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Materials databases for the computational materials scientist

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International Journal of Minerals,Metallurgy and Materials 2011年第3期18卷 303-308页

作者： Marcel H.F. Sluiter Darko Simonovic Emre S. Tasci Materials Science and Engineering 3ME Delft University of Technology Materials innovation institute M2i Física de la Materia Condensada Universidad del Pais Vasco

Until recently, many computational materials scientists have shown little interest in materials databases. This is now changing be-cause the amount of computational data is rapidly increasing and the potential for data mining provides unique opportunities for discovery and optimization. Here, a few examples of such opportunities are discussed relating to structural analysis and classification, discovery of correlations between materials properties, and discovery of unsuspected compounds.

关键词： database systems materials data mining ab initio prediction structural analysis

A Brief Review of Computational Gene prediction Methods

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Genomics, Proteomics & Bioinformatics 2004年第4期2卷 216-221页

作者： ZhuoWang YazhuChen YixueLi BiomedicalInstrumentInstitute ShanghaiJiaotongUniversityShanghai200030China／／ShanghaiCenterforBioinformationTechnologyShanghai200035China BiomedicalInstrumentInstitute ShanghaiJiaotongUniversityShanghai200030China ShanghaiCenterforBioinformationTechnology Shanghai200035China

With the development of genome sequencing for many organisms, more and moreraw sequences need to be annotated. Gene prediction by computational methods for finding thelocation of protein coding regions is one of the essential issues in bioinformatics. Two classes ofmethods are generally adopted: similarity based searches and ab initio prediction. Here, we reviewthe development of gene prediction methods, summarize the measures for evaluating predictor quality,highlight open problems in this area, and discuss future research directions.

关键词： gene prediction similarity searches ab initio prediction hidden markovmodel

Gene2DGE: A Perl Package for Gene Model Renewal with Digital Gene Expression Data

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Genomics, Proteomics & Bioinformatics 2012年第1期10卷 51-54页

作者： Xiaoli Tang Libin Deng Dake Zhang Jiari Lin Yi Wei Qinqin Zhou Xiang Li Guilin Li Shangdong Liang Faculty of Basic Medical Science Nanchang UniversityNanchang 330006China Institute of Translational Medicine Nanchang UniversityNanchang 330006China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100029China

For transcriptome analysis, it is critical to precisely define all the transcripts across the whole genome. More and more digital gene expression （DGE） scannings have indicated the presence of huge amount of novel transcripts in addition to the known gene models. However, almost all these studies still depend crucially on existing annotation. Here, we present Gene2DGE, a Perl software package for gene model renewal with DGE data. We applied Gene2DGE to the mouse blastomere transcriptome, and defined 98,532 read-enriched regions （RERs） by read clustering supported by more than four reads for each base pair. Taking advantage of this ab initio method, we refined 2,104 exonic regions （4% of a total of 48,501 annotated transcribed regions） with remarkable extension into un-annotated regions （〉50 bp）. For 5% of uniquely mapped reads falling within intron regions, we identified 13,291 additional possible exons. As a result, we renewed 4,788 gene models, which account for 39% of a total of 12,277 transcribed genes. Furthermore, we identified 12,613 intergenic RERs, suggesting the possible presence of novel genes outside the existing gene models. In this study, therefore, we have developed a suitable tool for renewal of known gene models by ab initio prediction in transcriptome dissection. The Gene2DGE package is freely available at http：//***/.

关键词： transcriptome annotation ab initio prediction