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Brooks-Warburton J, Modos D, Sudhakar P, Madgwick M, Thomas JP, Bohar B, Fazekas D, Zoufir A, Kapuy O, Szalay-Beko M, Verstockt B, Hall LJ, Watson A, Tremelling M, Parkes M, Vermeire S, Bender A, Carding SR, Korcsmaros T. A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis. Nat Commun 2022; 13:2299. [PMID: 35484353 PMCID: PMC9051123 DOI: 10.1038/s41467-022-29998-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 04/06/2022] [Indexed: 12/11/2022] Open
Abstract
We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients.
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Affiliation(s)
- Johanne Brooks-Warburton
- Earlham Institute, Norwich Research Park, Norwich, UK
- Gut Microbes and Health Programme, The Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
- Department of Clinical, Pharmaceutical and Biological Sciences, University of Hertfordshire, Hertford, UK
- Gastroenterology Department, Lister Hospital, Stevenage, UK
| | - Dezso Modos
- Earlham Institute, Norwich Research Park, Norwich, UK
- Gut Microbes and Health Programme, The Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
- Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Padhmanand Sudhakar
- Earlham Institute, Norwich Research Park, Norwich, UK
- Gut Microbes and Health Programme, The Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
- KU Leuven, Department of Chronic diseases, Metabolism and Ageing, Leuven, Belgium
| | - Matthew Madgwick
- Earlham Institute, Norwich Research Park, Norwich, UK
- Gut Microbes and Health Programme, The Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - John P Thomas
- Earlham Institute, Norwich Research Park, Norwich, UK
- Gut Microbes and Health Programme, The Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
- Department of Gastroenterology, Norfolk and Norwich University Hospitals, Norwich, UK
| | - Balazs Bohar
- Earlham Institute, Norwich Research Park, Norwich, UK
- Department of Genetics, Eötvös Loránd University, Budapest, Hungary
| | - David Fazekas
- Earlham Institute, Norwich Research Park, Norwich, UK
- Department of Genetics, Eötvös Loránd University, Budapest, Hungary
| | - Azedine Zoufir
- Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Orsolya Kapuy
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | | | - Bram Verstockt
- KU Leuven, Department of Chronic diseases, Metabolism and Ageing, Leuven, Belgium
- University Hospitals Leuven, Department of Gastroenterology and Hepatology, KU Leuven, Leuven, Belgium
| | - Lindsay J Hall
- Gut Microbes and Health Programme, The Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
- Norwich Medical School, University of East Anglia, Norwich, UK
- School of Life Sciences, ZIEL - Institute for Food & Health, Technical University of Munich, 80333, Freising, Germany
| | - Alastair Watson
- Department of Gastroenterology, Norfolk and Norwich University Hospitals, Norwich, UK
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Mark Tremelling
- Department of Gastroenterology, Norfolk and Norwich University Hospitals, Norwich, UK
| | - Miles Parkes
- Inflammatory Bowel Disease Research Group, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
| | - Severine Vermeire
- KU Leuven, Department of Chronic diseases, Metabolism and Ageing, Leuven, Belgium
- University Hospitals Leuven, Department of Gastroenterology and Hepatology, KU Leuven, Leuven, Belgium
| | - Andreas Bender
- Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Simon R Carding
- Gut Microbes and Health Programme, The Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.
- Norwich Medical School, University of East Anglia, Norwich, UK.
| | - Tamas Korcsmaros
- Earlham Institute, Norwich Research Park, Norwich, UK.
- Gut Microbes and Health Programme, The Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.
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Amilpur S, Bhukya R. EDeepSSP: Explainable deep neural networks for exact splice sites prediction. J Bioinform Comput Biol 2020; 18:2050024. [PMID: 32696716 DOI: 10.1142/s0219720020500249] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performance, their interpretability remains challenging. Moreover, all traditional machine learning methods manually extract features, which is tedious job. To address these challenges, we propose a deep learning-based approach (EDeepSSP) that employs convolutional neural networks (CNNs) architecture for automatic feature extraction and effectively predicts splice sites. Our model, EDeepSSP, divulges the opaque nature of CNN by extracting significant motifs and explains why these motifs are vital for predicting splice sites. In this study, experiments have been conducted on six benchmark acceptors and donor datasets of humans, cress, and fly. The results show that EDeepSSP has outperformed many state-of-the-art approaches. EDeepSSP achieves the highest area under the receiver operating characteristic curve (AUC_ROC) and area under the precision-recall curve (AUC_PR) of 99.32% and 99.26% on human donor datasets, respectively. We also analyze various filter activities, feature activations, and extracted significant motifs responsible for the splice site prediction. Further, we validate the learned motifs of our model against known motifs of JASPAR splice site database.
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Affiliation(s)
- Santhosh Amilpur
- Computer Science and Engineering, National Institute of Technology Warangal, Warangal, Telangana 506004, India
| | - Raju Bhukya
- Computer Science and Engineering, National Institute of Technology Warangal, Warangal, Telangana 506004, India
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Albaradei S, Magana-Mora A, Thafar M, Uludag M, Bajic VB, Gojobori T, Essack M, Jankovic BR. Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA. Gene 2020; 763S:100035. [PMID: 32550561 PMCID: PMC7285987 DOI: 10.1016/j.gene.2020.100035] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 05/06/2020] [Indexed: 12/21/2022]
Abstract
Background The accurate identification of the exon/intron boundaries is critical for the correct annotation of genes with multiple exons. Donor and acceptor splice sites (SS) demarcate these boundaries. Therefore, deriving accurate computational models to predict the SS are useful for functional annotation of genes and genomes, and for finding alternative SS associated with different diseases. Although various models have been proposed for the in silico prediction of SS, improving their accuracy is required for reliable annotation. Moreover, models are often derived and tested using the same genome, providing no evidence of broad application, i.e. to other poorly studied genomes. Results With this in mind, we developed the Splice2Deep models for SS detection. Each model is an ensemble of deep convolutional neural networks. We evaluated the performance of the models based on the ability to detect SS in Homo sapiens, Oryza sativa japonica, Arabidopsis thaliana, Drosophila melanogaster, and Caenorhabditis elegans. Results demonstrate that the models efficiently detect SS in other organisms not considered during the training of the models. Compared to the state-of-the-art tools, Splice2Deep models achieved significantly reduced average error rates of 41.97% and 28.51% for acceptor and donor SS, respectively. Moreover, the Splice2Deep cross-organism validation demonstrates that models correctly identify conserved genomic elements enabling annotation of SS in new genomes by choosing the taxonomically closest model. Conclusions The results of our study demonstrated that Splice2Deep both achieved a considerably reduced error rate compared to other state-of-the-art models and the ability to accurately recognize SS in other organisms for which the model was not trained, enabling annotation of poorly studied or newly sequenced genomes. Splice2Deep models are implemented in Python using Keras API; the models and the data are available at https://github.com/SomayahAlbaradei/Splice_Deep.git.
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Key Words
- AUC, area under curve
- AcSS, acceptor splice site
- Acc, accuracy
- Bioinformatics
- CNN, convolutional neural network
- CONV, convolutional layers
- DL, deep learning
- DNA, deoxyribonucleic acid
- DT, decision trees
- Deep-learning
- DoSS, donor splice site
- FC, fully connected layer
- ML, machine learning
- NB, naive Bayes
- NN, neural network
- POOL, pooling layer
- Prediction
- RF, random forest
- RNA, ribonucleic acid
- ReLU, rectified linear unit layer
- SS, splice site
- SVM, support vector machine
- Sn, sensitivity
- Sp, specificity
- Splice sites
- Splicing
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Affiliation(s)
- Somayah Albaradei
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.,Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia
| | - Arturo Magana-Mora
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.,Saudi Aramco, EXPEC-ARC, Drilling Technology Team, Dhahran 31311, Saudi Arabia
| | - Maha Thafar
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.,Faculty of Computers and Information Systems, Taif University, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Vladimir B Bajic
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.,Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Boris R Jankovic
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Splice sites detection using chaos game representation and neural network. Genomics 2019; 112:1847-1852. [PMID: 31704313 DOI: 10.1016/j.ygeno.2019.10.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 03/18/2019] [Accepted: 10/29/2019] [Indexed: 11/23/2022]
Abstract
A novel method is proposed to detect the acceptor and donor splice sites using chaos game representation and artificial neural network. In order to achieve high accuracy, inputs to the neural network, or feature vector, shall reflect the true nature of the DNA segments. Therefore it is important to have one-to-one numerical representation, i.e. a feature vector should be able to represent the original data. Chaos game representation (CGR) is an iterative mapping technique that assigns each nucleotide in a DNA sequence to a respective position on the plane in a one-to-one manner. Using CGR, a DNA sequence can be mapped to a numerical sequence that reflects the true nature of the original sequence. In this research, we propose to use CGR as feature input to a neural network to detect splice sites on the NN269 dataset. Computational experiments indicate that this approach gives good accuracy while being simpler than other methods in the literature, with only one neural network component. The code and data for our method can be accessed from this link: https://github.com/thoang3/portfolio/tree/SpliceSites_ANN_CGR.
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Meher PK, Sahu TK, Gahoi S, Satpathy S, Rao AR. Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition. Gene 2019; 705:113-126. [PMID: 31009682 DOI: 10.1016/j.gene.2019.04.047] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/27/2019] [Accepted: 04/17/2019] [Indexed: 02/02/2023]
Abstract
Identification of splice sites is imperative for prediction of gene structure. Machine learning-based approaches (MLAs) have been reported to be more successful than the rule-based methods for identification of splice sites. However, the strings of alphabets should be transformed into numeric features through sequence encoding before using them as input in MLAs. In this study, we evaluated the performances of 8 different sequence encoding schemes i.e., Bayes kernel, density and sparse (DS), distribution of tri-nucleotide and 1st order Markov model (DM), frequency difference distance measure (FDDM), paired-nucleotide frequency difference between true and false sites (FDTF), 1st order Markov model (MM1), combination of both 1st and 2nd order Markov model (MM1 + MM2) and 2nd order Markov model (MM2) in respect of predicting donor and acceptor splice sites using 5 supervised learning methods (ANN, Bagging, Boosting, RF and SVM). The encoding schemes and machine learning methods were first evaluated in 4 species i.e., A. thaliana, C. elegans, D. melanogaster and H. sapiens, and then performances were validated with another four species i.e., Ciona intestinalis, Dictyostelium discoideum, Phaeodactylum tricornutum and Trypanosoma brucei. In terms of ROC (receiver-operating-characteristics) and PR (precision-recall) curves, FDTF encoding approach achieved higher accuracy followed by either MM2 or FDDM. Further, SVM was found to achieve higher accuracy (in terms of ROC and PR curves) followed by RF across encoding schemes and species. In terms of prediction accuracy across species, the SVM-FDTF combination was optimum than other combinations of classifiers and encoding schemes. Further, splice site prediction accuracies were observed higher for the species with low intron density. To our limited knowledge, this is the first attempt as far as comprehensive evaluation of sequence encoding schemes for prediction of splice sites is concerned. We have also developed an R-package EncDNA (https://cran.r-project.org/web/packages/EncDNA/index.html) for encoding of splice site motifs with different encoding schemes, which is expected to supplement the existing nucleotide sequence encoding approaches. This study is believed to be useful for the computational biologists for predicting different functional elements on the genomic DNA.
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Affiliation(s)
- Prabina Kumar Meher
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
| | - Tanmaya Kumar Sahu
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India
| | - Shachi Gahoi
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India
| | - Subhrajit Satpathy
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India
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6
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Zeng Y, Yuan H, Yuan Z, Chen Y. A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples. Biol Direct 2019; 14:6. [PMID: 30975175 PMCID: PMC6460831 DOI: 10.1186/s13062-019-0236-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Accepted: 03/18/2019] [Indexed: 11/10/2022] Open
Abstract
Background Splice sites prediction has been a long-standing problem in bioinformatics. Although many computational approaches developed for splice site prediction have achieved satisfactory accuracy, further improvement in predictive accuracy is significant, for it is contributing to predict gene structure more accurately. Determining a proper window size before prediction is necessary. Overly long window size may introduce some irrelevant features, which would reduce predictive accuracy, while the use of short window size with maximum information may performs better in terms of predictive accuracy and time cost. Furthermore, the number of false splice sites following the GT–AG rule far exceeds that of true splice sites, accurate and rapid prediction of splice sites using imbalanced large samples has always been a challenge. Therefore, based on the short window size and imbalanced large samples, we developed a new computational method named chi-square decision table (χ2-DT) for donor splice site prediction. Results Using a short window size of 11 bp, χ2-DT extracts the improved positional features and compositional features based on chi-square test, then introduces features one by one based on information gain, and constructs a balanced decision table aimed at implementing imbalanced pattern classification. With a 2000:271,132 (true sites:false sites) training set, χ2-DT achieves the highest independent test accuracy (93.34%) when compared with three classifiers (random forest, artificial neural network, and relaxed variable kernel density estimator) and takes a short computation time (89 s). χ2-DT also exhibits good independent test accuracy (92.40%), when validated with BG-570 mutated sequences with frameshift errors (nucleotide insertions and deletions). Moreover, χ2-DT is compared with the long-window size-based methods and the short-window size-based methods, and is found to perform better than all of them in terms of predictive accuracy. Conclusions Based on short window size and imbalanced large samples, the proposed method not only achieves higher predictive accuracy than some existing methods, but also has high computational speed and good robustness against nucleotide insertions and deletions. Reviewers This article was reviewed by Ryan McGinty, Ph.D. and Dirk Walther. Electronic supplementary material The online version of this article (10.1186/s13062-019-0236-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ying Zeng
- Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha, 410128, Hunan, China.,Orient Science & Technology College, Hunan Agricultural University, Changsha, 410128, Hunan, China
| | - Hongjie Yuan
- Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha, 410128, Hunan, China
| | - Zheming Yuan
- Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha, 410128, Hunan, China. .,Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Hunan Agricultural University, Changsha, 410128, Hunan, China.
| | - Yuan Chen
- Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Hunan Agricultural University, Changsha, 410128, Hunan, China.
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Meher PK, Sahu TK, Saini V, Rao AR. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC. Sci Rep 2017; 7:42362. [PMID: 28205576 PMCID: PMC5304217 DOI: 10.1038/srep42362] [Citation(s) in RCA: 303] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Accepted: 01/09/2017] [Indexed: 11/13/2022] Open
Abstract
Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server iAMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/. The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs.
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Affiliation(s)
- Prabina Kumar Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India
| | - Tanmaya Kumar Sahu
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India
| | - Varsha Saini
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India.,Department of Bioinformatics, Janta Vedic College, Baraut, Baghpat-250611, Uttar Pradesh, India
| | - Atmakuri Ramakrishna Rao
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India
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8
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Meher PK, Sahu TK, Rao AR, Wahi SD. A computational approach for prediction of donor splice sites with improved accuracy. J Theor Biol 2016; 404:285-294. [PMID: 27302911 DOI: 10.1016/j.jtbi.2016.06.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 04/18/2016] [Accepted: 06/09/2016] [Indexed: 11/24/2022]
Abstract
Identification of splice sites is important due to their key role in predicting the exon-intron structure of protein coding genes. Though several approaches have been developed for the prediction of splice sites, further improvement in the prediction accuracy will help predict gene structure more accurately. This paper presents a computational approach for prediction of donor splice sites with higher accuracy. In this approach, true and false splice sites were first encoded into numeric vectors and then used as input in artificial neural network (ANN), support vector machine (SVM) and random forest (RF) for prediction. ANN and SVM were found to perform equally and better than RF, while tested on HS3D and NN269 datasets. Further, the performance of ANN, SVM and RF were analyzed by using an independent test set of 50 genes and found that the prediction accuracy of ANN was higher than that of SVM and RF. All the predictors achieved higher accuracy while compared with the existing methods like NNsplice, MEM, MDD, WMM, MM1, FSPLICE, GeneID and ASSP, using the independent test set. We have also developed an online prediction server (PreDOSS) available at http://cabgrid.res.in:8080/predoss, for prediction of donor splice sites using the proposed approach.
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Affiliation(s)
- Prabina Kumar Meher
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
| | - Tanmaya Kumar Sahu
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
| | - A R Rao
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
| | - S D Wahi
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
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Meher PK, Sahu TK, Rao AR, Wahi SD. Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features. Algorithms Mol Biol 2016; 11:16. [PMID: 27252772 PMCID: PMC4888255 DOI: 10.1186/s13015-016-0078-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2015] [Accepted: 05/17/2016] [Indexed: 11/16/2022] Open
Abstract
Background Identification of splice sites is essential for annotation of genes. Though existing approaches have achieved an acceptable level of accuracy, still there is a need for further improvement. Besides, most of the approaches are species-specific and hence it is required to develop approaches compatible across species. Results Each splice site sequence was transformed into a numeric vector of length 49, out of which four were positional, four were dependency and 41 were compositional features. Using the transformed vectors as input, prediction was made through support vector machine. Using balanced training set, the proposed approach achieved area under ROC curve (AUC-ROC) of 96.05, 96.96, 96.95, 96.24 % and area under PR curve (AUC-PR) of 97.64, 97.89, 97.91, 97.90 %, while tested on human, cattle, fish and worm datasets respectively. On the other hand, AUC-ROC of 97.21, 97.45, 97.41, 98.06 % and AUC-PR of 93.24, 93.34, 93.38, 92.29 % were obtained, while imbalanced training datasets were used. The proposed approach was found comparable with state-of-art splice site prediction approaches, while compared using the bench mark NN269 dataset and other datasets. Conclusions The proposed approach achieved consistent accuracy across different species as well as found comparable with the existing approaches. Thus, we believe that the proposed approach can be used as a complementary method to the existing methods for the prediction of splice sites. A web server named as ‘HSplice’ has also been developed based on the proposed approach for easy prediction of 5′ splice sites by the users and is freely available at http://cabgrid.res.in:8080/HSplice.
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