PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition

Chen, Wei; Lei, Tianyu; Jin, Dianchuan; Lin, Hao; Chou, Kuo‐Chen

doi:10.1016/j.ab.2014.04.001

Cited by 393 publications

(199 citation statements)

References 59 publications

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“…Ever since the concept of PseAAC [33,34] or Chou's PseAAC [35,36] was proposed, it has rapidly penetrated into nearly all fields of computational proteomics (see a long list papers cited in [36,37]). Stimulated by the great successes of PseAAC in dealing with protein/peptide sequences, the concept of PseAAC has been extended [38][39][40][41][42] to cover DNA/RNA sequences as well via the form of PseKNC (pseudo K-tuple nucleotide composition) [43,44], which has been proven very useful in studying many important genome analysis problems, as summarized in a recent review paper [45]. Also, because the concept of PseAAC has been increasingly and widely used in both computational proteomics and genomics, a very powerful web-server called "Pse-in-One" [46] was established that can be used to generate the pseudo components for both protein/peptide and DNA/RNA sequences.…”

Section: Introductionmentioning

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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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: Introductionmentioning

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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“…Ever since the concept of pseudo amino acid composition or Chou's PseAAC [55][56][57][58] was proposed, it has been widely used in many biomedicine and drug development areas [59,60] as well as nearly all the areas of computational proteomics(see, e.g., [39,43,45,[61][62][63][64][65][66][67][68][69][70][71][72][73] and a long list of references cited in two review papers [74,75]). Encouraged by the successes of using PseAAC to deal with protein/peptide sequences, its idea and approach have been extended to deal with DNA/RNA sequences [76][77][78][79][80][81][82] in computational genomics via PseKNC (Pseudo K-tuple Nucleotide Composition) [83,84]. Recently, a very powerful web-server called "Pse-in-One" [85] and its updated version "Pse-in-One 2.0" [86] were developed, by which users can generate any pseudo components for both protein/peptide and DNA/RNA sequences as they wish or define.…”

Section: Proteins Sample Formulationmentioning

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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“…[42]. Motivated by the wide and successful usage of pseudo amino acid composition or Chou's PseAAC in the areas of computational proteomics with protein/peptide sequences [43,44], recently, the concept of pseudo k-tuber nucleotide composition has been developed to deal with DNA/RNA sequences in computational genetics and genomics [45][46][47][48][49]. According to Ref.…”

Section: Feature Representation Of Rna Sequencementioning

confidence: 99%

Improving N6-methyladenosine site prediction with heuristic selection of nucleotide physical–chemical properties

Zhang

Sun

et al. 2016

Analytical Biochemistry

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PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition

Cited by 393 publications

References 59 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

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

Improving N6-methyladenosine site prediction with heuristic selection of nucleotide physical–chemical properties

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