DEAR EDITOR,Many functional elements associated with traits and diseases are located in non-coding regions and act on distant target genes via chromatin looping and folding,making it difficult for scientists to reveal...DEAR EDITOR,Many functional elements associated with traits and diseases are located in non-coding regions and act on distant target genes via chromatin looping and folding,making it difficult for scientists to reveal the genetic regulatory mechanisms.Capture Hi-C is a newly developed chromosome conformation capture technology based on hybridization capture between probes and target genomic regions.It can identify interactions among target loci and all other loci in a genome with low cost and high resolution.Here,we developed CaptureProbe,a user-friendly,graphical Java tool for the design of capture probes across a range of target sites or regions.Numerous parameters helped to achieve and optimize the designed probes.Design testing of CaptureProbe showed high efficiency in the design success ratio of target loci and probe specificity.Hence,this program will help scientists conduct genome spatial interaction research.展开更多
DEAR EDITOR,Cis-regulatory elements play an important role in the development of traits and disease in organisms(Ma et al.,2020;Woolfe et al., 2005) and their annotation could facilitate genetic studies. The Encyclope...DEAR EDITOR,Cis-regulatory elements play an important role in the development of traits and disease in organisms(Ma et al.,2020;Woolfe et al., 2005) and their annotation could facilitate genetic studies. The Encyclopedia of DNA Elements(ENCODE)(Davis et al., 2018) and Functional Annotation of Animal Genomes(FAANG)(FAANG Consortium et al., 2015)offer pioneering data on regulatory elements in several species. Currently, however, regulatory element annotation data remain limited for most organisms.展开更多
The basic high-temperature properties of iron ore play a crucial role in optimizing sintering and ore blending,but the testing process for these properties is complex and has significant lag time,which cannot meet the...The basic high-temperature properties of iron ore play a crucial role in optimizing sintering and ore blending,but the testing process for these properties is complex and has significant lag time,which cannot meet the actual needs of ore blending.A prediction model for the basic high-temperature properties of iron ore fines was thus proposed based on a combination of machine learning algorithms and genetic algorithms.First,the prediction accuracy of different machine learning models for the basic high-temperature properties of iron ore fines was compared.Then,a random forest model optimized by genetic algorithms was built,further improving the prediction accuracy of the model.The test results show that the random forest model optimized by genetic algorithms has the highest prediction accuracy for the lowest assimilation temperature and liquid phase fluidity of iron ore,with a determination coefficient of 0.903 for the lowest assimilation temperature and 0.927 for the liquid phase fluidity after optimization.The trained model meets the fluctuation requirements of on-site testing and has been successfully applied to actual production on site.展开更多
基金financially supported by the National Natural Science Foundation of China(No.51805181)the National Science Fund for Distinguished Young Scholars of China(No.51725504)the Fundamental Research Funds for the Central Universities of China(No.2020kfy XJJS049)。
基金supported by the Ministry of Agriculture of China(2016ZX08009003-006)Animal Branch of the Germplasm Bank of Wild Species,Chinese Academy of Sciences(Large Research Infrastructure Funding)
文摘DEAR EDITOR,Many functional elements associated with traits and diseases are located in non-coding regions and act on distant target genes via chromatin looping and folding,making it difficult for scientists to reveal the genetic regulatory mechanisms.Capture Hi-C is a newly developed chromosome conformation capture technology based on hybridization capture between probes and target genomic regions.It can identify interactions among target loci and all other loci in a genome with low cost and high resolution.Here,we developed CaptureProbe,a user-friendly,graphical Java tool for the design of capture probes across a range of target sites or regions.Numerous parameters helped to achieve and optimize the designed probes.Design testing of CaptureProbe showed high efficiency in the design success ratio of target loci and probe specificity.Hence,this program will help scientists conduct genome spatial interaction research.
基金This work was supported by the Chinese Academy of Sciences(XDA24010107)Ministry of Agriculture of China(2016ZX08009003-006)+2 种基金National Natural Science Foundation of China(31621062)Funding for Open Access Charge:Ministry of Agriculture of China(2016ZX08009003-006)Animal Branch of the Germplasm Bank of Wild Species,Chinese Academy of Sciences(Large Research Infrastructure Funding)。
文摘DEAR EDITOR,Cis-regulatory elements play an important role in the development of traits and disease in organisms(Ma et al.,2020;Woolfe et al., 2005) and their annotation could facilitate genetic studies. The Encyclopedia of DNA Elements(ENCODE)(Davis et al., 2018) and Functional Annotation of Animal Genomes(FAANG)(FAANG Consortium et al., 2015)offer pioneering data on regulatory elements in several species. Currently, however, regulatory element annotation data remain limited for most organisms.
基金Project supported by the Public Service Sectors Agriculture Research Projects of Ministry of Agriculture of China(No.201403051-07)the National Natural Science Foundation of China(No.31502025)the Chinese Universities Scientific Fund(No.2015DY003)
基金the National Natural Science Foundation of China(52204335)the Cross-disciplinary Research Project for Young Teachers of the University of Science and Technology Beijing(FRF-IDRY-22-004).
文摘The basic high-temperature properties of iron ore play a crucial role in optimizing sintering and ore blending,but the testing process for these properties is complex and has significant lag time,which cannot meet the actual needs of ore blending.A prediction model for the basic high-temperature properties of iron ore fines was thus proposed based on a combination of machine learning algorithms and genetic algorithms.First,the prediction accuracy of different machine learning models for the basic high-temperature properties of iron ore fines was compared.Then,a random forest model optimized by genetic algorithms was built,further improving the prediction accuracy of the model.The test results show that the random forest model optimized by genetic algorithms has the highest prediction accuracy for the lowest assimilation temperature and liquid phase fluidity of iron ore,with a determination coefficient of 0.903 for the lowest assimilation temperature and 0.927 for the liquid phase fluidity after optimization.The trained model meets the fluctuation requirements of on-site testing and has been successfully applied to actual production on site.
