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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/8509?offset=860</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29614/art-set-of-simulation-tools</guid>
	<pubDate>Thu, 03 Nov 2016 08:28:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29614/art-set-of-simulation-tools</link>
	<title><![CDATA[ART: Set of Simulation Tools]]></title>
	<description><![CDATA[<p>ART is a set of simulation tools to generate synthetic next-generation sequencing reads. ART simulates sequencing reads by mimicking real sequencing process with empirical error models or quality profiles summarized from large recalibrated sequencing data. ART can also simulate reads using user own read error model or quality profiles. ART supports simulation of single-end, paired-end/mate-pair reads of three major commercial next-generation sequencing platforms: Illumina's Solexa, Roche's 454 and Applied Biosystems' SOLiD. ART can be used to test or benchmark a variety of method or tools for next-generation sequencing data analysis, including read alignment, de novo assembly, SNP and structure variation discovery. ART was used as a primary tool for the simulation study of the <span><a href="http://www.1000genomes.org/" target="_blank">1000 Genomes Project<span></span></a></span> . ART is implemented in C++ with optimized algorithms and is highly efficient in read simulation. ART outputs reads in the FASTQ format, and alignments in the ALN format. ART can also generate alignments in the SAM alignment or UCSC BED file format. ART can be used together with genome variants simulators (e.g. <span><a href="http://bioinform.github.io/varsim/" target="_blank">VarSim<span></span></a></span>) for evaluating variant calling tools or methods.</p><p>Address of the bookmark: <a href="http://www.niehs.nih.gov/research/resources/software/biostatistics/art/" rel="nofollow">http://www.niehs.nih.gov/research/resources/software/biostatistics/art/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29635/r-graphs</guid>
	<pubDate>Fri, 04 Nov 2016 10:48:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29635/r-graphs</link>
	<title><![CDATA[R Graphs !!]]></title>
	<description><![CDATA[<p><span>The blog is a collection of script examples with example data and output plots. R produce excellent quality graphs for data analysis, science and business presentation, publications and other purposes. Self-help codes and examples are provided. Enjoy nice graphs !!</span></p><p>Address of the bookmark: <a href="http://rgraphgallery.blogspot.be/" rel="nofollow">http://rgraphgallery.blogspot.be/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/29652/bioistats-ppt</guid>
	<pubDate>Tue, 08 Nov 2016 07:09:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/file/view/29652/bioistats-ppt</link>
	<title><![CDATA[Bioistats PPT]]></title>
	<description><![CDATA[<p>Basics concepts of&nbsp;Probability: The Study of Randomness</p><p>Biostatistics is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological experiments, especially in medicine, pharmacy, agriculture and fishery; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results. A major branch of this is medical biostatistics, which is exclusively concerned with medicine and health.</p>]]></description>
	<dc:creator>Jit</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/29652" length="1663809" type="application/pdf" />
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29849/ra-bioinformatics-at-national-institute-of-cancer-prevention-research-icmr</guid>
  <pubDate>Thu, 17 Nov 2016 04:11:09 -0600</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at NATIONAL INSTITUTE OF CANCER PREVENTION &amp; RESEARCH (ICMR)]]></title>
  <description><![CDATA[
<p>NATIONAL INSTITUTE OF CANCER PREVENTION &amp; RESEARCH (ICMR)</p>

<p>Noida 201301 (U.P)</p>

<p>Applications are invited upto 21.11.2016 from interested candidates as per details available on NICPR website (www.nicpr.res.in)/ ICMR website (www.icmr.nic.in) to fill up following temporary position in the time bound DHR Project entitled “Next generation EGFR inhibitor identification using ligand based QSAR technique” under Dr. Subhash M. Agarwal, Scientist-D, Division of Bioinformatics.</p>

<p>Research Assistant (One)</p>

<p>Rs.27000/- p.m. (Fixed/temporary)</p>

<p>Essential: M.Sc. in Bioinformatics or related field.</p>

<p>Desirable: Experience in QSAR and structure based drug designing.</p>

<p>More Info : www.icmr.nic.in/icmrnews/NICPR_Advertisement%20for%20RA.pdf</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/29998/csir-nehru-science-postdoctoral-research-fellowship</guid>
  <pubDate>Tue, 29 Nov 2016 12:34:59 -0600</pubDate>
  <link></link>
  <title><![CDATA[CSIR Nehru Science Postdoctoral Research Fellowship]]></title>
  <description><![CDATA[
<p>CSIR Nehru Science Postdoctoral Research Fellowship</p>

<p>About Fellowship: <br />CSIR Nehru Science Postdoctoral Research Fellowship Scheme is an Research Fellowship awarded/given by HRD Ministry, Govt. of India every year to more than 100 fellows.</p>

<p>It was started to identify promising and young researchers with novel ideas and provide them research opportunities in the areas of basic science, engineering, medicine &amp; agriculture.</p>

<p>The fellowship aims at facilitating their transition from mentored to independent research career.</p>

<p>In addition, check these ICTS Research Fellowships:<br />1.) Max-Planck Partner Group Fellowships 2017-18<br />2.) ICTS-Simons Postdoctoral Fellowships 2017<br />3.) ICTS Post Doctoral Fellowships 2017<br />4.) Airbus Prize Postdoctoral Fellowship 2017-18</p>

<p>Eligibility: To be eligible for this fellowship, you:<br />1.) PhD degree (within 3 years of award of PhD degree), OR<br />2.) Those who have submitted PhD theses.<br />3.) Applicants should have research publications in good impact factor SCI journals.<br />4.) Indian nationals, Persons of Indian Origin (PIO) &amp; Overseas Citizen of India (OCI), a can also apply.<br />5.) Maximum Age Limit: 32 years.</p>

<p>Duration: <br />– 2 Years.<br />– extendable for a maximum of 1 more year based on performance.</p>

<p>Remuneration: <br />– Rs. 50,000/- per month plus House Rent Allowance (HRA)<br />– A contingency grant of Rs. 3.0 lakh per annum.<br />– 25% of the contingency grant can be used for domestic and international travel.</p>

<p>Mode of Selection: You can apply throughout the year, but selection will be made twice a year, in June and December.</p>

<p>How to Apply: <br />– Read the instructions, given at Annexure-I in this PDF file.<br />– And, application form is given as, Annexure-II.<br />– Fill the form &amp; send it to the given address in the PDF file.</p>

<p>Deadline: Rolling Deadline (Applications accepted throughout the year)</p>

<p>Also See: Research Internship/ Fellowship in India:<br />1.) IIT Bombay Research Internship Awards Programme 2016-17<br />2.) IIT Delhi Internship Program 2016-17<br />3.) DAAD WISE – International Internship in Germany<br />4.) Summer Research Fellowship Programme| JNCASR Bangalore<br />5.) Indian Academy of Sciences Summer Internships 2017<br />6.) Winter Internship – IIT Bombay NPDE-TCA<br />7.) Viterbi – India Program 2017 | Research Internship in US<br />8.) Internship – Centre for Stem Cell Research, Vellore</p>

<p>Accommodation &amp; other benefits: <br />– Accommodation may be provided by CSIR, if available.<br />– Medical benefits as per CSIR norms.</p>

<p>For more details: <br />– Check this PDF Notification of Fellowship.<br />– List of CSIR Labs &amp; their work/activities can be seen at www.csir.res.in.</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30018/bipype</guid>
	<pubDate>Thu, 01 Dec 2016 08:47:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30018/bipype</link>
	<title><![CDATA[bipype]]></title>
	<description><![CDATA[<p><span>Bipype is a very useful program, which prepare a lot of types of bioinformatics analyses. There are three input options: amplicons, WGS (whole genome sequences) and metatranscriptomic data. If amplicons are input data, then bipype does reconstruction and pairs merging. After that biodiversity is searching. There are two types of searching depending on the amplicons types (ITS or 16S). If WGS are chosen, then bipype finds the SA coordinates of the input reads and generates alignments in the SAM format given single-end reads, aligns reads to reference sequence(s). All of these analyses will be shown with Krona program, which allows to show hierarchical data with pie charts.</span></p><p>Address of the bookmark: <a href="https://readthedocs.org/projects/bipype/" rel="nofollow">https://readthedocs.org/projects/bipype/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30074/minia</guid>
	<pubDate>Thu, 08 Dec 2016 05:07:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30074/minia</link>
	<title><![CDATA[Minia]]></title>
	<description><![CDATA[<p>Minia is a short-read assembler based on a de Bruijn graph, capable of assembling a human genome on a desktop computer in a day. The output of Minia is a set of contigs. Minia produces results of similar contiguity and accuracy to other de Bruijn assemblers (e.g. Velvet).</p>
<h3>Download</h3>
<p><a href="https://github.com/GATB/minia/releases/download/v2.0.7/minia-v2.0.7-bin-Linux.tar.gz">Minia 2.0.7 Linux 64-bits binaries</a>&nbsp;(<a href="https://github.com/GATB/minia/releases/download/v2.0.7/minia-v2.0.7-Source.tar.gz">Source code</a>)&nbsp;<span>(<a href="http://minia.genouest.org/files/minia-1.6906.tar.gz">Legacy codebase</a>)</span></p>
<h3>For the impatient</h3>
<p>A typical Minia command line looks like:</p>
<pre>./minia -in <span>reads.fa</span> -kmer-size <span>31</span> -abundance-min <span>3</span> -out <span>output_prefix</span></pre>
<p>Type</p>
<pre>./minia</pre>
<p><span>for a quick explanation of the parameters.</span></p>
<p>For more information, refer to the&nbsp;<a href="http://minia.genouest.org/files/minia.pdf">manual</a>.</p>
<p><a href="http://kmergenie.bx.psu.edu/">KmerGenie</a>&nbsp;can be used to determine the best k-mer size, minimum abundance of correct k-mers, and genome size estimation for your dataset.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://minia.genouest.org/" rel="nofollow">http://minia.genouest.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30090/standardized-velvet-assembly-report</guid>
	<pubDate>Fri, 09 Dec 2016 03:59:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30090/standardized-velvet-assembly-report</link>
	<title><![CDATA[Standardized velvet assembly report]]></title>
	<description><![CDATA[<p>Requirements:</p>
<ul>
<li>velvet (velveth velvetg should be in your PATH)</li>
<li>R (with Sweave)</li>
<li>pdflatex (usually part of TeTeX)</li>
<li>ggplot2 (from R prompt type install.packages("ggplot2","proto","xtable"))</li>
<li>Perl</li>
</ul>
<p>Optional:</p>
<ul>
<li>BLAT or BLAST (to generate alignments against a reference genome). If using BLAT, add faToTwoBit,gfClient,gfServer to your PATH. If using BLAST, add blastall and formatdb.</li>
</ul>
<p>Edit permute.sh to your liking, paying particular attention to the kmer, cvCut, expCov, and other flags</p>
<p>To Run:</p>
<ol>
<li><code>perl fastaAllSize mysequences.fa &gt; mysequences.stat or gunzip -c mysequences.fa.gz | fastaAllSize &gt; mysequences.stat</code>&nbsp;Substitute fastqAllSize for fastq files.</li>
<li><code>./permute.sh mysequences</code>&nbsp;(leave out the .fa)</li>
</ol>
<p>https://github.com/leipzig/standardized-velvet-assembly-report</p><p>Address of the bookmark: <a href="https://github.com/leipzig/standardized-velvet-assembly-report" rel="nofollow">https://github.com/leipzig/standardized-velvet-assembly-report</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30111/eager</guid>
	<pubDate>Sat, 10 Dec 2016 18:07:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30111/eager</link>
	<title><![CDATA[EAGER]]></title>
	<description><![CDATA[<p><span>The automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process.</span></p>
<p><span>EAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.</span></p>
<p>https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0918-z</p><p>Address of the bookmark: <a href="https://github.com/apeltzer/EAGER-GUI" rel="nofollow">https://github.com/apeltzer/EAGER-GUI</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30144/bima-v3-an-aligner-customized-for-mate-pair-library-sequencing</guid>
	<pubDate>Wed, 14 Dec 2016 15:20:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30144/bima-v3-an-aligner-customized-for-mate-pair-library-sequencing</link>
	<title><![CDATA[BIMA V3: an aligner customized for mate pair library sequencing]]></title>
	<description><![CDATA[<p>Summary: Mate pair library sequencing is an effective and economical method for detecting genomic structural variants and chromosomal abnormalities. Unfortunately, the mapping and alignment of mate pair read pairs to a reference genome is a challenging and <br>time consuming process for most NGS alignment programs. Large insert sizes, introduction of library preparation protocol artifacts (biotin junction reads, paired-end read contamination, chimeras, etc.), and presence of structural variant breakpoints within reads increases mapping and alignment complexity. We describe an algorithm that is up to 20 times faster and 25% more accurate than popular NGS alignment programs when processing mate pair sequencing. <br>Availability: http://bioinformaticstools.mayo.edu/research/bima/ <br>Contact: vasmatzis.george@mayo.edu</p><p>Address of the bookmark: <a href="http://bioinformatics.oxfordjournals.org/content/early/2014/02/12/bioinformatics.btu078.full.pdf" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/early/2014/02/12/bioinformatics.btu078.full.pdf</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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