Welcome to smAMPsTK Toolkit

smAMPsTK is written in python to detect antimicrobial peptides from plant's transcriptome data. smAMPsTK detects peptides of four different activities i.e. antimicrobial, antibacterial, antifungal, and antiviral. smAMPsTK takes transcriptome data (in FASTA format) as input and produces an output file of predicted AMPs (regarding four different activities), their sequences, scores derived from SVM models, and prediction results per the threshold provided. The SVM models classified peptides are processed for one more level of filtering by aligning them with the AMPs database generated from PlantPepDB. The smORFs detection is carried out by utilizing three ORF prediction tools. The individual tools differ from one another by the start codon of ORFs and their length range. Prediction of small peptides (length of 5-100 aa) regarding four activities, that is, AMP, ABP, AFP, and AVP is entirely independent of each other. It is executed via four SVM models running in parallel. The developed pipeline can be applied to any plant specie's transcriptome data to identify mature AMPs. We applied this pipeline to extract AMPs from five selected organisms, followed by their downstream analysis of conservation and evolutionary facets.

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