Pengmian Feng

iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

Meiotic recombination is an important biological process. As a main driving force of evolution, recombination provides natural new combinations of genetic variations. Rather than randomly occurring across a genome, meiotic recombination takes place in some genomic regions (the so-called ‘hotspots’) with higher frequencies, and in the other regions (the so-called ‘coldspots’) with lower frequencies. Therefore, the information of the hotspots and coldspots would provide useful insights for in-depth studying of the mechanism of recombination and the genome evolution process as well. So far, the recombination regions have been mainly determined by experiments, which are both expensive and time-consuming. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the recombination regions. In this study, a predictor, called ‘iRSpot-PseDNC’, was developed for identifying the recombination hotspots and coldspots. In the new predictor, the samples of DNA sequences are formulated by a novel feature vector, the so-called ‘pseudo dinucleotide composition’ (PseDNC), into which six local DNA structural properties, i.e. three angular parameters (twist, tilt and roll) and three translational parameters (shift, slide and rise), are incorporated. It was observed by the rigorous jackknife test that the overall success rate achieved by iRSpot-PseDNC was >82% in identifying recombination spots in Saccharomyces cerevisiae, indicating the new predictor is promising or at least may become a complementary tool to the existing methods in this area. Although the benchmark data set used to train and test the current method was from S. cerevisiae, the basic approaches can also be extended to deal with all the other genomes. Particularly, it has not escaped our notice that the PseDNC approach can be also used to study many other DNA-related problems. As a user-friendly web-server, iRSpot-PseDNC is freely accessible at http://lin.uestc.edu.cn/server/iRSpot-PseDNC.

iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties.

Nucleosome positioning has important roles in key cellular processes. Although intensive efforts have been made in this area, the rules defining nucleosome positioning is still elusive and debated. In this study, we carried out a systematic comparison among the profiles of twelve DNA physicochemical features between the nucleosomal and linker sequences in the Saccharomyces cerevisiae genome. We found that nucleosomal sequences have some position-specific physicochemical features, which can be used for in-depth studying nucleosomes. Meanwhile, a new predictor, called iNuc-PhysChem, was developed for identification of nucleosomal sequences by incorporating these physicochemical properties into a 1788-D (dimensional) feature vector, which was further reduced to a 884-D vector via the IFS (incremental feature selection) procedure to optimize the feature set. It was observed by a cross-validation test on a benchmark dataset that the overall success rate achieved by iNuc-PhysChem was over 96% in identifying nucleosomal or linker sequences. As a web-server, iNuc-PhysChem is freely accessible to the public at http://lin.uestc.edu.cn/server/iNuc-PhysChem. For the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented just for the integrity in developing the predictor. Meanwhile, for those who prefer to run predictions in their own computers, the predictors code can be easily downloaded from the web-server. It is anticipated that iNuc-PhysChem may become a useful high throughput tool for both basic research and drug design.

iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition.

Heat shock proteins (HSPs) are a type of functionally related proteins present in all living organisms, both prokaryotes and eukaryotes. They play essential roles in protein-protein interactions such as folding and assisting in the establishment of proper protein conformation and prevention of unwanted protein aggregation. Their dysfunction may cause various life-threatening disorders, such as Parkinsons, Alzheimers, and cardiovascular diseases. Based on their functions, HSPs are usually classified into six families: (i) HSP20 or sHSP, (ii) HSP40 or J-class proteins, (iii) HSP60 or GroEL/ES, (iv) HSP70, (v) HSP90, and (vi) HSP100. Although considerable progress has been achieved in discriminating HSPs from other proteins, it is still a big challenge to identify HSPs among their six different functional types according to their sequence information alone. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop a high-throughput computational tool in this regard. To take up such a challenge, a predictor called iHSP-PseRAAAC has been developed by incorporating the reduced amino acid alphabet information into the general form of pseudo amino acid composition. One of the remarkable advantages of introducing the reduced amino acid alphabet is being able to avoid the notorious dimension disaster or overfitting problem in statistical prediction. It was observed that the overall success rate achieved by iHSP-PseRAAAC in identifying the functional types of HSPs among the aforementioned six types was more than 87%, which was derived by the jackknife test on a stringent benchmark dataset in which none of HSPs included has ≥40% pairwise sequence identity to any other in the same subset. It has not escaped our notice that the reduced amino acid alphabet approach can also be used to investigate other protein classification problems. As a user-friendly web server, iHSP-PseRAAAC is accessible to the public at http://lin.uestc.edu.cn/server/iHSP-PseRAAAC.

iACP: a sequence-based tool for identifying anticancer peptides

Cancer remains a major killer worldwide. Traditional methods of cancer treatment are expensive and have some deleterious side effects on normal cells. Fortunately, the discovery of anticancer peptides (ACPs) has paved a new way for cancer treatment. With the explosive growth of peptide sequences generated in the post genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying ACPs, so as to speed up their application in treating cancer. Here we report a sequence-based predictor called iACP developed by the approach of optimizing the g-gap dipeptide components. It was demonstrated by rigorous cross-validations that the new predictor remarkably outperformed the existing predictors for the same purpose in both overall accuracy and stability. For the convenience of most experimental scientists, a publicly accessible web-server for iACP has been established at http://lin.uestc.edu.cn/server/iACP, by which users can easily obtain their desired results.

iSS-PseDNC: Identifying Splicing Sites Using Pseudo Dinucleotide Composition

In eukaryotic genes, exons are generally interrupted by introns. Accurately removing introns and joining exons together are essential processes in eukaryotic gene expression. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapid and effective detection of splice sites that play important roles in gene structure annotation and even in RNA splicing. Although a series of computational methods were proposed for splice site identification, most of them neglected the intrinsic local structural properties. In the present study, a predictor called “iSS-PseDNC” was developed for identifying splice sites. In the new predictor, the sequences were formulated by a novel feature-vector called “pseudo dinucleotide composition” (PseDNC) into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on two benchmark datasets that the overall success rates achieved by iSS-PseDNC in identifying splice donor site and splice acceptor site were 85.45% and 87.73%, respectively. It is anticipated that iSS-PseDNC may become a useful tool for identifying splice sites and that the six DNA local structural properties described in this paper may provide novel insights for in-depth investigations into the mechanism of RNA splicing.

iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences

Catalyzed by adenosine deaminase (ADAR), the adenosine to inosine (A-to-I) editing in RNA is not only involved in various important biological processes, but also closely associated with a series of major diseases. Therefore, knowledge about the A-to-I editing sites in RNA is crucially important for both basic research and drug development. Given an uncharacterized RNA sequence that contains many adenosine (A) residues, can we identify which one of them can be of A-to-I editing, and which one cannot? Unfortunately, so far no computational method whatsoever has been developed to address such an important problem based on the RNA sequence information alone. To fill this empty area, we have proposed a predictor called iRNA-AI by incorporating the chemical properties of nucleotides and their sliding occurrence density distribution along a RNA sequence into the general form of pseudo nucleotide composition (PseKNC). It has been shown by the rigorous jackknife test and independent dataset test that the performance of the proposed predictor is quite promising. For the convenience of most experimental scientists, a user-friendly web-server for iRNA-AI has been established at http://lin.uestc.edu.cn/server/iRNA-AI/, by which users can easily get their desired results without the need to go through the mathematical details.

iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

There are many different types of RNA modifications, which are essential for numerous biological processes. Knowledge about the occurrence sites of RNA modifications in its sequence is a key for in-depth understanding of their biological functions and mechanism. Unfortunately, it is both time-consuming and laborious to determine these sites purely by experiments alone. Although some computational methods were developed in this regard, each one could only be used to deal with some type of modification individually. To our knowledge, no method has thus far been developed that can identify the occurrence sites for several different types of RNA modifications with one seamless package or platform. To address such a challenge, a novel platform called “iRNA-PseColl” has been developed. It was formed by incorporating both the individual and collective features of the sequence elements into the general pseudo K-tuple nucleotide composition (PseKNC) of RNA via the chemicophysical properties and density distribution of its constituent nucleotides. Rigorous cross-validations have indicated that the anticipated success rates achieved by the proposed platform are quite high. To maximize the convenience for most experimental biologists, the platform’s web-server has been provided at http://lin.uestc.edu.cn/server/iRNA-PseColl along with a step-by-step user guide that will allow users to easily achieve their desired results without the need to go through the mathematical details involved in this paper.

iDNA6mA-PseKNC: Identifying DNA N 6 -methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC

N6-methyladenine (6mA) is one kind of post-replication modification (PTM or PTRM) occurring in a wide range of DNA sequences. Accurate identification of its sites will be very helpful for revealing the biological functions of 6mA, but it is time-consuming and expensive to determine them by experiments alone. Unfortunately, so far, no bioinformatics tool is available to do so. To fill in such an empty area, we have proposed a novel predictor called iDNA6mA-PseKNC that is established by incorporating nucleotide physicochemical properties into Pseudo K-tuple Nucleotide Composition (PseKNC). It has been observed via rigorous cross-validations that the predictors sensitivity (Sn), specificity (Sp), accuracy (Acc), and stability (MCC) are 93%, 100%, 96%, and 0.93, respectively. For the convenience of most experimental scientists, a user-friendly web server for iDNA6mA-PseKNC has been established at http://lin-group.cn/server/iDNA6mA-PseKNC, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.

iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties

Motivation DNA N4‐methylcytosine (4mC) is an epigenetic modification. The knowledge about the distribution of 4mC is helpful for understanding its biological functions. Although experimental methods have been proposed to detect 4mC sites, they are expensive for performing genome‐wide detections. Thus, it is necessary to develop computational methods for predicting 4mC sites. Results In this work, we developed iDNA4mC, the first webserver to identify 4mC sites, in which DNA sequences are encoded with both nucleotide chemical properties and nucleotide frequency. The predictive results of the rigorous jackknife test and cross species test demonstrated that the performance of iDNA4mC is quite promising and holds high potential to become a useful tool for identifying 4mC sites. Availability and implementation The user‐friendly web‐server, iDNA4mC, is freely accessible at http://lin.uestc.edu.cn/server/iDNA4mC. Contact chenweiimu@gmail.com or hlin@uestc.edu.cn

Prediction of replication origins by calculating DNA structural properties

In this study, we introduced two DNA structural characteristics, namely, bendability and hydroxyl radical cleavage intensity to analyze origin of replication (ORI) in the Saccharomyces cerevisiae genome. We found that both DNA bendability and cleavage intensity in core replication regions were significantly lower than in the linker regions. By using these two DNA structural characteristics, we developed a computational model for ORI prediction and evaluated the model in a benchmark dataset. The predictive performance of the jackknife cross‐validation indicates that DNA bendability and cleavage intensity have the ability to describe core replication regions and our model is effective in ORI prediction.