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deepredeff provides a selection of models trained on sequences from different phytopathogen taxa. It is important to select the correct model. Each taxon is represented by just one model at the moment. You can select the model by setting the taxon in the main function, predict_effector(). Available values are bacteria, fungi and oomycete.
predict_effector(
input = "my_fungal_seqs.fa",
taxon = "fungi"
)For each taxon, deepredeff uses a different model. These models are:
bacteria: ensemble_weighted, which is a weighted ensemble model of CNN-LSTM, CNN-GRU, GRU Embedding, and LSTM-Embedding.fungi: CNN-LSTM.oomycete: CNN-LSTM.The model used for your prediction will also be shown when you run the function predict_effector() or when you run summary().
Here we will take example of fungal sequences in data frame format called input_fungi_df as shown below:
input_fungi_df | name | sequence |
|---|---|
| tr|A0A0A0S3X0|A0A0A0S3X0_LEPMC Avirulence protein OS=Leptosphaeria maculans OX=5022 GN=AvrLm2 PE=4 SV=1 | MRLANFLFYLAPMIVSSLAFDFVPLSGELDFSQEMVFINLTQQQFSELHL QHQQWHQKNILKRYTLTELDEICQQYNANFRFNSGFCSGKDRRWDCYDLN FPTTQSERRVQRRRVCRGEHQTCETIDVINAFGAHARFPQCVHRFELPIN DPIPYKDSYQGQYTVEKALDDSWEDILANTGGSHVDFSYQSGTQHYQGYG LTFACIHCIGGSILRMIHANDPARATVTIKFH |
| tr|A0A0A2ILW0|A0A0A2ILW0_PENEN Peptidoglycan-binding Lysin subgroup OS=Penicillium expansum OX=27334 GN=PEX2_020570 PE=4 SV=1 | MFFPSLILAAGSLSTLIQAIPHGAKHHHSLHRRAAATYAVMGGDGEASDG WPTISQWSEYETLWGLNQILIAASCDNSDDETSDINTSIKSIASETGVDA RFILAIIMQESKGCVRVQSTNNGVENTGLMQSHDGEGSCNKDGSKTTPCP SSMITQMIQDGTAGTTQGDGLKQCYEAQTGGTAAKYYKAARTYNSGSIAS SGNLGQGGATHCYASDIANRVRGWAGDVSECVEATIGTITSGVESALGGD DGSSSTSTSTTAAQSTETAEPVQTSSSAAEQPVTTEPIQTSSAPAQAAET SSAASSATSTETTSVAPAPTWTPSSNVQVAAQTTTPTPSWTTKSAPAATT APAASSSASGTAPLYPYASSSCQKYYTVKAGDFCDKVTEAVGISFLDLRS LNPGLDEKCSDLWLGYQYCIKA |
deepredeff accepts these directly. To predict fungal effectors, you can specify the model to taxon= "fungi".
pred_fungi <- predict_effector(
input = input_fungi_df,
taxon = "fungi"
)
#> Loaded models successfully!
#> Model used for taxon fungi: cnn_lstm.In the same way as with the dataframe and fasta files we can use a Bioconductor AAStringSet object:
input_oomycete_aastringset
#> AAStringSet object of length 10:
#> width seq names
#> [1] 820 MVKLYCAVVGVAGSAFSVRVDES...SKKGKTAMILSRMHYDDDEADL sp|A0A0M5K865|CR1...
#> [2] 111 MRLAQVVVVIAASFLVATDALST...FQRYQKKANKIIEKQKAAAKNA tr|A5YTY8|A5YTY8_...To predict oomycete effectors, you can specify the model to taxon= "oomycete".
pred_oomycete <- predict_effector(
input = input_oomycete_aastringset,
taxon = "oomycete"
)
#> Loaded models successfully!
#> Model used for taxon oomycete: cnn_lstm.The same applies if we provide a character vector. Let us take an example of data in string format.
input_bacteria_strings
#> [1] "MPINRPAFNLKLNTAIAQ..."
#> [2] "MQFMSRINRILFVAV..."To predict bacteria effectors, you can specify the model to taxon = "bacteria".
pred_bacteria <- predict_effector(
input = input_bacteria_strings,
taxon = "bacteria"
)
#> Loaded models successfully!
#> Model used for taxon bacteria: ensemble_weighted.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.