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| 題名: | Novel bioinformatics approaches for analysis of high-throughput biological data |
| 作者: | 吳立青;Weng, Julia Tzu-Ya;Wu, Li-Ching;Chang, Wen-Chi;Chang, Tzu-Hao;Akutsu, Tatsuya;Lee, Tzong-Yi |
| 貢獻者: | 生醫理工學院生醫科學與工程學系 |
| 關鍵詞: | Algorithms;Animals;Bioinformatics;Biology;Bladder cancer;Computational Biology;Computational Biology - methods;Computing Methodologies;Editorial;Electronic data processing;Enzymes;Experiments;Gene expression;Genomes;Humans;Innovations;Methods;Obesity;Proteins;Statistical analysis;Statistical methods;Studies |
| 日期: | 2014-12-28 |
| 上傳時間: | 2026-04-23 11:18:11 (UTC+8) |
| 出版者: | Hindawi Publishing Corporation;United States: Hindawi Publishing Corporation |
| 摘要: | 摘要： With the advent of high-throughput technologies, molecular biology is experiencing a surge in both growth and scope. As the amount of experimental data increases, the demand for the development of ways to analyze these results also increases. For example, the next-generation sequencing (NGS) technology has generated various sequencing data. Mass spectrometry- (MS-) based experiments are also widely applied in proteomics studies. Rapidly advancing technologies have offered us the opportunities to examine the genome, transcriptome, and proteome in comprehensive ways. Yet, extracting meaningful information from this vast sea of data and approaching biological problems from systems biology perspective have become the Holy Grail in bioinformatics. The main focus of this special issue is novelty: new ideas, original research findings, and practical applications that intend to answer biological questions through high-throughput technologies. The papers in this special issue present methods and experiments that demonstrate novel platforms and systems and new bioinformatics tools and models, as well as new data-analytical methods for high-throughput biological data. In this special issue, U. Rosani et al. attempted to unravel the genome of Mytilus galloprovincialis, the Mediterranean mussel, through a target capture and high-throughput massive sequencing approach to reduce whole genome sequencing cost and effort. However, inferences from sequencing data rely heavily on careful experimental design, as well as efficient detection and removal of artifacts. While analyzing restriction-based reduced representation genomic data, D. C. Ilut et al. demonstrated that, by setting an optimal clustering threshold, false homozygosity or heterozygosity can be effectively minimized. With the advancement of genomic researches, the number of sequences processed in comparative methods has grown immensely. E. A. Marucci et al. developed a parallel algorithm for multiple sequence similarities calculation using the k-mers counting method. Their tests showed that the algorithm provides a very good scalability and a nearly linear speedup. In “A de novo genome assembly algorithm for repeats and nonrepeats,” Z. Dai et al. proposed a new genome assembly algorithm called the sliding window assembler (SWA), which assembles repeats and nonrepeats by adopting a new overlapping extension strategy to extend each seed and implementing a compensational mechanism for low coverage datasets. Results of their analysis on three datasets support the practicability and efficiency of SWA as a promising algorithm for NGS data. High-throughput technology holds great promises for the efficient investigation of transcriptomes, but the enormous amount of gene expression data demands effective analytical tools. By combining three gene-set analytical methods in one R statistical package, C.-Y. Chien et al. presented MAVTgsa, offering a systematic pipeline for the identification of significant gene-set modules from a set of gene expression data. Often, genes are coexpressed and coregulated or interact together to orchestrate a series of biological processes. To decipher the complex genetic networks associated with different cellular functions, M. Huerta et al. proposed to study the expression dependence between not only coexpressed genes but also sets of coexpressed genes. In an attempt to predict the survival time in patients with oral squamous cell carcinoma, O. Hamidi et al. demonstrated that the three sparse variable selection techniques, when applied on gene expression microarray data, were able to yield better prediction results. For bladder cancer, Y.-H. Wong et al. proposed a statistical method based on carcinogenesis relevance values (CRVs) to identify 152 and 50 significant proteins and subsequently generated novel protein-protein interaction (PPI) network markers for early and late stage bladder cancer. Their findings not only provide new clues specific to cancer but also offer cancer researchers new directions for targeted cancer therapy. In metagenomics, C.-M. Chiu et al. developed a pipeline for the systematic analysis of the association between gut flora and obesity through high-throughput sequencing and bioinformatics approaches. Eighty-one stool samples were collected and the V4 region of 16S rRNA genes was selected for metagenomics analysis. The results demonstrate that bacterial communities in the gut could be clustered into the N-like (normal) group and OB-like (obese) group. Remarkably, most of the normal samples were clustered in the N-like group, and the OB-like group was enriched with case samples, indicating that bacterial communities in the gut were highly associated with obesity. The results provide new insights into the correlation of gut flora with the rising trend in obesity. In order to explore the molecular mechanism of flounder sex determination and development, Z. Fan et al. applied RNA-seq technology to investig ...
其他題名： Biomed Res Int
出版者： United States: Hindawi Publishing Corporation
出版日期： 2014-01-01
出處： BioMed Research International, 2014-01, Vol.2014, p.1-3
資源來源： ProQuest
版權： Copyright © 2014 Julia Tzu-Ya Weng et al.
版權： COPYRIGHT 2015 John Wiley & Sons, Inc.
版權： Copyright © 2014 Julia Tzu-Ya Weng et al. Julia Tzu-Ya Weng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
版權： Copyright © 2014 Julia Tzu-Ya Weng et al. 2014
識別號： ISSN: 2314-6133
識別號： ISSN: 2314-6141
識別號： EISSN: 2314-6141
識別號： DOI: 10.1155/2014/814092
識別號： PMID: 25610874 |
| 顯示於類別: | [生醫科學與工程學系] 期刊論文
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