New Genome Sequence Detection via Natural Vector Convex Hull Method

Constructing discriminative feature space for LncRNA-protein interaction based on deep autoencoder and marginal fisher analysis

Long non-coding RNAs (lncRNAs) play important roles by regulating proteins in many biological processes and life activities. To uncover molecular mechanisms of lncRNA, it is very necessary to identify interactions of lncRNA with proteins. Recently, some machine learning methods were proposed to detect lncRNA-protein interactions according to the distribution of known interactions. The performances of these methods were largely dependent upon: (1) how exactly the distribution of known interactions was characterized by feature space; (2) how discriminative the feature space was for distinguishing lncRNA-protein interactions. Because the known interactions may be multiple and complex model, it remains a challenge to construct discriminative feature space for lncRNA-protein interactions. To resolve this problem, a novel method named DFRPI was developed based on deep autoencoder and marginal fisher analysis in this paper. Firstly, some initial features of lncRNA-protein interactions were extracted from the primary sequences and secondary structures of lncRNA and protein. Secondly, a deep autoencoder was exploited to learn encode parameters of the initial features to describe the known interactions precisely. Next, the marginal fisher analysis was employed to optimize the encode parameters of features to characterize a discriminative feature space of the lncRNA-protein interactions. Finally, a random forest-based predictor was trained on the discriminative feature space to detect lncRNA-protein interactions. Verified by a series of experiments, the results showed that our predictor achieved the precision of 0.920, recall of 0.916, accuracy of 0.918, MCC of 0.836, specificity of 0.920, sensitivity of 0.916 and AUC of 0.906 respectively, which outperforms the concerned methods for predicting lncRNA-protein interaction. It may be suggested that the proposed method can generate a reasonable and effective feature space for distinguishing lncRNA-protein interactions accurately. The code and data are available on https://github.com/D0ub1e-D/DFRPI.

An efficient numerical representation of genome sequence: natural vector with covariance component

Background

The characterization and comparison of microbial sequences, including archaea, bacteria, viruses and fungi, are very important to understand their evolutionary origin and the population relationship. Most methods are limited by the sequence length and lack of generality. The purpose of this study is to propose a general characterization method, and to study the classification and phylogeny of the existing datasets.

Methods

We present a new alignment-free method to represent and compare biological sequences. By adding the covariance between each two nucleotides, the new 18-dimensional natural vector successfully describes 24,250 genomic sequences and 95,542 DNA barcode sequences. The new numerical representation is used to study the classification and phylogenetic relationship of microbial sequences.

Results

First, the classification results validate that the six-dimensional covariance vector is necessary to characterize sequences. Then, the 18-dimensional natural vector is further used to conduct the similarity relationship between giant virus and archaea, bacteria, other viruses. The nearest distance calculation results reflect that the giant viruses are closer to bacteria in distribution of four nucleotides. The phylogenetic relationships of the three representative families, Mimiviridae, Pandoraviridae and Marsellieviridae from giant viruses are analyzed. The trees show that ten sequences of Mimiviridae are clustered with Pandoraviridae, and Mimiviridae is closer to the root of the tree than Marsellieviridae. The new developed alignment-free method can be computed very fast, which provides an effective numerical representation for the sequence of microorganisms.

Geometric construction of viral genome space and its applications

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The first construction of viral genome space.

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The first demonstration of the convex hull principle of genomes.

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The first definition of a natural metric to describe the geometry of genome space.

Understanding the relationships between genomic sequences is essential to the classification and characterization of living beings. The classes and characteristics of an organism can be identified in the corresponding genome space. In the genome space, the natural metric is important to describe the distribution of genomes. Therefore, the similarity of two biological sequences can be measured. Here, we report that all of the viral genomes are in 32-dimensional Euclidean space, in which the natural metric is the weighted summation of Euclidean distance of k-mer natural vectors. The classification of viral genomes in the constructed genome space further proves the convex hull principle of taxonomy, which states that convex hulls of different families are mutually disjoint. This study provides a novel geometric perspective to describe the genome sequences.