Decontaminating eukaryotic genome assemblies with machine learning.BACKGROUND
 We introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences. 
machine learning
We introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences.  The major strength of the decision tree is that it can take any measured feature as input and does not require a priori identification of significant descriptors.  We use the decision tree to classify de novo assembled sequences and compare the method to published protocols.


 A decision tree performs better than existing methods when classifying sequences in eukaryotic de novo assemblies.  Importantly, a decision tree can be used to classify sequences according to measured descriptors and has potentially many uses in distilling biological datasets.
decision tree