The ability of several ab initio models to predict experimental 29Si-NMR chemical shift is examined. The shielding values of trimethylsilyl chloride (A), t-butyldimethylsilyl chloride (B) and allyltrimethylsilane (C) ...The ability of several ab initio models to predict experimental 29Si-NMR chemical shift is examined. The shielding values of trimethylsilyl chloride (A), t-butyldimethylsilyl chloride (B) and allyltrimethylsilane (C) are calculated by GIAO , CSGT and IGAIM methods, using HF/6-31G*, B3LYP/6-31G*, HF/6-311+G **, B3LYP/6-311+G ** and MPW1PW91/6-311+G ** models respectively. The 29Si chemical shifts calculated by GIAO method using HF/6-311+G ** model are highly in agreement with those obtained experimentally. All of the models above reproduce the trends of chemical shifts in all cases studied, suggesting that the models are of practical value.展开更多
Four isomers of the three-dimensionally connected bare boron cationic cluster B were investigated by using ab initio molecular orbital theory at the HF/6-31G level. The results show that the D5h symmetric isomer of B ...Four isomers of the three-dimensionally connected bare boron cationic cluster B were investigated by using ab initio molecular orbital theory at the HF/6-31G level. The results show that the D5h symmetric isomer of B is a possible isomer candidate of its stable geometries with closed structure.展开更多
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 dat...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.展开更多
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 e...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.展开更多
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 t...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：//bighapmap.big.ac.cn/.展开更多
文摘The ability of several ab initio models to predict experimental 29Si-NMR chemical shift is examined. The shielding values of trimethylsilyl chloride (A), t-butyldimethylsilyl chloride (B) and allyltrimethylsilane (C) are calculated by GIAO , CSGT and IGAIM methods, using HF/6-31G*, B3LYP/6-31G*, HF/6-311+G **, B3LYP/6-311+G ** and MPW1PW91/6-311+G ** models respectively. The 29Si chemical shifts calculated by GIAO method using HF/6-311+G ** model are highly in agreement with those obtained experimentally. All of the models above reproduce the trends of chemical shifts in all cases studied, suggesting that the models are of practical value.
文摘Four isomers of the three-dimensionally connected bare boron cationic cluster B were investigated by using ab initio molecular orbital theory at the HF/6-31G level. The results show that the D5h symmetric isomer of B is a possible isomer candidate of its stable geometries with closed structure.
文摘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.
文摘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.
基金supported by the National Nature Science Foundation of China (Grant No. 81171184, 31060139 and 30871384)Nature Science Foundation of Jiangxi Province (Grant No. 20114BAB215019)+1 种基金Department of Health of Jiangxi Province (Grant No. 20111209)Technology Pedestal and Society Development Project of Jiangxi Province (Grant No. 2010BSA09500 and 20111BBG70009-1)
文摘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：//bighapmap.big.ac.cn/.
