iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition

Xiao, Xuan; Ye, Han-Xiao; Liu, Zi; Jia, Jianhua; Chou, Kuo‐Chen

doi:10.18632/oncotarget.9057

Cited by 115 publications

(43 citation statements)

References 98 publications

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“…The feature analysis implies that the λ + 1 components beyond the amino acid composition play very important roles in the performance of the experiment. As shown in a series of recent publications (see, e.g., [58,[67][68][69][70][71][72]) in demonstrating new methods or approaches, user-friendly and publicly accessible web-servers will significantly enhance their impacts [73]. We will make efforts in our future work to further improve our method and provide a web-server for the new method presented.…”

Section: Discussionmentioning

confidence: 94%

Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

Lin

2016

Applied Sciences

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Abstract:The technique of comparison and analysis of biological sequences is playing an increasingly important role in the field of Computational Biology and Bioinformatics. One of the key steps in developing the technique is to identify an appropriate manner to represent a biological sequence. In this paper, on the basis of three physical-chemical properties of amino acids, a protein primary sequence is reduced into a six-letter sequence, and then a set of elements which reflect the global and local sequence-order information is extracted. Combining these elements with the frequencies of 20 native amino acids, a (21 + λ) dimensional vector is constructed to characterize the protein sequence. The utility of the proposed approach is illustrated by phylogenetic analysis and identification of DNA-binding proteins.

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Section: Discussionmentioning

confidence: 94%

Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

Lin

2016

Applied Sciences

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“…Since user-friendly and publicly-accessible web-servers represent the future direction [54] for developing practically more useful predictors and will significantly enhance their impacts [55,56], we have provided a web-server for the new method presented in this paper, as done in many recent works [39,43,48,49,57,58,59,60]. The server, named PredPPIS, provides a graphical user interface for users to submit their sequences and receive the predictive results.…”

Section: Resultsmentioning

confidence: 99%

“…This guideline provides a basic framework for many bioinformatics software tools, for example, see the recent publications [31,39,40,41,42,43,44,45,46,47,48,49]. It contains the following recommendations: (1) a benchmark training and testing dataset must be carefully constructed; (2) a mathematical representation of protein sequences must be introduced in such a way that the intrinsic biological properties can be effectively translated into numerical features; (3) a robust machine learning system should be employed; (4) an objective cross-validation experiment must be adopted to evaluate the predictor [21,22]; and (5) the predictor should be deployed as a publicly accessible web site.…”

Section: Introductionmentioning

confidence: 99%

Predicting Protein–Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

Kuo¹,

2016

IJMS

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Information about the interface sites of Protein–Protein Interactions (PPIs) is useful for many biological research works. However, despite the advancement of experimental techniques, the identification of PPI sites still remains as a challenging task. Using a statistical learning technique, we proposed a computational tool for predicting PPI interaction sites. As an alternative to similar approaches requiring structural information, the proposed method takes all of the input from protein sequences. In addition to typical sequence features, our method takes into consideration that interaction sites are not randomly distributed over the protein sequence. We characterized this positional preference using protein complexes with known structures, proposed a numerical index to estimate the propensity and then incorporated the index into a learning system. The resulting predictor, without using structural information, yields an area under the ROC curve (AUC) of 0.675, recall of 0.597, precision of 0.311 and accuracy of 0.583 on a ten-fold cross-validation experiment. This performance is comparable to the previous approach in which structural information was used. Upon introducing the B-factor data to our predictor, we demonstrated that the AUC can be further improved to 0.750. The tool is accessible at .

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“…Actually, user-friendly web-servers as shown in a series of recent publications [40,46,100,[107][108][109][110][111][112][114][115][116][117][118][119][120][121][122] will significantly enhance the impacts of theoretical work because they can attract the broad experimental scientists [52]. In view of this, the web-server for the new predictor pLoc-mGpos has been established at http://www.jci-bioinfo.cn/pLoc-mGpos/.…”

Section: Web Server and User Guidementioning

confidence: 99%

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

Xiao¹,

Cheng²,

Su³

et al. 2017

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The basic unit in life is cell. It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method, we developed a new predictor called "pLoc-mGpos" by in-depth extracting the key information from GO (Gene Ontology) into the Chou's general PseAAC (Pseudo Amino Acid Composition) for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to "iLoc-Gpos", the state-of-the-art predictor for the same purpose. To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at Natural Science http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.

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iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition

Cited by 115 publications

References 98 publications

Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

Predicting Protein–Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

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