Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences
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Summary
This article proposes a much more flexible web server called Pse-in-One, which can, through its 28 different modes, generate nearly all the possible feature vectors for DNA, RNA and protein sequences, and can also generate those feature vectors with the properties defined by users themselves.
- Type
- article
- Published
- 2015-05-09
- Cited by
- 694
- References
- 28
- Access
- Open access
- OpenAlex
- https://openalex.org/W1494484168
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14628312
Keywords
Web server, Biology, Sequence (biology), Computational biology, Genomics
References
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- iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition.
- Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology
- Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach
- PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition.
- iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition
- Some remarks on protein attribute prediction and pseudo amino acid composition
- PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition.
- AAindex: amino acid index database, progress report 2008
- PseAAC-General: Fast Building Various Modes of General Form of Chou’s Pseudo-Amino Acid Composition for Large-Scale Protein Datasets
- DiProDB: a database for dinucleotide properties
- Predicting Human Nucleosome Occupancy from Primary Sequence
- Predicting the in vivo signature of human gene regulatory sequence
- iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition
- A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis
- A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation
- repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects
- iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition
- PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions
- Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes
Cited by
- A functional module-based exploration between inflammation and cancer in esophagus
- iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework
- Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou's general pseudo amino acid composition.
- PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition
- A computational model for predicting integrase catalytic domain of retrovirus.
- iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components.
- HLPI-Ensemble: Prediction of human lncRNA-protein interactions based on ensemble strategy
- Benchmark data for identifying DNA methylation sites via pseudo trinucleotide composition
- Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts.
- repRNA: a web server for generating various feature vectors of RNA sequences
- Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae.
- mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor.
- Analysis of Conformational B-Cell Epitopes in the Antibody-Antigen Complex Using the Depth Function and the Convex Hull
- iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou׳s general PseAAC.
- iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples
- iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition.
- PGlcS: Prediction of protein O-GlcNAcylation sites with multiple features and analysis.
- Classification of membrane protein types using Voting Feature Interval in combination with Chou's Pseudo Amino Acid Composition.
- Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy.
- iTIS-PseKNC: Identification of Translation Initiation Site in human genes using pseudo k-tuple nucleotides composition
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