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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/27238?offset=290</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/22454/one-page-r-survival-guide</guid>
	<pubDate>Thu, 28 May 2015 21:10:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/22454/one-page-r-survival-guide</link>
	<title><![CDATA[One page R survival guide !!]]></title>
	<description><![CDATA[<p><span style="font-style: normal; color: #000000; float: none;">There any many of the documents have been developed and tested by scientist around the world. I found this one really useful. The data used is available for download as<span>&nbsp;</span></span><a href="http://onepager.togaware.com/data.zip">data.zip</a><span style="font-style: normal; color: #000000; float: none;">.</span></p><p><span style="font-style: normal; color: #000000; float: none;">Reference@http://www.datasciencecentral.com/profiles/blogs/one-page-r-a-survival-guide-to-data-science-with-r</span></p><ul>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Templates for the Data Scientist<ol style="margin: 0px; padding: 0px 0px 0px 1.5em; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">A Template for Preparing Data:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/DataO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/DataO.R">R</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">A Template for Building Models:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/ModelsO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/ModelsO.R">R</a></li>
</ol></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Getting Started as a Data Scientist<ol style="margin: 0px; padding: 0px 0px 0px 1.5em; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Getting Started with R and Rattle:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/StartL.pdf">Lecture</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/StartG.pdf">Laboratory</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Introducing and Interacting with R:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/IntroRL.pdf">Lecture</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/IntroRR.pdf">Laboratory</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">BasicR - OnePage(R) - Writing R scripts</li>
</ol></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Dealing With Data<ol style="margin: 0px; padding: 0px 0px 0px 1.5em; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Read Data into R:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/ReadO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/ReadO.R">R</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Explore and Summarise Data:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/SummaryO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/SummaryO.R">R</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Transform Data:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/TransformO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/TransformO.R">R</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><a href="http://togaware.com/onepager/DateTimeRB"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Dealing with Dates and Time:</span></a><span>&nbsp;</span>(<a href="http://onepager.togaware.com/DateTimeR.pdf">PDF</a>,<span>&nbsp;</span><a href="http://onepager.togaware.com/DateTimeR.R">R</a>) Dates and Time</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Visualising Data with GGPlot2:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/GGPlot2O.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/GGPlot2O.R">R</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Visualising Data with Maps</span><span>&nbsp;</span>*<a href="http://togaware.com/onepager/MapsO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/MapsO.R">R</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Spatial<span>&nbsp;</span>(R) Spatial Analysis</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Handling Big Data</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/BigDataO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/BigData.R">R</a></li>
</ol></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Descriptive Analytics<ol style="margin: 0px; padding: 0px 0px 0px 1.5em; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Cluster Analysis:</span><span>&nbsp;</span>*<a href="http://togaware.com/onepager/ClustersL.pdf">Lecture</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/ClustersO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/Clusters.R">R</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Association Analysis:</span><span>&nbsp;</span>*<a href="http://togaware.com/onepager/ARulesL.pdf">Lecture</a></li>
</ol></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Predictive Analytics<ol style="margin: 0px; padding: 0px 0px 0px 1.5em; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Decision Trees:</span><span>&nbsp;</span>*<a href="http://togaware.com/onepager/DTreesL.pdf">Lecture</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/DTreesO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/DTreesO.R">R</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/DTreesG.pdf">Rattle</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Ensembles of Decision Trees:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/EnsemblesL.pdf">Lecture</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/EnsemblesO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/EnsemblesO.R">R</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">SVM (R)</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">KernLab (R)</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">NeuralNetworks (R)</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">NNet (R)</li>
</ol></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Model Delivery<ol style="margin: 0px; padding: 0px 0px 0px 1.5em; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Evaluating Models:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/EvaluationO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/EvaluationO.R">R</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Evaluation (R)</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Scoring (R)</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">PMML (R) Exporting Models for Deployment</li>
</ol></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Advanced Topics<ol style="margin: 0px; padding: 0px 0px 0px 1.5em; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Text Mining:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/TextMiningO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/TextMiningO.R">R</a></li>
</ol></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Advanced R Topics<ol style="margin: 0px; padding: 0px 0px 0px 1.5em; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><a href="http://togaware.com/onepager/PlotsB"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Plots</span></a><span>&nbsp;</span>(<a href="http://onepager.togaware.com/Plots.pdf">PDF</a>,<span>&nbsp;</span><a href="http://onepager.togaware.com/Plots.R">R</a>) Miscellaneous Plots</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><a href="http://togaware.com/onepager/FunctionsB"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Functions</span></a><span>&nbsp;</span>(<a href="http://onepager.togaware.com/Functions.pdf">PDF</a>,<span>&nbsp;</span><a href="http://onepager.togaware.com/Functions.R">R</a>) Writing Functions in R</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><a href="http://togaware.com/onepager/ParallelB"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Parallel</span></a><span>&nbsp;</span>(<a href="http://onepager.togaware.com/Parallel.pdf">PDF</a>,<span>&nbsp;</span><a href="http://onepager.togaware.com/Parallel.R">R</a>) Parallel Execution</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Packaging (R) Pulling it Together into a Package</li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Doing R with Style:</span><span>&nbsp;</span>*<a href="http://onepager.togaware.com/StyleO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/StyleO.R">R</a></li>
<li style="margin: 0px; padding: 0px; border: 0px currentColor; font-style: inherit; font-weight: inherit; vertical-align: baseline;"><span style="margin: 0px; padding: 0px; border: 0px none currentcolor; font-style: inherit; font-weight: inherit; vertical-align: baseline;">Literate Data Mining with KnitR:</span><span>&nbsp;</span>*<a href="http://togaware.com/onepager/KnitRL.pdf">Lecture</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/KnitRO.pdf">OnePageR</a><span>&nbsp;</span>- *<a href="http://onepager.togaware.com/KnitRO.R"></a></li>
</ol></li>
</ul>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/11735/search-shell-command-history</guid>
	<pubDate>Thu, 12 Jun 2014 17:43:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/11735/search-shell-command-history</link>
	<title><![CDATA[Search Shell Command History]]></title>
	<description><![CDATA[<p>We use couple of hundreads of command in daily basis. Most of them are actually repeated several time. The question remain open how do I search old command history under bash shell and modify or reuse it? <br /><br />Now a days almost all modern shell allows you to search command history if enabled by user. Use history command to display the history list with line numbers. Lines listed with with a * have been modified by user.</p><p><br /><strong>Shell history search command</strong><br /><br />Type history at a shell prompt:<br />$ history</p><p>It will display the list of all used commandline history with an serial number.<br /><br />To search particular command, enter:<br />$ history | grep command-name<br />$ history | egrep -i 'scp|ssh|ftp'<br />Emacs Line-Edit Mode Command History Searching<br /><br />To get previous command containing string, hit [CTRL]+[r] followed by search string:<br /><br />(reverse-i-search): <br /><br />To get previous command, hit [CTRL]+[p]. You can also use up arrow key.<br /><br />CTRL-p<br /><br />To get next command, hit [CTRL]+[n]. You can also use down arrow key.<br /><br />CTRL-n<br /><br /></p><p><strong>fc command</strong></p><p>Apart from hostory command there are fc command to extract the command from history. The fc stands for either "find command" or "fix command.</p><p>For example list last 10 command, enter:<br />$ fc -l 10<br />To list commands 130 through 150, enter:<br />$ fc -l 130 150<br />To list all commands since the last command beginning with ssh, enter:<br />$ fc -l ssh<br />You can edit commands 1 through 5 using vi text editor, enter:<br />$ fc -e vi 1 5</p><p><strong>Delete command history</strong><br /><br />The -c option causes the history list to be cleared by deleting all of the entries:<br />$ history -c</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44865/snp-analysis-unlocking-the-secrets-in-our-dna</guid>
	<pubDate>Wed, 16 Jul 2025 01:31:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44865/snp-analysis-unlocking-the-secrets-in-our-dna</link>
	<title><![CDATA[SNP Analysis: Unlocking the Secrets in Our DNA]]></title>
	<description><![CDATA[<p>Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation in humans&mdash;and many other organisms. A single base change in the DNA sequence (for example, an A instead of a G) can influence everything from our eye color to our risk of developing diseases. Analyzing these tiny changes has become central to modern genetics, medicine, agriculture, and evolutionary biology.</p><p><strong>What are SNPs?</strong><br />SNPs (pronounced "snips") are positions in the genome where individuals differ by a single nucleotide. For example:</p><p>Reference: ...A T G C A T G A...<br />Variant:&nbsp; &nbsp; &nbsp;...A T G T A T G A...</p><p>Here, the C in the reference genome has been replaced by a T in the variant.</p><p>SNPs occur roughly every 300&ndash;1,000 bases in the human genome, meaning there are millions of them scattered throughout our DNA. Most SNPs have no effect on health, but some are linked to disease susceptibility, drug response, and other traits.</p><p><strong>Why Do We Analyze SNPs?</strong><br />1. Medical Genetics</p><p>Identify disease-associated variants (e.g., BRCA1/2 in breast cancer).</p><p>Predict drug response (pharmacogenomics).</p><p>Enable precision medicine by tailoring treatments.</p><p>2. Population Genetics &amp; Ancestry</p><p>Trace human migration and ancestry.</p><p>Study genetic diversity within and between populations.</p><p>3. Agriculture &amp; Animal Breeding</p><p>Select for desirable traits (drought resistance, yield, disease resistance).</p><p>Improve breeding efficiency in livestock.</p><p>4. Evolutionary Biology</p><p>Track natural selection.</p><p>Study adaptation in wild populations.</p><p><strong>How is SNP Analysis Performed?</strong><br />SNP analysis can be broadly divided into three steps:</p><p>SNP Detection<br />Genotyping arrays: Chips that test hundreds of thousands of known SNP positions simultaneously. Fast and affordable, widely used in consumer ancestry testing.</p><p>Whole-genome or whole-exome sequencing: Can detect known and novel SNPs across the genome.</p><p>Targeted sequencing or PCR: For focused analysis of specific regions.</p><p>Variant Calling<br />Sequencing data is aligned to a reference genome. Bioinformatics tools (e.g., GATK, bcftools) identify positions where the sequenced sample differs from the reference.</p><p>Annotation and Interpretation<br />Tools (e.g., SnpEff, VEP) predict the functional impact of SNPs.</p><p>Are the SNPs in coding regions? Do they cause amino acid changes? Are they known to be pathogenic?</p><p>Databases like dbSNP, ClinVar, and GWAS Catalog provide information on known associations.</p><p>Common Tools for SNP Analysis<br />Alignment: BWA, Bowtie2</p><p>Variant Calling: GATK, FreeBayes</p><p>Visualization: IGV, UCSC Genome Browser</p><p>Annotation: SnpEff, VEP</p><p>Statistical Analysis: PLINK, SNPTEST</p><p><strong>Challenges in SNP Analysis</strong><br />False positives/negatives: Sequencing errors, alignment issues.</p><p>Population stratification: Confounding in association studies.</p><p>Interpretation: Many SNPs have unknown or complex effects.</p><p>Researchers address these with rigorous quality control, large datasets, and increasingly sophisticated statistical models.</p><p><strong>The Future of SNP Analysis</strong><br />With advances in sequencing technology and AI-driven analysis, SNP studies are expanding:</p><p>Polygenic risk scores predict disease risk based on thousands of SNPs.</p><p>Large-scale biobanks (e.g., UK Biobank, All of Us) enable powerful genome-wide association studies (GWAS).</p><p>CRISPR and functional assays help validate SNP effects in the lab.</p><p>SNP analysis is at the heart of the genomic revolution, promising insights into biology, health, and evolution at unprecedented scale.</p><p><strong>Conclusion</strong><br />From diagnosing rare diseases to designing better crops, SNP analysis is a foundational tool in modern science. As our ability to sequence and interpret genomes improves, so will our understanding of these tiny&mdash;but mighty&mdash;variations in DNA.</p><p>&nbsp;</p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/11609/bioinformatician%E2%80%99s-pocket-reference</guid>
	<pubDate>Sun, 08 Jun 2014 09:56:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/11609/bioinformatician%E2%80%99s-pocket-reference</link>
	<title><![CDATA[Bioinformatician’s Pocket Reference !!]]></title>
	<description><![CDATA[<p><span>It is amusing how brain of bioinformaticians work! Learning a new programming language for days feels so much of fun that making 5 minute discussion with neighbours (unless under special circumstances!) in our own mother-tongue. Today every bioinformatician keeps more than few languages and core IT toolkits on their plate. It has become mandatory to be able to mould different code snippets to build our own custom workflows, and thus keeping syntax at our fingertips has become essential.Although Google is best way to get syntax problem solved, it is not a bad idea to keep reference sheets is our smartphones or stick out some printed sheets on the back of your door, in the old fashion way!!</span></p><p>Address of the bookmark: <a href="http://infoplatter.wordpress.com/2014/04/06/bioinformaticians-pocket-reference/" rel="nofollow">http://infoplatter.wordpress.com/2014/04/06/bioinformaticians-pocket-reference/</a></p>]]></description>
	<dc:creator>RAJESH DETROJA</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial</guid>
	<pubDate>Mon, 27 Nov 2017 16:24:29 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial</link>
	<title><![CDATA[Single Cell RNAseq data analysis tutorial !!]]></title>
	<description><![CDATA[<ul>
<li>A major breakthrough (replaced microarrays) in the late 00&rsquo;s and has been widely used since</li>
<li>Measures the&nbsp;average expression level&nbsp;for each gene across a large population of input cells</li>
<li>Useful for comparative transcriptomics, e.g.&nbsp;samples of the same tissue from different species</li>
<li>Useful for quantifying expression signatures from ensembles, e.g.&nbsp;in disease studies</li>
<li>Insufficient&nbsp;for studying heterogeneous systems, e.g.&nbsp;early development studies, complex tissues (brain)</li>
<li>Does&nbsp;not&nbsp;provide insights into the stochastic nature of gene expression</li>
</ul><p>Following are the useful links:</p><p><a href="http://hemberg-lab.github.io/scRNA.seq.course/scRNA-seq-course.pdf" target="_blank">Single Cell RNAseq data analysis Tutorial</a></p><p><a href="https://f1000research.com/articles/5-2122/v2" target="_blank">A step-by-step workflow for low-level analysis of single-cell RNA-seq data</a></p><p><a href="https://www.bioconductor.org/help/workflows/simpleSingleCell/" target="_blank">A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor</a></p><p>SCell: single-cell RNA-seq analysis software</p><p><a href="https://github.com/diazlab/SCell">https://github.com/diazlab/SCell</a></p><p>Beta-Poisson model for single-cell RNA-seq data analyses</p><p><a href="https://github.com/nghiavtr/BPSC">https://github.com/nghiavtr/BPSC</a></p><p>Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis</p><p><a href="https://research.cchmc.org/pbge/sincera.html">https://research.cchmc.org/pbge/sincera.html</a></p><p>SC3 &ndash; consensus clustering of single-cell RNA-Seq data</p><p><a href="http://biorxiv.org/content/early/2016/09/02/036558">http://biorxiv.org/content/early/2016/09/02/036558</a></p><p>Citrus: A toolkit for single cell sequencing analysis</p><p><a href="http://biorxiv.org/content/early/2016/09/14/045070">http://biorxiv.org/content/early/2016/09/14/045070</a></p><p>Single-Cell Resolution of Temporal Gene Expression during Heart Development</p><p><a href="http://www.cell.com/developmental-cell/fulltext/S1534-5807%2816%2930682-7">http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7</a></p><p>Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects</p><p><a href="http://biorxiv.org/content/early/2016/11/15/087775">http://biorxiv.org/content/early/2016/11/15/087775</a></p><p>Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes</p><p><a href="http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract">http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract</a></p><p>SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation</p><p><a href="http://biorxiv.org/content/early/2016/11/21/088856">http://biorxiv.org/content/early/2016/11/21/088856</a></p><p>SCOUP is a probabilistic model to analyze single-cell expression data during differentiation</p><p><a href="https://github.com/hmatsu1226/SCOUP">https://github.com/hmatsu1226/SCOUP</a></p><p>scLVM is a modelling framework for single-cell RNA-seq data</p><p><a href="https://github.com/PMBio/scLVM">https://github.com/PMBio/scLVM</a></p><p>Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories</p><p><a href="https://github.com/jw156605/SLICER">https://github.com/jw156605/SLICER</a></p><p>SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality</p><p><a href="http://www.morgridge.net/SinQC.html">http://www.morgridge.net/SinQC.html</a></p><p>TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis</p><p><a href="https://github.com/zji90/TSCAN">https://github.com/zji90/TSCAN</a></p><p>Visualization and cellular hierarchy inference of single-cell data using SPADE</p><p><a href="http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html">http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html</a></p><p>OEFinder: Identify ordering effect genes in single cell RNA-seq data</p><p><a href="https://github.com/lengning/OEFinder">https://github.com/lengning/OEFinder</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/12218/assistant-professor-in-medical-bioinformatics</guid>
  <pubDate>Tue, 24 Jun 2014 01:46:36 -0500</pubDate>
  <link></link>
  <title><![CDATA[Assistant Professor in Medical Bioinformatics]]></title>
  <description><![CDATA[
<p>Advt. No : ME-I/A-IV/03/14<br />No.of Posts:01 (SC)<br />Pay Scale:<br />Pay Band of Rs.15600-39100 + Rs.6000/- GP +NPA @ 25% of Basic Pay +Learning Resource Allowance @ Rs.20,000/-P.A.+ Conveyance Allowance @ Rs. 1650/-P.M.+ Academic Allowance @ Rs.2500/- P.M. and other admissible allowances.<br />Qualifications:<br />Area of Specialization:-<br />Bioinformatics/Computational/Biology/Genomics/ Proteomics/ Structural Biology<br />1. Postgraduate qualification, e.g. Master’s Degree in Biotechnology/Bioinformatics/ Biophysics.<br />2. A Doctorate Degree of recognized University/Institute in a basic or allied Medical Science subject e.g. Medical Biotechnology/Biophysics. Bioinformatics/X-ray Crystallography/<br />Immunology/Structural Biology etc<br />Experience:<br />1.Minimum three years teaching and/or research experience in a recognized medical/research Institution in an allied medical subject after obtaining doctorate degree and preferably in Medical<br />Molecular Biology/ Biophysics/Structural Biology/Genomics and Clinical Proteomics/Computational Biology.<br />2. Minimum two publication with atleast one in international journal and atleast one as first author<br />Desirable:-<br />Consistently excellent scholastic/academic record, demonstrated ability to write grant proposal/(s) successfully, Post Doctoral training in a frontier area of medical Bioinformatics Research and of direct relevance to clinical diagnosis or patient care (preferably from a recognized top-ranking medical institution abroad)<br />Send your applications to O/O, Deputy Registrar, Recruitment &amp; Establishment Cell, University of Health Sciences, Rohtak by 08.7.2014<br />For more details,please visit website: http://pgimsrohtak.nic.in/2014%20AP%20Advt.pdf<br />Last Apply Date: 08 Jul 2014</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/39307/awk-for-beginners</guid>
	<pubDate>Fri, 26 Apr 2019 16:19:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/39307/awk-for-beginners</link>
	<title><![CDATA[AWK for beginners !]]></title>
	<description><![CDATA[<p>AWK is a standard tool on every POSIX-compliant UNIX system. It&rsquo;s like flex/lex, from the command-line, perfect for text-processing tasks and other scripting needs. It has a C-like syntax, but without mandatory semicolons (although, you should use them anyway, because they are required when you&rsquo;re writing one-liners, something AWK excels at), manual memory management, or static typing. It excels at text processing. You can call to it from a shell script, or you can use it as a stand-alone scripting language.</p><p>Why use AWK instead of Perl? Readability. AWK is easier to read than Perl. For simple text-processing scripts, particularly ones that read files line by line and split on delimiters, AWK is probably the right tool for the job.</p><div><pre><span>#!/usr/bin/awk -f</span>

<span># Comments are like this</span>


<span># AWK programs consist of a collection of patterns and actions.</span>
<span>pattern1</span> <span>{</span> <span>action</span><span>;</span> <span>}</span> <span># just like lex</span>
<span>pattern2</span> <span>{</span> <span>action</span><span>;</span> <span>}</span>

<span># There is an implied loop and AWK automatically reads and parses each</span>
<span># record of each file supplied. Each record is split by the FS delimiter,</span>
<span># which defaults to white-space (multiple spaces,tabs count as one)</span>
<span># You can assign FS either on the command line (-F C) or in your BEGIN</span>
<span># pattern</span>

<span># One of the special patterns is BEGIN. The BEGIN pattern is true</span>
<span># BEFORE any of the files are read. The END pattern is true after</span>
<span># an End-of-file from the last file (or standard-in if no files specified)</span>
<span># There is also an output field separator (OFS) that you can assign, which</span>
<span># defaults to a single space</span>

<span>BEGIN</span> <span>{</span>

    <span># BEGIN will run at the beginning of the program. It's where you put all</span>
    <span># the preliminary set-up code, before you process any text files. If you</span>
    <span># have no text files, then think of BEGIN as the main entry point.</span>

    <span># Variables are global. Just set them or use them, no need to declare..</span>
    <span>count</span> <span>=</span> <span>0</span><span>;</span>

    <span># Operators just like in C and friends</span>
    <span>a</span> <span>=</span> <span>count</span> <span>+</span> <span>1</span><span>;</span>
    <span>b</span> <span>=</span> <span>count</span> <span>-</span> <span>1</span><span>;</span>
    <span>c</span> <span>=</span> <span>count</span> <span>*</span> <span>1</span><span>;</span>
    <span>d</span> <span>=</span> <span>count</span> <span>/</span> <span>1</span><span>;</span> <span># integer division</span>
    <span>e</span> <span>=</span> <span>count</span> <span>%</span> <span>1</span><span>;</span> <span># modulus</span>
    <span>f</span> <span>=</span> <span>count</span> <span>^</span> <span>1</span><span>;</span> <span># exponentiation</span>

    <span>a</span> <span>+=</span> <span>1</span><span>;</span>
    <span>b</span> <span>-=</span> <span>1</span><span>;</span>
    <span>c</span> <span>*=</span> <span>1</span><span>;</span>
    <span>d</span> <span>/=</span> <span>1</span><span>;</span>
    <span>e</span> <span>%=</span> <span>1</span><span>;</span>
    <span>f</span> <span>^=</span> <span>1</span><span>;</span>

    <span># Incrementing and decrementing by one</span>
    <span>a</span><span>++</span><span>;</span>
    <span>b</span><span>--</span><span>;</span>

    <span># As a prefix operator, it returns the incremented value</span>
    <span>++</span><span>a</span><span>;</span>
    <span>--</span><span>b</span><span>;</span>

    <span># Notice, also, no punctuation such as semicolons to terminate statements</span>

    <span># Control statements</span>
    <span>if</span> <span>(</span><span>count</span> <span>==</span> <span>0</span><span>)</span>
        <span>print</span> <span>"Starting with count of 0"</span><span>;</span>
    <span>else</span>
        <span>print</span> <span>"Huh?"</span><span>;</span>

    <span># Or you could use the ternary operator</span>
    <span>print</span> <span>(</span><span>count</span> <span>==</span> <span>0</span><span>)</span> <span>?</span> <span>"Starting with count of 0"</span> <span>:</span> <span>"Huh?"</span><span>;</span>

    <span># Blocks consisting of multiple lines use braces</span>
    <span>while</span> <span>(</span><span>a</span> <span>&lt;</span> <span>10</span><span>)</span> <span>{</span>
        <span>print</span> <span>"String concatenation is done"</span> <span>" with a series"</span> <span>" of"</span>
            <span>" space-separated strings"</span><span>;</span>
        <span>print</span> <span>a</span><span>;</span>

        <span>a</span><span>++</span><span>;</span>
    <span>}</span>

    <span>for</span> <span>(</span><span>i</span> <span>=</span> <span>0</span><span>;</span> <span>i</span> <span>&lt;</span> <span>10</span><span>;</span> <span>i</span><span>++</span><span>)</span>
        <span>print</span> <span>"Good ol' for loop"</span><span>;</span>

    <span># As for comparisons, they're the standards:</span>
    <span># a &lt; b   # Less than</span>
    <span># a &lt;= b  # Less than or equal</span>
    <span># a != b  # Not equal</span>
    <span># a == b  # Equal</span>
    <span># a &gt; b   # Greater than</span>
    <span># a &gt;= b  # Greater than or equal</span>

    <span># Logical operators as well</span>
    <span># a &amp;&amp; b  # AND</span>
    <span># a || b  # OR</span>

    <span># In addition, there's the super useful regular expression match</span>
    <span>if</span> <span>(</span><span>"foo"</span> <span>~</span> <span>"^fo+$"</span><span>)</span>
        <span>print</span> <span>"Fooey!"</span><span>;</span>
    <span>if</span> <span>(</span><span>"boo"</span> <span>!~</span> <span>"^fo+$"</span><span>)</span>
        <span>print</span> <span>"Boo!"</span><span>;</span>

    <span># Arrays</span>
    <span>arr</span><span>[</span><span>0</span><span>]</span> <span>=</span> <span>"foo"</span><span>;</span>
    <span>arr</span><span>[</span><span>1</span><span>]</span> <span>=</span> <span>"bar"</span><span>;</span>

    <span># You can also initialize an array with the built-in function split()</span>

    <span>n</span> <span>=</span> <span>split</span><span>(</span><span>"foo:bar:baz"</span><span>,</span> <span>arr</span><span>,</span> <span>":"</span><span>);</span>

    <span># You also have associative arrays (actually, they're all associative arrays)</span>
    <span>assoc</span><span>[</span><span>"foo"</span><span>]</span> <span>=</span> <span>"bar"</span><span>;</span>
    <span>assoc</span><span>[</span><span>"bar"</span><span>]</span> <span>=</span> <span>"baz"</span><span>;</span>

    <span># And multi-dimensional arrays, with some limitations I won't mention here</span>
    <span>multidim</span><span>[</span><span>0</span><span>,</span><span>0</span><span>]</span> <span>=</span> <span>"foo"</span><span>;</span>
    <span>multidim</span><span>[</span><span>0</span><span>,</span><span>1</span><span>]</span> <span>=</span> <span>"bar"</span><span>;</span>
    <span>multidim</span><span>[</span><span>1</span><span>,</span><span>0</span><span>]</span> <span>=</span> <span>"baz"</span><span>;</span>
    <span>multidim</span><span>[</span><span>1</span><span>,</span><span>1</span><span>]</span> <span>=</span> <span>"boo"</span><span>;</span>

    <span># You can test for array membership</span>
    <span>if</span> <span>(</span><span>"foo"</span> <span>in</span> <span>assoc</span><span>)</span>
        <span>print</span> <span>"Fooey!"</span><span>;</span>

    <span># You can also use the 'in' operator to traverse the keys of an array</span>
    <span>for</span> <span>(</span><span>key</span> <span>in</span> <span>assoc</span><span>)</span>
        <span>print</span> <span>assoc</span><span>[</span><span>key</span><span>];</span>

    <span># The command line is in a special array called ARGV</span>
    <span>for</span> <span>(</span><span>argnum</span> <span>in</span> <span>ARGV</span><span>)</span>
        <span>print</span> <span>ARGV</span><span>[</span><span>argnum</span><span>];</span>

    <span># You can remove elements of an array</span>
    <span># This is particularly useful to prevent AWK from assuming the arguments</span>
    <span># are files for it to process</span>
    <span>delete</span> <span>ARGV</span><span>[</span><span>1</span><span>];</span>

    <span># The number of command line arguments is in a variable called ARGC</span>
    <span>print</span> <span>ARGC</span><span>;</span>

    <span># AWK has several built-in functions. They fall into three categories. I'll</span>
    <span># demonstrate each of them in their own functions, defined later.</span>

    <span>return_value</span> <span>=</span> <span>arithmetic_functions</span><span>(</span><span>a</span><span>,</span> <span>b</span><span>,</span> <span>c</span><span>);</span>
    <span>string_functions</span><span>();</span>
    <span>io_functions</span><span>();</span>
<span>}</span>

<span># Here's how you define a function</span>
<span>function</span> <span>arithmetic_functions</span><span>(</span><span>a</span><span>,</span> <span>b</span><span>,</span> <span>c</span><span>,</span>     <span>d</span><span>)</span> <span>{</span>

    <span># Probably the most annoying part of AWK is that there are no local</span>
    <span># variables. Everything is global. For short scripts, this is fine, even</span>
    <span># useful, but for longer scripts, this can be a problem.</span>

    <span># There is a work-around (ahem, hack). Function arguments are local to the</span>
    <span># function, and AWK allows you to define more function arguments than it</span>
    <span># needs. So just stick local variable in the function declaration, like I</span>
    <span># did above. As a convention, stick in some extra whitespace to distinguish</span>
    <span># between actual function parameters and local variables. In this example,</span>
    <span># a, b, and c are actual parameters, while d is merely a local variable.</span>

    <span># Now, to demonstrate the arithmetic functions</span>

    <span># Most AWK implementations have some standard trig functions</span>
    <span>localvar</span> <span>=</span> <span>sin</span><span>(</span><span>a</span><span>);</span>
    <span>localvar</span> <span>=</span> <span>cos</span><span>(</span><span>a</span><span>);</span>
    <span>localvar</span> <span>=</span> <span>atan2</span><span>(</span><span>b</span><span>,</span> <span>a</span><span>);</span> <span># arc tangent of b / a</span>

    <span># And logarithmic stuff</span>
    <span>localvar</span> <span>=</span> <span>exp</span><span>(</span><span>a</span><span>);</span>
    <span>localvar</span> <span>=</span> <span>log</span><span>(</span><span>a</span><span>);</span>

    <span># Square root</span>
    <span>localvar</span> <span>=</span> <span>sqrt</span><span>(</span><span>a</span><span>);</span>

    <span># Truncate floating point to integer</span>
    <span>localvar</span> <span>=</span> <span>int</span><span>(</span><span>5.34</span><span>);</span> <span># localvar =&gt; 5</span>

    <span># Random numbers</span>
    <span>srand</span><span>();</span> <span># Supply a seed as an argument. By default, it uses the time of day</span>
    <span>localvar</span> <span>=</span> <span>rand</span><span>();</span> <span># Random number between 0 and 1.</span>

    <span># Here's how to return a value</span>
    <span>return</span> <span>localvar</span><span>;</span>
<span>}</span>

<span>function</span> <span>string_functions</span><span>(</span>    <span>localvar</span><span>,</span> <span>arr</span><span>)</span> <span>{</span>

    <span># AWK, being a string-processing language, has several string-related</span>
    <span># functions, many of which rely heavily on regular expressions.</span>

    <span># Search and replace, first instance (sub) or all instances (gsub)</span>
    <span># Both return number of matches replaced</span>
    <span>localvar</span> <span>=</span> <span>"fooooobar"</span><span>;</span>
    <span>sub</span><span>(</span><span>"fo+"</span><span>,</span> <span>"Meet me at the "</span><span>,</span> <span>localvar</span><span>);</span> <span># localvar =&gt; "Meet me at the bar"</span>
    <span>gsub</span><span>(</span><span>"e+"</span><span>,</span> <span>"."</span><span>,</span> <span>localvar</span><span>);</span> <span># localvar =&gt; "m..t m. at th. bar"</span>

    <span># Search for a string that matches a regular expression</span>
    <span># index() does the same thing, but doesn't allow a regular expression</span>
    <span>match</span><span>(</span><span>localvar</span><span>,</span> <span>"t"</span><span>);</span> <span># =&gt; 4, since the 't' is the fourth character</span>

    <span># Split on a delimiter</span>
    <span>n</span> <span>=</span> <span>split</span><span>(</span><span>"foo-bar-baz"</span><span>,</span> <span>arr</span><span>,</span> <span>"-"</span><span>);</span> <span># a[1] = "foo"; a[2] = "bar"; a[3] = "baz"; n = 3</span>

    <span># Other useful stuff</span>
    <span>sprintf</span><span>(</span><span>"%s %d %d %d"</span><span>,</span> <span>"Testing"</span><span>,</span> <span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>);</span> <span># =&gt; "Testing 1 2 3"</span>
    <span>substr</span><span>(</span><span>"foobar"</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>);</span> <span># =&gt; "oob"</span>
    <span>substr</span><span>(</span><span>"foobar"</span><span>,</span> <span>4</span><span>);</span> <span># =&gt; "bar"</span>
    <span>length</span><span>(</span><span>"foo"</span><span>);</span> <span># =&gt; 3</span>
    <span>tolower</span><span>(</span><span>"FOO"</span><span>);</span> <span># =&gt; "foo"</span>
    <span>toupper</span><span>(</span><span>"foo"</span><span>);</span> <span># =&gt; "FOO"</span>
<span>}</span>

<span>function</span> <span>io_functions</span><span>(</span>    <span>localvar</span><span>)</span> <span>{</span>

    <span># You've already seen print</span>
    <span>print</span> <span>"Hello world"</span><span>;</span>

    <span># There's also printf</span>
    <span>printf</span><span>(</span><span>"%s %d %d %d\n"</span><span>,</span> <span>"Testing"</span><span>,</span> <span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>);</span>

    <span># AWK doesn't have file handles, per se. It will automatically open a file</span>
    <span># handle for you when you use something that needs one. The string you used</span>
    <span># for this can be treated as a file handle, for purposes of I/O. This makes</span>
    <span># it feel sort of like shell scripting, but to get the same output, the string</span>
    <span># must match exactly, so use a variable:</span>

    <span>outfile</span> <span>=</span> <span>"/tmp/foobar.txt"</span><span>;</span>

    <span>print</span> <span>"foobar"</span> <span>&gt;</span> <span>outfile</span><span>;</span>

    <span># Now the string outfile is a file handle. You can close it:</span>
    <span>close</span><span>(</span><span>outfile</span><span>);</span>

    <span># Here's how you run something in the shell</span>
    <span>system</span><span>(</span><span>"echo foobar"</span><span>);</span> <span># =&gt; prints foobar</span>

    <span># Reads a line from standard input and stores in localvar</span>
    <span>getline</span> <span>localvar</span><span>;</span>

    <span># Reads a line from a pipe (again, use a string so you close it properly)</span>
    <span>cmd</span> <span>=</span> <span>"echo foobar"</span><span>;</span>
    <span>cmd</span> <span>|</span> <span>getline</span> <span>localvar</span><span>;</span> <span># localvar =&gt; "foobar"</span>
    <span>close</span><span>(</span><span>cmd</span><span>);</span>

    <span># Reads a line from a file and stores in localvar</span>
    <span>infile</span> <span>=</span> <span>"/tmp/foobar.txt"</span><span>;</span>
    <span>getline</span> <span>localvar</span> <span>&lt;</span> <span>infile</span><span>;</span> 
    <span>close</span><span>(</span><span>infile</span><span>);</span>
<span>}</span>

<span># As I said at the beginning, AWK programs consist of a collection of patterns</span>
<span># and actions. You've already seen the BEGIN pattern. Other</span>
<span># patterns are used only if you're processing lines from files or standard</span>
<span># input.</span>
<span>#</span>
<span># When you pass arguments to AWK, they are treated as file names to process.</span>
<span># It will process them all, in order. Think of it like an implicit for loop,</span>
<span># iterating over the lines in these files. these patterns and actions are like</span>
<span># switch statements inside the loop. </span>

<span>/^fo+bar$/</span> <span>{</span>

    <span># This action will execute for every line that matches the regular</span>
    <span># expression, /^fo+bar$/, and will be skipped for any line that fails to</span>
    <span># match it. Let's just print the line:</span>

    <span>print</span><span>;</span>

    <span># Whoa, no argument! That's because print has a default argument: $0.</span>
    <span># $0 is the name of the current line being processed. It is created</span>
    <span># automatically for you.</span>

    <span># You can probably guess there are other $ variables. Every line is</span>
    <span># implicitly split before every action is called, much like the shell</span>
    <span># does. And, like the shell, each field can be access with a dollar sign</span>

    <span># This will print the second and fourth fields in the line</span>
    <span>print</span> <span>$</span><span>2</span><span>,</span> <span>$</span><span>4</span><span>;</span>

    <span># AWK automatically defines many other variables to help you inspect and</span>
    <span># process each line. The most important one is NF</span>

    <span># Prints the number of fields on this line</span>
    <span>print</span> <span>NF</span><span>;</span>

    <span># Print the last field on this line</span>
    <span>print</span> <span>$</span><span>NF</span><span>;</span>
<span>}</span>

<span># Every pattern is actually a true/false test. The regular expression in the</span>
<span># last pattern is also a true/false test, but part of it was hidden. If you</span>
<span># don't give it a string to test, it will assume $0, the line that it's</span>
<span># currently processing. Thus, the complete version of it is this:</span>

<span>$</span><span>0</span> <span>~</span> <span>/^fo+bar$/</span> <span>{</span>
    <span>print</span> <span>"Equivalent to the last pattern"</span><span>;</span>
<span>}</span>

<span>a</span> <span>&gt;</span> <span>0</span> <span>{</span>
    <span># This will execute once for each line, as long as a is positive</span>
<span>}</span>

<span># You get the idea. Processing text files, reading in a line at a time, and</span>
<span># doing something with it, particularly splitting on a delimiter, is so common</span>
<span># in UNIX that AWK is a scripting language that does all of it for you, without</span>
<span># you needing to ask. All you have to do is write the patterns and actions</span>
<span># based on what you expect of the input, and what you want to do with it.</span>

<span># Here's a quick example of a simple script, the sort of thing AWK is perfect</span>
<span># for. It will read a name from standard input and then will print the average</span>
<span># age of everyone with that first name. Let's say you supply as an argument the</span>
<span># name of a this data file:</span>
<span>#</span>
<span># Bob Jones 32</span>
<span># Jane Doe 22</span>
<span># Steve Stevens 83</span>
<span># Bob Smith 29</span>
<span># Bob Barker 72</span>
<span>#</span>
<span># Here's the script:</span>

<span>BEGIN</span> <span>{</span>

    <span># First, ask the user for the name</span>
    <span>print</span> <span>"What name would you like the average age for?"</span><span>;</span>

    <span># Get a line from standard input, not from files on the command line</span>
    <span>getline</span> <span>name</span> <span>&lt;</span> <span>"/dev/stdin"</span><span>;</span>
<span>}</span>

<span># Now, match every line whose first field is the given name</span>
<span>$</span><span>1</span> <span>==</span> <span>name</span> <span>{</span>

    <span># Inside here, we have access to a number of useful variables, already</span>
    <span># pre-loaded for us:</span>
    <span># $0 is the entire line</span>
    <span># $3 is the third field, the age, which is what we're interested in here</span>
    <span># NF is the number of fields, which should be 3</span>
    <span># NR is the number of records (lines) seen so far</span>
    <span># FILENAME is the name of the file being processed</span>
    <span># FS is the field separator being used, which is " " here</span>
    <span># ...etc. There are plenty more, documented in the man page.</span>

    <span># Keep track of a running total and how many lines matched</span>
    <span>sum</span> <span>+=</span> <span>$</span><span>3</span><span>;</span>
    <span>nlines</span><span>++</span><span>;</span>
<span>}</span>

<span># Another special pattern is called END. It will run after processing all the</span>
<span># text files. Unlike BEGIN, it will only run if you've given it input to</span>
<span># process. It will run after all the files have been read and processed</span>
<span># according to the rules and actions you've provided. The purpose of it is</span>
<span># usually to output some kind of final report, or do something with the</span>
<span># aggregate of the data you've accumulated over the course of the script.</span>

<span>END</span> <span>{</span>
    <span>if</span> <span>(</span><span>nlines</span><span>)</span>
        <span>print</span> <span>"The average age for "</span> <span>name</span> <span>" is "</span> <span>sum</span> <span>/</span> <span>nlines</span><span>;</span>
<span>}</span>
</pre><p><span>&nbsp;</span></p></div>]]></description>
	<dc:creator>BioJoker</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/12582/postdoc-position-at-centre-mediterraneen-de-medecine-moleculaire</guid>
  <pubDate>Sun, 06 Jul 2014 11:23:06 -0500</pubDate>
  <link></link>
  <title><![CDATA[Postdoc position at Centre Méditerranéen de Médecine Moléculaire]]></title>
  <description><![CDATA[
<p>The research group of Dr. Michele Trabucchi at the Centre Méditerranéen de Médecine Moléculaire (C3M) at INSERM U1065 (University of Nice Sophia-Antipolis, France) is seeking candidates for a Postdoctoral fellow position to start on October 2014 for 3 years funded by FRM (Fondation pour la Recherche Médicale).<br />The broad interest of the lab is in understanding the expression control and function of small RNAs in activated myeloid cells (visit our webpage to check research interests and publications of the group : http://www.unice.fr/c3m/EN/Equipe10.html ). </p>

<p>The work will focus on the functional studies of small RNAs by using next-generation sequencing approaches.<br /> <br />Candidates should hold a Ph.D. degree and have strong background in bioinformatics.<br />The University of Nice Sophia-Antipolis provides a wide range of facilities and training essential for biomedical research.<br />Interested applicants should send a PDF with a cover letter stating research interests and qualifications, an updated CV, a summary of previous research experience and contact information for two references to Michele Trabucchi ( mtrabucchi@unice.fr )</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43863/snakemake-tutorials</guid>
	<pubDate>Mon, 09 May 2022 05:20:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43863/snakemake-tutorials</link>
	<title><![CDATA[Snakemake Tutorials !]]></title>
	<description><![CDATA[<p>A lesson introducing the Snakemake workflow system for bioinformatics analysis.</p>
<blockquote>
<h2 id="prerequisites">Prerequisites<a href="https://carpentries-incubator.github.io/snakemake-novice-bioinformatics/index.html#prerequisites"></a></h2>
<p>This is an intermediate lesson and assumes learners have already done some bioinformatics:</p>
<ul>
<li>Familiarity with the BASH command shell, including concepts like pipes, variables and loops.</li>
<li>Knowledge of bioinformatics fundamentals like the FASTQ file format and transcriptome sequencing, in order to understand the example workflow.</li>
</ul>
<p>No previous knowledge of Snakemake or workflow systems is required.</p>
<p>https://carpentries-incubator.github.io/snakemake-novice-bioinformatics/index.html</p>
</blockquote><p>Address of the bookmark: <a href="https://carpentries-incubator.github.io/snakemake-novice-bioinformatics/aio/index.html" rel="nofollow">https://carpentries-incubator.github.io/snakemake-novice-bioinformatics/aio/index.html</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/12870/nuclear-dynamics-lab</guid>
  <pubDate>Thu, 17 Jul 2014 15:03:27 -0500</pubDate>
  <link></link>
  <title><![CDATA[Nuclear Dynamics Lab]]></title>
  <description><![CDATA[
<p>Lab focus is to elucidate fundamental principles, new mechanisms, machineries and emergent properties that are involved in maintaining the genome and gene expression programmes for improvements in lifelong health and well-being for all.</p>

<p>More at http://www.babraham.ac.uk/our-research/nuclear-dynamics/</p>
]]></description>
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