pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC

Jia, Jianhua; Zhang, Liuxia; Liu, Zi; Xiao, Xuan; Chou, Kuo‐Chen

doi:10.1093/bioinformatics/btw387

Cited by 172 publications

(66 citation statements)

References 105 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%

“…In literature, a set of metrics are often used to measure the prediction quality. To make it intuitive and easy to understand for readers, here we adopt the definition and notations used in [40,41,[56][57][58][59][60] to describe the corresponding evaluation metrics: N + . It should be pointed out that the set of metrics above is valid only for the single-label system (such as the case at hand).…”

Section: Experiments Ii: Identification Of Dna-binding Proteinsmentioning

confidence: 99%

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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%

Section: Experiments Ii: Identification Of Dna-binding Proteinsmentioning

confidence: 99%

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

Lin

2016

Applied Sciences

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show abstract

“…Another future work is to establish a web-server to make our predictor publicly available just as shown in many recent publications (see, e.g., [52,53,55,57,63,64,65,66,67,68,69]) and to significantly enhance the influence of our predictor.…”

Section: Discussionmentioning

confidence: 99%

“…As illustrated by many recently published papers [51,52,53,54,55,56,57], to establish a powerful predictor for a biological problem, one should obey the following five steps: (a) build or choose a benchmark dataset to train and test the predictor; (b) transform the raw biological sequences into mathematical feature vectors that can truly extract their intrinsic features from the target to be predicted; (c) employ or create a powerful algorithm to operate the prediction; (d) accurately use cross-validation tests to impersonally evaluate the predicting ability of the predictor; and (e) build a user-friendly web-server to make their dataset and predictor publicly available. Next, the current study will be organized following these steps one-by-one.…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human

Yao

et al. 2017

IJMS

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DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation.

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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

Self Cite

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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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pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC

Abstract: jjia@gordonlifescience.org, xxiao@gordonlifescience.org or kcchou@gordonlifescience.orgSupplementary information: Supplementary data are available at Bioinformatics online.

Cited by 172 publications

References 105 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

Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human

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

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