Poly(A) motif prediction using spectral latent features from human DNA sequences
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Computer Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/325438
MetadataShow full item record
AbstractMotivation: 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.Results: 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 used 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 the previous state-of-the-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 makes ?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. The Author 2013.
CitationXie B, Jankovic BR, Bajic VB, Song L, Gao X (2013) Poly(A) motif prediction using spectral latent features from human DNA sequences. Bioinformatics 29: i316-i325. doi:10.1093/bioinformatics/btt218.
PublisherOxford University Press (OUP)
PubMed Central IDPMC3694652
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact email@example.com
- An improved poly(A) motifs recognition method based on decision level fusion.
- Authors: Zhang S, Han J, Liu J, Zheng J, Liu R
- Issue date: 2015 Feb
- Dragon PolyA Spotter: predictor of poly(A) motifs within human genomic DNA sequences.
- Authors: Kalkatawi M, Rangkuti F, Schramm M, Jankovic BR, Kamau A, Chowdhary R, Archer JA, Bajic VB
- Issue date: 2012 Jan 1
- An in-silico method for prediction of polyadenylation signals in human sequences.
- Authors: Liu H, Han H, Li J, Wong L
- Issue date: 2003
- A novel genome-wide polyadenylation sites recognition system based on condition random field.
- Authors: Han J, Zhang S, Liu J, Liu R
- Issue date: 2014
- Discovering cis-regulatory RNAs in Shewanella genomes by Support Vector Machines.
- Authors: Xu X, Ji Y, Stormo GD
- Issue date: 2009 Apr