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Poly(A) motif Prediction Using Spectral Latent Features from Human DNA Sequences

Banff International Research Station for Mathematical Innovation and Discovery

Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA. Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge. We propose a novel machine learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we employed hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine tune the classification performance. We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14,740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of previous state-of-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false negative rate and false positive rate by 26%, 15% and 35%, respectively. Meanwhile, our method made about 30% fewer error predictions relative to the other string kernels. Furthermore, our method can be used to visualize the importance of oligomers and positions in predicting poly(A) motifs, from which we can observe a number of characteristics in the surrounding regions of true and false motifs that have not been reported before.

Mathematics; Statistics; Biology and other natural sciences; Applied statistics

video/mp4

Author affiliation: King Abdullah University of Science and Technology (KAUST)

2014-08-07

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/49846

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/