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hotspots
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### This is the sixth tool in the eQTL backend pipeline: 
lookup, classification, frequency, sliding window frequency, hotspots, GO enrichment

Link to the workflow (for import into Galaxy): http://chewbacca.bi.up.ac.za:8080/u/nanette/w/back-end-workflow-2

Identify the max number of eQTL expected by chance per cM using a permutation approach.

Eliminate differential gene density as an explanatory factor for eQTL hotspots, by performing a chi-squared test per bin.

* Calculate the proportion of genes to eQTLs, use this as the population estimates and test the null hypothesis that the number of genes and eQTLs in each interval is consistent.
* Mark bins where the expected number (genes + eQTLs) of every interval is not 5 or more (assumption for chi-squared test). For these bins the chi-squared test cannot be performed.

Extract lists of eQTLs linked to each unbiased eQTL hotspot.

Genome wide eQTL freqeuncy plots.

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Installation
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The eQTL backend pipeline is available for: 
* command line usage
* integration into Galaxy servers


Requirements: 	Python 2.7
		R 3.1.1