  Background: In organisms' genomes, promoters are short DNA sequences on the
upstream of structural genes, with the function of controlling genes'
transcription. Promoters can be roughly divided into two classes: constitutive
promoters and inducible promoters. Promoters with clear functional annotations
are practical synthetic biology biobricks. Many statistical and machine
learning methods have been introduced to predict the functions of candidate
promoters. Spectral Eigenmap has been proved to be an effective clustering
method to classify biobricks, while support vector machine (SVM) is a powerful
machine learning algorithm, especially when dataset is small. Methods: The two
algorithms: spectral embedding and SVM are applied to the same dataset with 375
prokaryotic promoters. For spectral embedding, a Laplacian matrix is built with
edit distance, followed by K-Means Clustering. The sequences are represented by
numeric vector to serve as dataset for SVM trainning. Results: SVM achieved a
high predicting accuracy of 93.07% in 10-fold cross validation for
classification of promoters' transcriptional functions. Laplacian eigenmap
(spectral embedding) based on editing distance may not be capable for
extracting discriminative features for this task.

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