Browsing by Author "Yang, Mary Qu"

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    3D Protein structure prediction with genetic tabu search algorithm
    (BMC, 2010-05-28) Zhang, Xiaolong; Wang, Ting; Luo, Huiping; Yang, Jack Y.; Deng, Youping; Tang, Jinshan; Yang, Mary Qu; Medicine, School of Medicine
    Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.
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    Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms
    (Springer (Biomed Central Ltd.), 2014) Yang, Jack Y.; Dunker, A. Keith; Liu, Jun S.; Qin, Xiang; Arabnia, Hamid R.; Yang, William; Niemierko, Andrzej; Chen, Zhongxue; Luo, Zuojie; Wang, Liangjiang; Liu, Yunlong; Xu, Dong; Deng, Youping; Tong, Weida; Yang, Mary Qu; Department of Biochemistry and Molecular Biology, IU School of Medicine
    Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.
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    BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features
    (BMC, 2010-05-28) Wang, Liangjiang; Huang, Caiyan; Yang, Mary Qu; Yang, Jack Y.; Medicine, School of Medicine
    Background Understanding how biomolecules interact is a major task of systems biology. To model protein-nucleic acid interactions, it is important to identify the DNA or RNA-binding residues in proteins. Protein sequence features, including the biochemical property of amino acids and evolutionary information in terms of position-specific scoring matrix (PSSM), have been used for DNA or RNA-binding site prediction. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modelling DNA or RNA-binding sites in protein sequences. Results In the present study, several new descriptors of evolutionary information have been developed and evaluated for sequence-based prediction of DNA and RNA-binding residues using support vector machines (SVMs). The new descriptors were shown to improve classifier performance. Interestingly, the best classifiers were obtained by combining the new descriptors and PSSM, suggesting that they captured different aspects of evolutionary information for DNA and RNA-binding site prediction. The SVM classifiers achieved 77.3% sensitivity and 79.3% specificity for prediction of DNA-binding residues, and 71.6% sensitivity and 78.7% specificity for RNA-binding site prediction. Conclusions Predictions at this level of accuracy may provide useful information for modelling protein-nucleic acid interactions in systems biology studies. We have thus developed a web-based tool called BindN+ (http://bioinfo.ggc.org/bindn+/) to make the SVM classifiers accessible to the research community.
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    The emerging genomics and systems biology research lead to systems genomics studies
    (Springer (Biomed Central Ltd.), 2014) Yang, Mary Qu; Yoshigoe, Kenji; Yang, William; Tong, Weida; Qin, Xiang; Dunker, A. Keith; Chen, Zhongxue; Arbania, Hamid R.; Liu, Jun S.; Niemierko, Andrzej; Yang, Jack Y.; Department of Biochemistry and Molecular Biology, IU School of Medicine
    Synergistically integrating multi-layer genomic data at systems level not only can lead to deeper insights into the molecular mechanisms related to disease initiation and progression, but also can guide pathway-based biomarker and drug target identification. With the advent of high-throughput next-generation sequencing technologies, sequencing both DNA and RNA has generated multi-layer genomic data that can provide DNA polymorphism, non-coding RNA, messenger RNA, gene expression, isoform and alternative splicing information. Systems biology on the other hand studies complex biological systems, particularly systematic study of complex molecular interactions within specific cells or organisms. Genomics and molecular systems biology can be merged into the study of genomic profiles and implicated biological functions at cellular or organism level. The prospectively emerging field can be referred to as systems genomics or genomic systems biology. The Mid-South Bioinformatics Centre (MBC) and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and University of Arkansas for Medical Sciences are particularly interested in promoting education and research advancement in this prospectively emerging field. Based on past investigations and research outcomes, MBC is further utilizing differential gene and isoform/exon expression from RNA-seq and co-regulation from the ChiP-seq specific for different phenotypes in combination with protein-protein interactions, and protein-DNA interactions to construct high-level gene networks for an integrative genome-phoneme investigation at systems biology level.
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    Identification of genes and pathways involved in kidney renal clear cell carcinoma
    (Springer (Biomed Central Ltd.), 2014) Yang, William; Yoshigoe, Kenji; Qin, Xiang; Liu, Jun S.; Yang, Jack Y.; Niemierko, Andrzej; Deng, Youping; Liu, Yunlong; Dunker, A. Keith; Chen, Zhongxue; Wang, Liangjiang; Xu, Dong; Arabnia, Hamid R.; Tong, Weida; Yang, Mary Qu; Department of Medical and Molecular Genetics, IU School of Medicine
    BACKGROUND: Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development. RESULTS: Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets. CONCLUSIONS: Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.