DiProDB: a database for dinucleotide properties

Abstract

DiProDB (http://diprodb.fli-leibniz.de) is a database of conformational and thermodynamic dinucleotide properties. It includes datasets both for DNA and RNA, as well as for single and double strands. The data have been shown to be important for understanding different aspects of nucleic acid structure and function, and they can also be used for encoding nucleic acid sequences. The database is intended to facilitate further applications of dinucleotide properties. A number of property datasets is highly correlated. Therefore, the database comes with a correlation analysis facility. Authors having determined new sets of dinucleotide property values are invited to submit these data to DiProDB.

A new method for identification of pre-microRNAs based on hybrid features

“…According to the physical and chemical properties of dinucleotides and the intrinsic characteristics of Hilbert-Huang transform, we selected 15 physical and chemical properties for RNAs from the database [47], including: enthalpy, enthalpy2, entropy, entropy2, free energy, free energy2, hydrophilicity, hydrophilicity2, rise, roll, shift, slide, stackingenergy, tilt, twist.…”

A high-order representation and classification method for transcription factor binding sites recognition in Escherichia coli

“…and geometrical parameters of base pairs (shear, stretch, and shift, etc.). In this paper, we focus on the physicochemical properties recorded as 2-mers to characterize the specific TFBSs, and the extended physicochemical properties recorded as 3-mers, 4-mers, and 7-mers from two recent studies [14,28]. For 2-mers, we collect all dinucleotide properties from DiProDB database (http://diprodb.fli-leibniz.de/ShowTable.php), and the total number of corresponding properties is 110.…”

“…Additionally, we also incorporate the letter features to provide the same information as used in PWM-based approaches. As described in previous studies [14,28], letter features are generated by designating the four kinds of nucleotides -A, C, G, and T -as mutually orthogonal 4D vectors (1,0,0,0), (0,1,0,0), (0,0,1,0), and (0,0,0,1), respectively.…”

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

“…In iRNA-Methyl [29], only three physical-chemical properties of nucleotides, i.e., enthalpy, entropy, and free energy, were used to encode each RNA segment into a pseudo dinucleotide composition (PseDNC) feature; while in pRNAm-PC [28], ten physical-chemical properties of nucleotides were used to construct the combined feature based on auto-covariance (AC) and cross-covariance (CC) transformations, and the prediction performance has been remarkably improved. However, there are many other physical-chemical properties of nucleotides can be used, such as local structural properties [33], entropy and enthalpy [34], energy [35,36], nucleotides content [37], hydrophilicity [38], elastic behavior [39], and so on. Nevertheless, in both iRNA-Methyl [29] and pRNAm-PC [28], the authors only provided the prediction performances under several physical-chemical properties but did not explain why these properties were selected.…”