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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/22793?offset=30</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/22571/pattern-matching-problem-solution-with-perl</guid>
	<pubDate>Tue, 09 Jun 2015 23:58:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/22571/pattern-matching-problem-solution-with-perl</link>
	<title><![CDATA[Pattern Matching Problem Solution with Perl]]></title>
	<description><![CDATA[<p>Problem at http://rosalind.info/problems/1c/</p><p>#Find all occurrences of a pattern in a string.<br />#Given: Strings Pattern and Genome.<br />#Return: All starting positions in Genome where Pattern appears as a substring. Use 0-based indexing.<br /><br />use strict;<br />use warnings;<br /><br />my $string="GATATATGCATATACTT";<br />my $subStr="ATAT";<br />my $kmer=length($subStr);<br /><br />kmerMatch ($string, $subStr, $kmer);<br /><br />sub kmerMatch { #Check the exact matching kmers with sliding window<br />my ($string, $myStr, $kmer)=@_;<br />for (my $aa=0; $aa&lt;=(length($string)-$kmer); $aa++) {<br />&nbsp;&nbsp;&nbsp; my $myWin=substr&nbsp; $string, $aa,$kmer;<br />&nbsp;&nbsp;&nbsp; if ($myWin eq $myStr) {<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; #print "$myWin eq $myStr\n";<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; print $aa;<br />&nbsp;&nbsp;&nbsp; }<br />}<br />}</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/poll/view/22906/at-what-age-did-you-gain-passion-in-bioinformatics</guid>
	<pubDate>Tue, 23 Jun 2015 10:39:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/poll/view/22906/at-what-age-did-you-gain-passion-in-bioinformatics</link>
	<title><![CDATA[At what age did you gain passion in Bioinformatics?]]></title>
	<description><![CDATA[<p>Most of the bioinformatician were biologist ( yeah ... not all ;), and at later stage they gain a passion in Bioinformatics and learn it. When did you get inclined towards computational analysis of biological data?</p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/23892/bioinformatics-made-easy-search-bioinformatics-tools-and-run-genomic-analysis-in-the-cloud</guid>
	<pubDate>Thu, 20 Aug 2015 02:21:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/23892/bioinformatics-made-easy-search-bioinformatics-tools-and-run-genomic-analysis-in-the-cloud</link>
	<title><![CDATA[Bioinformatics Made Easy Search: Bioinformatics tools and run genomic analysis in the cloud]]></title>
	<description><![CDATA[<p>InsideDNA makes hundreds of bioinformatics tools immediately available to run via an easy-to-use web interface and allows an accurate search across all functions, tools and pipelines.</p>
<p>With InsideDNA, you can upload and store your own genomic/genetic datasets in a limitless cloud space, and instantly analyze it with a powerful compute instance, without any tool installation or set up hassle.</p>
<p>More at https://insidedna.me/</p><p>Address of the bookmark: <a href="https://insidedna.me/" rel="nofollow">https://insidedna.me/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/24264/cancer-research-database</guid>
	<pubDate>Tue, 01 Sep 2015 17:36:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/24264/cancer-research-database</link>
	<title><![CDATA[Cancer research database]]></title>
	<description><![CDATA[<p>Researchers in Andhra Pradesh have developed a database to identify genes that are common in tumours to provide their colleagues with easy access to insights into the genetic alterations in cancer.<br /> &nbsp;<br /> The database, hosted at the Sri Venkateswara University (SVU) in Tirupati, will integrate information on cancer genes and markers with experimental data.<br /> &nbsp;<br /> The <a href="http://cgmd.in/" target="_blank">Cancer Gene Markers Database</a> (CGMD) is meant to help scientists better understand tumour genes and markers at a molecular level by combining data with literature on treatment regimen and recent advances in cancer therapy.<br /> <br /> The database is free to access, and already includes 309 genes and 206 markers that correspond to 40 different human cancers. Accompanying literature comes from databases such as the United States&rsquo; <a href="http://www.ncbi.nlm.nih.gov/" target="_blank">National Center for Biotechnology Information</a> and the <a href="http://www.genome.jp/kegg/" target="_blank">Kyoto Encyclopedia of Genes and Genomes</a>. It also includes experimental data from <a href="http://www.ncbi.nlm.nih.gov/pubmed" target="_blank">PubMed</a>.<br /> <br /> In a paper <a href="http://dx.doi.org/10.1038/srep12035" target="_blank">published</a> last month in <em>Nature Scientific Reports</em>, the researchers from SVU&rsquo;s department of animal biotechnology, describes the need for a database for different genes and markers along with their molecular characteristics and pathway associations.</p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26322/liftover</guid>
	<pubDate>Mon, 08 Feb 2016 15:45:03 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26322/liftover</link>
	<title><![CDATA[liftover]]></title>
	<description><![CDATA[<p><span>Convenient conversions between genome assemblie.&nbsp;The liftover package makes it easy to remap genomic coordinates to a different genome assembly. </span></p>
<p><span>More at https://github.com/aaronwolen/liftover<br></span></p>
<p><span>https://www.bioconductor.org/help/workflows/liftOver/</span></p><p>Address of the bookmark: <a href="https://github.com/aaronwolen/liftover" rel="nofollow">https://github.com/aaronwolen/liftover</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
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	<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/opportunity/view/11528/post-doctoral-research-assistant-in-genetics</guid>
  <pubDate>Thu, 05 Jun 2014 16:01:39 -0500</pubDate>
  <link></link>
  <title><![CDATA[Post-doctoral Research Assistant in Genetics]]></title>
  <description><![CDATA[
<p>Post-doctoral Research Assistant in Genetics<br />Camden, North London<br />£31.1K per annum inclusive of London Weighting</p>

<p>This is a fixed term post for 36 months.</p>

<p>We wish to recruit a highly motivated, postdoctoral scientist to carry out a BBSRC funded project in the laboratory of Dr. Denis Larkin. The project is focused on developing and applying new algorithms to study genome and chromosome evolution in birds, mammals and other vertebrate species using whole-genome sequences and existing algorithms. The post holder will use cutting edge computational and laboratory approaches to generate chromosomal assemblies for sequenced genomes, study chromosomal structures and genome differences between bird and other vertebrate species in attempt to identify species- and clade-specific genome signatures.</p>

<p>Applicants must have a Ph.D. and a track record of success, as indicated by first-author publications in international journals. They must possess excellent organisation skills and be capable of individual initiative and of interacting as part of a team. Applicants with extensive practical experience in bioinformatics or computer science, programming, visualization, handling of large data sets, high-performance computing are encouraged to apply. The post will involve collaboration with a wide range of academic partners both within the UK, EU and worldwide. In addition to leading their own project the post holder will have opportunities to contribute to multiple international genome initiatives.</p>

<p>Experience in programming, bioinformatics and comparative genome analysis is essential. Applicants should have a minimum of a degree and preferably a higher degree in a relevant subject.</p>

<p>The Royal Veterinary College has the largest range of veterinary, para-veterinary and animal science undergraduate and postgraduate courses of any veterinary school in the world and is one of the largest veterinary schools in Europe.</p>

<p>Prospective applicants are encouraged to contact Dr. Denis Larkin, Comparative Biomedical Sciences Department on +442071211906 or email: dlarkin@rvc.ac.uk</p>

<p>We offer a generous reward package.</p>

<p>For further information and to apply on-line please visit our website: www.rvc.ac.uk<br />Job reference CBS-0025-14A</p>

<p>Closing date: 4 July 2014<br />Interviews are likely to be held in July 2014</p>

<p>We promote equality of opportunity and diversity within the workplace and welcome applications from all sections of the community.</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/8943/roth-lab</guid>
  <pubDate>Tue, 11 Mar 2014 17:43:45 -0500</pubDate>
  <link></link>
  <title><![CDATA[Roth Lab]]></title>
  <description><![CDATA[
<p>The Roth Lab seeks insight into biological systems through genome- and proteome-scale experimentation and analysis.</p>

<p>Current computational interests:</p>

<p>Systematic analysis of genetic epistasis to identify redundant or compensatory systems and to reveal order of action in genetic pathways.<br />Using knockout, knockdown, or overexpression, or other perturbation experiments in combinations of genes in S. cerevisiae, C. elegans or mouse.<br />Using genome-scale genotyping of natural polymorphisms in S. cerevisiae and human populations.<br />Alternative splicing and its relationship to protein interaction networks.<br />Integrating large-scale studies including phenotype, genetic epistasis, protein-protein and transcription-regulatory interactions and sequence patterns to quantitatively assign function to genes and guide experimentation.</p>

<p>More at http://llama.mshri.on.ca/index.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/13842/swabs-to-genomes-a-comprehensive-workflow</guid>
	<pubDate>Sun, 10 Aug 2014 03:01:21 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/13842/swabs-to-genomes-a-comprehensive-workflow</link>
	<title><![CDATA[Swabs to Genomes: A Comprehensive Workflow]]></title>
	<description><![CDATA[<p>The sequencing, assembly, and basic analysis of microbial genomes, once a painstaking and expensive undertaking, has become almost trivial for research labs with access to standard molecular biology and computational tools. However, there are a wide variety of options available for DNA library preparation and sequencing, and inexperience with bioinformatics can pose a significant barrier to entry for many who may be interested in microbial genomics. The objective of the present study was to design, test, troubleshoot, and publish a simple, comprehensive workflow from the collection of an environmental sample (a swab) to a published microbial genome; empowering even a lab or classroom with limited resources and bioinformatics experience to perform it.</p><p>Address of the bookmark: <a href="https://peerj.com/preprints/453.pdf" rel="nofollow">https://peerj.com/preprints/453.pdf</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/19636/google-genomics</guid>
	<pubDate>Thu, 18 Dec 2014 11:05:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/19636/google-genomics</link>
	<title><![CDATA[Google Genomics]]></title>
	<description><![CDATA[<ul>
<li>
<p><strong>Explore genetic variation interactively.</strong> Compare entire cohorts in seconds with SQL-like queries. Compute transition/transversion ratios, genome-wide association, allelic frequency and more.</p>
</li>
<li>
<p><strong>Process big genomic data easily.</strong> Run batch analyses like principal component analysis and Hardy-Weinberg equilibrium on as many samples as you like, in minutes or hours, with just a little code.</p>
</li>
<li>
<p><strong>Use Google's infrastructure and big data expertise.</strong> Store one genome or a million using Google Genomics and take advantage of the same infrastructure that powers Search, Maps, YouTube, Gmail and Drive.</p>
</li>
<li>
<p><strong>Support emerging global standards.</strong> Google Genomics is implementing the API defined by the Global Alliance for Genomics and Health for visualization, analysis and more. Compliant software can access Google Genomics, local servers, or any other implementation.</p>
</li>
</ul><p>Address of the bookmark: <a href="https://cloud.google.com/genomics/" rel="nofollow">https://cloud.google.com/genomics/</a></p>]]></description>
	<dc:creator>Tenzin Paul</dc:creator>
</item>

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