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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/31371?offset=960</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/3917/the-story-of-you-encode-and-the-human-genome</guid>
	<pubDate>Sat, 24 Aug 2013 18:49:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/3917/the-story-of-you-encode-and-the-human-genome</link>
	<title><![CDATA[The Story of You: ENCODE and the human genome]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/TwXXgEz9o4w" frameborder="0" allowfullscreen></iframe><p>Ever since a monk called Mendel started breeding pea plants we've been learning about our genomes. In 1953, Watson, Crick and Franklin described the structure of the molecule that makes up our genomes: the DNA double helix. Then, in 2001, scientists wrote down the entire 3-billion letter code contained in the average human genome. Now they're trying to interpret that code; to work out how it's used to make different types of cells and different people. The ENCODE project, as it's called, is the latest chapter in the story of you. To read the ENCODE research papers and more, visit http://www.nature.com/ENCODE</p>]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/8317/new-version-of-modeller-913</guid>
	<pubDate>Thu, 13 Feb 2014 09:07:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/8317/new-version-of-modeller-913</link>
	<title><![CDATA[New version of Modeller, 9.13]]></title>
	<description><![CDATA[<p>The new version of Modeller, 9.13, is now available for download! Please see the download page at <a href="http://www.facebook.com/l.php?u=http%3A%2F%2Fsalilab.org%2Fmodeller%2F&amp;h=mAQG5wo_Z&amp;enc=AZOoq2B7BxT95AT3Mw3za3VlbmRFke43YMI5vAjCAbBlIcf3bptn8pmFC1Idxrssy98117S03IgdcNmEWcQBi9bmi8Or_ut1D1yybt1ZonvPoCT3_LOglcYV7o6bEaa442_6LhbjefEaelkq0aq6dl0w&amp;s=1" target="_blank">http://salilab.org/modeller/</a> for more information.</p><p><img src="http://salilab.org/modeller/gifs/modeller.jpg" alt="image" width="848" height="272" style="border: 0px; border: 0px;"><br /> <br /> If you have a license key for Modeller 8 or 9, there is no need to reregister for Modeller 9.13 - the same license key will work. (It won't <span>do any harm to reregister if you want to, though!)<br /> <br /> 9.13 is primarily a bugfix release relative to the last public release(9.12). Major user-visible changes include:<br /> <br /> # Modeller now includes a variety of SOAP (statistically optimized atomic potential) scores for assessing proteins, loops, and interfaces.<br /> <br /> # The Lennard-Jones interaction energy is now artificially truncated at very short distance; this makes simulations with poor starting conditions much less likely to 'blow up'.<br /> <br /> # model.get_insertions(), model.get_deletions() and model.loops() now have an include_termini option; if False, residue ranges that include chain termini are excluded from the output.<br /> <br /> See the Modeller manual for a full change log: <a href="http://salilab.org/modeller/9.13/manual/node39.html" target="_blank">http://salilab.org/modeller/9.13/manual/node39.html</a><br /> <br /> If you encounter bugs in Modeller 9.13, please see <a href="http://salilab.org/modeller/9.13/manual/node10.html" target="_blank">http://salilab.org/modeller/9.13/manual/node10.html</a> for information on how to report them.</span></p><p><span>Reference:</span></p><p><span>http://salilab.org/modeller/</span></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4960/genome-epigenome-new-understanding-of-the-pathogens-in-your-food</guid>
	<pubDate>Fri, 27 Sep 2013 11:30:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4960/genome-epigenome-new-understanding-of-the-pathogens-in-your-food</link>
	<title><![CDATA[Genome + Epigenome = New Understanding of the Pathogens in Your Food]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/hGtHs_C1BFA" frameborder="0" allowfullscreen></iframe>UC Davis's Bart Weimer describes foodborne pathogens and their proclivity for rapid genome rearrangement. The 100K Pathogen Genome Project he leads is using PacBio long-read sequencing to close genomes and analyze methylation; Weimer reports that his team has already discovered new epigenetic modifications in Salmonella and Listeria with the technology. www.pacb.com/microbe]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/fun/view/8509/the-best-bioinformatics-computational-biology-quotes</guid>
	<pubDate>Wed, 26 Feb 2014 17:50:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/fun/view/8509/the-best-bioinformatics-computational-biology-quotes</link>
	<title><![CDATA[The Best Bioinformatics / Computational Biology Quotes]]></title>
	<description><![CDATA[<p><img src="http://bioinformaticsonline.com/mod//photo/hahaha.png" style="border: 0; border: 0px;" alt="image"></p><p>Bioinformatician are not anti-social; We are just genome friendly.</p><p>Bioinformatician would love to change the biological world, but they won't give us the genetic code :P</p><p>If at first you don't succeed; call it version 1.0</p><p>The glass is neither half-full nor half-empty: it's actually have several genomes.</p><p>I'm BioGeek.</p><p>Fedup with LIPS, try God script.</p><p>Idiot, Go ahead, make my data!</p><p>Thank god, my genome just compiled.</p><p>Error message: "Out of space on genome drive:"</p><p>Shut up mobile elements, or i'll flush you out.</p><p>Never underestimate the internet bandwidth, u gotta incomplete.</p><p>Applied fuzzy logic to understand God's logic?</p><p>Warning! Overflow, delete chromosome !</p><p>Be nice to the BioGeek, for all you know they might be the next curator!</p><p>Beware of computational biologist they screw genes and protein.</p><p>Warning! Your genome is full of garbage, delete it !</p><p>Bad or missing mouse genome. Spank the cat? (Y/N)</p><p>Genome make very fast, very accurate mistakes.</p><p>Let's BLAST it.</p><p>Some genome never has transposons. It just develops random features.</p><p>Go watch CINEMA and have BLAST.</p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/18653/genetic-code-amino-acid</guid>
	<pubDate>Sun, 26 Oct 2014 07:45:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/18653/genetic-code-amino-acid</link>
	<title><![CDATA[Genetic code - Amino Acid]]></title>
	<description><![CDATA[<p>The genetic code consists of 64 triplets of nucleotides. These triplets are called codons.With three exceptions, each codon encodes for one of the 20 amino acids used in the synthesis of proteins. That produces some redundancy in the code: most of the amino acids being encoded by more than one codon.</p><p>The image summarise all in one.</p><p>More at http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/C/Codons.html</p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/18653" length="226605" type="image/jpeg" />
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/8987/the-dna-of-a-successful-bioinformatician-decoded</guid>
	<pubDate>Wed, 12 Mar 2014 13:41:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/8987/the-dna-of-a-successful-bioinformatician-decoded</link>
	<title><![CDATA[The DNA of a Successful Bioinformatician decoded !!!]]></title>
	<description><![CDATA[<p>Many blogs exist about successful bioinformatician, but this blog so far now is my personal view on characteristics of successful bioinformatician or computational biologist. &nbsp;Hmm &hellip; of course these views are subjective to my own personal experiences and therefore I don't claim that the view listed here is complete. As a human, I don&rsquo;t take them too serious. The success must not be the only target of your work. The target is to work on your own virtues; some of those virtues are the topic of this blog.</p><p><img src="http://bioinformaticsonline.com/mod/photo/genome_decode.png" alt="image" width="509" height="458" style="border: 0px; border: 0px;"><br /> <br /> <strong>1. Update new things continuously<br /></strong>As per my personal experience, it&rsquo;s not always easy to work as a bioinformatician! &nbsp;There are couple of reasons to say that; First computational part of biology make our life&rsquo;s a little harder compared to other professional categories. The fact - for instance - that the technology cycle in the bioinformatics world is very short, the actual knowledge becomes outdated in a few months or years. Therefore, we need to learn continuously - new things get important. Second, to stay on top of things we really need the strong will to be good at our job. That's probably the most important characteristic to bioinformatician. They are usually an excellent knowledge worker with great technical abilities, and have the will to be that over decades!<br /> <br /> <strong>2. Avoid the sentence </strong><strong>"I did not know what to do!"</strong><br /> In our computational biology lab, we generally face lots of technical problems. But as you know, it's impossible to know everything to do the computational biology jobs ( Yup.. because you need diverse and multidisciplinary knowledge to understand biological problems and resolve their respective solutions), therefore it's absolutely necessary that a bioinformatician finds its way through a new topic. How I typically do that is I use google and I talk to other experts in our laboratory or online biostar community to find out what they think. "I did not know what to do!" should not be an argument for us.<strong><br /><br /> <strong>3. To make oneself useful</strong></strong><br /> Several time it does happen, you finished our task earlier than expected; in such cases if you have some time left then: Take a coffee and play chess; reversi, etc. In my case I take a rest. Afterwards I think about what I could do that helps the team to achieve its targets, 'cause some of my team mates probably didn't finish! (at least if I didn't met them at coffee bar !!)</p><p><strong>4. Care for all</strong><br /> During my rigorous research duration; I attended several workshop organized by my University departments. I had a discussion with other research fellow, professors; I generally ask &hellip; what it really takes to make a team successful or to be a successful research leader. They always said: "Well, you need some caring people!" I think there is a lot truth in that statement. If we do not care about quality, timelines, good team culture, respectful communication (!!), clean code, if all this doesn&rsquo;t matter to us, then I believe the probability is higher that we fail in research and analysis. <br /> <br /> <strong>5. Be good with people</strong><br /> Because bioinformatician and computational biologist jobs typically involves to work in a (most wanted J cross-departmental!) team, therefore it's important that we're (more or less) good in dealing with other individuals. Everyone have their own strengths and weaknesses, just like us. It's important to treat all the research team mates with respect, regardless of their technical competence or contributions. Of course, sometimes people deserve a clear statement (!!!), but try to do these things one-on-one. Make sure nobody loses his face. Attend the meetings at the coffee bar; be good at table top soccer and go out once in a while to have a beer with your team. You know what I'm talking about.</p><p>At the end of a week I look back and I ask myself what I have produced. This could be paperwork, community days or (best!!) programming code. Always remember there is always a solution to a problem. Most of the times there are at least three solutions. So, don&rsquo;t just blame, suggest a solution.<br /> <br /> That's it. I am looking forward to your thoughts and comments!</p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33221/genome-annotation-transfer-utility-gatu</guid>
	<pubDate>Mon, 29 May 2017 05:54:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33221/genome-annotation-transfer-utility-gatu</link>
	<title><![CDATA[Genome Annotation Transfer Utility (GATU)]]></title>
	<description><![CDATA[<p>Genome Annotation Transfer Utility (GATU) was designed to facilitate quick, efficient annotation of similar genomes using genomes that have already been annotated. For example, whenever a new strain of SARS coronavirus is sequenced, it is possible, using GATU, to automatically annotate the new strain using a previously-annotated strain of SARS CoV. This saves researchers from tedious manual annotation of these sequences.</p>
<p>The program utilizes tBLASTn and BLASTn algorithms to map genes from the reference genome (the annotated strain) to the new sequence (the unannotated strain). The goal is to annotate the majority of the new genome&rsquo;s genes in a single step. ORFs present in the target genome and absent from the reference genome are also identified; these ORFs can be further analyzed using BLAST, VGO and BBB. Afterwards, they can either be accepted for/rejected from annotation. GATU can handle multiple-exon genes as well as mature peptides. Although it was designed for use with viral genomes, GATU can also be used to help annotate larger genomes (ie. bacterial genomes).</p>
<p>The output is saved in GenBank, XML, or EMBL file format.</p><p>Address of the bookmark: <a href="https://virology.uvic.ca/help/tool-help/help-books/genome-annotation-transfer-utility-gatu-documentation/" rel="nofollow">https://virology.uvic.ca/help/tool-help/help-books/genome-annotation-transfer-utility-gatu-documentation/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/9028/linux-for-bioinformatician</guid>
	<pubDate>Thu, 13 Mar 2014 16:59:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/9028/linux-for-bioinformatician</link>
	<title><![CDATA[Linux for bioinformatician !!!]]></title>
	<description><![CDATA[<p>Linux, free operating system for computers, provides several powerful admin tools and utilities which will help you to manage your systems effectively and handle huge amount of genomic/biological data with an ease. The field of bioinformatics relies heavily on Linux-based computers and software. Although most bioinformatics programs can be compiled to run. If you don&rsquo;t know what these no so user-friendly tools are and how to use them, you could be spending lot of time trying to perform even the basic admin tasks. The focus of this linux series is to help you understand system admin as well as basic tools, which will help you to become an effective bioinformatician and computational biologist.<br /><br /></p><p>For knowledge about Linux and their importance amongst bioinformatician plesae read this article "<a href="http://www.ualberta.ca/~stothard/downloads/linux_for_bioinformatics.pdf">An introduction to Linux for bioinformatics</a>" by Paul Stothard.</p><p>Linux cheat sheet at http://bioinformaticsonline.com/file/view/87/linux-cheat-sheet</p><p>Please browse for futher useful linux pages on right hand side ...</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35823/regen-ancestral-genome-reconstruction-for-bacteria</guid>
	<pubDate>Tue, 06 Mar 2018 05:02:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35823/regen-ancestral-genome-reconstruction-for-bacteria</link>
	<title><![CDATA[REGEN: Ancestral Genome Reconstruction for Bacteria]]></title>
	<description><![CDATA[<p><span>REGEN infers evolutionary events, including gene creation and deletion and replicon fission and fusion. The reconstruction can be performed by either a maximum parsimony or a maximum likelihood method. Gene content reconstruction is based on the concept of neighboring gene pairs. REGEN was designed to be used with any set of genomes that are sufficiently related, which will usually be the case for bacteria within the same taxonomic order.&nbsp;</span></p><p>Address of the bookmark: <a href="http://www.mdpi.com/2073-4425/3/3/423" rel="nofollow">http://www.mdpi.com/2073-4425/3/3/423</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/9204/keep-your-important-ssh-session-running-when-you-disconnect-from-server</guid>
	<pubDate>Sat, 15 Mar 2014 21:39:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/9204/keep-your-important-ssh-session-running-when-you-disconnect-from-server</link>
	<title><![CDATA[Keep Your Important SSH Session Running when You Disconnect from Server !!!]]></title>
	<description><![CDATA[<p>As a Bioinformatician/ Computational biologist we swim in the ocean of genomic/proteomics data, and play with them with an ease. In our day to day simulation, analysis, comparative study we do need to run exhaustive programs, which might take more than a week. In such cases we do need to disconnect from sever in a way that our program/session should not get terminated. To do so there are lots of software, tools such as tmux ( <a href="http://tmux.sourceforge.net/">http://tmux.sourceforge.net/</a>, nohup (<a href="http://ss64.com/bash/nohup.html">http://ss64.com/bash/nohup.html</a>) , byobu (<a href="https://help.ubuntu.com/10.04/serverguide/byobu.html">https://help.ubuntu.com/10.04/serverguide/byobu.html</a>) and other commands (disown -a &amp;&amp; exit), but following are the ones I use the most.</p><p>Screen is like a window manager for your console. It will allow you to keep multiple terminal sessions running and easily switch between them. It also protects you from disconnection, because the screen session doesn&rsquo;t end when you get disconnected.<br /><br />You&rsquo;ll need to make sure that screen is installed on the server you are connecting to. If that server is Ubuntu or Debian, just use this command:<br /><br />sudo apt-get install screen<br /><br />Now you can start a new screen session by just typing screen at the command line. You&rsquo;ll be shown some information about screen. Hit enter, and you&rsquo;ll be at a normal prompt.<br /><br /><strong>To disconnect (but leave the session running)</strong><br /><br />Hit Ctrl + A and then Ctrl + D in immediate succession. You will see the message [detached]<br /><br /><strong>To reconnect to an already running session</strong><br /><br />screen -r<br /><br /><strong>To reconnect to an existing session, or create a new one if none exists</strong><br /><br />screen -D -r<br /><br /><strong>To create a new window inside of a running screen session</strong><br /><br />Hit Ctrl + A and then C in immediate succession. You will see a new prompt.<br /><br /><strong>To switch from one screen window to another</strong><br /><br />Hit Ctrl + A and then Ctrl + A in immediate succession.<br /><br /><strong>To list open screen windows</strong><br /><br />Hit Ctrl + A and then W in immediate succession</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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