1.20.0

[mothur.git] / chimerauchimecommand.cpp

diff --git a/chimerauchimecommand.cpp b/chimerauchimecommand.cpp
index 375964c306651e93c07966d464b02cf24acb187c..af427d8b6445e39185661bf51782bec1efc54fe6 100644 (file)
--- a/chimerauchimecommand.cpp
+++ b/chimerauchimecommand.cpp
@@ -65,7 +65,7 @@ string ChimeraUchimeCommand::getHelpString(){
                helpString += "The abskew parameter can only be used with template=self. Minimum abundance skew. Default 1.9. Abundance skew is: min [ abund(parent1), abund(parent2) ] / abund(query).\n";
                helpString += "The chimealns parameter allows you to indicate you would like a file containing multiple alignments of query sequences to parents in human readable format. Alignments show columns with differences that support or contradict a chimeric model.\n";
                helpString += "The minh parameter - mininum score to report chimera. Default 0.3. Values from 0.1 to 5 might be reasonable. Lower values increase sensitivity but may report more false positives. If you decrease xn you may need to increase minh, and vice versa.\n";
-               helpString += "The mindiv parameter - minimum divergence ratio, default 0.5. Div ratio is 100%% - %%identity between query sequence and the closest candidate for being a parent. If you don't care about very close chimeras, then you could increase mindiv to, say, 1.0 or 2.0, and also decrease --min h, say to 0.1, to increase sensitivity. How well this works will depend on your data. Best is to tune parameters on a good benchmark.\n";
+               helpString += "The mindiv parameter - minimum divergence ratio, default 0.5. Div ratio is 100%% - %%identity between query sequence and the closest candidate for being a parent. If you don't care about very close chimeras, then you could increase mindiv to, say, 1.0 or 2.0, and also decrease minh, say to 0.1, to increase sensitivity. How well this works will depend on your data. Best is to tune parameters on a good benchmark.\n";
                helpString += "The xn parameter - weight of a no vote. Default 8.0. Decreasing this weight to around 3 or 4 may give better performance on denoised data.\n";
                helpString += "The dn parameter - pseudo-count prior on number of no votes. Default 1.4. Probably no good reason to change this unless you can retune to a good benchmark for your data. Reasonable values are probably in the range from 0.2 to 2.\n";
                helpString += "The xa parameter - weight of an abstain vote. Default 1. So far, results do not seem to be very sensitive to this parameter, but if you have a good training set might be worth trying. Reasonable values might range from 0.1 to 2.\n";