Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences

Abstract

With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems in computational biology is how to effectively formulate the sequence of a biological sample (such as DNA, RNA or protein) with a discrete model or a vector that can effectively reflect its sequence pattern information or capture its key features concerned. Although several web servers and stand-alone tools were developed to address this problem, all these tools, however, can only handle one type of samples. Furthermore, the number of their built-in properties is limited, and hence it is often difficult for users to formulate the biological sequences according to their desired features or properties. In this article, with a much larger number of built-in properties, we are to propose a much more flexible web server called Pse-in-One (http://bioinformatics.hitsz.edu.cn/Pse-in-One/), which can, through its 28 different modes, generate nearly all the possible feature vectors for DNA, RNA and protein sequences. Particularly, it can also generate those feature vectors with the properties defined by users themselves. These feature vectors can be easily combined with machine-learning algorithms to develop computational predictors and analysis methods for various tasks in bioinformatics and system biology. It is anticipated that the Pse-in-One web server will become a very useful tool in computational proteomics, genomics, as well as biological sequence analysis. Moreover, to maximize users’ convenience, its stand-alone version can also be downloaded from http://bioinformatics.hitsz.edu.cn/Pse-in-One/download/, and directly run on Windows, Linux, Unix and Mac OS.

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

“…Evolved from the original Pse-in-One package [226], Pse-in-One 2.0 is much more flexible and Natural Science powerful than the former. In comparison with the 2015 version that has been widely used in bioinformatics and computational biology and biomedicine within a very short period of time, the new version is even more powerful for conducting various genome analyses and proteome analyses.…”

“…Nucleic acid composition 1) increment of diversity (IDKmer) [226,270,291] 2) The occurrences of kmers, allowing at most m mismatches (Mismatch) [264,265,292] 3) The occurrences of kmers, allowing non-contiguous matches (Subsequence) [265,292,293] Autocorrelation 4) Moran autocorrelation (MAC) [268,294] 5) Geary autocorrelation (GAC) [217,295] 6) Normalized Moreau-Broto autocorrelation (NMBAC) [217,296] Table 2. List of the 8 new modes for RNA 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.…”

A new signal characterization and signal-based Chou's PseAAC representation of protein sequences

“…Since its introduction by Chou, the PseAAC has become rather popular, being applied in almost all areas of computational proteomics and, more recently, the idea has also been extended to DNA and RNA sequences (see the recent paper by Liu et al 37 ). Although originally only some physicochemical properties of amino acids were used (hydrophobicity, hydrophilicity or side-chain mass), in fact any physicochemical properties, such as those listed in the amino acid index database, 23 can be used.…”

“…Chou's PseAAC has been used for a wide variety of applications. 37 In order to compare the PseAAC based on physicochemical properties of amino acids with that one based on the proposed representation in 20 signals we will use it for the same task described in Ref. 11.…”