate the overall characteristics of a genome, including genome size, heterozygosity rate, and repeat content from unprocessed short reads]]></title>
	<description><![CDATA[<div>
<div>
<div>
<div id="content-block-markup">
<div>
<div id="abstract-1">
<p id="p-2">Summary: GenomeScope is an open-source web tool to rapidly estimate the overall characteristics of a genome, including genome size, heterozygosity rate, and repeat content from unprocessed short reads. These features are essential for studying genome evolution, and help to choose parameters for downstream analysis. We demonstrate its accuracy on 324 simulated and 16 real datasets with a wide range in genome sizes, heterozygosity levels, and error rates. Availability and Implementation: http://qb.cshl.edu/genomescope/, https://github.com/schatzlab/genomescope.git</p>
</div>
<span></span></div>
<span></span></div>
</div>
</div>
</div><p>Address of the bookmark: <a href="http://qb.cshl.edu/genomescope/" rel="nofollow">http://qb.cshl.edu/genomescope/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29578/plink2</guid>
	<pubDate>Thu, 27 Oct 2016 11:24:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29578/plink2</link>
	<title><![CDATA[PLINK2]]></title>
	<description><![CDATA[<p><span>This is a comprehensive update to Shaun Purcell's&nbsp;</span><a href="http://pngu.mgh.harvard.edu/~purcell/plink/">PLINK</a><span>&nbsp;command-line program, developed by&nbsp;</span><a href="mailto:chrchang@alumni.caltech.edu">Christopher Chang</a><span>&nbsp;with support from the&nbsp;</span><a href="http://www.niddk.nih.gov/">NIH-NIDDK</a><span>'s Laboratory of Biological Modeling, the&nbsp;</span><a href="http://research.mssm.edu/statgen/">Purcell Lab</a><span>&nbsp;at Mount Sinai School of Medicine, and others. (</span><a href="https://www.cog-genomics.org/plink2/#new">What's new?</a><span>) (</span><a href="https://www.cog-genomics.org/plink2/credits">Credits.</a><span>) (</span><a href="http://www.gigasciencejournal.com/content/4/1/7">Methods paper.</a><span>)</span></p><p>Address of the bookmark: <a href="https://www.cog-genomics.org/plink2/" rel="nofollow">https://www.cog-genomics.org/plink2/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/29601/statistics-using-r-with-biological-examples</guid>
	<pubDate>Thu, 03 Nov 2016 04:55:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/29601/statistics-using-r-with-biological-examples</link>
	<title><![CDATA[Statistics Using R   with Biological Examples]]></title>
	<description><![CDATA[<p>This book is a manifestation of my desire to teach researchers in biology a bit more about statistics than an ordinary introductory course covers and to introduce the utilization of R as a tool for analyzing their data. My goal is to reach those with little or no training in higher level statistics so that they can do more of their own data analysis, communicate more with statisticians, and appreciate the great potential statistics has to offer as a tool to answer biological questions. </p><p>This is necessary in light of the increasing use of higher level statistics in biomedical research. I hope it accomplishes this mission and encourage its free distribution and use as a course text or supplement.</p><p>K Seefeld, May 2007</p>]]></description>
	<dc:creator>Neel</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/29601" length="4581031" type="application/pdf" />
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29624/information-officer-at-iiar-gujarat</guid>
  <pubDate>Fri, 04 Nov 2016 05:19:13 -0500</pubDate>
  <link></link>
  <title><![CDATA[Information Officer at IIAR, Gujarat]]></title>
  <description><![CDATA[
<p>Walk in interview at 10.30 am on Nov 11th, 2016 for the following position at Distributed Information Sub-Centre (DISC) established by Dept. Of Biotechnology, Govt. of India at Indian Institute of Advanced Research, Gandhinagar, Gujarat</p>

<p>Position (scale), qualifications and experience</p>

<p>1. Information Officer (Rs 8000-275-13500): one post <br />Qualifications and experience: MCA, Post-Graduate in any field of biosciences, bioinformatics with at least two years of experience in working in a bioinformatics setup and good knowledge of linux operating system and computer networking.</p>

<p>General terms and conditions: <br />1. The above engagements is presently till March 31st, 2017. However on extension of project grant and satisfactory performance of the candidate, your services can be extended beyond March 31st, 2017 based on the terms and conditions of extension of project grant.</p>

<p>2. It is not an appointment with the institute and will not confer any right to the incumbent to any claim implicit or explicit on any position.</p>

<p>3.No TA/DA will be paid for attending the interview. Outstation candidates have to make their own arrangement for their stay.</p>

<p>4.Candidates, appearing in the walk-in interview are requested to bring the hard copy of application addressed to Dr. Anju Pappachan with latest photograph, CV mentioning qualifications, work experience and name of two referees and one page write up as to why you would like to join the project.</p>

<p>Address: <br />Dr. Anju Pappachan <br />Indian Institute of Advanced Research (IIAR), <br />University  and Institute of Advanced Research, <br />The Puri Foundation for Education in India, <br />Koba Institutional Area, <br />Gandhinagar- 382 007, Gujarat, India. Contact no. 079-30514152 <br />e-mail- anju@iiar.res.in</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29957/record</guid>
	<pubDate>Fri, 25 Nov 2016 08:23:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29957/record</link>
	<title><![CDATA[RECORD]]></title>
	<description><![CDATA[<p>Background. Next-generation sequencing technologies are now producing multiple times the genome size in total reads from a single experiment. This is enough information to reconstruct at least some of the differences between the individual genome studied in the experiment and the reference genome of the species. However, in most typical protocols, this information is disregarded and the reference genome is used. Results. We provide a new approach that allows researchers to reconstruct genomes very closely related to the reference genome (e.g., mutants of the same species) directly from the reads used in the experiment. Our approach applies de novo assembly software to experimental reads and so-called pseudoreads and uses the resulting contigs to generate a modified reference sequence. In this way, it can very quickly, and at no additional sequencing cost, generate new, modified reference sequence that is closer to the actual sequenced genome and has a full coverage. In this paper, we describe our approach and test its implementation called RECORD. We evaluate RECORD on both simulated and real data. We made our software publicly available on sourceforge. Conclusion. Our tests show that on closely related sequences RECORD outperforms more general assisted-assembly software.</p>
<p>More at&nbsp;https://sourceforge.net/projects/record-genome-assembler/files/</p><p>Address of the bookmark: <a href="https://www.ncbi.nlm.nih.gov/pubmed/26558255" rel="nofollow">https://www.ncbi.nlm.nih.gov/pubmed/26558255</a></p>]]></description>
	<dc:creator>Bulbul</dc:creator>
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