The Chinese yew（Taxus wallichiana）,which is widely distributed in the Himalayas and in southern China,is now on the edge of extinction.In order to understand the evolutionary processes that control the current diver...The Chinese yew（Taxus wallichiana）,which is widely distributed in the Himalayas and in southern China,is now on the edge of extinction.In order to understand the evolutionary processes that control the current diversity within this species at the genetic and ecological levels,its genetic patterns and range dynamics must first be identified and mapped.This knowledge can then be applied in the development of an effective conservation strategy.Based on molecular data obtained from 48 populations of T.wallichiana,we used GIS-based interpolation approach for the explicit visualization of patterns of genetic divergence and diversity,and a number of potential evolutionary hotspots have been specifically identified within the genetic landscape maps.Within the maps of genetic divergence and diversity,five areas of high inter-population genetic divergence and six areas of high intra-population genetic diversity have been highlighted in a number of separate mountain regions,and these evolutionary hotspots should have the priority to be protected.Furthermore,four geographical barriers have been identified： the eastern Himalayas,the Yunnan Plateau,the Hengduan Mountains and the Taiwan Strait.According to ecological niche modeling（ENM）,the populations of T.wallichiana within the Sino-Himalayan Forest floristic subkingdom experienced westward expansion from the periods of Last Inter-glacial to Last Glacial Maximum（LGM）.Following the LGM,the distribution range overall became reduced and fragmented.These findings challenge the classic mode of contraction-expansion in response to the last glaciation.In conclusion,our findings suggest that the changes in geographical landscapes and climate that occurred during the Quaternary resulted in current genetic landscape patterns.展开更多
Informative proteins are the proteins that play critical functional roles inside cells.They are the fundamental knowledge of translating bioinformatics into clinical practices.Many methods of identifying informative b...Informative proteins are the proteins that play critical functional roles inside cells.They are the fundamental knowledge of translating bioinformatics into clinical practices.Many methods of identifying informative biomarkers have been developed which are heuristic and arbitrary,without considering the dynamics characteristics of biological processes.In this paper,we present a generative model of identifying the informative proteins by systematically analyzing the topological variety of dynamic protein-protein interaction networks(PPINs).In this model,the common representation of multiple PPINs is learned using a deep feature generation model,based on which the original PPINs are rebuilt and the reconstruction errors are analyzed to locate the informative proteins.Experiments were implemented on data of yeast cell cycles and different prostate cancer stages.We analyze the effectiveness of reconstruction by comparing different methods,and the ranking results of informative proteins were also compared with the results from the baseline methods.Our method is able to reveal the critical members in the dynamic progresses which can be further studied to testify the possibilities for biomarker research.展开更多
基金National Basic Research Program of China(No.2010CB951704)National Natural Science Foundation of China(No.41271068)
文摘The Chinese yew（Taxus wallichiana）,which is widely distributed in the Himalayas and in southern China,is now on the edge of extinction.In order to understand the evolutionary processes that control the current diversity within this species at the genetic and ecological levels,its genetic patterns and range dynamics must first be identified and mapped.This knowledge can then be applied in the development of an effective conservation strategy.Based on molecular data obtained from 48 populations of T.wallichiana,we used GIS-based interpolation approach for the explicit visualization of patterns of genetic divergence and diversity,and a number of potential evolutionary hotspots have been specifically identified within the genetic landscape maps.Within the maps of genetic divergence and diversity,five areas of high inter-population genetic divergence and six areas of high intra-population genetic diversity have been highlighted in a number of separate mountain regions,and these evolutionary hotspots should have the priority to be protected.Furthermore,four geographical barriers have been identified： the eastern Himalayas,the Yunnan Plateau,the Hengduan Mountains and the Taiwan Strait.According to ecological niche modeling（ENM）,the populations of T.wallichiana within the Sino-Himalayan Forest floristic subkingdom experienced westward expansion from the periods of Last Inter-glacial to Last Glacial Maximum（LGM）.Following the LGM,the distribution range overall became reduced and fragmented.These findings challenge the classic mode of contraction-expansion in response to the last glaciation.In conclusion,our findings suggest that the changes in geographical landscapes and climate that occurred during the Quaternary resulted in current genetic landscape patterns.
基金supported by National Natural Science Foundation of China(30970780)Ph.D.Programs Foundation of Ministry of Education of China(20091103110005)+4 种基金the Project for the Innovation Team of Beijing,National Natural Science Foundation of China(81370038)the Beijing Natural Science Foundation(7142012)the Science and Technology Project of Beijing Municipal Education Commission(km201410005003)the Rixin Fund of Beijing University of Technology(2013-RX-L04)the Basic Research Fund of Beijing University of Technology
文摘Informative proteins are the proteins that play critical functional roles inside cells.They are the fundamental knowledge of translating bioinformatics into clinical practices.Many methods of identifying informative biomarkers have been developed which are heuristic and arbitrary,without considering the dynamics characteristics of biological processes.In this paper,we present a generative model of identifying the informative proteins by systematically analyzing the topological variety of dynamic protein-protein interaction networks(PPINs).In this model,the common representation of multiple PPINs is learned using a deep feature generation model,based on which the original PPINs are rebuilt and the reconstruction errors are analyzed to locate the informative proteins.Experiments were implemented on data of yeast cell cycles and different prostate cancer stages.We analyze the effectiveness of reconstruction by comparing different methods,and the ranking results of informative proteins were also compared with the results from the baseline methods.Our method is able to reveal the critical members in the dynamic progresses which can be further studied to testify the possibilities for biomarker research.
