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Output format

EffectorP will return the output as shown in the example below. First, it will return the predicted effectors in your set as FASTA sequences, if there are any. Second, a summary table will be shown which shows the predictions (effector or non-effector) for each submitted protein.

As a probabilistic classifier (Naive Bayes), EffectorP returns a probability that a tested instance will belong to either the effector or non-effector class and these probabilities are included in the web server output as additional information to researchers. However, in Naive Bayes classification these are known to be only rough estimations and should therefore not be overinterpreted.

We deliberately did not recommend a probability threshold over which a protein would be classified as an effector candidate, as we believe it should remain up to the individual user to interpret their results in the context of additional resources available. For example, a researcher might like to predict the full effector candidate complement using EffectorP and overlay this with in planta expression data to prioritize candidates, whereas in other situations without additional information a list of high-priority candidates as determined by the EffectorP probabilities might be more appropriate.

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Example output for EffectorP 3.0

3 proteins were provided as input.

Predicted effectors: 3 (100.0%)
Predicted cytoplasmic effectors: 0 (0.0%)
Predicted apoplastic effectors: 3 (100.0%)

66.7% of the apoplastic effectors are predicted dual-localized (apoplastic/cytoplasmic).

Predicted effectors (download as FASTA file here):

Summary table

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#Identifier	Cytoplasmic effector	Apoplastic effector	Non-effector	Prediction
ToxA	Y (0.551)	Y (0.767)	-	Apoplastic/cytoplasmic effector
Tox1	Y (0.715)	Y (0.946)	-	Apoplastic/cytoplasmic effector
Tox3	-	Y (0.573)	-	Apoplastic effector

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Example output for EffectorP 2.0

3 of your 3 submitted proteins are predicted to be effectors (100.0%).

Predicted effectors (download as FASTA file here):

Summary table

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#Identifier	Prediction	Probability
ToxA	Effector	0.998
Tox1	Effector	1.0
Tox3	Effector	0.87

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