<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36191?offset=450</link>
	<atom:link href="https://bioinformaticsonline.com/related/36191?offset=450" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
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  <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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/9029/syntax-for-secure-copy-scp</guid>
	<pubDate>Thu, 13 Mar 2014 17:01:32 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/9029/syntax-for-secure-copy-scp</link>
	<title><![CDATA[Syntax for Secure Copy (scp)]]></title>
	<description><![CDATA[<div><p>In our day to day research activity, we need to securely copy our data from several to local computer and visa-versa. I am jotting down some of the commonly used SCP command for your future help. Hope you all will like it</p><p>What is Secure Copy?<br /><br />scp allows files to be copied to, from, or between different hosts. It uses ssh for data transfer and provides the same authentication and same level of security as ssh.</p><p><br />Examples</p><p><br /><strong>Copy the file "gene.txt" from a remote host to the local host</strong><br /><br />&nbsp;&nbsp;&nbsp; $ scp your_username@remotehost.edu:gene.txt /some/local/directory<br /><br /><strong>Copy the file "foobar.txt" from the local host to a remote host</strong><br /><br />&nbsp;&nbsp;&nbsp; $ scp gene.txt your_username@remotehost.edu:/some/remote/directory<br /><br /><strong>Copy the directory "chromosome" from the local host to a remote host's directory "bar"</strong><br /><br />&nbsp;&nbsp;&nbsp; $ scp -r chromosome your_username@remotehost.edu:/some/remote/directory/bar<br /><br /><strong>Copy the file "gene.txt" from remote host "rh1.edu" to remote host "rh2.edu"</strong><br /><br />&nbsp;&nbsp;&nbsp; $ scp your_username@rh1.edu:/some/remote/directory/gene.txt \<br />&nbsp;&nbsp;&nbsp; your_username@rh2.edu:/some/remote/directory/<br /><br /><strong>Copying the files "gene.txt" and "cancer.txt" from the local host to your home directory on the remote host</strong><br /><br />&nbsp;&nbsp;&nbsp; $ scp gene.txt cancer.txt your_username@remotehost.edu:~<br /><br /><strong>Copy the file "gene.txt" from the local host to a remote host using port 2264</strong><br /><br />&nbsp;&nbsp;&nbsp; $ scp -P 2264 gene.txt your_username@remotehost.edu:/some/remote/directory<br /><br /><strong>Copy multiple files from the remote host to your current directory on the local host</strong><br /><br />&nbsp;&nbsp;&nbsp; $ scp your_username@remotehost.edu:/some/remote/directory/\{a,b,c\} .<br /><br />&nbsp;&nbsp;&nbsp; $ scp your_username@remotehost.edu:~/\{gene.txt,cancer.txt\} .<br /><br /><strong>scp Performance</strong><br /><br />By default scp uses the Triple-DES cipher to encrypt the data being sent. Using the Blowfish cipher has been shown to increase speed. This can be done by using option -c blowfish in the command line.<br /><br />&nbsp;&nbsp;&nbsp; $ scp -c blowfish some_file your_username@remotehost.edu:~<br /><br />It is often suggested that the -C option for compression should also be used to increase speed. The effect of compression, however, will only significantly increase speed if your connection is very slow. Otherwise it may just be adding extra burden to the CPU. An example of using blowfish and compression:<br /><br />&nbsp;&nbsp;&nbsp; $ scp -c blowfish -C local_file your_username@remotehost.edu:~</p></div>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/9327/jarvis%E2%80%99-laboratory</guid>
  <pubDate>Tue, 18 Mar 2014 18:53:47 -0500</pubDate>
  <link></link>
  <title><![CDATA[Jarvis’ laboratory]]></title>
  <description><![CDATA[
<p>Dr. Jarvis’ laboratory studies the neurobiology of vocal communication. We want to know how the brain generates, perceives, and learns behavior. We use vocal communication as a model behavior. Emphasis is placed on the molecular pathways involved in the perception and production of learned vocalizations. We use an integrative approach that combines behavioral, anatomical, electrophysiological, and molecular biological techniques. The main animal model used is songbirds, one of the few vertebrate groups that evolved the ability to learn vocalizations. The overall goal of the research is to advance knowledge of the neural mechanisms for vocal learning and basic mechanisms of brain function.</p>

<p>Lab page: http://jarvislab.net/</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/9639/find-certain-filesdocuments-in-linux-os</guid>
	<pubDate>Sun, 06 Apr 2014 23:56:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/9639/find-certain-filesdocuments-in-linux-os</link>
	<title><![CDATA[Find certain files/documents in Linux OS]]></title>
	<description><![CDATA[<p>As bioinformatician I know the fact that we usually handle the large dataset and lost in the huge numbers of files and folders. In order to search the missing file a strong search command is required. The Linux Find Command is one of the most important and much used command in Linux sytems. Find command used to search and locate list of files and directories based on conditions you specify for files that match the arguments. Find can be used in variety of conditions like you can find files by permissions, users, groups, file type, date, size and other possible criteria.<br /><br />Through this article we are sharing our day-to-day Linux find command experience and its usage in the form of examples. In this article we will show you the most used 35 Find Commands examples in Linux. We have divided the section into Five parts from basic to advance usage of find command.</p><p><strong>Part I &ndash; Basic Find Commands for Finding Files with Names</strong><br />1. Find Files Using Name in Current Directory<br /><br />Find all the files whose name is gene.txt in a current working directory.<br /><br /># find . -name gene.txt<br /><br />./gene.txt<br /><br />2. Find Files Under Home Directory<br /><br />Find all the files under /home directory with name gene.txt.<br /><br /># find /home -name gene.txt<br /><br />/home/gene.txt<br /><br />3. Find Files Using Name and Ignoring Case<br /><br />Find all the files whose name is gene.txt and contains both capital and small letters in /home directory.<br /><br /># find /home -iname gene.txt<br /><br />./gene.txt<br />./Gene.txt<br /><br />4. Find Directories Using Name<br /><br />Find all directories whose name is Gene in / directory.<br /><br /># find / -type d -name Gene<br /><br />/Gene<br /><br />5. Find fasta Files Using Name<br /><br />Find all php files whose name is gene.fasta in a current working directory.<br /><br /># find . -type f -name gene.fasta<br /><br />./gene.fasta<br /><br />6. Find all PHP Files in Directory<br /><br />Find all fasta files in a directory.<br /><br /># find . -type f -name "*.fasta"<br /><br />./gene.fasta<br />./cancer.fasta<br />./allgene.fasta<br /><br /><strong>Part II &ndash; Find Files Based on their Permissions</strong><br />7. Find Files With 777 Permissions<br /><br />Find all the files whose permissions are 777.<br /><br /># find . -type f -perm 0777 -print<br /><br />8. Find Files Without 777 Permissions<br /><br />Find all the files without permission 777.<br /><br /># find / -type f ! -perm 777<br /><br />9. Find SGID Files with 644 Permissions<br /><br />Find all the SGID bit files whose permissions set to 644.<br /><br /># find / -perm 2644<br /><br />10. Find Sticky Bit Files with 551 Permissions<br /><br />Find all the Sticky Bit set files whose permission are 551.<br /><br /># find / -perm 1551<br /><br />11. Find SUID Files<br /><br />Find all SUID set files.<br /><br /># find / -perm /u=s<br /><br />12. Find SGID Files<br /><br />Find all SGID set files.<br /><br /># find / -perm /g+s<br /><br />13. Find Read Only Files<br /><br />Find all Read Only files.<br /><br /># find / -perm /u=r<br /><br />14. Find Executable Files<br /><br />Find all Executable files.<br /><br /># find / -perm /a=x<br /><br />15. Find Files with 777 Permissions and Chmod to 644<br /><br />Find all 777 permission files and use chmod command to set permissions to 644.<br /><br /># find / -type f -perm 0777 -print -exec chmod 644 {} \;<br /><br />16. Find Directories with 777 Permissions and Chmod to 755<br /><br />Find all 777 permission directories and use chmod command to set permissions to 755.<br /><br /># find / -type d -perm 777 -print -exec chmod 755 {} \;<br /><br />17. Find and remove single File<br /><br />To find a single file called gene.txt and remove it.<br /><br /># find . -type f -name "gene.txt" -exec rm -f {} \;<br /><br />18. Find and remove Multiple File<br /><br />To find and remove multiple files such as .fa or .gb, then use.<br /><br /># find . -type f -name "*.fa" -exec rm -f {} \;<br /><br />OR<br /><br /># find . -type f -name "*.gb" -exec rm -f {} \;<br /><br />19. Find all Empty Files<br /><br />To file all empty files under certain path.<br /><br /># find /tmp -type f -empty<br /><br />20. Find all Empty Directories<br /><br />To file all empty directories under certain path.<br /><br /># find /tmp -type d -empty<br /><br />21. File all Hidden Files<br /><br />To find all hidden files, use below command.<br /><br /># find /tmp -type f -name ".*"<br /><br /><strong>Part III &ndash; Search Files Based On Owners and Groups</strong><br />22. Find Single File Based on User<br /><br />To find all or single file called gene.txt under / root directory of owner root.<br /><br /># find / -user root -name gene.txt<br /><br />23. Find all Files Based on User<br /><br />To find all files that belongs to user Rahul under /home directory.<br /><br /># find /home -user rahul<br /><br />24. Find all Files Based on Group<br /><br />To find all files that belongs to group Developer under /home directory.<br /><br /># find /home -group developer<br /><br />25. Find Particular Files of User<br /><br />To find all .txt files of user Rahul under /home directory.<br /><br /># find /home -user rahul -iname "*.txt"<br /><br /><strong>Part IV &ndash; Find Files and Directories Based on Date and Time</strong><br />26. Find Last 50 Days Modified Files<br /><br />To find all the files which are modified 50 days back.<br /><br /># find / -mtime 50<br /><br />27. Find Last 50 Days Accessed Files<br /><br />To find all the files which are accessed 50 days back.<br /><br /># find / -atime 50<br /><br />28. Find Last 50-100 Days Modified Files<br /><br />To find all the files which are modified more than 50 days back and less than 100 days.<br /><br /># find / -mtime +50 &ndash;mtime -100<br /><br />29. Find Changed Files in Last 1 Hour<br /><br />To find all the files which are changed in last 1 hour.<br /><br /># find / -cmin -60<br /><br />30. Find Modified Files in Last 1 Hour<br /><br />To find all the files which are modified in last 1 hour.<br /><br /># find / -mmin -60<br /><br />31. Find Accessed Files in Last 1 Hour<br /><br />To find all the files which are accessed in last 1 hour.<br /><br /># find / -amin -60<br /><br /><strong>Part V &ndash; Find Files and Directories Based on Size</strong><br />32. Find 50MB Files<br /><br />To find all 50MB files, use.<br /><br /># find / -size 50M<br /><br />33. Find Size between 50MB &ndash; 100MB<br /><br />To find all the files which are greater than 50MB and less than 100MB.<br /><br /># find / -size +50M -size -100M<br /><br />34. Find and Delete 100MB Files<br /><br />To find all 100MB files and delete them using one single command.<br /><br /># find / -size +100M -exec rm -rf {} \;<br /><br />35. Find Specific Files and Delete<br /><br />Find all .gb files with more than 10MB and delete them using one single command.<br /><br /># find / -type f -name *.gb -size +10M -exec rm {} \;</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/10260/%E2%80%9Con%E2%80%9D-and-%E2%80%9Coff%E2%80%9D-the-neuron</guid>
	<pubDate>Fri, 25 Apr 2014 19:31:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/10260/%E2%80%9Con%E2%80%9D-and-%E2%80%9Coff%E2%80%9D-the-neuron</link>
	<title><![CDATA[“On” and “Off” the neuron !!!]]></title>
	<description><![CDATA[<p><span>Optogenetics is a recent innovation in neuroscience that gives researchers the ability to control the activity of neurons with light. With this powerful tool, researchers are teasing apart the biological basis of memory, behavior, and disease (see &ldquo;<a href="http://www.technologyreview.com/news/517226/scientists-make-mice-remember-things-that-didnt-happen/"><span>Scientists Make Mice &lsquo;Remember&rsquo; Things That Didn&rsquo;t Happen</span></a>&rdquo; and &ldquo;<a href="http://www.technologyreview.com/news/423254/an-on-off-switch-for-anxiety/"><span>An On-Off Switch for Anxiety</span></a>,&rdquo;). But for the first several years of this technology&rsquo;s existence, the proteins that scientists added to neurons to make them react to light were only good at activating neurons. That limited researchers&rsquo; ability to understand neuronal circuits, sets of interconnected neurons that are thought to control behavior and, when misfiring, to underlie many brain conditions. Problems can arise from any imbalance in circuit activity, whether too much or too little.&nbsp;</span></p><p><span>Now, two research groups have engineered new optogenetic proteins that can be used to efficiently silence neurons.&nbsp;<span><span>One of the two new proteins comes from the lab of<span>&nbsp;</span><a href="http://www.stanford.edu/group/dlab/about_pi.html" target="_blank">Karl Deisseroth</a>, a psychiatrist and neuroscientist at Stanford University who helped develop optogenetics as a research tool.&nbsp;His group&rsquo;s new &ldquo;off&rdquo; switch for neurons was created by changing 10 of the 333 amino acids in an existing optogenetic protein, which itself had been engineered by combining natural proteins from<span>&nbsp;</span></span></span><a href="http://genome.jgi-psf.org/Chlre3/Chlre3.home.html" target="_blank"><span>green algae</span></a><span><span>. That advance&nbsp;</span><span>&ldquo;creates a powerful tool that allows neuroscientists to apply a brake in any specific circuit with millisecond precision,&rdquo; said Thomas&nbsp;Insel, director of the National Institute of Mental Health, in a released statement.&nbsp;</span><a href="http://www.sciencemag.org/content/344/6182/409" target="_blank"><span>The other new silencing protein</span></a>, developed by scientists at the H</span><span>umboldt University of Berlin and collaborators, was created by changing amino acids in the same existing optogenetic protein.&nbsp;</span></span></p><p><span><span>Some researchers are also looking to optogenetics as a potential treatment for patients with a variety of conditions (see &ldquo;</span></span><span><a href="http://www.technologyreview.com/news/524771/for-mice-and-maybe-men-pain-is-gone-in-a-flash/"><span>For Mice, and Maybe Men, Pain Is Gone in a Flash</span></a><span><span>,&rdquo; and &ldquo;</span></span><a href="http://www.technologyreview.com/news/506981/flipping-on-the-lights-to-halt-seizures/"><span>Flipping on the Lights to Halt Seizures</span></a><span><span>&rdquo;) but there are huge challenges to overcome. The method requires genetic modification of cells to make them light-sensitive. It also requires implanted light sources for all but the shallowest of nerve endings. <br /></span></span></span></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/10659/gps-dna-tracking-university-of-sheffield</guid>
	<pubDate>Sat, 10 May 2014 04:33:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/10659/gps-dna-tracking-university-of-sheffield</link>
	<title><![CDATA[GPS DNA tracking - University of Sheffield]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/Aap-s1kle4Q" frameborder="0" allowfullscreen></iframe>University of Sheffield geneticist and bioinformatics expert Dr Eran Elhaik demonstrates the power of his new DNA research, which allows people to discover their genetic homeland from 1000 years ago. Find out more about our biological research here http://www.sheffield.ac.uk/aps]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/10925/a-brief-bioinformatics-tutorial</guid>
	<pubDate>Wed, 21 May 2014 12:50:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/10925/a-brief-bioinformatics-tutorial</link>
	<title><![CDATA[A Brief Bioinformatics Tutorial]]></title>
	<description><![CDATA[<p>This is about how to use a computer to find what is known about a gene of interest and also how to get new insights about it.</p>
<p>The tutorial is divided in three main parts:</p>
<ul>
<li>In the <strong>Sequence </strong>part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.</li>
<li>The <strong>Structure </strong>part is about molecular visualization, homology modeling and structural domain prediction.</li>
<li>In the <strong>Function </strong>part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...</li>
</ul>
<p>During all the three parts, we will use the <em>S. cerevisiae </em>VPS36 protein as an example.</p><p>Address of the bookmark: <a href="http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html" rel="nofollow">http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/13014/bioinformatics-jrf-vacancy-at-icgeb-new-delhi</guid>
  <pubDate>Wed, 23 Jul 2014 16:07:15 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics JRF vacancy at ICGEB, New Delhi]]></title>
  <description><![CDATA[
<p>Junior Research Fellow for a DBT sponsored project entitled "Computational and experimental characterization of stage specific arginine methylation in P. falciparum proteome". </p>

<p>Candidates should have a 1st class MSc/MTech/BTech degree in Bioinformatics. Please send complete CV, quoting Application for RMETH-JRF-2014, by email to Dr. Dinesh Gupta: dinesh@icgeb.res.in</p>

<p>Closing date for applications: 6 August 2014</p>

<p>More at http://www.icgeb.org/tl_files/Vacancies/JRF.pdf</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/11313/linux-sort-commands-for-bioinformatics</guid>
	<pubDate>Sat, 31 May 2014 15:41:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/11313/linux-sort-commands-for-bioinformatics</link>
	<title><![CDATA[Linux Sort Commands for Bioinformatics]]></title>
	<description><![CDATA[<p>Almost all the scripting languages such as Perl, Python etc have built-in sort, but unfortunately none of them are as flexible as sort command. But one when it come to space efficiency GNU sort stands at the top. It can sort a 20Gb file with less than 2Gb memory. It is not trivial to implement so powerful a sort by yourself.</p><p>sort a space-delimited file based on its first column, then the second if the first is the same, and so on:<br />sort input.txt</p><p>sort a huge file (GNU sort ONLY):<br />sort -S 1500M -t $HOME/tmp input.txt &gt; sorted.txt</p><p>sort starting from the third column, skipping the first two columns:<br />sort +2 input.txt</p><p>sort the second column as numbers, descending order; if identical, sort the 3rd as strings, ascending order:<br />sort -k2,2nr -k3,3 input.txt</p><p>sort starting from the 4th character at column 2, as numbers:<br />sort -k2.4n input.txt</p><p>More Linxu sort command information<br /><br />If you have any sort commands you'd like to share, please add them to our comments section below. For more help, you can also type:<br /><br />man sort<br /><br />or<br /><br />sort --help<br /><br />on your Unix/Linux system.</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/11399/next-generation-sequencing-in-r-or-bioconductor-environment</guid>
	<pubDate>Mon, 02 Jun 2014 18:03:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/11399/next-generation-sequencing-in-r-or-bioconductor-environment</link>
	<title><![CDATA[Next generation sequencing in R or bioconductor environment]]></title>
	<description><![CDATA[<p>There are many R software and bioconductor packages for NGS data analysis, some of them are as follows</p><h3><a name="TOC-Biostrings" id="TOC-Biostrings"></a>Biostrings</h3><p>The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R. It contains many speed and memory effective string containers, string matching algorithms, and other utilities, for fast manipulation of large sets of biological sequences. The objects and functions provided by Biostrings form the basis for many other sequence analysis packages. <a href="http://bioconductor.org/packages/release/bioc/html/Biostrings.html">Documentation</a></p><div><div style="text-align: left;"><div style="color: #000000;"><h4><a name="TOC-IRanges-Overview" id="TOC-IRanges-Overview"></a>IRanges Overview</h4><p>IRanges provides the low-level infrastructure and containers for handling sets of integer ranges within Bioconductor's BioC-Seq domain. Its classes and methods provide support for many more high-level packages like GenomicRanges, ShortRead, Rsamtools, etc. <a href="http://bioconductor.org/packages/release/bioc/html/IRanges.html">Documentation</a></p><div style="text-align: right;"><div style="text-align: left;"><h4><a name="TOC-GenomicRanges-Overview" id="TOC-GenomicRanges-Overview"></a>GenomicRanges Overview</h4><p>The <em>GenomicRanges</em> package serves as the foundation for representing genomic locations within the Bioconductor project. It is built upon the <em>IRanges</em> infrastructure and defines three major data containers - <em>GRanges, GRangesList</em> and <em>GappedAlignments</em> - which are supporting other important BioC-Seq packages including <em>ShortRead, Rsamtools, rtracklayer, GenomicFeatures</em> and <em>BSgenome</em>.&nbsp; Compared to the IRanges container, the GRanges/<em>GRangesList</em> classes are more flexible and extensible to store additional information about sequence ranges, such as chromosome identifiers (sequence space), strand information and annotation data. <a href="http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html">Documentation</a></p></div></div></div></div><h3><a name="TOC-Motif-Discovery" id="TOC-Motif-Discovery"></a>Motif Discovery</h3><h4><a name="TOC-cosmo" id="TOC-cosmo"></a>cosmo</h4><p>The cosmo package allows to search a set of unaligned DNA sequences for a shared motif that may function as transcription factor binding site. The algorithm extends the popular motif discovery tool MEME (Bailey and Elkan, 1995) in that it allows the search to be supervised by specifying a set of constraints that the motif to be discovered must satisfy. <a href="http://bioconductor.org/packages/release/bioc/html/cosmo.html">Documentation</a></p></div><div>
<p><span></span><span></span></p>
<div style="color: #0000ff;"><h4><a name="TOC-BCRANK" id="TOC-BCRANK"></a>BCRANK</h4><p>BCRANK is a method that takes a ranked list of genomic regions as input and outputs short DNA sequences that are overrepresented in some part of the list. The algorithm was developed for detecting transcription factor (TF) binding sites in a large number of enriched regions from high-throughput ChIP-chip or ChIP-seq experiments, but it can be applied to any ranked list of DNA sequences. Documentation</p>
<p><a href="http://bioconductor.org/packages/release/bioc/html/BCRANK.html"></a></p>
<p>rGADEM: <a href="http://bioconductor.org/packages/devel/bioc/html/rGADEM.html">Documentation</a></p><p>MotIV: <a href="http://bioconductor.org/packages/devel/bioc/html/MotIV.html">Documentation</a></p></div><h3><a name="TOC-ShortRead" id="TOC-ShortRead"></a>ShortRead</h3><p>The ShortRead package provides input, quality control, filtering, parsing, and manipulation functionality for short read sequences produced by high throughput sequencing technologies. While support is provided for many sequencing technologies, this package is primairly focused on Solexa/Illumina reads. <a href="http://bioconductor.org/packages/release/bioc/html/ShortRead.html">Documentation</a></p><h3><a name="TOC-Rsamtools" id="TOC-Rsamtools"></a>Rsamtools</h3><p>Rsamtools provides functions for parsing and inspecting samtools BAM formatted binary alignment data. SAM/BAM is quickly becoming a universal standard alignment format, and is now supported by a wide variety of alignment tools. <a href="http://bioconductor.org/help/bioc-views/2.7/bioc/html/Rsamtools.html">Documentation</a></p>
<p><a href="http://samtools.sourceforge.net/">Samtools Website</a><br /> <a href="http://bio-bwa.sourceforge.net/">BWA (Burrows-Wheeler Alignment) Website</a><br /><span style="color: #0000ff;"></span></p>
<div style="color: #000000;">&nbsp;</div></div><div>
<p><span style="color: #000000;">Additional tools for SNP analysis:&nbsp;</span></p>
<p><a href="http://bioconductor.org/help/bioc-views/release/bioc/html/snpMatrix.html">snpMatrix</a></p><h3><a name="TOC-BSgenome" id="TOC-BSgenome"></a>BSgenome</h3><p>BSgenome provides an object oriented infrastructure for interacting with a Biostring based genome sequence. BSgenome packages exist for many common genomes, and can be created to represent custom genomes. See the "How to forge a BSgenome data package" Vignette for instructions to create a new BSgenome package if a prebuilt package does not exist for your organism. <a href="http://bioconductor.org/packages/release/bioc/html/BSgenome.html">Documentation</a></p><h3><a name="TOC-rtracklayer" id="TOC-rtracklayer"></a>rtracklayer</h3><p>rtracklayer provides an interface for exporting annotation feature data to various genome browsers and file formats (such as GFF). See the Small RNA Profiling exercise for an example of using rtracklayer to visualize alignment coverage. <a href="http://bioconductor.org/packages/release/bioc/html/rtracklayer.html">Documentation</a></p><h3><a name="TOC-biomaRt" id="TOC-biomaRt"></a>biomaRt</h3><p>The biomaRt package, provides an interface to a growing collection of databases implementing the BioMart software suite (http:// www.biomart.org). The package enables online retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas. This data is retrieved automatically via the Internet, so it's recommended that you cache the data locally, or check versions if your code will be adversely affected by updates to these data. <a href="http://bioconductor.org/packages/release/bioc/html/biomaRt.html">Documentation</a></p><h3><a name="TOC-ChIP-Seq-Analysis-Packages" id="TOC-ChIP-Seq-Analysis-Packages"></a>ChIP-Seq Analysis Packages</h3><p>Bioconductor provides various packages for analyzing and visualizing ChIP-Seq data. Only a small selection of these packages is introduced here. Additional useful introductions to this topic are: <a href="http://www.bioconductor.org/workshops/2009/SeattleJan09/ChIP-seq/">BioC ChIP-seq Case Study</a> and BioC <a href="http://www.bioconductor.org/help/course-materials/2009/SeattleNov09/ChIP-seq/">ChIP-Seq</a>.</p><h4><a name="TOC-chipseq" id="TOC-chipseq"></a>chipseq</h4><p>The chipseq package combines a variety of HT-Seq packages to a pipeline for ChIP-Seq data analysis. <a href="http://bioconductor.org/packages/release/bioc/html/chipseq.html">Documentation</a></p><h4><a name="TOC-BayesPeak" id="TOC-BayesPeak"></a>BayesPeak</h4><p>BayesPeak is a peak calling package for identifying DNA binding sites of proteins in ChIP-Seq experiments. Its algorithm uses hidden Markov models (HMM) and Bayesian statistical methods. The following sample code introduces the identification of peaks with the BayesPeak package as well as the incorporation of read coverage information obtained by the chipseq package. <a href="http://bioconductor.org/packages/release/bioc/html/BayesPeak.html">Documentation</a> [ <a href="http://www.biomedcentral.com/1471-2105/10/299">Publication</a> ]</p><h4><a name="TOC-PICS" id="TOC-PICS"></a>PICS</h4><p>The PICS package applies probabilistic inference to aligned-read ChIP-Seq data in order to identify regions bound by transcription factors. PICS identifies enriched regions by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. The following sample code uses the test data set from the above BayesPeak package in order to compare the results from both methods by identifying their consensus peak set. <a href="http://www.bioconductor.org/packages/release/bioc/html/PICS.html">Documentation</a> [ <a href="http://www.hubmed.org/display.cgi?uids=20528864">Publication</a> ]</p><h4><a name="TOC-ChIPpeakAnno" id="TOC-ChIPpeakAnno"></a>ChIPpeakAnno</h4><p>The ChIPpeakAnno package provides. batch annotation of the peaks identified from either ChIP-seq or ChIP-chip experiments. It includes functions to retrieve the sequences around peaks, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. The package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. <a href="http://bioconductor.org/packages/release/bioc/html/ChIPpeakAnno.html">Documentation</a></p><h4><a name="TOC-Additional-ChIP-Seq-Packages" id="TOC-Additional-ChIP-Seq-Packages"></a>Additional ChIP-Seq Packages</h4><p>DiffBind: <a href="http://www.bioconductor.org/packages/release/bioc/html/DiffBind.html">Documentation</a></p><p>MOSAICS: <a href="http://bioconductor.org/packages/devel/bioc/html/mosaics.html">Documentation</a></p><p>iSeq: <a href="http://bioconductor.org/packages/release/bioc/html/iSeq.html">Documentation</a></p><p>ChIPseqR: <a href="http://bioconductor.org/packages/release/bioc/html/ChIPseqR.html">Documentation</a></p><p>ChiPsim: <a href="http://bioconductor.org/packages/release/bioc/html/ChIPsim.html">Documentation</a></p><p>CSAR: <a href="http://www.bioconductor.org/packages/devel/bioc/html/CSAR.html">Documentation</a></p><p>ChIP-Seq Pipeline: <a href="http://www.bioconductor.org/packages/release/bioc/html/PICS.html">PICS</a>, rGADEM and MotIV (<a href="http://www.rglab.org/pics-and-bioconductor/">developer web site</a>)</p><p>SPP: <a href="http://compbio.med.harvard.edu/Supplements/ChIP-seq/">ChIP-seq processing pipeline</a></p><p><a href="http://compbio.med.harvard.edu/Supplements/ChIP-seq/tutorial.html">SPP Tutorial</a></p><p><a href="http://liulab.dfci.harvard.edu/MACS/index.html">MACS</a></p><p><a href="http://gmdd.shgmo.org/Computational-Biology/ChIP-Seq/download/SIPeS">SIPeS</a></p><h3><a name="TOC-RNA-Seq-Analysis" id="TOC-RNA-Seq-Analysis"></a>RNA-Seq Analysis</h3><h4><a name="TOC-Counting-Reads-that-Overlap-with-Annotation-Ranges-" id="TOC-Counting-Reads-that-Overlap-with-Annotation-Ranges-"></a>Counting Reads that Overlap with Annotation Ranges&nbsp;</h4><p>The GenomicRanges package provides support for importing into R short read alignment data in BAM format (via Rsamtools) and associating them with genomic feature ranges, such as exons or genes. This way one can quantify the number of reads aligning to annotated genomic regions. The package defines general purpose containers for storing genomic intervals as well as more specialized containers for storing alignments against a reference genome. The two main functions for read counting provided by this infrastructure are <span>countOverlaps <span style="color: #000000;"><span>and</span></span> summarizeOverlaps</span>. For their proper usage, it is important to read the corresponding <a href="http://www.bioconductor.org/packages/devel/bioc/vignettes/GenomicRanges/inst/doc/summarizeOverlaps.pdf">PDF manual</a>. <a href="http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html">Documentation</a></p><h4><a name="TOC-Differential-Gene-Expression-Analysis-with-DESeq" id="TOC-Differential-Gene-Expression-Analysis-with-DESeq"></a>Differential Gene Expression Analysis with DESeq</h4><p>The DESeq package contains functions to call differentially expressed genes (DEGs) in count tables based on a model using the negative binomial distribution. It expects as input a data frame with the raw read counts per region/gene of interest (rows) for each test sample (columns).&nbsp; Such a count table can be imported into R or generated from BAM alignment files using the <span>countOverlaps</span> function as introduced above. <a href="http://www.bioconductor.org/packages/release/bioc/html/DESeq.html">Documentation</a></p><h4><a name="TOC-Differential-Gene-Expression-Analysis-with-edgeR" id="TOC-Differential-Gene-Expression-Analysis-with-edgeR"></a>Differential Gene Expression Analysis with edgeR</h4><p>The edgeR package uses empirical Bayes estimation and exact tests based on the negative binomial distribution to call differentially expressed genes (DEGs) in count data.&nbsp;</p>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/edgeR.html">Documentation</a></p>
<p><span style="color: #000000;">A variety of additional R packages are available for normalizing RNA-Seq read count data and identifying differentially expressed genes (DEG): <br /> </span></p><p><a href="http://bioconductor.org/packages/devel/bioc/html/easyRNASeq.html">easyRNASeq</a> (simplifies read counting per genome feature)</p><p><a href="http://www.bioconductor.org/packages/release/bioc/html/DEXSeq.html">DEXSeq</a> (Inference of differential exon usage);&nbsp;<a href="http://www.bioconductor.org/packages/release/data/experiment/html/parathyroidSE.html">parathyroidSE</a> explains how to generate exon read counts in R</p><p><a href="http://bioconductor.org/packages/release/bioc/html/DEGseq.html">DEGseq</a></p><p><a href="http://www.bioconductor.org/packages/release/bioc/html/baySeq.html">baySeq</a> (also see: <a href="http://www.bioconductor.org/packages/release/bioc/html/segmentSeq.html">segmentSeq</a>)</p><p><a href="http://bioconductor.org/packages/release/bioc/html/Genominator.html">Genominator</a> (<a href="http://www.hubmed.org/display.cgi?uids=20167110">Bullard et al. 2010</a>)</p><div style="text-align: right;"><div style="text-align: left;"><h4><a name="TOC-Detection-of-Alternative-Splice-Junctions" id="TOC-Detection-of-Alternative-Splice-Junctions"></a>Detection of Alternative Splice Junctions</h4>
<p><span style="color: #000000;">Another utility of RNA-Seq experiments is the analysis of splice junctions. The following software suggestions provide this utility:</span></p>
<p><a href="http://woldlab.caltech.edu/rnaseq/">ERANGE<br /> </a><a href="http://tophat.cbcb.umd.edu/">TopHat</a></p><p><a href="http://biogibbs.stanford.edu/%7Ekinfai/SpliceMap/">SpliceMap</a></p><p><a href="http://solidsoftwaretools.com/gf/project/splitseek/">SplitSeek</a></p><h3><a name="TOC-DNA-Methylation-Data-Analysis" id="TOC-DNA-Methylation-Data-Analysis"></a>DNA-Methylation Data Analysis</h3><div><ul>
<li><span style="font-size: 10pt;"><a href="http://www.bioconductor.org/help/course-materials/2012/BiocEurope2012/mattia_pelizzola_methylPipe.pdf">methylPipe</a></span></li>
<li><span style="font-size: 10pt;"><a href="http://www.bioconductor.org/packages/devel/bioc/html/bsseq.html">bsseq</a></span></li>
<li><a href="http://www.bioconductor.org/packages/devel/bioc/html/BiSeq.html">BiSeq</a></li>
<li>Much more under <a href="http://www.bioconductor.org/packages/devel/BiocViews.html#___DNAMethylation">BiocViews</a></li>
</ul></div></div></div><h3><a name="TOC-HT-Seq-Data-Visualization" id="TOC-HT-Seq-Data-Visualization"></a>HT-Seq Data Visualization</h3>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/ggbio.html">ggbio</a>: ggplot2 extension for genomics data (<a href="http://tengfei.github.com/ggbio/">online manual</a>) <a href="http://www.bioconductor.org/packages/devel/bioc/html/Gviz.html">Gviz</a>:&nbsp;Plotting data and annotation information along genomic coordinates <a href="http://bioconductor.org/packages/release/bioc/html/HilbertVis.html">HilbertVis</a>: Hilbert genome plots</p>
<p><a href="http://bioconductor.org/packages/release/bioc/html/GenomeGraphs.html">GenomeGraphs</a>: Plotting genomic information from Ensembl</p><p><a href="http://www.hubmed.org/display.cgi?uids=18507856">TileQC</a>: Flow Cell Quality Visualization</p><p><a href="http://bioconductor.org/packages/release/bioc/html/rtracklayer.html">rtracklayer</a>: R interface to genome browsers</p><p><a href="http://genoplotr.r-forge.r-project.org/">genoPlotR</a>: Plotting maps of genes and genomes</p><p><a href="http://bioconductor.org/packages/release/bioc/html/Genominator.html">Genominator</a>: Tools for storing, accessing, analyzing and visualizing genomic data.</p><p>&nbsp;</p><p>To install all packages</p><blockquote><p>source("http://bioconductor.org/biocLite.R")<br />biocLite()<br />biocLite(c("ShortRead", "Biostrings", "IRanges", "BSgenome", "rtracklayer", "biomaRt", "chipseq", "ChIPpeakAnno", "Rsamtools", "BayesPeak", "PICS", "GenomicRanges", "DESeq", "edgeR", "leeBamViews", "GenomicFeatures", "BSgenome.Celegans.UCSC.ce2"))</p></blockquote></div>]]></description>
	<dc:creator>John Parker</dc:creator>
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