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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44516?offset=1040</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29586/eforgev12</guid>
	<pubDate>Fri, 28 Oct 2016 09:06:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29586/eforgev12</link>
	<title><![CDATA[eFORGE.v1.2]]></title>
	<description><![CDATA[<p><span>The eFORGE tool provides a method to view the tissue specific regulatory component of a set of EWAS DMPs. eFORGE analysis takes a set of DMPs, such as those hits above genome-wide significance threshold in an EWAS study, and analyses whether there is enrichment for overlap of putative functional elements compared to matched background DMPs. It assesses enrichment on a per cell type basis, since functional elements are differentially active in different cell types, and hence can expose tissue-specific signals of enrichment for the given test DMP set. This can reveal the sites of action underlying the EWAS signal, and provide confirmation of the validity of the EWAS where a tissue-specific mechanism is known or expected for the phenotype. Conversely unknown tissue involvements can also be revealed.</span></p><p>Address of the bookmark: <a href="http://eforge.cs.ucl.ac.uk/eFORGE.v1.2/?documentation" rel="nofollow">http://eforge.cs.ucl.ac.uk/eFORGE.v1.2/?documentation</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<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" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/29679/comparative-genomics-educational-material-and-papers-bookmarks</guid>
	<pubDate>Wed, 09 Nov 2016 16:23:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/29679/comparative-genomics-educational-material-and-papers-bookmarks</link>
	<title><![CDATA[Comparative genomics educational material and papers bookmarks]]></title>
	<description><![CDATA[<p><span>Alignment of the porcine genome against seven other mammalian genomes (</span><a href="http://www.nature.com/nature/journal/v491/n7424/full/nature11622.html#supplementary-information">Supplementary Information</a><span>) identified homologous synteny blocks (HSBs). Using porcine HSBs and stringent filtering criteria, 192 pig-specific evolutionary breakpoint regions (EBRs) were located. The number of porcine EBRs </span><span>is comparable to the number of bovine-lineage-specific EBRs (100) reported earlier using a slightly lower resolution (500</span><span><span>&thinsp;</span></span><span>kilobases (kb)), indicating that both lineages evolved with an average rate of ~2.1 large-scale rearrangements per million years after the divergence from a common cetartiodactyl ancestor ~60</span><span><span>&thinsp;</span></span><span>Myr ago</span><sup><a href="http://www.nature.com/nature/journal/v491/n7424/full/nature11622.html#ref2" title="Meredith, R. W. et al. Impacts of the Cretaceous Terrestrial Revolution and KPg extinction on mammal diversification. Science 334, 521-524 (2011)">2</a></sup><span>. This rate compares to ~1.9 rearrangements per million years within the primate lineage (</span><a href="http://www.nature.com/nature/journal/v491/n7424/full/nature11622.html#supplementary-information">Supplementary Table 11</a><span>). A total of 20 and 18 cetartiodactyl EBRs (shared by pigs and cattle) were detected using the pig and human genomes as a reference, respectively.</span></p><p>Address of the bookmark: <a href="http://www.nature.com/nature/journal/v491/n7424/abs/nature11622.html" rel="nofollow">http://www.nature.com/nature/journal/v491/n7424/abs/nature11622.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <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/29915/professor-all-levels-in-bioinformatics-and-computational-biology</guid>
  <pubDate>Tue, 22 Nov 2016 05:43:38 -0600</pubDate>
  <link></link>
  <title><![CDATA[Professor (all levels) in Bioinformatics and Computational Biology]]></title>
  <description><![CDATA[
<p>King Abdullah University of Science and Technology (KAUST) (kaust.edu.sa) is seeking a highly motivated and skilled faculty member for the Bioinformatics track whose research focuses on development of methods and tools for Bioinformatics and Computational Biology.<br />KAUST is an international, graduate-level research university dedicated to advancing science and technology through interdisciplinary research, education, and innovation. Located on the shores of the Red Sea in Saudi Arabia, KAUST offers superb research facilities, generous assured research funding, and internationally competitive salaries, attracting top international faculty, scientists, engineers, and students to conduct fundamental and goal-oriented research to address the world’s pressing scientific and technological challenges in the areas of food, water, energy, and the environment.<br />The successful applicant is expected to develop world-leading research in domain of bioinformatics/computational biology with focus on development of novel computational approaches for efficient and accurate methods of analyzing biological phenomena at molecular level. The faculty member will be part of the Computational Bioscience Research Center (CBRC) within the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division. The position will remain open until filled.<br /> <br />Requirements:<br /> <br />PhD or equivalent in a Computer Science, Mathematics or Engineering discipline. Candidates should be well-established within the research field relevant to the position grade. They should demonstrate original research and experience at the highest international level.<br /> <br />Responsibilities and tasks:<br /> <br />Research competence in the following areas is preferred:<br />Analysis of next generation sequencing (NGS) and other ‘omics’ data (e.g. CAGE, ChIP-Seq, DHS, RNA-Seq, Ribo-Seq, proteomic, metabolic and NMR spectra, etc.).<br />Signaling, regulatory and metabolic pathways analysis.<br />Development of tools (web-based and standalone) suited for efficient computational biology/bioinformatics.<br /> <br /> <br />Visit cemse.kaust.edu.sa to apply.</p>
]]></description>
</item>
<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>
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