Ruiling Liu

CarSPred: A Computational Tool for Predicting Carbonylation Sites of Human Proteins

Protein carbonylation is one of the most pervasive oxidative stress-induced post-translational modifications (PTMs), which plays a significant role in the etiology and progression of several human diseases. It has been regarded as a biomarker of oxidative stress due to its relatively early formation and stability compared with other oxidative PTMs. Only a subset of proteins is prone to carbonylation and most carbonyl groups are formed from lysine (K), arginine (R), threonine (T) and proline (P) residues. Recent advancements in analysis of the PTM by mass spectrometry provided new insights into the mechanisms of protein carbonylation, such as protein susceptibility and exact modification sites. However, the experimental approaches to identifying carbonylation sites are costly, time-consuming and capable of processing a limited number of proteins, and there is no bioinformatics method or tool devoted to predicting carbonylation sites of human proteins so far. In the paper, a computational method is proposed to identify carbonylation sites of human proteins. The method extracted four kinds of features and combined the minimum Redundancy Maximum Relevance (mRMR) feature selection criterion with weighted support vector machine (WSVM) to achieve total accuracies of 85.72%, 85.95%, 83.92% and 85.72% for K, R, T and P carbonylation site predictions respectively using 10-fold cross-validation. The final optimal feature sets were analysed, the position-specific composition and hydrophobicity environment of flanking residues of modification sites were discussed. In addition, a software tool named CarSPred has been developed to facilitate the application of the method. Datasets and the software involved in the paper are available at https://sourceforge.net/projects/hqlstudio/files/CarSPred-1.0/.

A novel computational method for the identification of plant alternative splice sites

Alternative splicing (AS) increases protein diversity by generating multiple transcript isoforms from a single gene in higher eukaryotes. Up to 48% of plant genes exhibit alternative splicing, which has proven to be involved in some important plant functions such as the stress response. A hybrid feature extraction approach which combing the position weight matrix (PWM) with the increment of diversity (ID) was proposed to represent the base conservative level (BCL) near splice sites and the similarity level of two datasets, respectively. Using the extracted features, the support vector machine (SVM) was applied to classify alternative and constitutive splice sites. By the proposed algorithm, 80.8% of donor sites and 85.4% of acceptor sites were correctly classified. It is anticipated that the novel computational method is promising for the identification of AS sites in plants.

An Improved Multihop Distance Estimation for DV-Hop Localization Algorithm in Wireless Sensor Networks

Range-free, distributed localization method is an important and challenging issue in wireless sensor networks. Typical hop-count-based method always assumes that the average hop size for different hop count is the same. In this paper, we analyze the hop progress for different hop counts, and modify the assumption as the hop progress is the same for different hop counts except the 1-hop. We propose two improved DV-Hop estimation distance methods based on the new assumption. simulation result shows that the proposed methods obtain more accurate estimation distance and get better performance of localization.

Computational identification of circular RNAs based on conformational and thermodynamic properties in the flanking introns

Circular RNAs (circRNAs) were found more than 30 years ago, but have been treated as molecular flukes in a long time. Combining deep sequencing studies with bioinformatics technique, thousands of endogenous circRNAs have been found in mammalian cells, and some researchers have proved that several circRNAs act as competing endogenous RNAs (ceRNAs) to regulate gene expression. However, the mechanism by which the precursor mRNA to be transformed into a circular RNA or a linear mRNA is largely unknown. In this paper, we attempted to bioinformatically identify shared genomic features that might further elucidate the mechanism of formation and proposed a SVM-based model to distinguish circRNAs from non-circularized, expressed exons. Firstly, conformational and thermodynamic dinucleotide properties in the flanking introns were extracted as potential features. Secondly, two feature selection methods were applied to gain the optimal feature subset. Our 10-fold cross-validation results showed that the model can be used to distinguish circRNAs from non-circularized, expressed exons with an Sn of 0.884, Sp of 0.900, ACC of 0.892, MCC of 0.784, respectively. The identification results suggest that conformational and thermodynamic properties in the flanking introns are closely related to the formation of circRNAs. Datasets and the tool involved in this paper are all available at https://sourceforge.net/projects/predicircrnatool/files/.

Genome-wide identification and predictive modeling of lincRNAs polyadenylation in cancer genome

Long noncoding RNAs (lncRNAs) play essential regulatory roles in the human cancer genome. Many identified lncRNAs are transcribed by RNA polymerase II in which they are polyadenylated, whereby the long intervening noncoding RNAs (lincRNAs) have been widely used for the researches of lncRNAs. To date, the mechanism of lincRNAs polyadenylation related to cancer is rarely fully understood yet. In this paper, first we reported a comprehensive map of global lincRNAs polyadenylation sites (PASs) in five human cancer genomes; second we proposed a grouping method based on the pattern of genes expression and the manner of alternative polyadenylation (APA); third we investigated the distribution of motifs surrounding PASs. Our analysis reveals that about 70% of PASs are located in the sense strand of lincRNAs. Also more than 90% PASs in the antisense strand of lincRNAs are located in the intron regions. In addition, around 40% of lincRNA genes with PASs has APA sites. Four obvious motifs i.e., AATAAA, TTTTTTTT, CCAGSCTGG, and RGYRYRGTGG were detected in the sequences surrounding PASs in the normal and cancer tissues. Furthermore, a novel algorithm was proposed to recognize the lincRNAs PASs of tumor tissues based on support vector machine (SVM). The algorithm can achieve the accuracies up to 96.55% and 89.48% for identification the tumor lincRNAs PASs from the non-polyadenylation sites and the non-lincRNA PASs, respectively.

ISDTool 2.0: A computational model for predicting immunosuppressive domain of retroviruses

Immunosuppressive domain (ISD) is a conserved region of transmembrane proteins (TM) in envelope gene (env) of retroviruses. in vitro and vivo, a synthetic peptide (CKS-17) that shows homology to ISD inhibits immune function. Evidence has shown that ISD suppresses lymphocyte proliferation and allows escape from immune effectors of the innate and adaptive arms in mouse immune system. Previously, we have developed a tool ISDTool 1.0 to identify ISD of human endogenous retrovirus (HERV). However, several other important retroviruses exist and no method is devoted to ISD prediction of them so far. In the paper, a computational model is proposed to identify ISD of six typical retroviruses from three species. The model combines the minimum Redundancy Maximum Relevance (mRMR) feature selection criterion with weighted extreme learning machine (WELM) to achieve high identification accuracies of 98.95%, 96.34% and 96.87% using self-consistency, 5-fold and 10-fold cross-validation, respectively. A software tool named ISDTool 2.0 has been developed to facilitate the application of the model and a large number of new putative ISDs of the six retroviruses were predicted. In addition, motifs of ISD in these retroviruses were analyzed and the evolutionary relationship was discussed. Datasets and the software involved in the paper are available at http://sourceforge.net/projects/isdtool/files/ISDTool-2.0/.

ISDTool: a computational model for predicting immunosuppressive domain of HERVs.

Human endogenous retroviruses (HERVs) have been found to act as etiological cofactors in several chronic diseases, including cancer, autoimmunity and neurological dysfunction. Immunosuppressive domain (ISD) is a conserved region of transmembrane protein (TM) in envelope gene (env) of retroviruses. In vitro and vivo, evidence has shown that retroviral TM is highly immunosuppressive and a synthetic peptide (CKS-17) that shows homology to ISD inhibits immune function. ISD is probably a potential pathogenic element in HERVs. However, only less than one hundred ISDs of HERVs have been annotated by researchers so far, and universal software for domain prediction could not achieve sufficient accuracy for specific ISD. In this paper, a computational model is proposed to identify ISD in HERVs based on genome sequences only. It has a classification accuracy of 97.9% using Jack-knife test. 117 HERVs families were scanned with the model, 1002 new putative ISDs have been predicted and annotated in the human chromosomes. This model is also applicable to search for ISDs in human T-lymphotropic virus (HTLV), simian T-lymphotropic virus (STLV) and murine leukemia virus (MLV) because of the evolutionary relationship between endogenous and exogenous retroviruses. Furthermore, software named ISDTool has been developed to facilitate the application of the model. Datasets and the software involved in the paper are all available at https://sourceforge.net/projects/isdtool/files/ISDTool-1.0.

A novel method for in silico identification of regulatory SNPs in human genome.

Regulatory single nucleotide polymorphisms (rSNPs), kind of functional noncoding genetic variants, can affect gene expression in a regulatory way, and they are thought to be associated with increased susceptibilities to complex diseases. Here a novel computational approach to identify potential rSNPs is presented. Different from most other rSNPs finding methods which based on hypothesis that SNPs causing large allele-specific changes in transcription factor binding affinities are more likely to play regulatory functions, we use a set of documented experimentally verified rSNPs and nonfunctional background SNPs to train classifiers, so the discriminating features are found. To characterize variants, an extensive range of characteristics, such as sequence context, DNA structure and evolutionary conservation etc. are analyzed. Support vector machine is adopted to build the classifier model together with an ensemble method to deal with unbalanced data. 10-fold cross-validation result shows that our method can achieve accuracy with sensitivity of ~78% and specificity of ~82%. Furthermore, our method performances better than some other algorithms based on aforementioned hypothesis in handling false positives. The original data and the source matlab codes involved are available at https://sourceforge.net/projects/rsnppredict/.

A computational model for predicting fusion peptide of retroviruses

As a pivotal domain within envelope protein, fusion peptide (FP) plays a crucial role in pathogenicity and therapeutic intervention. Taken into account the limited FP annotations in NCBI database and absence of FP prediction software, it is urgent and desirable to develop a bioinformatics tool to predict new putative FPs (np-FPs) in retroviruses. In this work, a sequence-based FP model was proposed by combining Hidden Markov Method with similarity comparison. The classification accuracies are 91.97% and 92.31% corresponding to 10-fold and leave-one-out cross-validation. After scanning sequences without FP annotations, this model discovered 53,946 np-FPs. The statistical results on FPs or np-FPs reveal that FP is a conserved and hydrophobic domain. The FP software programmed for windows environment is available at https://sourceforge.net/projects/fptool/files/?source=navbar.

A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining

A filter feature selection technique has been widely used to mine biomedical data. Recently, in the classical filter method minimal-Redundancy-Maximal-Relevance (mRMR), a risk has been revealed that a specific part of the redundancy, called irrelevant redundancy, may be involved in the minimal-redundancy component of this method. Thus, a few attempts to eliminate the irrelevant redundancy by attaching additional procedures to mRMR, such as Kernel Canonical Correlation Analysis based mRMR (KCCAmRMR), have been made. In the present study, a novel filter feature selection method based on the Maximal Information Coefficient (MIC) and Gram-Schmidt Orthogonalization (GSO), named Orthogonal MIC Feature Selection (OMICFS), was proposed to solve this problem. Different from other improved approaches under the max-relevance and min-redundancy criterion, in the proposed method, the MIC is used to quantify the degree of relevance between feature variables and target variable, the GSO is devoted to calculating the orthogonalized variable of a candidate feature with respect to previously selected features, and the max-relevance and min-redundancy can be indirectly optimized by maximizing the MIC relevance between the GSO orthogonalized variable and target. This orthogonalization strategy allows OMICFS to exclude the irrelevant redundancy without any additional procedures. To verify the performance, OMICFS was compared with other filter feature selection methods in terms of both classification accuracy and computational efficiency by conducting classification experiments on two types of biomedical datasets. The results showed that OMICFS outperforms the other methods in most cases. In addition, differences between these methods were analyzed, and the application of OMICFS in the mining of high-dimensional biomedical data was discussed. The Matlab code for the proposed method is available at https://github.com/lhqxinghun/bioinformatics/tree/master/OMICFS/.