iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC

Fan, Yongxian; Wang, Wanru; Zhu, Qingqi

doi:10.1371/journal.pone.0228479

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…Specifically, the content characteristics of single nucleotides (A, C, G, T) in each DNA sequence was utilized as features. Four base characteristics (GC-skew, GC-profile, AT-skew, AT-profile) were considered in this paper [ 3 , 19 – 22 ]. …”

Section: Methodsmentioning

confidence: 99%

Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features

Fan

Wang

2021

BMC Bioinformatics

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Background The origin is the starting site of DNA replication, an extremely vital part of the informational inheritance between parents and children. More importantly, accurately identifying the origin of replication has great application value in the diagnosis and treatment of diseases related to genetic information errors, while the traditional biological experimental methods are time-consuming and laborious. Results We carried out research on the origin of replication in a variety of eukaryotes and proposed a unique prediction method for each species. Throughout the experiment, we collected data from 7 species, including Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Kluyveromyces lactis, Pichia pastoris and Schizosaccharomyces pombe. In addition to the commonly used sequence feature extraction methods PseKNC-II and Base-content, we designed a feature extraction method based on TF-IDF. Then the two-step method was utilized for feature selection. After comparing a variety of traditional machine learning classification models, the multi-layer perceptron was employed as the classification algorithm. Ultimately, the data and codes involved in the experiment are available at https://github.com/Sarahyouzi/EukOriginPredict. Conclusions The prediction accuracy of the training set of the above-mentioned seven species after 100 times fivefold cross validation reach 92.60%, 90.80%, 91.22%, 96.15%, 96.72%, 99.86%, 96.72%, respectively. It denotes that compared with other methods, the methods we designed could accomplish superior performance. In addition, our experiments reveals that the models of multiple species could predict each other with high accuracy, and the results of STREME shows that they have a certain common motif.

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Section: Methodsmentioning

confidence: 99%

Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features

Fan

Wang

2021

BMC Bioinformatics

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“…PseKNC has been successfully applied to the identification of RNA or DNA modification by forming the physicochemical properties of oligonucleotides [38], such as MSLP [39], iterb-PPse [40], and mRNALocater [41]. Each sample can be expressed utilizing the following formula:…”

Section: Pseudo K-tuple Composition (Pseknc)mentioning

confidence: 99%

m6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier

Liu

Lan

Liu

et al. 2023

IJMS

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As one of the most important post-transcriptional modifications, m6Am plays a fairly important role in conferring the m6Am stability and in the progression of cancers. The accurate identification of m6Am sites is critical for explaining its biological significance and developing its application in the medical field. However, conventional experimental approaches are time-consuming and expensive, making them unsuitable for the large-scale identification of the m6Am sites. To address this challenge, we exploit a CatBoost-based method, m6Aminer, to identify the m6Am sites on mRNA. For feature extraction, nine different feature-encoding schemes (pseudo electron–ion interaction potential, hash decimal conversion method, dinucleotide binary encoding, nucleotide chemical properties, pseudo k-tuple composition, dinucleotide numerical mapping, K monomeric units, series correlation pseudo trinucleotide composition, and K-spaced nucleotide pair frequency) were utilized to form the initial feature space. To obtain the optimized feature subset, the ExtraTreesClassifier algorithm was adopted to perform feature importance ranking, and the top 300 features were selected as the optimal feature subset. With different performance assessment methods, 10-fold cross-validation and independent test, m6Aminer achieved average AUC of 0.913 and 0.754, demonstrating a competitive performance with the state-of-the-art models m6AmPred (0.905 and 0.735) and DLm6Am (0.897 and 0.730). The prediction model developed in this study can be used to identify the m6Am sites in the whole transcriptome, laying a foundation for the functional research of m6Am.

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Phage Annotation Guide: Guidelines for Assembly and High-Quality Annotation

Turner

Adriaenssens

Tolstoy

et al. 2021

PHAGE

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iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC

Cited by 4 publications

References 46 publications

Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features

Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features

m6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier

Phage Annotation Guide: Guidelines for Assembly and High-Quality Annotation

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