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.

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

Facing the explosive growth of biological sequence data, such as those of protein/peptide and DNA/RNA, generated in the post-genomic age, many bioinformatical and mathematical approaches as well as physicochemical concepts have been introduced to timely derive useful informations from these biological sequences, in order to stimulate the development of medical science and drug design. Meanwhile, because of the rapid penetrations from these disciplines, medicinal chemistry is currently undergoing an unprecedented revolution. In this minireview, we are to summarize the progresses by focusing on the following six aspects. (1) Use the pseudo amino acid composition or PseAAC to predict various attributes of protein/peptide sequences that are useful for drug development. (2) Use pseudo oligonucleotide composition or PseKNC to do the same for DNA/RNA sequences. (3) Introduce the multi-label approach to study those systems where the constituent elements bear multiple characters and functions. (4) Utilize the graphical rules and "wenxiang" diagrams to analyze complicated biomedical systems. (5) Recent development in identifying the interactions of drugs with its various types of target proteins in cellular networking. (6) Distorted key theory and its application in developing peptide drugs.

Impacts of bioinformatics to medicinal chemistry.

Motivation Enhancers are of short regulatory DNA elements. They can be bound with proteins (activators) to activate transcription of a gene, and hence play a critical role in promoting gene transcription in eukaryotes. With the avalanche of DNA sequences generated in the post-genomic age, it is a challenging task to develop computational methods for timely identifying enhancers from extremely complicated DNA sequences. Although some efforts have been made in this regard, they were limited at only identifying whether a query DNA element being of an enhancer or not. According to the distinct levels of biological activities and regulatory effects on target genes, however, enhancers should be further classified into strong and weak ones in strength. Results In view of this, a two-layer predictor called ' IENHANCER-2L: ' was proposed by formulating DNA elements with the 'pseudo k-tuple nucleotide composition', into which the six DNA local parameters were incorporated. To the best of our knowledge, it is the first computational predictor ever established for identifying not only enhancers, but also their strength. Rigorous cross-validation tests have indicated that IENHANCER-2L: holds very high potential to become a useful tool for genome analysis. Availability and implementation For the convenience of most experimental scientists, a web server for the two-layer predictor was established at http://bioinformatics.hitsz.edu.cn/iEnhancer-2L/, by which users can easily get their desired results without the need to go through the mathematical details. Contact bliu@gordonlifescience.org, bliu@insun.hit.edu.cn, xlan@stanford.edu, kcchou@gordonlifescience.org Supplementary information Supplementary data are available at Bioinformatics online.

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iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition

https://www.kau.edu.sa/Files/117/Researches/67082_38614.pdf

iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition.

pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach

Associate Editor: John HancockABSTRACTSummary: The avalanche of genomic sequences generated in thepost-genomic age requires efficient computational methods for rapidlyand accurately identifying biological features from sequence informa-tion. Towards this goal, we developed a freely available and open-source package, called PseKNC-General (the general form ofpseudo k-tuple nucleotide composition), that allows for fast and ac-curate computation of all the widely used nucleotide structural andphysicochemical properties of both DNA and RNA sequences.PseKNC-General can generate several modes of pseudo nucleotidecompositions, including conventional k-tuple nucleotide compositions,Moreau–Broto autocorrelation coefficient, Moran autocorrelation coef-ficient, Geary autocorrelation coefficient, Type I PseKNC and Type IIPseKNC. In every mode,4100 physicochemical properties are avail-able for choosing. Moreover, it is flexible enough to allow the users tocalculate PseKNC with user-defined properties. The package can berun on Linux, Mac and Windows systems and also provides a graph-ical user interface.Availability and implementation: The package is freely available at:http://lin.uestc.edu.cn/server/pseknc.Contact: chenweiimu@gmail.com or lqzhang@vt.edu or kcchou@gor-donlifescience.org.Supplementary information: Supplementary data are available atBioinformatics online.Received on July 22, 2014; revised on August 19, 2014; accepted onAugust 31, 2014

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Jordan Brooker

Papers

PseKNC-General: A cross-platform package for generating various modes of pseudo nucleotide compositions