iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach

Abstract

A microRNA (miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating that they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the post-genomic age, it is highly desired to develop computational sequence-based methods for effectively identifying the human pre-miRNAs. Here, we propose a predictor called "iMiRNA-PseDPC", in which the RNA sequences are formulated by a novel feature vector called "pseudo distance-pair composition" (PseDPC) with 10 types of structure statuses. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark data-set showed that our approach has remarkably outperformed the existing ones in either prediction accuracy or efficiency, indicating the new predictor is quite promising or at least may become a complementary tool to the existing predictors in this area. For the convenience of most experimental scientists, a user-friendly web server for the new predictor has been established at http://bioinformatics.hitsz.edu.cn/iMiRNA-PseDPC/, by which users can easily get their desired results without the need to go through the mathematical details. It is anticipated that the new predictor may become a useful high throughput tool for genome analysis particularly in dealing with large-scale data.

Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts

“…Later on the concept of PseAAC was extended to cover all the feature vectors of proteins (Chou, 2009(Chou, , 2011. Furthermore, the concept of PseAAC has been extended to deal with DNA/RNA sequences Lin et al, 2014;Liu et al, 2015bLiu et al, , 2015c. Because it has been widely and increasingly used in many areas of computational biology, recently a web server called 'Pse-in One' was established to generate various modes of pseudocomponents (Liu et al, 2015e), which is the first web server ever that can generate nearly all the features of pseudocomponents of DNA, RNA, and protein sequences in one package.…”

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

“…Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [54,55]. That is why researchers have a preference for the jackknife test for examining the quality of various machine learning based predictors such as [30,31,46]. Hence, we also use the jackknife test and independent dataset test to evaluate the accuracy of the current predictor in this work.…”

“…In order to compare with previous works, we use the benchmark dataset in the works of Liu et al [27,28,29,30] and Khan et al [31], which consists of positive samples (true pre-microRNAs) and negative samples (pseudo pre-microRNAs). As in the above works, we derived the positive samples from the miRBase (released on 20 June, 2013) [32], which is composed of 1872 experimentally confirmed premicroRNA sequences of homo sapiens.…”

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences

“…1) The occurrences of kmers, allowing at most m mismatches (Mismatch) [264,265,292] 2) The occurrences of kmers, allowing non-contiguous matches (Subsequence) [265,292,293] Autocorrelation 3) Moran autocorrelation (MAC) [217,294] 4) Geary autocorrelation (GAC) [217,295] 5) Normalized Moreau-Broto autocorrelation (NMBAC) [217,296] Predicted structure composition 6) Local structure-sequence triplet element (Triplet) [266] 7) Pseudo-structure status composition (PseSSC) [226] 8) Pseudo-distance structure status pair composition (PseDPC) [10] 2) PseAAC of Distance-Pairs and Reduced Alphabet (Distance Pair) [271] Autocorrelation 3) Physicochemical distance transformation (PDT) [270] Profile-based features 4) Select and combine the n most frequenct amino acids according to their frequencies (Top-n-gram) [269] 5) Profile-based Physicochemical distance transformation (PDT-Pofile) [270] 6) Distance-based Top-n-gram (DT) [271] 7) Profile-based Auto covariance (AC-PSSM) [272] 8) Profile-based Cross covariance (CC-PSSM) [272] 9) Profile-based Auto-cross covariance (ACC-PSSM) [272] Natural Science Mismatch [264] and Subsequence [265]; and 3 are added into the autocorrelation category, i.e., Moran autocorrelation, Geary autocorrelation, and Normalized Moreau-Broto autocorrelation [268]. PseAAC-General is designed to generate the feature vectors for protein sequences.…”

“…This is because almost all the existing machine-learning algorithms, such as "Neural Network" or NN algorithm [1][2][3] "Support Vector Machine" or SVM algorithm [4][5][6][7][8][9][10][11][12] "Nearest Neighbor" or NN algorithm [13,14] and "Random Forest" algorithm [15][16][17][18][19][20][21][22] can only handle vectors but not sequence samples as elucidated in a review paper [23]. Unfortunately, if using the sequential model, i.e., the model in which all the samples are represented by their original sequences, it is hardly able to train a machine learning model that can cover all the possible cases concerned, as elaborated in [24].…”