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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/28141?offset=1140</link>
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	<description><![CDATA[]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27696/methylkit</guid>
	<pubDate>Fri, 03 Jun 2016 10:09:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27696/methylkit</link>
	<title><![CDATA[methylKit]]></title>
	<description><![CDATA[<p><em>methylKit</em> is an <a href="http://en.wikipedia.org/wiki/R_%28programming_language%29">R</a> package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from <a href="http://www.nature.com/nprot/journal/v6/n4/abs/nprot.2010.190.html">RRBS</a> and its variants, but also target-capture methods such as <a href="http://www.halogenomics.com/sureselect/methyl-seq">Agilent SureSelect methyl-seq</a>. In addition, methylKit can deal with base-pair resolution data for 5hmC obtained from Tab-seq or oxBS-seq. It can also handle whole-genome bisulfite sequencing data if proper input format is provided.</p><p>Address of the bookmark: <a href="https://github.com/al2na/methylKit" rel="nofollow">https://github.com/al2na/methylKit</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</guid>
	<pubDate>Mon, 04 Jul 2016 00:44:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28269/4dgenome</link>
	<title><![CDATA[4DGenome]]></title>
	<description><![CDATA[<p><span>Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies.&nbsp;</span><br><br><span>All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology.&nbsp;</span></p><p>Address of the bookmark: <a href="http://4dgenome.research.chop.edu/" rel="nofollow">http://4dgenome.research.chop.edu/</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/27827/guest-faculty-centre-for-bioinformatics-at-pondicherry-university</guid>
  <pubDate>Wed, 15 Jun 2016 03:44:31 -0500</pubDate>
  <link></link>
  <title><![CDATA[Guest Faculty Centre for Bioinformatics at Pondicherry University]]></title>
  <description><![CDATA[
<p>Guest Faculty Centre For Bioinformatics Jobs opportunity in Pondicherry University<br />Qualification : M.Phil. (with NET/SLET)/ M.Tech. / M.E. in Computer Science with a minimum of 55% of marks as per UGC norms.<br />Desirable : Ph.D and Teaching experience in Perl and Java programming.<br />Honorarium : Rs. 1,000/- per lecture (subject to a maximum of Rs. 25,000/- per month)<br />How to apply<br />Walk-in-Interview will be held on 29.06.2016 (Wednesday) at 2:30 P.M at the office of Centre for Bioinformatics, Pondicherry University, Puducherry — 605 014. Interested eligible candidates may attend the Walk-in-Interview along with all original certificates, self attested photocopies and testimonials with a copy of their bio-data. Candidates reporting after 2:30 P.M will not be entertained.</p>

<p>More at http://www.pondiuni.edu.in/news?quicktabs_2=5#quicktabs-2</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27847/anvio</guid>
	<pubDate>Thu, 16 Jun 2016 18:15:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27847/anvio</link>
	<title><![CDATA[Anvio]]></title>
	<description><![CDATA[<p>In a nutshell</p>
<p>Anvi&rsquo;o is an analysis and visualization platform for &lsquo;omics data.</p>
<p>Please find the methods paper here: https://peerj.com/articles/1319/</p>
<p>Anvi&rsquo;o would not have been possible without the help of many people who directly or indirectly contributed to its development. Here is the acknowledgements section of our methods paper</p>
<p><span>An analysis and visualization platform for 'omics data</span><span>&nbsp;</span><span><a href="http://merenlab.org/projects/anvio">http://merenlab.org/projects/anvio</a></span></p>
<p><span>Paper&nbsp;https://peerj.com/articles/1839/</span></p><p>Address of the bookmark: <a href="https://github.com/meren/anvio" rel="nofollow">https://github.com/meren/anvio</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27961/nearhgt</guid>
	<pubDate>Wed, 22 Jun 2016 05:41:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27961/nearhgt</link>
	<title><![CDATA[NearHGT]]></title>
	<description><![CDATA[<p>Horizontal gene transfer (HGT), the transfer of genetic material between organisms, is crucial for genetic innovation and the evolution of genome architecture. Existing HGT detection algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from ancestral (vertically derived) genes in its recipient genome. Detecting HGT between closely related species or strains is challenging, as the phylogenetic signal is usually weak and the nucleotide composition is normally nearly identical. Nevertheless, there is a great importance in detecting HGT between congeneric species or strains, especially in clinical microbiology, where understanding the emergence of new virulent and drug-resistant strains is crucial, and often time-sensitive.</p>
<p>We developed a novel, self-contained technique named&nbsp;<em>Near HGT</em>, based on the&nbsp;<em>synteny index</em>, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the&nbsp;<em>constant relative mutability</em>&nbsp;(CRM). Using CRM, the algorithm assigns a confidence score based on &ldquo;unusual&rdquo; sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three&nbsp;<em>E. coli</em>&nbsp;strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set.</p>
<p>When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain.</p>
<p><strong>Availability:</strong><span>&nbsp;The method is publicly available at</span><a href="http://research.haifa.ac.il/~ssagi/software/nearHGT.zip">http://research.haifa.ac.il/~ssagi/software/nearHGT.zip</a></p><p>Address of the bookmark: <a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004408" rel="nofollow">http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004408</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27973/wgsim</guid>
	<pubDate>Thu, 23 Jun 2016 07:26:49 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27973/wgsim</link>
	<title><![CDATA[WgSim]]></title>
	<description><![CDATA[<p>Reads simulator</p>
<p>Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.<br><br>Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.<br><br></p><p>Address of the bookmark: <a href="https://github.com/lh3/wgsim" rel="nofollow">https://github.com/lh3/wgsim</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28117/quin%E2%80%99s-web-server</guid>
	<pubDate>Mon, 27 Jun 2016 10:44:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28117/quin%E2%80%99s-web-server</link>
	<title><![CDATA[QuIN’s web server]]></title>
	<description><![CDATA[<p><span>Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.&nbsp;</span></p>
<p><strong>AVAILABILITY:</strong><span>&nbsp;QuIN&rsquo;s web server is available at&nbsp;</span><a href="http://quin.jax.org/">http://quin.jax.org</a><span>&nbsp;QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub:</span><a href="https://github.com/UcarLab/QuIN/">https://github.com/UcarLab/QuIN/</a><span>.</span></p><p>Address of the bookmark: <a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004809" rel="nofollow">http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004809</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28303/fancy-oneliner-for-bioinformatics</guid>
	<pubDate>Thu, 07 Jul 2016 12:05:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28303/fancy-oneliner-for-bioinformatics</link>
	<title><![CDATA[Fancy Oneliner for Bioinformatics !!]]></title>
	<description><![CDATA[<p><span>This webpage lists some of the one-liners that we frequently use in metagenomic analyses. You can click on the following links to browse through different topics. You can copy/paste the commands as they are in your terminal screen, provided you follow the same naming conventions and folder structures as we have. We are sharing these codes with the intention that if they are useful and help you in your analyses, then we will be appropriately credited as considerable effort has been put into devising them.</span></p><p>Address of the bookmark: <a href="http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/oneliners.html" rel="nofollow">http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/oneliners.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/28439/binc-exam-preparation-tips</guid>
	<pubDate>Fri, 15 Jul 2016 20:53:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/28439/binc-exam-preparation-tips</link>
	<title><![CDATA[BINC exam preparation tips !!]]></title>
	<description><![CDATA[<p>How to prepare for <span>BINC (BioInformatics National Certification)</span>&nbsp;exam? What are the expected questions?</p><p>These are just a scant few of the common questions asked by bioinformatics students as they ready themselves for the next exam sitting. If you read the entire <a href="http://bioinformaticsonline.com/bookmarks/view/2334/binc-bioinformatics-national-certification-website-address">Syllabus</a> (and I know that everyone does), you will see a section devoted to study and exam techniques. The section discusses such broad concepts as motivation, scheduling, and retention. Upon reading this section, however, I find the "hints" to be too general. Much of the advice boils down to read, study, understand, and memorize the material. The techniques mentioned apply to everyone and thus the overall advice ends up as a broad overview of the learning process.</p><p>The idea behind this article is to give students ideas on different approaches and techniques in the preparation for exams. By providing various ways to prepare for the exam process, fascinated readers may gain some additional insight to help complement their studying methodology. There are, of course, many common themes expressed in this small empirical sample of students' study habits. The idea of note cards, memorization, and problem solving are frequently mentioned by all students. No matter what technique a candidate uses, it always takes a significant amount of time and personal resources to successfully complete the examination process.</p><p>1 Explain it in your own word</p><p>Your teacher or lecturer can explain something to you, you can learn it from a text book, your friends can study with you, even your own notes can explain it to you but all these explanations are of little use if, by the end, you can&rsquo;t explain what you have learned to yourself. The BINC exam looking for ability to write and explain the concept in your own word. You, therefore, need to illustrate in an exam to get top exam results, then you won&rsquo;t be happy with your end exam result. So don&rsquo;t just memorise and tick off the list &ndash; make sure you understand your theory.</p><p>2 Be an examiner yourself</p><p>Of course, depending on what you&rsquo;re studying, it may be quite difficult to get into a position to understand a concept, theory or other information you need to learn. Ask &lsquo;stupid&rsquo; question to yourself and train yourself for the worst! Embrace your curiosity, for as William Arthur Ward said: &ldquo;Curiosity is the wick in the candle of learning.&rdquo; Doing so will allow you to fill in the blanks and better prepare you for exams.</p><p>3 Quiz yourself</p><p>Once you feel you understand topic, it is important to test yourself regularly. Try yourself to replicate exam conditions as much as possible: turn your phone off, don&rsquo;t talk, time yourself etc. You can set yourself a study quiz or practice exam questions and, so long as you approach it with the right mindset, you can get a very good idea of how much you know. You gain a greater insight into where you stand in relation to what you&rsquo;ve studied so far.</p><p>4 Online study</p><p>Keeping the fact that, bioinformatics is ever changing subject, you might need to update yourself on timely basis. Don&rsquo;t feel obliged to just sit in front of a book with a highlighter; there are many different ways to improve your bioinformatics knowledge. Login and check almost all web servers and keep yourself updated, like how many genomes sequenced, sizes, techniques used, software names etc.</p><p>5 Study plan</p><p>In order to achieve exam success, you need to know what you want to achieve and focus on. That&rsquo;s why it is extremely important to set your Study Goals now and outline to yourself what you need to do. With your study goals in mind, you properly need to attention all subjects. It should be broad enough to allow you to add and change aspects but concise enough so you know you&rsquo;re covering each subject/topic as best you can at this point.</p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28554/megan6</guid>
	<pubDate>Mon, 25 Jul 2016 05:45:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28554/megan6</link>
	<title><![CDATA[MEGAN6]]></title>
	<description><![CDATA[<p>Microbiome analysis using a single application</p>
<p>MEGAN6 is a comprehensive toolbox for interactively analyzing microbiome data. All the interactive tools you need in one application.</p>
<ul>
<li>Taxonomic analysis using the NCBI taxonomy or a customized taxonomy such as SILVA</li>
<li>Functional analysis using InterPro2GO, SEED, eggNOG or KEGG</li>
<li>Bar charts, word clouds, Voronoi tree maps and many other charts</li>
<li>PCoA, clustering and networks</li>
<li>Supports metadata</li>
<li>MEGAN parses many different types of input</li>
</ul>
<p>Why use MEGAN6?</p>
<div>&nbsp;The software is:</div>
<div><ol>
<li>Easy to use. MEGAN6 is a single application and all features are available through menus, toolbars and graphics. No scripting skills required.</li>
<li>Powerful. MEGAN6 allows you to work with hundreds of samples containing&nbsp;hundreds of millions of sequencing reads. Blast-like analysis can be performed using DIAMOND.</li>
<li>Comprehensive. MEGAN6 offers a large range of analysis tools, and is under active development.</li>
</ol></div><p>Address of the bookmark: <a href="https://ab.inf.uni-tuebingen.de/software/megan6" rel="nofollow">https://ab.inf.uni-tuebingen.de/software/megan6</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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