Details

The prodigal gene prediction pipeline is implemented in an ab-initio manner: a training model will be learned from the input contigs assembly sequence at first(when the model parameter is not specified), and then the gene finding algorithm is running based on the dynamic programming score of the coding/non-coding hexamer and the RBS motif of the trained model.

Authors

Value

a collection of the gene prediction result: each element in the collection(PredictionResult) is the gene prediction result of the corresponding contigs sequence in the input fasta sequence data.

this function returns a R# error message object if the input sequence data is nothing or can not be cast to a fasta sequence collection.

clr value class

• PredictionResult

Examples

 imports "bioseq.fasta" from "seqtoolkit";
 imports "bifrost" from "seqtoolkit";
 imports "annotation.genomics" from "seqtoolkit";
 
 # an example workflow script for run prodigal gene prediction on MAGs contigs assembly sequence, 
 # and export the result to files. The input contigs assembly sequence is in FASTA format, 
 # and the output gene prediction result is in PredictionResult format, which can be further 
 # converted to GFF3 format, or gene/protein FASTA format. The example workflow script is 
 # as follows:
 
 # read the contigs assembly sequence from a FASTA file
 let MAGs <- "MAGs_contigs.fasta";
 let contigs <- read.fasta(MAGs);
 # predict genes on the contigs assembly sequence 
 let result <- prodigal(contigs, min.ORF.len = 90);
 
 # export result to files
 write.csv(as.data.frame(result), file = "gene_predicts.csv");
 # export the gene prediction result to GFF3 format
 write.gff3(as.gff3(result), file = "gene_predicts.gff3");
 # export gene/protein fasta sequence to file
 write.fasta(as.genes(result), file = "gene_predicts.fna");
 write.fasta(as.proteins(result), file = "protein_predicts.faa");