Detection of RNA polymerase II promoters and polyadenylation sites in human DNA sequence

Matis, S.; Xu, Ying; Shah, M.; Guan, Xiaojun; Einstein, J.R.; Mural, Richard J.; Uberbacher, Edward C.

doi:10.1016/s0097-8485(96)80015-5

Cited by 30 publications

(7 citation statements)

References 12 publications

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“…Despite decades of work, computational identification of promoters remains a stubborn problem [ 222 ]. Researchers have used neural networks for promoter recognition as early as 1996 [ 223 ]. Recently, a CNN recognized promoter sequences with sensitivity and specificity exceeding 90% [ 224 ].…”

Section: Deep Learning To Study the Fundamental Biological Processes mentioning

confidence: 99%

Opportunities and obstacles for deep learning in biology and medicine

Ching

Himmelstein

Beaulieu‐Jones

et al. 2018

J. R. Soc. Interface.

1,595

1,001

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Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

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Section: Deep Learning To Study the Fundamental Biological Processes mentioning

confidence: 99%

Opportunities and obstacles for deep learning in biology and medicine

Ching

Himmelstein

Beaulieu‐Jones

et al. 2018

J. R. Soc. Interface.

1,595

1,001

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“…EUKARYOTIC PROMOTER RECOGNITION GENOME RESEARCH 871 ponent of integrated gene structure prediction (Matis et al 1996). The promoter recognition module combines matrix scores for the TATA-, GC-and CAAT-boxes, the Inr, and the translation start site with constraints on the distances between these elements, using a neural network.…”

Section: Grail Includes Promoter Recognition As One Com-mentioning

confidence: 99%

Eukaryotic Promoter Recognition

Fickett¹,

1997

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“…ATTAAA ) as true PAS but do not involve in polyadenylation. Earlier studies (Helden et al , 2000; Matis et al , 1996; Tabaska and Zhang, 1999) focus on exploring the statistical information of PAS surrounding sequences. Based on prior knowledge of DNA sequences, many carefully hand-crafted features have been proposed by experts which formed the basis of most PAS recognition models (Akhtar et al , 2010; Cheng et al , 2006; Hu et al , 2005; Liu et al , 2003; Salamov and Solovyev, 1997; Tabaska and Zhang, 1999).…”

Section: Introductionmentioning

confidence: 99%

DeeReCT-PolyA: a robust and generic deep learning method for PAS identification

et al. 2018

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Motivation Polyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PASs) identification is not only desired for the purpose of better transcripts’ end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif- and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals. Results In this work, we propose a robust, PAS motif agnostic, and highly interpretable and transferrable deep learning model for accurate PAS recognition, which requires no prior knowledge or human-designed features. We show that our single model trained over all human PAS motifs not only outperforms the state-of-the-art methods trained on specific motifs, but can also be generalized well to two mouse datasets. Moreover, we further increase the prediction accuracy by transferring the deep learning model trained on the data of one species to the data of a different species. Several novel underlying poly(A) patterns are revealed through the visualization of important oligomers and positions in our trained models. Finally, we interpret the deep learning models by converting the convolutional filters into sequence logos and quantitatively compare the sequence logos between human and mouse datasets. Availability and implementation https://github.com/likesum/DeeReCT-PolyA Supplementary information Supplementary data are available at Bioinformatics online.

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Detection of RNA polymerase II promoters and polyadenylation sites in human DNA sequence

Cited by 30 publications

References 12 publications

Opportunities and obstacles for deep learning in biology and medicine

Opportunities and obstacles for deep learning in biology and medicine

Eukaryotic Promoter Recognition

DeeReCT-PolyA: a robust and generic deep learning method for PAS identification

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