Abstract and Applied Analysis,2012年
Nan-jing Huang, Xing Wang, Lei Wang
LicenseType:CC BY | 英文
Abstract and Applied Analysis,2012年
Nan-jing Huang, Xing Wang, Lei Wang
LicenseType:CC BY | 英文
3 Modelling and Regularity of Nonlinear Impulsive Switching Dynamical System in Fed-Batch Culture [期刊论文]
Abstract and Applied Analysis,2012年
Lei Wang
LicenseType:CC BY | 英文
4 Modelling and Regularity of Nonlinear Impulsive Switching Dynamical System in Fed-Batch Culture [期刊论文]
Abstract and Applied Analysis,2012年
Lei Wang
LicenseType:CC BY | 英文
BMC Bioinformatics,2012年
Peter Sørensen, David Edwards, Lei Wang
LicenseType:CC BY |
BackgroundAlthough genome-scale expression experiments are performed routinely in biomedical research, methods of analysis remain simplistic and their interpretation challenging. The conventional approach is to compare the expression of each gene, one at a time, between treatment groups. This implicitly treats the gene expression levels as independent, but they are in fact highly interdependent, and exploiting this enables substantial power gains to be realized.ResultsWe assume that information on the dependence structure between the expression levels of a set of genes is available in the form of a Bayesian network (directed acyclic graph), derived from external resources. We show how to analyze gene expression data conditional on this network. Genes whose expression is directly affected by treatment may be identified using tests for the independence of each gene and treatment, conditional on the parents of the gene in the network. We apply this approach to two datasets: one from a hepatotoxicity study in rats using a PPAR pathway, and the other from a study of the effects of smoking on the epithelial transcriptome, using a global transcription factor network.ConclusionsThe proposed method is straightforward, simple to implement, gives rise to substantial power gains, and may assist in relating the experimental results to the underlying biology.
BMC Genomics,2012年
Qinyu Ge, Yunfei Bai, Shengqin Wang, Jing Tu, Lei Wang, Qi Yang, Beili Sun, Zuhong Lu
LicenseType:Unknown |
BackgroundThe multiplexing becomes the major limitation of the next-generation sequencing (NGS) in application to low complexity samples. Physical space segregation allows limited multiplexing, while the existing barcode approach only permits simultaneously analysis of up to several dozen samples.ResultsHere we introduce pair-barcode sequencing (PBS), an economic and flexible barcoding technique that permits parallel analysis of large-scale multiplexed samples. In two pilot runs using SOLiD sequencer (Applied Biosystems Inc.), 32 independent pair-barcoded miRNA libraries were simultaneously discovered by the combination of 4 unique forward barcodes and 8 unique reverse barcodes. Over 174,000,000 reads were generated and about 64% of them are assigned to both of the barcodes. After mapping all reads to pre-miRNAs in miRBase, different miRNA expression patterns are captured from the two clinical groups. The strong correlation using different barcode pairs and the high consistency of miRNA expression in two independent runs demonstrates that PBS approach is valid.ConclusionsBy employing PBS approach in NGS, large-scale multiplexed pooled samples could be practically analyzed in parallel so that high-throughput sequencing economically meets the requirements of samples which are low sequencing throughput demand.