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opticall committed 8275a6b

spelling mistakes fixed

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                 </p>
                 <pre>tar xvzf &lt;filename&gt;</pre>
                 <p class="lead">
-                which creates a folder of the same name as filename. cd into the optical folder:
+                which creates a folder of the same name as filename. cd into the optiCall folder:
                 </p>
                 <pre>cd &lt;foldername&gt;/opticall</pre>
                 <p class="lead">
-                then compile the opticall code by running the make command:
+                then compile the optiCall code by running the make command:
                 </p>
                 <pre>make</pre>
                 <p class="lead">
-                after which the opticall executable will appear, and you've successfully installed opticall!
+                after which the optiCall executable will appear, and you've successfully installed optiCall!
                 </p>
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                     <p class="lead">
 
 
-                        opticall reads in a file containing Illumina normalized intensities. 
+                        optiCall reads in a file containing Illumina normalized intensities. 
                         The intensity input file is tab separated, with SNPs are rows, and samples as columns. So a line would be:
                     
                     </p>
                         -nointcutoff
                     </h4>
                     <p class="lead">
-                        By default, when fitting the genotype mixture model, optiCall doesn't consider samples with outlying intensity values. They are still called once the mixture model has been fit. This option stops optiCall excluding such outliers from model fitting. Use this if you already have filtered samples/SNPs for intensity outliers
+                        By default, when fitting the genotype mixture model, optiCall doesn't consider samples with outlying intensity values. They are still called once the mixture model has been fit. This option stops optiCall excluding such outliers from model fitting. Use this if you already have filtered samples/SNPs for intensity outliers.
                     </p>
                 </div>
             </div>