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

By Bin Liu, Fule Liu, Xiaolong Wang, Junjie Chen, Longyun Fang, Kuo-Chen Chou. Nucleic Acids Res., 2015

692 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0c83d37a4e326ef06cee371042eeda497eb82deb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Bin Liu
2. Xuan Xiao
3. Jianhua Jia
4. Zi Liu
5. Bingxiang Liu
6. Junjie Chen
7. Xin Gao
8. Kuo-Chen Chou

Citations in the corpus

• BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches (2019, background): “As shown in some recent studies, machine learning techniques are playing more and more important roles in biological sequence analysis (73,74), such as protein remote homology detection (75), protein fold recognition (76), etc.”
• Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA (2018, background): “Related features are then proposed for representing the sliding window sequences with equal length (Liu et al. 2015, 2018; He et al. 2018).”
• A comprehensive review and comparison of different computational methods for protein remote homology detection (2018, methodology): “Recently, studies [65, 112] show that the sequenceorder information [44, 113], the physicochemical properties [45, 80], pseudo protein representation [18, 89] and profile/ HMM representation [37, 49, 51] are useful features for constructing computational predictors, and a web server called Pse-in-On”
• Protein remote homology detection based on bidirectional long short-term memory (2017, methodology): “During the vectorization process, the sequence-order information and the position dependency effects are lost, and this information is critical for protein sequence analysis and nucleic acid analysis [32 – 34].”
• pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. (2016, methodology): “Particularly, recently a powerful web-server called Pse-in-One [56] has been established that can be used to generate any desired pseudo components for protein/peptide and DNA/RNA sequences according to users’ own need.”