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Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition.

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Title Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition.
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Creator Prabina Kumar Meher
Tanmaya Kumar Sahu
Shachi Gahoi
Subhrajit Satpathy
Atmakuri Ramakrishna Rao
 
Subject Gene prediction
Intron density
Markov model
Sequence encoding
Supervised learning
 
Description Not Available
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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Date 2022-08-07T07:34:34Z
2022-08-07T07:34:34Z
2019-07-01
 
Type Research Paper
 
Identifier Prabina Kumar Meher, Tanmaya Kumar Sahu, Shachi Gahoi, Subhrajit Satpathy, Atmakuri Ramakrishna Rao, (2019). Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition, Gene, 705, 113-126, https://doi.org/10.1016/j.gene.2019.04.047
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http://krishi.icar.gov.in/jspui/handle/123456789/73739
 
Language English
 
Relation Not Available;
 
Publisher Not Available