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	<link>https://bioinformaticsonline.com/related/19555?offset=400</link>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26827/kamaleshwar-singh-lab</guid>
  <pubDate>Fri, 25 Mar 2016 10:46:49 -0500</pubDate>
  <link></link>
  <title><![CDATA[Kamaleshwar Singh Lab]]></title>
  <description><![CDATA[
<p>The focus of Dr. Singh’s research and teaching is on the molecular mechanistic basis for environmental carcinogen-induced genetic (DNA damage) and epigenetic changes, and susceptibility to human cancer development</p>

<p>More at http://www.tiehh.ttu.edu/dr.-kamaleshwar-singh.html</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/25406/assistant-professor-bioinformatics-at-alagappa-university</guid>
  <pubDate>Thu, 03 Dec 2015 23:30:43 -0600</pubDate>
  <link></link>
  <title><![CDATA[ASSISTANT PROFESSOR Bioinformatics at ALAGAPPA UNIVERSITY]]></title>
  <description><![CDATA[
<p>ALAGAPPA UNIVERSITY<br /> Recruitment and Sarkari Naukri for the Post of ASSISTANT PROFESSOR Bioinformatics<br />Job Description UGC scale of pay is applicable. For eligibility qualifications and other norms, please refer to the ?Instructions to the Candidates? available with the application forms which can be had on payment of Rs.520/- inclusive of Rs.20/- for postage. For SC/ST, Rs.320/- inclusive of Rs.20/- for postage on enclosure of a copy of the community certificate. Payment is to be made by means of Demand Draft drawn on any nationalized bank in favour of ?The Registrar, Alagappa University? payable at Karaikudi. Candidates can also download the application form and instructions to the candidates from httpwww.alagappauniversity.ac Filled-in application should reach on or before 19.10.2015<br />Salary for Job : Rs.15600-39100+AGP Rs.6000<br />Education : Good academic record with at least 55 marks (or an equivalent grade in a point scale wherever grading system is followed) at the Masters Degree level in a relevant subject from an Indian University, or an equivalent degree from an accredited foreign university. Besides fulfilling the above qualifications, the candidate must have cleared the National Eligibility Test (NET) conducted by the UGC, CSIR or similar test accredited by the UGC like SLET/SET.Notwithstanding anything contained in sub-clauses (i) and (ii) above, the candidates, who are, or have been awarded a Ph.D. degree in accordance with the University Grants Commission (Minimum Standards and Procedure for Award of Ph.D. Degree) Regulations 2009, shall be exempted from the requirement of the minimum eligibility condition of NET/SLET/SET for recruitment and appointment of Assistant Professor or equivalent positions in Universities/Colleges/Institutions.<br />Number of Vacancies : 02<br />Naukri Location : Other City(s) in Tamil Nadu<br />Address : KARAIKUDI ? 630 003<br />Last Date to Apply : 2015-12-04<br />Apply Process : written test/interview</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26426/genome-browser-gbrowse</guid>
	<pubDate>Fri, 19 Feb 2016 09:22:43 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26426/genome-browser-gbrowse</link>
	<title><![CDATA[Genome Browser : GBrowse]]></title>
	<description><![CDATA[<p>Generic Genome Browser Version 2: A Tutorial for Administrators</p>
<p>This is an extensive tutorial to take you through the main features and gotchas of configuring GBrowse as a server. This tutorial assumes that you have successfully set up Perl, GD, BioPerl and the other GBrowse dependencies. If you haven't, please see the <a href="http://gmod.org/wiki/GBrowse_2.0_HOWTO">GBrowse HOWTO</a> During most of the tutorial, we will be using the "in-memory" GBrowse database (no relational database required!) Later we will show how to set up a genome size database using the berkeleydb and MySQL adaptors.</p>
<p>More at http://elp.ucdavis.edu/tutorial/tutorial.html</p><p>Address of the bookmark: <a href="http://elp.ucdavis.edu/tutorial/tutorial.html" rel="nofollow">http://elp.ucdavis.edu/tutorial/tutorial.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26539/scikit-learn</guid>
	<pubDate>Mon, 29 Feb 2016 17:39:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26539/scikit-learn</link>
	<title><![CDATA[scikit-learn]]></title>
	<description><![CDATA[<p>Machine Learning in Python</p>
<p>Simple and efficient tools for data mining and data analysis<br> Accessible to everybody, and reusable in various contexts<br> Built on NumPy, SciPy, and matplotlib<br> Open source, commercially usable - BSD license</p>
<p>More at&nbsp;http://scikit-learn.org/stable/index.html</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://scikit-learn.org/stable/auto_examples/index.html" rel="nofollow">http://scikit-learn.org/stable/auto_examples/index.html</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26999/discovar</guid>
	<pubDate>Mon, 18 Apr 2016 11:59:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26999/discovar</link>
	<title><![CDATA[DISCOVAR]]></title>
	<description><![CDATA[<p><strong>DISCOVAR</strong> is a new variant caller and <strong>DISCOVAR <em>de novo</em></strong> a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or longer &mdash; produced on MiSeq or HiSeq 2500 &mdash; and from a single PCR-free library. These data enable a level of completeness and continuity that was not previously possible.</p>
<p><strong>DISCOVAR</strong> can call variants on a region by region basis, potentially tiling an entire large genome. DISCOVAR variant calling is under active development and transitioning to VCF.</p>
<p><strong>DISCOVAR <em>de novo</em></strong> can generate <em>de novo</em> assemblies for both large and small genomes. It currently does not call variants.</p>
<p>More at https://www.broadinstitute.org/software/discovar/blog/?page_id=14</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/software/discovar/blog/" rel="nofollow">https://www.broadinstitute.org/software/discovar/blog/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27078/homer-software-for-motif-discovery-and-next-gen-sequencing-analysis</guid>
	<pubDate>Tue, 26 Apr 2016 03:48:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27078/homer-software-for-motif-discovery-and-next-gen-sequencing-analysis</link>
	<title><![CDATA[HOMER:  Software for motif discovery and next-gen sequencing analysis]]></title>
	<description><![CDATA[<p><span>This tutorial covers topics independently of HOMER, and represents knowledge which is important to know before diving head first into more advanced analysis tools such as HOMER.</span></p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/computerSetup.html">Setting up your computing environment</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/retrieveFiles.html">Retrieving and storing sequencing files</a>&nbsp;(your own data or from public sources)</li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/fastqFiles.html">Checking sequence quality, trimming, general sequence manipulation</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/mapping.html">Mapping reads to a reference genome</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/samfiles.html">Manipulating SAM/BAM alignment files</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/genomeBrowsers.html">Visualizing data in a genome browser</a></li>
</ol>
<p><br>RNA-Seq</p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/rnaseqCufflinks.html">De novo transcript discovery and differential analysis with Cufflinks</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/rnaseqR.html">Differential expression analysis with R/Bioconductor</a></li>
<li><a href="http://homer.salk.edu/homer/basicTutorial/clustering.html">Clustering of large expression datasets (microarray or RNA-Seq)</a></li>
</ol>
<p><br><span>Microarray</span></p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/affymetrix.html">Basic analysis of Affymetrix Gene Expression Arrays using R/Bioconductor</a></li>
</ol>
<p><span>General Tips for Data Analysis</span></p>
<ol>
<li><a href="http://homer.salk.edu/homer/basicTutorial/excelTips.html">Excel workarounds, adding gene annotation, X-Y plots tips, etc.</a></li>
</ol><p>Address of the bookmark: <a href="http://homer.salk.edu/homer/basicTutorial/" rel="nofollow">http://homer.salk.edu/homer/basicTutorial/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27104/gatb-genome-analysis-toolbox-with-de-bruijn-graph</guid>
	<pubDate>Thu, 28 Apr 2016 11:16:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27104/gatb-genome-analysis-toolbox-with-de-bruijn-graph</link>
	<title><![CDATA[GATB : Genome Analysis Toolbox with de-Bruijn graph]]></title>
	<description><![CDATA[<p>The&nbsp;<strong><strong>Genome Analysis Toolbox with de-Bruijn graph</strong> (GATB)</strong> provides a set of <a href="https://gatb.inria.fr/gatb-global-architecture/">highly efficient algorithms to analyse NGS data sets</a>. These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (<em>e.g.</em> metagenomes).</p>
<p>More at https://gatb.inria.fr/</p><p>Address of the bookmark: <a href="https://gatb.inria.fr/" rel="nofollow">https://gatb.inria.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27331/andi</guid>
	<pubDate>Fri, 13 May 2016 05:16:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27331/andi</link>
	<title><![CDATA[Andi]]></title>
	<description><![CDATA[<p>This is the <code>andi</code> program for estimating the evolutionary distance between closely related genomes. These distances can be used to rapidly infer phylogenies for big sets of genomes. Because <code>andi</code> does not compute full alignments, it is so efficient that it scales even up to thousands of bacterial genomes.</p>
<p>This readme covers all necessary instructions for the impatient to get <code>andi</code> up and running. For extensive instructions please consult the <a href="https://github.com/EvolBioInf/andi/blob/master/andi-manual.pdf">manual</a>.</p>
<p>More at https://github.com/evolbioinf/andi/</p><p>Address of the bookmark: <a href="http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27477/cytoscape</guid>
	<pubDate>Mon, 23 May 2016 02:32:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27477/cytoscape</link>
	<title><![CDATA[Cytoscape]]></title>
	<description><![CDATA[<p>Cytoscape is an <a href="http://www.cytoscape.org/download.php">open source</a> software platform for visualizing complex networks and integrating these with any type of attribute data. A lot of <a href="http://apps.cytoscape.org/"><em>Apps</em></a> are available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web.</p><p>Address of the bookmark: <a href="http://www.cytoscape.org/" rel="nofollow">http://www.cytoscape.org/</a></p>]]></description>
	<dc:creator>Anjana</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27818/gaemr</guid>
	<pubDate>Tue, 14 Jun 2016 06:18:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27818/gaemr</link>
	<title><![CDATA[GAEMR]]></title>
	<description><![CDATA[<p>The&nbsp;<span>G</span>enome&nbsp;<span>A</span>ssembly&nbsp;<span>E</span>valuation&nbsp;<span>M</span>etrics and&nbsp;<span>R</span>eporting (GAEMR) package is an assembly analysis framework composed a number of integrated modules. These modules can be executed as a single program to generate a complete analysis report, or executed individually to generate specific charts and tables. GAEMR standardizes input by converting a variety of read types to Binary Alignment Map (BAM) format, allowing a single input format to be entered into GAEMR&rsquo;s analysis pipeline, hence enabling the generation of standard reports.</p>
<p>GAEMR&rsquo;s analysis philosophy is centered on contiguity, correctness, and completeness -- how many pieces in an assembly composed of, how well those pieces accurately represent the genome sequenced, and how much of that genome is represented by those pieces. By performing over twenty different analyses based on these principles, GAEMR gives a clear picture of the condition of a genome assembly.&nbsp;</p><p>Address of the bookmark: <a href="https://www.broadinstitute.org/software/gaemr/" rel="nofollow">https://www.broadinstitute.org/software/gaemr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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