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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/2267?offset=20</link>
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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Here, the C in the reference genome has been replaced by a T in the variant.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/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/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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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43631/pangolin-tutorial</guid>
	<pubDate>Fri, 10 Dec 2021 05:58:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43631/pangolin-tutorial</link>
	<title><![CDATA[Pangolin tutorial !]]></title>
	<description><![CDATA[<p><span>This is a tutorial for using the Pangolin Web Application. For information on using the command line tool, please visit the&nbsp;</span><a href="https://cov-lineages.org/resources/pangolin/usage.html">command line tool usage page</a><span>.</span></p>
<p>https://cov-lineages.org/resources/pangolin/tutorial.html</p><p>Address of the bookmark: <a href="https://cov-lineages.org/resources/pangolin/tutorial.html" rel="nofollow">https://cov-lineages.org/resources/pangolin/tutorial.html</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/19556/genome-origami</guid>
	<pubDate>Fri, 12 Dec 2014 22:48:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/19556/genome-origami</link>
	<title><![CDATA[Genome Origami]]></title>
	<description><![CDATA[<p>There are several interesting factoid about our genomes, one of them is their folding. If we stretched out the DNA in a single cell, which is only a few millionths of an inch wide, it would span more than six feet. In other word, the size of six feet DNA fold themself to fit in a few millionths of an inch wide space. These DNA folding is a dynamic process that changes over time (!!). Researchers around the world have been trying to understand how DNA folds itself up so efficiently, and a recent post on the NIH Director&rsquo;s Blog highlights new research illustrating how the human genome folds inside the cell&rsquo;s nucleus, as well as how DNA folding affects gene regulation. The research team created this delightful video that demonstrates the principles involved using origami art.</p><p>http://bioinformaticsonline.com/videolist/watch/19555/a-3d-map-of-the-human-genome<br /><br />Researchers have been working to determine how cells regulate gene expression for nearly as long as we&rsquo;ve known about DNA. How, for example, do nerve cells know to turn off only nerve cell genes and turn off bone cell genes? DNA folding loops are part of the answer. This research team, which published their findings in a paper in Cell http://www.cell.com/cell/abstract/S0092-8674%2814%2901497-4 , found that the number of loops is much lower than expected. There are only 10,000 loops instead of the predicted millions, and they form on/off switches in DNA.<br /><br /></p><p>More at http://www.eurekalert.org/pub_releases/2014-12/ru-3mr121114.php</p><p>Reference http://www.cell.com/cell/abstract/S0092-8674%2814%2901497-4</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4043/what-is-bioinformatics</guid>
	<pubDate>Wed, 28 Aug 2013 06:53:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4043/what-is-bioinformatics</link>
	<title><![CDATA[What is Bioinformatics?]]></title>
	<description><![CDATA[<iframe src="http://player.vimeo.com/video/71581534?byline=0" width="" height="" frameborder="0" webkitAllowFullScreen allowFullScreen></iframe>Illustration and Animation: Rachel Robinson Script: Tiffany Trent Voice-over: Kris Monger Sound: Glisten Carefully by Guennadi Malyshevski]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42672/introduction-to-bioinformatics-and-computational-biology</guid>
	<pubDate>Mon, 25 Jan 2021 01:32:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42672/introduction-to-bioinformatics-and-computational-biology</link>
	<title><![CDATA[Introduction to Bioinformatics and Computational Biology]]></title>
	<description><![CDATA[<p><span>This is the course material for STAT115/215 BIO/BST282 at Harvard University.</span></p>
<p>Xiaole Shirley Liu (lead instructor)<br>Joshua Starmer<br>Martin Hemberg<br>Ting Wang<br>Feng Yue</p>
<p>Ming Tang<br>Yang Liu<br>Jack Kang<br>Scarlett Ge<br>Jiazhen Rong<br>Phillip Nicol<br>Maartin De Vries</p>
<p>We thank many colleagues in the community, who helped Dr.&nbsp;Liu in prepare the STAT115/215 BIO/BST282 course over the years.&nbsp;</p><p>Address of the bookmark: <a href="https://liulab-dfci.github.io/bioinfo-combio/" rel="nofollow">https://liulab-dfci.github.io/bioinfo-combio/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2253/best-practices-in-bioinformatics-training-for-life-scientists</guid>
	<pubDate>Tue, 13 Aug 2013 15:47:34 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2253/best-practices-in-bioinformatics-training-for-life-scientists</link>
	<title><![CDATA[Best practices in bioinformatics training for life scientists]]></title>
	<description><![CDATA[<p>Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts.</p>
<p>Find the detail paper at http://bib.oxfordjournals.org/content/early/2013/06/25/bib.bbt043.full</p><p>Address of the bookmark: <a href="http://bib.oxfordjournals.org/content/early/2013/06/25/bib.bbt043.full" rel="nofollow">http://bib.oxfordjournals.org/content/early/2013/06/25/bib.bbt043.full</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/28564/dbt-%E2%80%93-bioinformatics-industrial-training-programme-biitp-2016-%E2%80%93-17</guid>
	<pubDate>Wed, 27 Jul 2016 04:09:59 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/28564/dbt-%E2%80%93-bioinformatics-industrial-training-programme-biitp-2016-%E2%80%93-17</link>
	<title><![CDATA[DBT – Bioinformatics Industrial Training Programme (BIITP) 2016 – 17]]></title>
	<description><![CDATA[<p>BIITP is a programme of Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India, managed by Biotech Consortium India Limited (BCIL).The objective of BIITP is to provide an opportunity to bioinformatics students to acquire practical skills and experience by working on projects alongside industry experts as well as to provide an opportunity for the industry to identify potential employees.</p><p><strong>DBT Invites online applications from the bioinformatics&nbsp;students and requisitions from biotech/bioinformatics companies.</strong></p><p><strong>Biotech Industry</strong>&nbsp;:</p><p>Biotech/Bioinformatics companies interested to provide hands on industrial training to the students of Bioinformatics under BIITP may apply online. The companies would have no obligation towards any payments to trainees. The companies would be paid bench fee to cover expenses towards training. Trainees would be provided to companies subject to availability.</p><p><strong>Attn: Bioinformatics Students</strong></p><p>Bioinformatics students interested in training in biotech / bioinformatics companies may apply online.&nbsp;<strong>Stipend of Rs. 10,000/- per month</strong>&nbsp;will be paid to candidates placed for training. The candidates will be selected for training through an interview.</p><p><strong>Eligiblity</strong>&nbsp;:</p><p>a) B.E /B.Tech./M.Sc./M.Tech./Advanced Post Graduate Diploma in Bioinformatics from an Indian recognized university with minimum 55% marks or equivalent grade at highest degree/diploma completed in the year 2015 or 2016 are only eligible to apply.</p><p>b) The Advanced Post Graduate diploma should be of at least one year duration after graduation.</p><p>c)&nbsp; Students whose result of last semester/final year is not declared can also apply mentioning their marks upto the semester/year upto which result declared. The final result with original mark sheet(s) of all the semesters/years will have to be produced at the time of interview.</p><p><strong>Application Procedure</strong>&nbsp;:</p><p>The online application form is available below :</p><p><strong><a href="https://www.biotecnika.org/2016/07/dbt-bioinformatics-industrial-training-programme-biitp-2016-17/?xurl=%3A%2F%2Fwww.bcil.nic.in%2Fbiitp2016-17%2Fregistration1.asp" target="_blank">Application Form For Students (New User)</a></strong></p><p><strong><a href="https://www.biotecnika.org/2016/07/dbt-bioinformatics-industrial-training-programme-biitp-2016-17/?xurl=%3A%2F%2Fwww.bcil.nic.in%2Fbiitp2016-17%2Fregistration.asp%3FT1%3DCompany" target="_blank">Requisition form for companies (New User)</a></strong></p><p><strong><a href="https://www.biotecnika.org/2016/07/dbt-bioinformatics-industrial-training-programme-biitp-2016-17/?xurl=%3A%2F%2Fwww.bcil.nic.in%2Fbiitp2016-17%2Findex1.asp" target="_blank">Already registered User Click Here</a></strong></p><p>The following documents are to be sent to Mr. Manoj Gupta, Manager, Biotech Consortium India Limited, 5th floor, Anuvrat Bhawan, 210, Deen Dayal UpadhyayaMarg, New Delhi-110002.</p><p>More at&nbsp;http://www.bcil.nic.in/biitp2016-17/index.asp</p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/42813/bioinformatics-in-africa-part5-nigeria</guid>
	<pubDate>Sat, 06 Feb 2021 21:13:47 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/42813/bioinformatics-in-africa-part5-nigeria</link>
	<title><![CDATA[Bioinformatics in Africa: Part5 - Nigeria]]></title>
	<description><![CDATA[<p>Covenant University (CU)&shy;Ota:<br />Covenant University (with her enriching and growing state&shy;of&shy;the&shy;art laboratories in the area of &nbsp;science and technology, arts, business and social sciences) is presently the Best University in &nbsp;Nigeria (Private University category), based on the recent over&shy;all rating just concluded by the &nbsp;Nigeria &nbsp; University &nbsp; Commission &nbsp; (NUC). &nbsp; Recently, &nbsp; Covenant &nbsp; University &nbsp; has &nbsp; initiated &nbsp; the &nbsp;establishment of a Centre for Applied Biotech, Bio&shy;Informatics and Microbiology (CBBM) to be &nbsp;situated at the University. The institute has been designed to be a Public&shy;Private Partnership for a productive synergy b/w Academia, Industry and Government. The whole concept is still evolving &nbsp;and more details will be release soon. As regards CBBM, a dedicated computing lab is in plan, but even our computing capacity is &nbsp;presently enormous. In the department of Computer and Information Sciences, we have more than &nbsp;250 Pentium 4 PCs set aside for teaching and research purposes. Furthermore, we have several &nbsp;moderate speed PCs at the Postgraduate research lab and our engineering departments and units. &nbsp;Our wet lab facilities is presently minimal (basic for teaching), the Centre requirement as it touches &nbsp;the wet&shy;laboratories is also set to upgrade this to basic tools expected at an international centre of learning.</p><p>University&nbsp;of&nbsp;Ibadan&nbsp;(UIB)&shy;Ibadan:<br />There&nbsp;has&nbsp;been&nbsp;significant&nbsp;increase&nbsp;in&nbsp;the&nbsp;number&nbsp;of&nbsp;bioinformatics&nbsp;activities&nbsp;in&nbsp;Nigeria&nbsp;(and&nbsp;West Africa)&nbsp;since&nbsp;2003&nbsp;when&nbsp;the&nbsp;program&nbsp;was&nbsp;initiated&nbsp;by&nbsp;the&nbsp;West&nbsp;African&nbsp;Biotechnology&nbsp;Workshops Series&nbsp;(WABWS,&nbsp;http://www.wabw.org)&nbsp;at&nbsp;the&nbsp;University&nbsp;of&nbsp;Ibadan,&nbsp;Nigeria&nbsp;(in&nbsp;collaboration&nbsp;with&nbsp; the&nbsp;South&nbsp;African&nbsp;National&nbsp;Bioinformatics&nbsp;Institute&nbsp;(SANBI,&nbsp;http:/www.sanbi.ac.za).&nbsp;Workshops&nbsp; that&nbsp;were&nbsp;open&nbsp;to&nbsp;scientists&nbsp;from&nbsp;all&nbsp;African&nbsp;countries&nbsp;have&nbsp;seen&nbsp;a&nbsp;very&nbsp;high&nbsp;number&nbsp;of&nbsp;applications&nbsp; from&nbsp;scientists&nbsp;based&nbsp;in&nbsp;West&nbsp;Africa.&nbsp;The&nbsp;encouraging&nbsp;desire&nbsp;to&nbsp;acquire&nbsp;cutting&shy;edge&nbsp;skills&nbsp;to&nbsp; computational&nbsp;process&nbsp;data&nbsp;and&nbsp;extract&nbsp;useful&nbsp;knowledge&nbsp;from&nbsp;genome&nbsp;projects&nbsp;led&nbsp;to&nbsp;the&nbsp;interest&nbsp;of&nbsp; the&nbsp;West&nbsp;African&nbsp;Biotechnology&nbsp;Workshops&nbsp;(WABW)&nbsp;to&nbsp;develop&nbsp;an&nbsp;agenda&nbsp;to&nbsp;address&nbsp;the&nbsp; bioinformatics&nbsp;skills&nbsp;gap&nbsp;among&nbsp;scientists&nbsp;in&nbsp;West&nbsp;Africa.&nbsp;An&nbsp;increased&nbsp;commitment&nbsp;from&nbsp;agencies&nbsp; like&nbsp;NEPAD&nbsp;would&nbsp;be&nbsp;required&nbsp;in&nbsp;the&nbsp;provision&nbsp;of&nbsp;infrastructure&nbsp;to&nbsp;establish&nbsp;and&nbsp;sustain&nbsp;regional&nbsp; and&nbsp;national&nbsp;networks.</p><p>University&nbsp;of&nbsp;Ilorin&nbsp;(UIL)&shy;Ilorin:<br />The&nbsp;University&nbsp;of&nbsp;Ilorin&nbsp;was&nbsp;established&nbsp;in&nbsp;1976&nbsp;by&nbsp;the&nbsp;Federal&nbsp;Government&nbsp;of&nbsp;Nigeria.&nbsp; Bioinformatics&nbsp;activities&nbsp;started&nbsp;at&nbsp;the&nbsp;University&nbsp;in&nbsp;February&nbsp;2003&nbsp;with&nbsp;the&nbsp;establishment&nbsp;of&nbsp;the&nbsp; West&nbsp;African&nbsp;Bioinformatics&nbsp;Research&nbsp;Initiative&nbsp;(WABRI).&nbsp;However,&nbsp;progress&nbsp;has&nbsp;been&nbsp;rather&nbsp;slow&nbsp; due&nbsp;to&nbsp;inadequate&nbsp;funding.&nbsp;We&nbsp;are&nbsp;mainly&nbsp;engaged&nbsp;in&nbsp;Bioinformatics&nbsp;training&nbsp;at&nbsp;the&nbsp;introductory&nbsp; level&nbsp;and&nbsp;proteomics&nbsp;studies&nbsp;on&nbsp;various&nbsp;species&nbsp;of&nbsp;malaria&nbsp;parasites.&nbsp;Recently,&nbsp;we&nbsp;became&nbsp;interested&nbsp; in&nbsp;comparative&nbsp;genome&nbsp;analysis&nbsp;of&nbsp;various&nbsp;species&nbsp;of &nbsp;Plasmodium&nbsp; and&nbsp;the&nbsp;comparison&nbsp;of&nbsp; chloroquine&nbsp;sensitive&nbsp;and&nbsp;chloroquine&nbsp;resistant&nbsp;strains&nbsp;of&nbsp;Plasmodium&nbsp;falciparum.&nbsp;Other&nbsp;activities&nbsp; and&nbsp;areas&nbsp;of&nbsp;interest&nbsp;can&nbsp;be&nbsp;seen&nbsp;on&nbsp;our&nbsp;website,&nbsp;http://www.wabri.org,&nbsp;although&nbsp;not&nbsp;all&nbsp;our&nbsp; proposed&nbsp;interests&nbsp;have&nbsp;been&nbsp;fully&nbsp;implemented&nbsp;due&nbsp;to&nbsp;our&nbsp;level&nbsp;of&nbsp;funding.</p><p>Training:<br />The&nbsp;University&nbsp;of&nbsp;Ilorin&nbsp;has&nbsp;introduced&nbsp;M.Sc.&nbsp;and&nbsp;Ph.D.&nbsp;programmes&nbsp;in&nbsp;Computer&nbsp;Science&nbsp;(with&nbsp; options&nbsp;in&nbsp;Bioinformatics).&nbsp;The&nbsp;programme&nbsp;is&nbsp;based&nbsp;in&nbsp;the&nbsp;Department&nbsp;of&nbsp;Computer&nbsp;Science&nbsp;and&nbsp; emphasis&nbsp;is&nbsp;on&nbsp;the&nbsp;development&nbsp;of&nbsp;algorithms&nbsp;to&nbsp;solve&nbsp;problems&nbsp;in&nbsp;bioinformatics. The&nbsp;Covenant&nbsp;University&nbsp;offers&nbsp;M.Sc.&nbsp;and&nbsp;Ph.D&nbsp;in&nbsp;Computer&nbsp;Science&nbsp;with&nbsp;option&nbsp;in&nbsp;Bioinformatics&nbsp; (Computational&nbsp;Biology).&nbsp;Furthermore,&nbsp;through&nbsp;affiliated&nbsp;departments,&nbsp;the&nbsp;CBBM&nbsp;is&nbsp;been&nbsp;design&nbsp;to&nbsp;award&nbsp;Diploma&nbsp;and&nbsp;Degree&nbsp;certificates&nbsp;in&nbsp;Biotechnology.</p><p>Web&nbsp;sites&nbsp;and&nbsp;links: http://www.covenantuniversity.com http://www.run.edu.ng http://www.uniben.edu http://www.wabri.org http://www.wabw.org http://www.unilorin.edu.ng http://www.wabri.org http://www.asopah.org</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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