Abstract:Motivation
Classification of protein sequences is one big task in bioinformatics and has many applications. Different machine learning methods exist and are applied on these problems, such as support vector machines (SVM), random forests (RF) and neural networks (NN). All of these methods have in common that protein sequences have to be made machine-readable and comparable in the first step, for which different encodings exist. These encodings are typically based on physical or chemical prope… Show more
Background
Nucleosome plays an important role in the process of genome expression, DNA replication, DNA repair and transcription. Therefore, the research of nucleosome positioning has invariably received extensive attention. Considering the diversity of DNA sequence representation methods, we tried to integrate multiple features to analyze its effect in the process of nucleosome positioning analysis. This process can also deepen our understanding of the theoretical analysis of nucleosome positioning.
Results
Here, we not only used frequency chaos game representation (FCGR) to construct DNA sequence features, but also integrated it with other features and adopted the principal component analysis (PCA) algorithm. Simultaneously, support vector machine (SVM), extreme learning machine (ELM), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and convolutional neural networks (CNN) are used as predictors for nucleosome positioning prediction analysis, respectively. The integrated feature vector prediction quality is significantly superior to a single feature. After using principal component analysis (PCA) to reduce the feature dimension, the prediction quality of H. sapiens dataset has been significantly improved.
Conclusions
Comparative analysis and prediction on H. sapiens, C. elegans, D. melanogaster and S. cerevisiae datasets, demonstrate that the application of FCGR to nucleosome positioning is feasible, and we also found that integrative feature representation would be better.
Background
Nucleosome plays an important role in the process of genome expression, DNA replication, DNA repair and transcription. Therefore, the research of nucleosome positioning has invariably received extensive attention. Considering the diversity of DNA sequence representation methods, we tried to integrate multiple features to analyze its effect in the process of nucleosome positioning analysis. This process can also deepen our understanding of the theoretical analysis of nucleosome positioning.
Results
Here, we not only used frequency chaos game representation (FCGR) to construct DNA sequence features, but also integrated it with other features and adopted the principal component analysis (PCA) algorithm. Simultaneously, support vector machine (SVM), extreme learning machine (ELM), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and convolutional neural networks (CNN) are used as predictors for nucleosome positioning prediction analysis, respectively. The integrated feature vector prediction quality is significantly superior to a single feature. After using principal component analysis (PCA) to reduce the feature dimension, the prediction quality of H. sapiens dataset has been significantly improved.
Conclusions
Comparative analysis and prediction on H. sapiens, C. elegans, D. melanogaster and S. cerevisiae datasets, demonstrate that the application of FCGR to nucleosome positioning is feasible, and we also found that integrative feature representation would be better.
“… Fig. 3 Chaos Game Representation, order of vertices: A-T/C-G, created with R package kaos [14] . Upper left: random sequence.…”
Section: Chaos Game For Dnamentioning
confidence: 99%
“… Fig. 4 CGR with a scaling factor of 0.5 for different numbers of vertices (5 to 8 from left to right), created with R package kaos [14] . With a random input sequence the possible space within the CGR is covered.…”
Section: Chaos Game For Dnamentioning
confidence: 99%
“…The scaling factor for n-flakes is also known as the kissing number [20] . The coordinates of the vertices for a polyflake can be calculated using the following equation [14] : …”
Section: Generalized Chaos Game Representationmentioning
“…This CGR feature established its effective use in the identification of new members of the specific peptide type. CGR has further benefits over the sequence based alignment tools usually use to search sequence homology that the CGR has a great potential to unveil the functional and evolutionary association among the proteins/peptides that have no substantial sequence similarity (homology) [119], [120]. C-GRex [121] is a standalone tool available to derive the CGR features.…”
Section: M: Chaos Game Representation (Cgr)mentioning
Peptides, short-chained amino acids, have shown great potentials toward the investigation and evolution of novel medications for treatment or therapy. The wet-lab based discovery of potential therapeutic peptides and eventually drug development is a hard and time-consuming process. The computational prediction using machine learning (ML) methods can expedite and facilitate the discovery process of potential prospects with therapeutic effects. ML approaches have been practiced favorably and extensively within the area of proteins, DNA, and RNA to discover the hidden features and functional activities, moreover, recently been utilized for functional discovery of peptides for various therapeutics. In this paper, a systematic literature review (SLR) has been presented to recognize the data-sources, ML classifiers, and encoding schemes being utilized in the state-of-the-art computational models to predict therapeutic peptides. To conduct the SLR, fourty-one research articles have been selected carefully based on well-defined selection criteria. To the best of our knowledge, there is no such SLR available that provides a comprehensive review in this domain. In this article, we have proposed a taxonomy based on identified feature encodings, which may offer relational understandings to researchers. Similarly, the framework model for the computational prediction of the therapeutic peptides has been introduced to characterize the best practices and levels involved in the development of peptide prediction models. Lastly, common issues and challenges have been discussed to facilitate the researchers with encouraging future directions in the field of computational prediction of therapeutic peptides.
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