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Number of citations per year for the bioinformatics software tool repDNA

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repDNA specifications


Unique identifier OMICS_24336
Name repDNA
Alternative name representations of DNAs
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Python
Computer skills Advanced
Version 1.1.4
Stability Stable
Maintained Yes




No version available



  • person_outline Bin Liu
  • person_outline Kuo-Chen Chou

Publication for representations of DNAs

repDNA citations


A Novel Modeling in Mathematical Biology for Classification of Signal Peptides

Sci Rep
PMCID: 5773712
PMID: 29348418
DOI: 10.1038/s41598-018-19491-y

[…] such as PSFM-DBT, 2L-piRNA, Pse-Analysis, ProtDec-LTR, ProtDec-LTR2.0, etc. Some powerful protein analysis methods have been proposed for the formulation of biological sequences, such as Pse-in-One, repDNA, based on different functions to produce feature vector for biological sequences. HMMTOP was developed for the prediction of localization of helical transmembrane. Kuo-Chen Chou devised quasi-s […]


Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

PMCID: 5704239
PMID: 29149087
DOI: 10.3390/genes8110326

[…] se tools incorporated traditional machine learning techniques such as support vector machine (SVM), neural networks and k-nearest neighbor (KNN). In [], the authors developed a Python package called “repDNA” to produce the features representing physicochemical properties and the sequence-order effects of DNAs and nucleotides. Fifteen types of features built from DNA sequences can be calculated usi […]


Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO SVM algorithm

PLoS One
PMCID: 5667738
PMID: 29095904
DOI: 10.1371/journal.pone.0187371

[…] ls were proposed in an open-source Python package designed to formulate comprehensive built-in and user-defined features for DNA, RNA and protein sequences; these are known as representations of DNA (repDNA) [], repRNA [] and Pse-in-One [], respectively. The repDNA tool was used to develop powerful computational predictors for use in identifying the biological features or attributes of DNAs by gen […]


iDNAProt ES: Identification of DNA binding Proteins Using Evolutionary and Structural Features

Sci Rep
PMCID: 5668250
PMID: 29097781
DOI: 10.1038/s41598-017-14945-1

[…] Ontology” mode, and “Sequential Evolution” or “PSSM” mode. Encouraged by the successes of using PseAAC to deal with protein or peptide sequences, four web-servers called ‘PseKNC’, ‘PseKNC-General’, ‘repDNA’, and ‘repRNA’ were developed for generating various feature vectors for DNA/RNA sequences as well. Particularly, recently a very powerful web-server called Pse-in-One has been established that […]


iSS PC: Identifying Splicing Sites via Physical Chemical Properties Using Deep Sparse Auto Encoder

Sci Rep
PMCID: 5557945
PMID: 28811565
DOI: 10.1038/s41598-017-08523-8

[…] good results. However, with the further research of feature extraction methods and the development of computer technology, more and more web servers have been emerged, such as Pse-in-One, repRNA, and repDNA. Then, many features such as pseudo amino acid composition (PseAAC), pseudo dinucleotide composition (PseDNC), pseudo trinucleotide composition (PseTNC), dinucleotide-based auto covariance (DAC […]


Multi scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree

PLoS One
PMCID: 5549711
PMID: 28792503
DOI: 10.1371/journal.pone.0181426

[…] uous (MCD) [], and Multi-scale Local Feature Representation (MLD) []. Fortunately enough, recent advances in developing numerous web servers for extracting features from biological sequences, such as RepDNA [], RepRNA [] and Pse-in-One [] for DNA, RNA and protein sequence respectively, make the procedure quickly and effectively.Sample classification is another important issue for predicting PPIs c […]

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repDNA institution(s)
School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China; Gordon Life Science Institute, Belmont, MA, USA; Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
repDNA funding source(s)
Supported by the National Natural Science Foundation of China [grant no 61300112, 61370010 and 61272383] and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.

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