# Introduction to ampir

#### Legana Fingerhut

#### 2019-11-26

# Background

The ampir (short for antimicrobial peptide prediction in r ) package was designed to be a fast and user-friendly method to predict antimicrobial peptides (AMPs) from any given size protein dataset. ampir uses a supervised statistical machine learning approach to predict AMPs. It incorporates a support vector machine classification model that has been trained on publicly available antimicrobial peptide data.

## Usage

Standard input to ampir is a `data.frame` with sequence names in the first column and protein sequences in the second column.

    library(ampir)

Read in a FASTA formatted file as a `data.frame` with `read_faa()`

    my_protein_df <- read_faa(system.file("extdata/bat_protein.fasta", package = "ampir"))

seq_name | seq_aa
--- | ---
G1P6H5_MYOLU | MALTVRIQAACLLLLLLASLTSYSLLLSQTTQLADLQTQDTAGAT…

Calculate the probability that each protein is an antimicrobial peptide with `predict_amps()`

Note that amino acid sequences that are shorter than five amino acids long and/or contain anything other than the standard 20 amino acids are not evaluated and will contain an `NA` as their `prob_AMP` value.

    my_prediction <- predict_amps(my_protein_df)

seq_name | seq_aa | prob_AMP
--- | --- | ---
G1P6H5_MYOLU | MALTVRIQAACLLLLLLASLTSYSLLLSQTTQLADLQTQDTAGAT… | 0.934

Predicted proteins with a specified predicted probability value could then be extracted and written to a FASTA file:

    my_predicted_amps <- my_protein_df[my_prediction[,3] >= 0.8,]

seq_name | seq_aa
--- | ---
G1P6H5_MYOLU | MALTVRIQAACLLLLLLASLTSYSLLLSQTTQLADLQTQDTAGAT…

Write the `data.frame` with sequence names in the first column and protein sequences in the second column to a FASTA formatted file with `df_to_faa()`

    df_to_faa(my_predicted_amps, tempfile("my_predicted_amps", tempdir()))