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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/33398?offset=30</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44373/mitohifi-a-python-pipeline-for-mitochondrial-genome-assembly-from-pacbio-high-fidelity-reads</guid>
	<pubDate>Tue, 05 Sep 2023 07:31:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44373/mitohifi-a-python-pipeline-for-mitochondrial-genome-assembly-from-pacbio-high-fidelity-reads</link>
	<title><![CDATA[MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads]]></title>
	<description><![CDATA[<p dir="auto">MitoHiFi v3.2 is a python pipeline distributed under&nbsp;<a href="https://github.com/marcelauliano/MitoHiFi/blob/master/LICENSE">MIT License</a>&nbsp;!</p>
<p dir="auto">MitoHiFi was first developed to assemble the mitogenomes for a wide range of species in the Darwin Tree of Life Project (DToL)</p>
<p dir="auto">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05385-y&nbsp;</p>
<p dir="auto"><a href="https://github.com/marcelauliano/MitoHiFi/blob/master/docs/dtol-logo-round-300x132.png" target="_blank"><img src="https://github.com/marcelauliano/MitoHiFi/raw/master/docs/dtol-logo-round-300x132.png" alt="" style="border: 0px; border: 0px;"></a></p><p>Address of the bookmark: <a href="https://github.com/marcelauliano/MitoHiFi" rel="nofollow">https://github.com/marcelauliano/MitoHiFi</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/2267/added-video-feature-in-bol</guid>
	<pubDate>Tue, 13 Aug 2013 17:42:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/2267/added-video-feature-in-bol</link>
	<title><![CDATA[Added video feature in BOL]]></title>
	<description><![CDATA[<p>Just in: Added video features in BOL, now you can watch and share your&nbsp;favourite bioinformatics video tutorials.</p><p>Share your favourite video tutorial or lectures on BOL at http://bioinformaticsonline.com/videolist/all . You can also add video in you groups.</p><p>Note: Other than bioinformatics video material/tutorial will be deleted without any prior warning.</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/4037/perl-and-bioperl-tutorials</guid>
	<pubDate>Wed, 28 Aug 2013 05:51:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/4037/perl-and-bioperl-tutorials</link>
	<title><![CDATA[Perl and BioPerl Tutorials]]></title>
	<description><![CDATA[<p>This bookmark is created to store the useful Perl and BioPerl tutorial links at one place. Feel free to share and add more useful tutorial links here ....&nbsp;</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://cbb.sjtu.edu.cn/course/database/beginning.pdf" rel="nofollow">http://cbb.sjtu.edu.cn/course/database/beginning.pdf</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/12787/integrative-genomics-viewer-igv-tutorial</guid>
	<pubDate>Sat, 12 Jul 2014 15:16:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/12787/integrative-genomics-viewer-igv-tutorial</link>
	<title><![CDATA[Integrative Genomics Viewer (IGV) tutorial]]></title>
	<description><![CDATA[<p>The <a href="http://www.broadinstitute.org/igv/">Integrative Genomics Viewer (IGV)</a> from the Broad Center allows you to view several types of data files involved in any NGS analysis that employs a reference genome, including how reads from a dataset are mapped, gene annotations, and predicted genetic variants.</p>
<p>http://www.broadinstitute.org/igv/</p><p>Address of the bookmark: <a href="https://wikis.utexas.edu/display/bioiteam/Integrative+Genomics+Viewer+%28IGV%29+tutorial" rel="nofollow">https://wikis.utexas.edu/display/bioiteam/Integrative+Genomics+Viewer+%28IGV%29+tutorial</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/21443/a-guide-for-complete-r-beginners-getting-data-into-r</guid>
	<pubDate>Tue, 24 Feb 2015 20:15:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/21443/a-guide-for-complete-r-beginners-getting-data-into-r</link>
	<title><![CDATA[A guide for complete R beginners :- Getting data into R]]></title>
	<description><![CDATA[<p>For a beginner this can be is the hardest part, it is also the most important to get right.</p><p>It is possible to create a vector by typing data directly into R using the combine function &lsquo;c&rsquo;</p><blockquote><p><strong>x </strong></p></blockquote><p>same as</p><blockquote><p><strong>x </strong></p></blockquote><p>creates the vector x with the numbers between 1 and 5.</p><p>You can see what is in an object at any time by typing its name;</p><blockquote><p><strong>x</strong></p></blockquote><p>will produce the output<strong> &lsquo;[1] 1 2 3 4 5&prime;</strong></p><p>Note that names need to be quoted</p><blockquote><p><strong>daysofweek </strong><strong>&larr; c(&lsquo;Monday&rsquo;, &lsquo;Tuesday&rsquo;, &lsquo;Wednesday&rsquo;, &lsquo;Thursday&rsquo;, &lsquo;Friday&rsquo;);</strong></p></blockquote><p>Usually however you want to input from a file. We have touched on the &lsquo;read.table&rsquo; function already.</p><blockquote><p><strong>mydata </strong></p></blockquote><p>Now <strong>mydata</strong> is a data frame with multiple vectors</p><p>each vector can be identified by the default syntax</p><p>#if any of these are typed it will print to screen</p><blockquote><p><strong>mydata$V1 mydata$V2 mydata$V3 </strong></p></blockquote><p>By default the function assumes certain things from the file</p><ul>
<li>The file is a plain text file (there are function to read excel files: <em>not covered here</em>)</li>
<li>columns are separated by any number of tabs or spaces</li>
<li>there is the same number of data points in each column</li>
<li>there is no header row (labels for the columns)</li>
<li>there is no column with names for the rows** [I&rsquo;ll explain].</li>
</ul><p><span style="text-decoration: underline;">If any of these are false, we need to tell that to the function</span></p><p>If it has a header column</p><blockquote><p><strong>mydata <em>header=T also works</em></strong></p></blockquote><p>Note that there is a comma between different parts of the functions arguments</p><p>If there is one less column in the header row, then R assumes that the 1<sup>st</sup> column of data after the header are the row names</p><p>Now the vectors (columns) are identified by their name</p><p>#if any of these are typed it will print to screen</p><blockquote><p><strong>mydata$A mydata$B mydata$C </strong></p></blockquote><p># Summary about the whole data frame</p><blockquote><p><strong>summary(mydata)</strong></p></blockquote><p># Summary information of column A</p><blockquote><p><strong>summary(mydata$A) </strong></p></blockquote><p>We can shortcut having to type the data frame each time by attaching it</p><blockquote><p><strong>attach(mydata)</strong></p></blockquote><p># summary of column B as &lsquo;mydata&rsquo; is attached</p><blockquote><p><strong>summary(B)</strong></p></blockquote><p><span style="text-decoration: underline;">Two other important options for </span><em><span style="text-decoration: underline;">read.table</span></em></p><p>If is is separated only by tabs and has a header</p><blockquote><p><strong>mydata </strong></p></blockquote><p>Really useful if you have spaces in the contents of some columns, so R does not mess up reading the columns . However if the columns or of an uneven length it will tell you.</p><p>If you know that the file has uneven columns</p><blockquote><p><strong>mydata </strong></p></blockquote><p>This causes R to fill empty spaces in a columns with &lsquo;NA&rsquo; .</p><p>The last two examples will still work with our file and give the same result as with only headers=T</p><p><span style="text-decoration: underline;">Graphs</span></p><p>to get an idea of what R is capable of type</p><blockquote><p><strong>demo(graphics)</strong></p></blockquote><p>steps through the examples, and the code is printed to the screen</p><p>We will work with simpler examples that have immediate use to biologists.</p><p>Remember to get more information about the options to a function type &lsquo;?function&rsquo;</p><p><span style="text-decoration: underline;">Histogram of A</span><span style="text-decoration: underline;"></span></p><blockquote><p><strong>hist(mydata$A)</strong></p></blockquote><p>If there was more data we could increase the number of vertical columns with the option, breaks=50 (or another relevant number).</p><blockquote><p><strong>boxplot(mydata)</strong></p></blockquote><p>We can get rid of the need to type the data frame each time by using the <strong>attach</strong> function</p><p># if not already done so</p><blockquote><p><strong>attach(mydata) </strong></p><p><strong>boxplot(mydata$A, mydata$B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p>same as</p><blockquote><p><strong>boxplot(A, B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p><span style="text-decoration: underline;">Scatter plot</span></p><p># if not already done so</p><blockquote><p><strong>attach(mydata) </strong></p><p><strong>plot(A,B) # or plot(mydata$A, mydata$B)</strong></p></blockquote><p><strong><span style="text-decoration: underline;">SAVING an image</span></strong></p><p>Windows users (Rgui) RIGHT click on image and select which you want.</p><p><span style="text-decoration: underline;">These instructions work for everyone.</span></p><p>You need to create a new device of the type of file you need, then send the data to that device</p><p>to save as a png file (easy to load into the likes of powerpoint, also great for web applications.</p><blockquote><p><strong>png(&lsquo;filename&rsquo;) </strong></p><p><strong>boxplot(A, B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p>or to save as a pdf</p><blockquote><p><strong>pdf(&lsquo;filename&rsquo;) </strong></p><p><strong>boxplot(A, B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p><span style="text-decoration: underline;">Note</span></p><ul>
<li>Nothing will appear on screen, the output is going to the file</li>
<li>Also it may not be saved immediately but will once the device (or R) is turned quit.</li>
</ul><p>To quit R type</p><p><strong>q() # </strong>If you save your session, next time you start R, you will have your data preloaded.</p><p>Or if you want to remain in R</p><blockquote><pre><strong>dev.off() #</strong>turns of the png (or pdf etc) device, thus forces the data to save</pre></blockquote>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/23160/opencpu</guid>
	<pubDate>Sun, 05 Jul 2015 18:34:46 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/23160/opencpu</link>
	<title><![CDATA[OpenCPU]]></title>
	<description><![CDATA[<p>OpenCPU is a system for embedded scientific computing and reproducible research. The OpenCPU server provides a reliable and interoperable <a href="https://www.opencpu.org/api.html">HTTP API</a> for data analysis based on R.</p><p>The OpenCPU <a href="https://www.opencpu.org/jslib.html">JavaScript client library</a> provides the most seamless integration of R and JavaScript available today.</p><p>OpenCPU uses standard R packaging to develop, ship and deploy web applications. Several open source <a href="https://www.opencpu.org/apps.html">example apps</a> are available from Github.</p><p>Installing your own OpenCPU server is <a href="https://www.opencpu.org/download.html">super easy</a> and only takes a few minutes.</p><p>More at https://www.opencpu.org/</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26424/biotoolbox</guid>
	<pubDate>Fri, 19 Feb 2016 09:14:44 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26424/biotoolbox</link>
	<title><![CDATA[BioToolbox]]></title>
	<description><![CDATA[<p>This is a collection of libraries and high-quality end-user scripts for bioinformatic analysis, including working with gene annotation, collecting data scores from a variety of modern file formats, and conversion between file formats. The Bio::ToolBox libraries provide a unified, abstracted interface to multiple common gene annotation formats and the collection of data from multiple data files. They rely on BioPerl SeqFeature libraries and related adaptors to access binary file formats including Bam, BigWig, BigBed, and USeq. The Bio::ToolBox package includes scripts for setting up databases of annotation, collecting annotated features, collecting genomic data relative to features, manipulating and analyzing data, and data format conversion.</p>
<p>More at http://cpansearch.perl.org/src/TJPARNELL/</p><p>Address of the bookmark: <a href="http://cpansearch.perl.org/src/TJPARNELL/" rel="nofollow">http://cpansearch.perl.org/src/TJPARNELL/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27104/gatb-genome-analysis-toolbox-with-de-bruijn-graph</guid>
	<pubDate>Thu, 28 Apr 2016 11:16:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27104/gatb-genome-analysis-toolbox-with-de-bruijn-graph</link>
	<title><![CDATA[GATB : Genome Analysis Toolbox with de-Bruijn graph]]></title>
	<description><![CDATA[<p>The&nbsp;<strong><strong>Genome Analysis Toolbox with de-Bruijn graph</strong> (GATB)</strong> provides a set of <a href="https://gatb.inria.fr/gatb-global-architecture/">highly efficient algorithms to analyse NGS data sets</a>. These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (<em>e.g.</em> metagenomes).</p>
<p>More at https://gatb.inria.fr/</p><p>Address of the bookmark: <a href="https://gatb.inria.fr/" rel="nofollow">https://gatb.inria.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27971/samtools-primer</guid>
	<pubDate>Thu, 23 Jun 2016 07:18:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27971/samtools-primer</link>
	<title><![CDATA[Samtools Primer !!]]></title>
	<description><![CDATA[<p>SAMtools: Primer / Tutorial by Ethan Cerami, Ph.D.<br><br>keywords: samtools, next-gen, next-generation, sequencing, bowtie, sam, bam, primer, tutorial, how-to, introduction<br>Revisions<br><br>&nbsp;&nbsp;&nbsp; 1.0: May 30, 2013: First public release on biobits.org.<br>&nbsp;&nbsp;&nbsp; 1.1: July 24, 2013: Updated with Disqus Comments / Feedback section.<br>&nbsp;&nbsp;&nbsp; 1.2: December 19, 2014: Multiple updates, including:<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Updated to use samtools 1.1 and bcftools 1.2.<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Updated usage for bcftools.<br><br>About<br><br>SAMtools is a popular open-source tool used in next-generation sequence analysis. This primer provides an introduction to SAMtools, and is geared towards those new to next-generation sequence analysis. The primer is also designed to be self-contained and hands-on, meaning that you only need to install SAMtools, and no other tools, and sample data sets are provided. Terms in bold are also explained in the glossary at the end of the document.</p><p>Address of the bookmark: <a href="http://biobits.org/samtools_primer.html" rel="nofollow">http://biobits.org/samtools_primer.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32011/fools-guide</guid>
	<pubDate>Sun, 02 Apr 2017 14:31:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32011/fools-guide</link>
	<title><![CDATA[Fools guide]]></title>
	<description><![CDATA[<p><span>This website and accompaning documents are intended as a tool to help researchers dealing with non-model organisms acquire and process transcriptomic high-throughput sequencing data without having to learn extensive bioinformatics skills. It covers all steps from tissue collection, sample preparation and computer setup, through addressing biological questions with gene expression and SNP data.</span></p>
<p>http://sfg.stanford.edu/denovo.html</p>
<p>http://sfg.stanford.edu/sequencing.html</p>
<p>http://sfg.stanford.edu/BLAST.html</p>
<p>http://sfg.stanford.edu/denovo.html&nbsp;</p><p>Address of the bookmark: <a href="http://sfg.stanford.edu/guide.html" rel="nofollow">http://sfg.stanford.edu/guide.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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