Prediction Models for DNA Transcription Termination Based on SOM Networks

Bajic, Vladimir B.; Charn, Tze Howe; Xu, Jingting; Panda, Sucheta; Krishnan, S. P. T.

doi:10.1109/iembs.2005.1615543

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…ERPIN used position weight matrices computed for each di-nucleotide in a window of 600 nt surrounding the PAS (−300, +300), and achieved a prediction specificity of 85% for a sensitivity of 56%, resulting in a specificity improvement of 9.7% relative to the polyadq method. Bajic et al [ 26 ] developed the Dragon PolyA tool based on artificial neural networks and self-organized maps for predicting the two most common PAS variants in human (AATAAA and ATTAAA). Their tool improved both sensitivity and specificity by ~5% and 5% on AATAAA variant, respectively, and 11.3% and 7.9% on ATTAAA variant, respectively, relative to those obtained by polyadq.…”

Section: Introductionmentioning

confidence: 99%

Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA

2017

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BackgroundPolyadenylation is a critical stage of RNA processing during the formation of mature mRNA, and is present in most of the known eukaryote protein-coding transcripts and many long non-coding RNAs. The correct identification of poly(A) signals (PAS) not only helps to elucidate the 3′-end genomic boundaries of a transcribed DNA region and gene regulatory mechanisms but also gives insight into the multiple transcript isoforms resulting from alternative PAS. Although progress has been made in the in-silico prediction of genomic signals, the recognition of PAS in DNA genomic sequences remains a challenge.ResultsIn this study, we analyzed human genomic DNA sequences for the 12 most common PAS variants. Our analysis has identified a set of features that helps in the recognition of true PAS, which may be involved in the regulation of the polyadenylation process. The proposed features, in combination with a recognition model, resulted in a novel method and tool, Omni-PolyA. Omni-PolyA combines several machine learning techniques such as different classifiers in a tree-like decision structure and genetic algorithms for deriving a robust classification model. We performed a comparison between results obtained by state-of-the-art methods, deep neural networks, and Omni-PolyA. Results show that Omni-PolyA significantly reduced the average classification error rate by 35.37% in the prediction of the 12 considered PAS variants relative to the state-of-the-art results.ConclusionsThe results of our study demonstrate that Omni-PolyA is currently the most accurate model for the prediction of PAS in human and can serve as a useful complement to other PAS recognition methods. Omni-PolyA is publicly available as an online tool accessible at www.cbrc.kaust.edu.sa/omnipolya/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-4033-7) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA

2017

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“…Several computational studies have been performed to characterize patterns in the cis-elements around used poly(A) sites obtained from experiments (Beaudoing et al, 2000; Legendre and Gautheret, 2003; Tian et al, 2005; Ara et al, 2006; Nunes et al, 2010; Ozsolak et al, 2010). Computational methods have also been developed to identify poly(A) sites (Tabaska and Zhang, 1999; Graber et al, 2002; Hajarnavis et al, 2004; Bajic et al, 2005; Cheng et al, 2006; Retelska et al, 2006; Akhtar et al, 2010). …”

Section: Methods For Characterizing Poly(a) Site Usagementioning

confidence: 99%

Alterations in Polyadenylation and Its Implications for Endocrine Disease

Rehfeld¹,

Plass²,

Krogh³

et al. 2013

Front. Endocrinol.

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Introduction: Polyadenylation is the process in which the pre-mRNA is cleaved at the poly(A) site and a poly(A) tail is added – a process necessary for normal mRNA formation. Genes with multiple poly(A) sites can undergo alternative polyadenylation (APA), producing distinct mRNA isoforms with different 3′ untranslated regions (3′ UTRs) and in some cases different coding regions. Two thirds of all human genes undergo APA. The efficiency of the polyadenylation process regulates gene expression and APA plays an important part in post-transcriptional regulation, as the 3′ UTR contains various cis-elements associated with post-transcriptional regulation, such as target sites for micro-RNAs and RNA-binding proteins.Implications of alterations in polyadenylation for endocrine disease: Alterations in polyadenylation have been found to be causative of neonatal diabetes and IPEX (immune dysfunction, polyendocrinopathy, enteropathy, X-linked) and to be associated with type I and II diabetes, pre-eclampsia, fragile X-associated premature ovarian insufficiency, ectopic Cushing syndrome, and many cancer diseases, including several types of endocrine tumor diseases.Perspectives: Recent developments in high-throughput sequencing have made it possible to characterize polyadenylation genome-wide. Antisense elements inhibiting or enhancing specific poly(A) site usage can induce desired alterations in polyadenylation, and thus hold the promise of new therapeutic approaches.Summary: This review gives a detailed description of alterations in polyadenylation in endocrine disease, an overview of the current literature on polyadenylation and summarizes the clinical implications of the current state of research in this field.

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“…To recognize poly(A) region of Saccharomyces cerevisiae [30] and Caenorhabditis elegans [31], the weight-matrix-only approaches have been developed. To identify PASs, Bajic et al [32] have developed another program, Dragon PolyAtt , the accuracy of which was substantially better than that obtained by the polyadq program. It predicted the two most frequent poly(A) sites, AAUAAA and AUUAAA, with a sensitivity of 48.4% and 13.6%, and specificity of 74% and 79.1%, respectively.…”

Section: Introductionmentioning

confidence: 99%

POLYAR, a new computer program for prediction of poly(A) sites in human sequences

et al. 2010

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BackgroundmRNA polyadenylation is an essential step of pre-mRNA processing in eukaryotes. Accurate prediction of the pre-mRNA 3'-end cleavage/polyadenylation sites is important for defining the gene boundaries and understanding gene expression mechanisms.Results28761 human mapped poly(A) sites have been classified into three classes containing different known forms of polyadenylation signal (PAS) or none of them (PAS-strong, PAS-weak and PAS-less, respectively) and a new computer program POLYAR for the prediction of poly(A) sites of each class was developed. In comparison with polya_svm (till date the most accurate computer program for prediction of poly(A) sites) while searching for PAS-strong poly(A) sites in human sequences, POLYAR had a significantly higher prediction sensitivity (80.8% versus 65.7%) and specificity (66.4% versus 51.7%) However, when a similar sort of search was conducted for PAS-weak and PAS-less poly(A) sites, both programs had a very low prediction accuracy, which indicates that our knowledge about factors involved in the determination of the poly(A) sites is not sufficient to identify such polyadenylation regions.ConclusionsWe present a new classification of polyadenylation sites into three classes and a novel computer program POLYAR for prediction of poly(A) sites/regions of each of the class. In tests, POLYAR shows high accuracy of prediction of the PAS-strong poly(A) sites, though this program's efficiency in searching for PAS-weak and PAS-less poly(A) sites is not very high but is comparable to other available programs. These findings suggest that additional characteristics of such poly(A) sites remain to be elucidated. POLYAR program with a stand-alone version for downloading is available at http://cub.comsats.edu.pk/polyapredict.htm.

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Prediction Models for DNA Transcription Termination Based on SOM Networks

Cited by 5 publications

References 14 publications

Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA

Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA

Alterations in Polyadenylation and Its Implications for Endocrine Disease

POLYAR, a new computer program for prediction of poly(A) sites in human sequences

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