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We have designed a high-throughput system for the identification of novel crystal protein genes (cry) from Bacillus thuringiensis strains. The system was developed with two goals: (i) to acquire the mixed plasmid-enriched genomic sequence of B. thuringiensis using next-generation sequencing biotechnology, and (ii) to identify cry genes with a computational pipeline (using BtToxin_scanner). In our pipeline method, we employed three different kinds of well-developed prediction methods, BLAST, hidden Markov model (HMM), and support vector machine (SVM), to predict the presence of Cry toxin genes. The pipeline proved to be fast (average speed, 1.02 Mb/min for proteins and open reading frames [ORFs] and 1.80 Mb/min for nucleotide sequences), sensitive (it detected 40% more protein toxin genes than a keyword extraction method using genomic sequences downloaded from GenBank), and highly specific …