392 Genome Informatics 14 392–393 (2003) Conservation of DNA Curvature Signals in Gene Reg
徐云碧-从分子数量遗传学到分子植物育种

国际上最早的水稻QTL论文之一
博士论文的总结报告 Xu, Yun-Bi, Zong-Tan Shen, Ji-Chen Xu, Ying Chen and LiHuang Zhu. 1993. Mapping quantitative trait loci via restriction fragment length polymorphism markers in rice. Rice Genetics Newsletter 10:135-138.
1
Molecular Quantitative Genetics in China (1990-1994)
数量性状遗传改良的希望和曙光
Paterson, A. H., E. S. Lander, J. D. Hewitt, S. Peterson, S. E. Lincoln and S. D. Tanksley. 1988. Resolution of quantitative traits into Mendelian factors by using a complete linkage map of restriction fragment length polymorphisms. Nature 335:721-726. Received 8 July 1988 Accepted 9 September 1988 Google 被引用次数:1155 (8:25am, Aug 24, 2011) Lander, E. S. and D. Botstein. 1989. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185-199. Manuscript received August 2, 1988 Accepted for publication October 6, 1988 Google被引用次数:3614 (8:28am, Aug 24, 2011)
诺禾致源2014产品手册

CONTENTS
建库测序
06 建库测序服务
基因组测序
08 动植物基因组测序 10 基因组特征评估 11 基因组de novo测序 14 泛基因组测序(pan-genome) 16 动植物重测序 17 变异检测(基于全基因组重测序) 19 变异检测(基于简化基因组测序) 21 单个性状定位 24 遗传图谱(基于全基因组重测序) 26 遗传图谱(基于简化基因组测序) 28 群体进化(基于全基因组重测序) 30 群体进化(基于简化基因组测序)
[2] Zhi X Y, Yao J C, Li H W, et al. Genome-wide identification, domain architectures and phylogenetic analysis provide new insights into the early evolution of shikimate pathway in prokaryotes[J]. Molecular phylogenetics and evolution, 2014, 75: 154-164.
[8] Xu X, Dong G X, Hu X S, et al. The genetic basis of white tigers[J]. Current Biology, 2013, 23(11): 1031-1035. [9] Jiang W, Liu Y, Xia E, et al. Prevalent role of gene features in determining evolutionary fates of whole-genome duplication duplicated genes in flowering plants[J]. Plant physiology, 2013, 161(4): 1844-1861. [10] Zhang G, Cowled C, Shi Z, et al. Comparative analysis of bat genomes provides insight into the evolution of flight and immunity[J]. Science, 2013, 339(6118): 456-460. [11] Fan Y, Huang Z Y, Cao C C, et al. Genome of the Chinese tree shrew[J]. Nature communications, 2013, 4: 1426. [12] Wang M Y, Zhao P M, Cheng H Q, et al. The Cotton transcription factor TCP14 functions in auxin-mediated epidermal cell differentiation and elongation[J]. Plant physiology, 2013, 162(3): 1669-1680. [13] Lu S, Zong C, Fan W, et al. Probing meiotic recombination and aneuploidy of single sperm cells by wholegenome sequencing[J]. Science, 2012, 338(6114): 1627-1630. [14] Guo S, Zhang J, Sun H, et al. The draft genome of watermelon (Citrullus lanatus) and resequencing of 20 diverse accessions[J]. Nature genetics, 2013, 45(1): 51-58. [15] Li S, Li R, Li H, et al. SOAPindel: Efficient identification of indels from short paired reads[J]. Genome research, 2013, 23(1): 195-200. [16] Li M, Wu H, Luo Z, et al. An atlas of DNA methylomes in porcine adipose and muscle tissues[J]. Nature communications, 2012, 3: 850. [17] Fan W, Li R. Test driving genome assemblers[J]. Nature biotechnology, 2012, 30(4): 330. [18] Liu C M, Wong T, Wu E, et al. SOAP3: ultra-fast GPU-based parallel alignment tool for short reads[J]. Bioinformatics, 2012, 28(6): 878-879. [19] Hvilsom C, Qian Y, Bataillon T, et al. Extensive X-linked adaptive evolution in central chimpanzees[J]. Proceedings of the National Academy of Sciences, 2012, 109(6): 2054-2059. [20] Zhang G, Fang X, Guo X, et al. The oyster genome reveals stress adaptation and complexity of shell formation[J]. Nature, 2012, 490(7418): 49-54.
欧盟拟禁止在食品中使用山梨酸钙

研究论文李洪波,等:茯苓菌丝蛋白双向电泳体系的建立及质谱鉴定[5 ] RIOS J L. Chemical constituents and pharmacological properties of P oria cocos [J]. Planta Med ,2011,77(7): 681-691.[6 ] CHEN X,ZHANG L,CHEUNG P C.Immunopotentiation and anti-tumor activity of carboxymethylated-sulfated beta- (l->3)-d-glucan from Poria cocos [J]. Int Immunopharmacol, 2010,10(4) : 398-405.[7 ] CHANG H H,YEH C H,SHEU F. A novel immunomodulatory protein from Poria cocos induces toll-like receptor 4-dependentactivation within mouse peritoneal macrophages[J]. J Agric Food Chem,2009,57(14) $6129-6139.[8 ] CHANG H H, SHEU F. A Novel fungal immunomodulatory protein (PCP) isolated from Poria cocos activates mouse peritonealmacrophage involved in Toll-like receptor 4[J]. FASEB J ,2007,21 (702) : 15.[9 ] WU Zongxun,SUN Zhiwen. Construction and analysis of EST library in Wolfiporia cocos [J]. Bio Formosa, 2007,42 (1) : 47-53.(in Chinese)[10] LI Hongbo,YANG Ruixue,CHEN Jing,et al. Preparation and analysis of the antibody against Poria cocos immunomodulatoryprotein WCFIP1[J]. Chinese Traditional and Herbal Drugs,2014,45(20):2912-2916.(in Chinese)[11] HOU Y,ZHENG Z,XU S,et al. Proteomic analysis of F usarium graminearum treated by the fungicide JS399-19[J]. Pestic BiochemPhysiol,2013,107(1):86-92[12] SU Y,GUO Q,TU J,et al. Proteins differentially expressed in conidia and mycelia of the entomopathogenic fungus Metarhiziumanisopliae sensu stricto[J]. Can J Microbiol ,2013,59(7) : 443-448.[13] QIU J,SU Y,GELBIC I,et al. Proteomic analysis of proteins differentially expressed in conidia and mycelium of theentomopathogenic fungus Aschersonia placenta[J].Can J Microbiol ,2012,58(12):1327-34.[14] LI Yi,YANG Sheng,LI Chen,et al. Compound medicinal medium of flask liquid fermentation of Poria cocos and its chemicalconstituents[J]. Chinese Traditional and Herbal Drugs,2012,43(8): 1519-1522.(in Chinese)科技信息欧盟拟禁止在食品中使用山梨酸钙2017年11月17日,据《食品导航》网站消息,由于缺乏山梨酸钙防腐剂的使用安全性数据,欧盟将在食品中禁止 使用山梨酸钙。
Analysis of Genetic Diversity and Population Structure

Agricultural Sciences in China2010, 9(9): 1251-1262September 2010Received 30 October, 2009 Accepted 16 April, 2010Analysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of ChinaLIU Zhi-zhai 1, 2, GUO Rong-hua 2, 3, ZHAO Jiu-ran 4, CAI Yi-lin 1, W ANG Feng-ge 4, CAO Mo-ju 3, W ANG Rong-huan 2, 4, SHI Yun-su 2, SONG Yan-chun 2, WANG Tian-yu 2 and LI Y u 21Maize Research Institute, Southwest University, Chongqing 400716, P.R.China2Institue of Crop Sciences/National Key Facility for Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences,Beijing 100081, P.R.China3Maize Research Institute, Sichuan Agricultural University, Ya’an 625014, P.R.China4Maize Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100089, P.R.ChinaAbstractUnderstanding genetic diversity and population structure of landraces is important in utilization of these germplasm in breeding programs. In the present study, a total of 143 core maize landraces from the South Maize Region (SR) of China,which can represent the general profile of the genetic diversity in the landraces germplasm of SR, were genotyped by 54DNA microsatellite markers. Totally, 517 alleles (ranging from 4 to 22) were detected among these landraces, with an average of 9.57 alleles per locus. The total gene diversity of these core landraces was 0.61, suggesting a rather higher level of genetic diversity. Analysis of population structure based on Bayesian method obtained the samilar result as the phylogeny neighbor-joining (NJ) method. The results indicated that the whole set of 143 core landraces could be clustered into two distinct groups. All landraces from Guangdong, Hainan, and 15 landraces from Jiangxi were clustered into group 1, while those from the other regions of SR formed the group 2. The results from the analysis of genetic diversity showed that both of groups possessed a similar gene diversity, but group 1 possessed relatively lower mean alleles per locus (6.63) and distinct alleles (91) than group 2 (7.94 and 110, respectively). The relatively high richness of total alleles and distinct alleles preserved in the core landraces from SR suggested that all these germplasm could be useful resources in germplasm enhancement and maize breeding in China.Key words :maize, core landraces, genetic diversity, population structureINTRODUCTIONMaize has been grown in China for nearly 500 years since its first introduction into this second biggest pro-duction country in the world. Currently, there are six different maize growing regions throughout the coun-try according to the ecological conditions and farming systems, including three major production regions,i.e., the North Spring Maize Region, the Huang-Huai-Hai Summer Maize Region, and the Southwest MaizeRegion, and three minor regions, i.e., the South Maize Region, the Northwest Maize Region, and the Qingzang Plateau Maize Region. The South Maize Region (SR)is specific because of its importance in origin of Chi-nese maize. It is hypothesized that Chinese maize is introduced mainly from two routes. One is called the land way in which maize was first brought to Tibet from India, then to Sichuan Province in southwestern China. The other way is that maize dispersed via the oceans, first shipped to the coastal areas of southeast China by boats, and then spread all round the country1252LIU Zhi-zhai et al.(Xu 2001; Zhou 2000). SR contains all of the coastal provinces and regions lie in southeastern China.In the long-term cultivation history of maize in south-ern China, numerous landraces have been formed, in which a great amount of genetic variation was observed (Li 1998). Similar to the hybrid swapping in Europe (Reif et al. 2005a), the maize landraces have been al-most replaced by hybrids since the 1950s in China (Li 1998). However, some landraces with good adapta-tions and yield performances are still grown in a few mountainous areas of this region (Liu et al.1999). Through a great effort of collection since the 1950s, 13521 accessions of maize landraces have been cur-rently preserved in China National Genebank (CNG), and a core collection of these landraces was established (Li et al. 2004). In this core collection, a total of 143 maize landrace accessions were collected from the South Maize Region (SR) (Table 1).Since simple sequence repeat ( SSR ) markers were firstly used in human genetics (Litt and Luty 1989), it now has become one of the most widely used markers in the related researches in crops (Melchinger et al. 1998; Enoki et al. 2005), especially in the molecular characterization of genetic resources, e.g., soybean [Glycine max (L.) Merr] (Xie et al. 2005), rice (Orya sativa L.) (Garris et al. 2005), and wheat (Triticum aestivum) (Chao et al. 2007). In maize (Zea mays L.), numerous studies focusing on the genetic diversity and population structure of landraces and inbred lines in many countries and regions worldwide have been pub-lished (Liu et al. 2003; Vegouroux et al. 2005; Reif et al. 2006; Wang et al. 2008). These activities of documenting genetic diversity and population structure of maize genetic resources have facilitated the under-standing of genetic bases of maize landraces, the utili-zation of these resources, and the mining of favorable alleles from landraces. Although some studies on ge-netic diversity of Chinese maize inbred lines were con-ducted (Yu et al. 2007; Wang et al. 2008), the general profile of genetic diversity in Chinese maize landraces is scarce. Especially, there are not any reports on ge-netic diversity of the maize landraces collected from SR, a possibly earliest maize growing area in China. In this paper, a total of 143 landraces from SR listed in the core collection of CNG were genotyped by using SSR markers, with the aim of revealing genetic diver-sity of the landraces from SR (Table 2) of China and examining genetic relationships and population struc-ture of these landraces.MATERIALS AND METHODSPlant materials and DNA extractionTotally, 143 landraces from SR which are listed in the core collection of CNG established by sequential strati-fication method (Liu et al. 2004) were used in the present study. Detailed information of all these landrace accessions is listed in Table 1. For each landrace, DNA sample was extracted by a CTAB method (Saghi-Maroof et al. 1984) from a bulk pool constructed by an equal-amount of leaves materials sampled from 15 random-chosen plants of each landrace according to the proce-dure of Reif et al. (2005b).SSR genotypingA total of 54 simple sequence repeat (SSR) markers covering the entire maize genome were screened to fin-gerprint all of the 143 core landrace accessions (Table 3). 5´ end of the left primer of each locus was tailed by an M13 sequence of 5´-CACGACGTTGTAAAACGAC-3´. PCR amplification was performed in a 15 L reac-tion containing 80 ng of template DNA, 7.5 mmol L-1 of each of the four dNTPs, 1×Taq polymerase buffer, 1.5 mmol L-1 MgCl2, 1 U Taq polymerase (Tiangen Biotech Co. Ltd., Beijing, China), 1.2 mol L-1 of forward primer and universal fluorescent labeled M13 primer, and 0.3 mol L-1 of M13 sequence tailed reverse primer (Schuelke 2000). The amplification was carried out in a 96-well DNA thermal cycler (GeneAmp PCR System 9700, Applied Biosystem, USA). PCR products were size-separated on an ABI Prism 3730XL DNA sequencer (HitachiHigh-Technologies Corporation, Tokyo, Japan) via the software packages of GENEMAPPER and GeneMarker ver. 6 (SoftGenetics, USA).Data analysesAverage number of alleles per locus and average num-ber of group-specific alleles per locus were identifiedAnalysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China 1253Table 1 The detailed information about the landraces used in the present studyPGS revealed by Structure1) NJ dendragram revealed Group 1 Group 2 by phylogenetic analysis140-150tian 00120005AnH-06Jingde Anhui 0.0060.994Group 2170tian00120006AnH-07Jingde Anhui 0.0050.995Group 2Zixihuangyumi00120007AnH-08Zixi Anhui 0.0020.998Group 2Zixibaihuangzayumi 00120008AnH-09Zixi Anhui 0.0030.997Group 2Baiyulu 00120020AnH-10Yuexi Anhui 0.0060.994Group 2Wuhuazi 00120021AnH-11Yuexi Anhui 0.0030.997Group 2Tongbai 00120035AnH-12Tongling Anhui 0.0060.994Group 2Yangyulu 00120036AnH-13Yuexi Anhui 0.0040.996Group 2Huangli 00120037AnH-14Tunxi Anhui 0.0410.959Group 2Baiyumi 00120038AnH-15Tunxi Anhui 0.0030.997Group 2Dapigu00120039AnH-16Tunxi Anhui 0.0350.965Group 2150tianbaiyumi 00120040AnH-17Xiuning Anhui 0.0020.998Group 2Xiuning60tian 00120042AnH-18Xiuning Anhui 0.0040.996Group 2Wubaogu 00120044AnH-19ShitaiAnhui 0.0020.998Group 2Kuyumi00130001FuJ-01Shanghang Fujian 0.0050.995Group 2Zhongdouyumi 00130003FuJ-02Shanghang Fujian 0.0380.962Group 2Baixinyumi 00130004FuJ-03Liancheng Fujian 0.0040.996Group 2Hongxinyumi 00130005FuJ-04Liancheng Fujian 0.0340.966Group 2Baibaogu 00130008FuJ-05Changding Fujian 0.0030.997Group 2Huangyumi 00130011FuJ-06Jiangyang Fujian 0.0020.998Group 2Huabaomi 00130013FuJ-07Shaowu Fujian 0.0020.998Group 2Huangbaomi 00130014FuJ-08Songxi Fujian 0.0020.998Group 2Huangyumi 00130016FuJ-09Wuyishan Fujian 0.0460.954Group 2Huabaogu 00130019FuJ-10Jian’ou Fujian 0.0060.994Group 2Huangyumi 00130024FuJ-11Guangze Fujian 0.0010.999Group 2Huayumi 00130025FuJ-12Nanping Fujian 0.0040.996Group 2Huangyumi 00130026FuJ-13Nanping Fujian 0.0110.989Group 2Hongbaosu 00130027FuJ-14Longyan Fujian 0.0160.984Group 2Huangfansu 00130029FuJ-15Loangyan Fujian 0.0020.998Group 2Huangbaosu 00130031FuJ-16Zhangping Fujian 0.0060.994Group 2Huangfansu 00130033FuJ-17Zhangping Fujian0.0040.996Group 2Baolieyumi 00190001GuangD-01Guangzhou Guangdong 0.9890.011Group 1Nuomibao (I)00190005GuangD-02Shixing Guangdong 0.9740.026Group 1Nuomibao (II)00190006GuangD-03Shixing Guangdong 0.9790.021Group 1Zasehuabao 00190010GuangD-04Lechang Guangdong 0.9970.003Group 1Zihongmi 00190013GuangD-05Lechang Guangdong 0.9880.012Group 1Jiufengyumi 00190015GuangD-06Lechang Guangdong 0.9950.005Group 1Huangbaosu 00190029GuangD-07MeiGuangdong 0.9970.003Group 1Bailibao 00190032GuangD-08Xingning Guangdong 0.9980.002Group 1Nuobao00190038GuangD-09Xingning Guangdong 0.9980.002Group 1Jinlanghuang 00190048GuangD-10Jiangcheng Guangdong 0.9960.004Group 1Baimizhenzhusu 00190050GuangD-11Yangdong Guangdong 0.9940.006Group 1Huangmizhenzhusu 00190052GuangD-12Yangdong Guangdong 0.9930.007Group 1Baizhenzhu 00190061GuangD-13Yangdong Guangdong 0.9970.003Group 1Baiyumi 00190066GuangD-14Wuchuan Guangdong 0.9880.012Group 1Bendibai 00190067GuangD-15Suixi Guangdong 0.9980.002Group 1Shigubaisu 00190068GuangD-16Gaozhou Guangdong 0.9960.004Group 1Zhenzhusu 00190069GuangD-17Xinyi Guangdong 0.9960.004Group 1Nianyaxixinbai 00190070GuangD-18Huazhou Guangdong 0.9960.004Group 1Huangbaosu 00190074GuangD-19Xinxing Guangdong 0.9950.005Group 1Huangmisu 00190076GuangD-20Luoding Guangdong 0.940.060Group 1Huangmi’ai 00190078GuangD-21Luoding Guangdong 0.9980.002Group 1Bayuemai 00190084GuangD-22Liannan Guangdong 0.9910.009Group 1Baiyumi 00300001HaiN-01Haikou Hainan 0.9960.004Group 1Baiyumi 00300003HaiN-02Sanya Hainan 0.9970.003Group 1Hongyumi 00300004HaiN-03Sanya Hainan 0.9980.002Group 1Baiyumi00300011HaiN-04Tongshi Hainan 0.9990.001Group 1Zhenzhuyumi 00300013HaiN-05Tongshi Hainan 0.9980.002Group 1Zhenzhuyumi 00300015HaiN-06Qiongshan Hainan 0.9960.004Group 1Aiyumi 00300016HaiN-07Qiongshan Hainan 0.9960.004Group 1Huangyumi 00300021HaiN-08Qionghai Hainan 0.9970.003Group 1Y umi 00300025HaiN-09Qionghai Hainan 0.9870.013Group 1Accession name Entry code Analyzing code Origin (county/city)Province/Region1254LIU Zhi-zhai et al .Baiyumi00300032HaiN-10Tunchang Hainan 0.9960.004Group 1Huangyumi 00300051HaiN-11Baisha Hainan 0.9980.002Group 1Baihuangyumi 00300055HaiN-12BaishaHainan 0.9970.003Group 1Machihuangyumi 00300069HaiN-13Changjiang Hainan 0.9900.010Group 1Hongyumi00300073HaiN-14Dongfang Hainan 0.9980.002Group 1Xiaohonghuayumi 00300087HaiN-15Lingshui Hainan 0.9980.002Group 1Baiyumi00300095HaiN-16Qiongzhong Hainan 0.9950.005Group 1Y umi (Baimai)00300101HaiN-17Qiongzhong Hainan 0.9980.002Group 1Y umi (Xuemai)00300103HaiN-18Qiongzhong Hainan 0.9990.001Group 1Huangmaya 00100008JiangS-10Rugao Jiangsu 0.0040.996Group 2Bainian00100012JiangS-11Rugao Jiangsu 0.0080.992Group 2Bayebaiyumi 00100016JiangS-12Rudong Jiangsu 0.0040.996Group 2Chengtuohuang 00100021JiangS-13Qidong Jiangsu 0.0050.995Group 2Xuehuanuo 00100024JiangS-14Qidong Jiangsu 0.0020.998Group 2Laobaiyumi 00100032JiangS-15Qidong Jiangsu 0.0050.995Group 2Laobaiyumi 00100033JiangS-16Qidong Jiangsu 0.0010.999Group 2Huangwuye’er 00100035JiangS-17Hai’an Jiangsu 0.0030.997Group 2Xiangchuanhuang 00100047JiangS-18Nantong Jiangsu 0.0060.994Group 2Huangyingzi 00100094JiangS-19Xinghua Jiangsu 0.0040.996Group 2Xiaojinhuang 00100096JiangS-20Yangzhou Jiangsu 0.0010.999Group 2Liushizi00100106JiangS-21Dongtai Jiangsu 0.0030.997Group 2Kangnandabaizi 00100108JiangS-22Dongtai Jiangsu 0.0020.998Group 2Shanyumi 00140020JiangX-01Dexing Jiangxi 0.9970.003Group 1Y umi00140024JiangX-02Dexing Jiangxi 0.9970.003Group 1Tianhongyumi 00140027JiangX-03Yushan Jiangxi 0.9910.009Group 1Hongganshanyumi 00140028JiangX-04Yushan Jiangxi 0.9980.002Group 1Zaoshuyumi 00140032JiangX-05Qianshan Jiangxi 0.9970.003Group 1Y umi 00140034JiangX-06Wannian Jiangxi 0.9970.003Group 1Y umi 00140038JiangX-07De’an Jiangxi 0.9940.006Group 1Y umi00140045JiangX-08Wuning Jiangxi 0.9740.026Group 1Chihongyumi 00140049JiangX-09Wanzai Jiangxi 0.9920.008Group 1Y umi 00140052JiangX-10Wanzai Jiangxi 0.9930.007Group 1Huayumi 00140060JiangX-11Jing’an Jiangxi 0.9970.003Group 1Baiyumi 00140065JiangX-12Pingxiang Jiangxi 0.9940.006Group 1Huangyumi00140066JiangX-13Pingxiang Jiangxi 0.9680.032Group 1Nuobaosuhuang 00140068JiangX-14Ruijin Jiangxi 0.9950.005Group 1Huangyumi 00140072JiangX-15Xinfeng Jiangxi 0.9960.004Group 1Wuningyumi 00140002JiangX-16Jiujiang Jiangxi 0.0590.941Group 2Tianyumi 00140005JiangX-17Shangrao Jiangxi 0.0020.998Group 2Y umi 00140006JiangX-18Shangrao Jiangxi 0.0310.969Group 2Baiyiumi 00140012JiangX-19Maoyuan Jiangxi 0.0060.994Group 260riyumi 00140016JiangX-20Maoyuan Jiangxi 0.0020.998Group 2Shanyumi 00140019JiangX-21Dexing Jiangxi 0.0050.995Group 2Laorenya 00090002ShangH-01Chongming Shanghai 0.0050.995Group 2Jinmeihuang 00090004ShangH-02Chongming Shanghai 0.0020.998Group 2Zaobaiyumi 00090006ShangH-03Chongming Shanghai 0.0020.998Group 2Chengtuohuang 00090007ShangH-04Chongming Shanghai 0.0780.922Group 2Benyumi (Huang)00090008ShangH-05Shangshi Shanghai 0.0020.998Group 2Bendiyumi 00090010ShangH-06Shangshi Shanghai 0.0040.996Group 2Baigengyumi 00090011ShangH-07Jiading Shanghai 0.0020.998Group 2Huangnuoyumi 00090012ShangH-08Jiading Shanghai 0.0040.996Group 2Huangdubaiyumi 00090013ShangH-09Jiading Shanghai 0.0440.956Group 2Bainuoyumi 00090014ShangH-10Chuansha Shanghai 0.0010.999Group 2Laorenya 00090015ShangH-11Shangshi Shanghai 0.0100.990Group 2Xiaojinhuang 00090016ShangH-12Shangshi Shanghai 0.0050.995Group 2Gengbaidayumi 00090017ShangH-13Shangshi Shanghai 0.0020.998Group 2Nongmeiyihao 00090018ShangH-14Shangshi Shanghai 0.0540.946Group 2Chuanshazinuo 00090020ShangH-15Chuansha Shanghai 0.0550.945Group 2Baoanshanyumi 00110004ZheJ-01Jiangshan Zhejiang 0.0130.987Group 2Changtaixizi 00110005ZheJ-02Jiangshan Zhejiang 0.0020.998Group 2Shanyumibaizi 00110007ZheJ-03Jiangshan Zhejiang 0.0020.998Group 2Kaihuajinyinbao 00110017ZheJ-04Kaihua Zhejiang 0.0100.990Group 2Table 1 (Continued from the preceding page)PGS revealed by Structure 1) NJ dendragram revealed Group1 Group2 by phylogenetic analysisAccession name Entry code Analyzing code Origin (county/city)Province/RegoinAnalysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China 1255Liputianzi00110038ZheJ-05Jinhua Zhejiang 0.0020.998Group 2Jinhuaqiuyumi 00110040ZheJ-06Jinhua Zhejiang 0.0050.995Group 2Pujiang80ri 00110069ZheJ-07Pujiang Zhejiang 0.0210.979Group 2Dalihuang 00110076ZheJ-08Yongkang Zhejiang 0.0140.986Group 2Ziyumi00110077ZheJ-09Yongkang Zhejiang 0.0020.998Group 2Baiyanhandipinzhong 00110078ZheJ-10Yongkang Zhejiang 0.0030.997Group 2Duosuiyumi00110081ZheJ-11Wuyi Zhejiang 0.0020.998Group 2Chun’an80huang 00110084ZheJ-12Chun’an Zhejiang 0.0020.998Group 2120ribaiyumi 00110090ZheJ-13Chun’an Zhejiang 0.0020.998Group 2Lin’anliugu 00110111ZheJ-14Lin’an Zhejiang 0.0030.997Group 2Qianhuangyumi00110114ZheJ-15Lin’an Zhejiang 0.0030.997Group 2Fenshuishuitianyumi 00110118ZheJ-16Tonglu Zhejiang 0.0410.959Group 2Kuihualiugu 00110119ZheJ-17Tonglu Zhejiang 0.0030.997Group 2Danbaihuang 00110122ZheJ-18Tonglu Zhejiang 0.0020.998Group 2Hongxinma 00110124ZheJ-19Jiande Zhejiang 0.0030.997Group 2Shanyumi 00110136ZheJ-20Suichang Zhejiang 0.0030.997Group 2Bai60ri 00110143ZheJ-21Lishui Zhejiang 0.0050.995Group 2Zeibutou 00110195ZheJ-22Xianju Zhejiang 0.0020.998Group 2Kelilao00110197ZheJ-23Pan’an Zhejiang 0.0600.940Group 21)The figures refered to the proportion of membership that each landrace possessed.Table 1 (Continued from the preceding page)PGS revealed by Structure 1) NJ dendragram revealed Group 1 Group 2 by phylogenetic analysisAccession name Entry code Analyzing code Origin (county/city)Province/Regoin Table 2 Construction of two phylogenetic groups (SSR-clustered groups) and their correlation with geographical locationsGeographical location SSR-clustered groupChi-square testGroup 1Group 2Total Guangdong 2222 χ2 = 124.89Hainan 1818P < 0.0001Jiangxi 15621Anhui 1414Fujian 1717Jiangsu 1313Shanghai 1515Zhejiang 2323Total5588143by the software of Excel MicroSatellite toolkit (Park 2001). Average number of alleles per locus was calcu-lated by the formula rAA rj j¦1, with the standarddeviation of1)()(12¦ r A AA rj jV , where A j was thenumber of distinct alleles at locus j , and r was the num-ber of loci (Park 2001).Unbiased gene diversity also known as expected heterozygosity, observed heterozygosity for each lo-cus and average gene diversity across the 54 SSR loci,as well as model-based groupings inferred by Struc-ture ver. 2.2, were calculated by the softwarePowerMarker ver.3.25 (Liu et al . 2005). Unbiased gene diversity for each locus was calculated by˅˄¦ 2ˆ1122ˆi x n n h , where 2ˆˆ2ˆ2¦¦z ji ijij i X X x ,and ij X ˆwas the frequency of genotype A i A jin the sample, and n was the number of individuals sampled.The average gene diversity across 54 loci was cal-culated as described by Nei (1987) as follows:rh H rj j ¦1ˆ, with the variance ,whereThe average observed heterozygosity across the en-tire loci was calculated as described by (Hedrick 1983)as follows: r jrj obsobs n h h ¦1, with the standard deviationrn h obs obsobs 1V1256LIU Zhi-zhai et al.Phylogenetic analysis and population genetic structureRelationships among all of the 143 accessions collected from SR were evaluated by using the unweighted pair group method with neighbor-joining (NJ) based on the log transformation of the proportion of shared alleles distance (InSPAD) via PowerMarker ver. 3.25 (FukunagaTable 3 The PIC of each locus and the number of alleles detected by 54 SSRsLocus Bin Repeat motif PIC No. of alleles Description 2)bnlg1007y51) 1.02AG0.7815Probe siteumc1122 1.06GGT0.639Probe siteumc1147y41) 1.07CA0.2615Probe sitephi961001) 2.00ACCT0.298Probe siteumc1185 2.03GC0.7215ole1 (oleosin 1)phi127 2.08AGAC0.577Probe siteumc1736y21) 2.09GCA T0.677Probe sitephi453121 3.01ACC0.7111Probe sitephi374118 3.03ACC0.477Probe sitephi053k21) 3.05A TAC0.7910Probe sitenc004 4.03AG0.4812adh2 (alcohol dehydrogenase 2)bnlg490y41) 4.04T A0.5217Probe sitephi079 4.05AGATG0.495gpc1(glyceraldehyde-3-phosphate dehydrogenase 1) bnlg1784 4.07AG0.6210Probe siteumc1574 4.09GCC0.719sbp2 (SBP-domain protein 2)umc1940y51) 4.09GCA0.4713Probe siteumc1050 4.11AA T0.7810cat3 (catalase 3)nc130 5.00AGC0.5610Probe siteumc2112y31) 5.02GA0.7014Probe sitephi109188 5.03AAAG0.719Probe siteumc1860 5.04A T0.325Probe sitephi085 5.07AACGC0.537gln4 (glutamine synthetase 4)phi331888 5.07AAG0.5811Probe siteumc1153 5.09TCA0.7310Probe sitephi075 6.00CT0.758fdx1 (ferredoxin 1)bnlg249k21) 6.01AG0.7314Probe sitephi389203 6.03AGC0.416Probe sitephi299852y21) 6.07AGC0.7112Probe siteumc1545y21)7.00AAGA0.7610hsp3(heat shock protein 3)phi1127.01AG0.5310o2 (opaque endosperm 2)phi4207018.00CCG0.469Probe siteumc13598.00TC0.7814Probe siteumc11398.01GAC0.479Probe siteumc13048.02TCGA0.335Probe sitephi1158.03A TAC0.465act1(actin1)umc22128.05ACG0.455Probe siteumc11218.05AGAT0.484Probe sitephi0808.08AGGAG0.646gst1 (glutathione-S-transferase 1)phi233376y11)8.09CCG0.598Probe sitebnlg12729.00AG0.8922Probe siteumc20849.01CTAG0.498Probe sitebnlg1520k11)9.01AG0.5913Probe sitephi0659.03CACCT0.519pep1(phosphoenolpyruvate carboxylase 1)umc1492y131)9.04GCT0.2514Probe siteumc1231k41)9.05GA0.2210Probe sitephi1084119.06AGCT0.495Probe sitephi4488809.06AAG0.7610Probe siteumc16759.07CGCC0.677Probe sitephi041y61)10.00AGCC0.417Probe siteumc1432y61)10.02AG0.7512Probe siteumc136710.03CGA0.6410Probe siteumc201610.03ACAT0.517pao1 (polyamine oxidase 1)phi06210.04ACG0.337mgs1 (male-gametophyte specific 1)phi07110.04GGA0.515hsp90 (heat shock protein, 90 kDa)1) These primers were provided by Beijing Academy of Agricultural and Forestry Sciences (Beijing, China).2) Searched from Analysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China1257et al. 2005). The unrooted phylogenetic tree was finally schematized with the software MEGA (molecular evolu-tionary genetics analysis) ver. 3.1 (Kumar et al. 2004). Additionally, a chi-square test was used to reveal the correlation between the geographical origins and SSR-clustered groups through FREQ procedure implemented in SAS ver. 9.0 (2002, SAS Institute, Inc.).In order to reveal the population genetic structure (PGS) of 143 landrace accessions, a Bayesian approach was firstly applied to determine the number of groups (K) that these materials should be assigned by the soft-ware BAPS (Bayesian Analysis of Population Structure) ver.5.1. By using BAPS, a fixed-K clustering proce-dure was applied, and with each separate K, the num-ber of runs was set to 100, and the value of log (mL) was averaged to determine the appropriate K value (Corander et al. 2003; Corander and Tang 2007). Since the number of groups were determined, a model-based clustering analysis was used to assign all of the acces-sions into the corresponding groups by an admixture model and a correlated allele frequency via software Structure ver.2.2 (Pritchard et al. 2000; Falush et al. 2007), and for the given K value determined by BAPS, three independent runs were carried out by setting both the burn-in period and replication number 100000. The threshold probability assigned individuals into groupswas set by 0.8 (Liu et al. 2003). The PGS result carried out by Structure was visualized via Distruct program ver. 1.1 (Rosenberg 2004).RESULTSGenetic diversityA total of 517 alleles were detected by the whole set of54 SSRs covering the entire maize genome through all of the 143 maize landraces, with an average of 9.57 alleles per locus and ranged from 4 (umc1121) to 22 (bnlg1272) (Table 3). Among all the alleles detected, the number of distinct alleles accounted for 132 (25.53%), with an av-erage of 2.44 alleles per locus. The distinct alleles dif-fered significantly among the landraces from different provinces/regions, and the landraces from Guangdong, Fujian, Zhejiang, and Shanghai possessed more distinct alleles than those from the other provinces/regions, while those from southern Anhui possessed the lowest distinct alleles, only counting for 3.28% of the total (Table 4).Table 4 The genetic diversity within eight provinces/regions and groups revealed by 54 SSRsProvince/Region Sample size Allele no.1)Distinct allele no.Gene diversity (expected heterozygosity)Observed heterozygosity Anhui14 4.28 (4.19) 69 (72.4)0.51 (0.54)0.58 (0.58)Fujian17 4.93 (4.58 80 (79.3)0.56 (0.60)0.63 (0.62)Guangdong22 5.48 (4.67) 88 (80.4)0.57 (0.59)0.59 (0.58)Hainan18 4.65 (4.26) 79 (75.9)0.53 (0.57)0.55 (0.59)Jiangsu13 4.24 700.500.55Jiangxi21 4.96 (4.35) 72 (68.7)0.56 (0.60)0.68 (0.68)Shanghai15 5.07 (4.89) 90 (91.4)0.55 (0.60)0.55 (0.55)Zhejiang23 5.04 (4.24) 85 (74)0.53 (0.550.60 (0.61)Total/average1439.571320.610.60GroupGroup 155 6.63 (6.40) 91 (89.5)0.57 (0.58)0.62 (0.62)Group 2887.94 (6.72)110 (104.3)0.57 (0.57)0.59 (0.58)Total/Average1439.571320.610.60Provinces/Regions within a groupGroup 1Total55 6.69 (6.40) 910.57 (0.58)0.62 (0.62)Guangdong22 5.48 (4.99) 86 (90.1)0.57 (0.60)0.59 (0.58)Hainan18 4.65 (4.38) 79 (73.9)0.53 (0.56)0.55 (0.59)Jiangxi15 4.30 680.540.69Group 2Total887.97 (6.72)110 (104.3)0.57 (0.57)0.59 (0.58)Anhui14 4.28 (3.22) 69 (63.2)0.51 (0.54)0.58 (0.57)Fujian17 4.93 (3.58) 78 (76.6)0.56 (0.60)0.63 (0.61)Jiangsu13 4.24 (3.22) 71 (64.3)0.50 (0.54)0.55 (0.54)Jiangxi6 3.07 520.460.65Shanghai15 5.07 (3.20) 91 (84.1)0.55 (0.60)0.55 (0.54)Zhejiang23 5.04 (3.20) 83 (61.7)0.53 (0.54)0.60 (0.58)1258LIU Zhi-zhai et al.Among the 54 loci used in the study, 16 (or 29.63%) were dinucleotide repeat SSRs, which were defined as type class I-I, the other 38 loci were SSRs with a longer repeat motifs, and two with unknown repeat motifs, all these 38 loci were defined as the class of I-II. In addition, 15 were located within certain functional genes (defined as class II-I) and the rest were defined as class II-II. The results of comparison indicated that the av-erage number of alleles per locus captured by class I-I and II-II were 12.88 and 10.05, respectively, which were significantly higher than that by type I-II and II-I (8.18 and 8.38, respectively). The gene diversity re-vealed by class I-I (0.63) and II-I (0.63) were some-what higher than by class I-II (0.60) and II-II (0.60) (Table 5).Genetic relationships of the core landraces Overall, 143 landraces were clustered into two groups by using neighbor-joining (NJ) method based on InSPAD. All the landraces from provinces of Guangdong and Hainan and 15 of 21 from Jiangxi were clustered together to form group 1, and the other 88 landraces from the other provinces/regions formed group 2 (Fig.-B). The geographical origins of all these 143 landraces with the clustering results were schematized in Fig.-D. Revealed by the chi-square test, the phylogenetic results (SSR-clustered groups) of all the 143 landraces from provinces/regions showed a significant correlation with their geographical origin (χ2=124.89, P<0.0001, Table 2).Revealed by the phylogenetic analysis based on the InSPAD, the minimum distance was observed as 0.1671 between two landraces, i.e., Tianhongyumi (JiangX-03) and Hongganshanyumi (JiangX-04) collected from Jiangxi Province, and the maximum was between two landraces of Huangbaosu (FuJ-16) and Hongyumi (HaiN-14) collected from provinces of Fujian and Hainan, respectively, with the distance of 1.3863 (data not shown). Two landraces (JiangX-01 and JiangX-21) collected from the same location of Dexing County (Table 1) possessing the same names as Shanyumi were separated to different groups, i.e., JiangX-01 to group1, while JiangX-21 to group 2 (Table 1). Besides, JiangX-01 and JiangX-21 showed a rather distant distance of 0.9808 (data not shown). These results indicated that JiangX-01 and JiangX-21 possibly had different ances-tral origins.Population structureA Bayesian method was used to detect the number of groups (K value) of the whole set of landraces from SR with a fixed-K clustering procedure implemented in BAPS software ver. 5.1. The result showed that all of the 143 landraces could also be assigned into two groups (Fig.-A). Then, a model-based clustering method was applied to carry out the PGS of all the landraces via Structure ver. 2.2 by setting K=2. This method as-signed individuals to groups based on the membership probability, thus the threshold probability 0.80 was set for the individuals’ assignment (Liu et al. 2003). Accordingly, all of the 143 landraces were divided into two distinct model-based groups (Fig.-C). The landraces from Guangdong, Hainan, and 15 landraces from Jiangxi formed one group, while the rest 6 landraces from the marginal countries of northern Jiangxi and those from the other provinces formed an-other group (Table 1, Fig.-D). The PGS revealed by the model-based approach via Structure was perfectly consistent with the relationships resulted from the phy-logenetic analysis via PowerMarker (Table 1).DISCUSSIONThe SR includes eight provinces, i.e., southern Jiangsu and Anhui, Shanghai, Zhejiang, Fujian, Jiangxi, Guangdong, and Hainan (Fig.-C), with the annual maize growing area of about 1 million ha (less than 5% of theTable 5 The genetic diversity detected with different types of SSR markersType of locus No. of alleles Gene diversity Expected heterozygosity PIC Class I-I12.880.630.650.60 Class I-II8.180.600.580.55 Class II-I8.330.630.630.58。
材料基因组数据库建设与数据驱动的新材料创新

材料基因组数据库建设与数据驱动的新材料创新近年来,材料基因组已经成为材料科学领域的一个热门话题。
“材料基因组”(materials genome)一词的出现,很大程度上受到成功的人类基因组计划的启发。
传统上,新材料和新工艺的发现和开发依赖于科学直觉和漫长的试错过程。
多年来,材料科学家渴望找到某种类似于生物基因的材料基本构造单元,其排序及缺陷结构或可决定材料的性质或功能。
通过了解这些构件,他们希望能够按需设计材料,从而加速材料的发现和开发,并降低成本。
自2011年美国启动“材料基因组计划”[1,2] 以来,其他主要经济体如欧盟[3,4]、日本[5]和中国都在国家层面设立了类似的科学计划。
然而关于什么是“材料基因组”,一直众说纷纭,难下定论。
近期取得的共识是其仅作为设计预测材料研发模式的代称[6]。
材料基因工程(materials genome engineering, MGE)意味着通过交叉融合高通量计算、高通量实验和材料信息学技术,速度更快、效率更高、成本更少地掌握成分-组织-工艺-性能间的关联关系——这些恰恰构成了材料设计的基础。
材料基因工程的工作模式可大致可分为实验驱动、计算驱动和数据驱动[7]三种。
实验驱动模式基于高通量合成与表征实验,直接快速优化与筛选材料。
这种模式的典型代表是高通量组合材料芯片技术[8]。
计算驱动模式基于计算模拟,预测有希望的候选材料,再进行实验验证[9],大大缩小实验范围。
数据驱动模式基于大量数据,借助材料信息学方法建立模型,即利用人工智能(AI)方法,如机器学习,解析多参数间复杂的关联关系,预测出候选材料[10]。
从人类认识自然的过程来看,数千年来,科学探索跨越了实验观测、理论推演、计算仿真几个阶段。
今天,利用前所未有的计算能力和大规模的数据收集能力,现代科学正在进入“第四范式”[11],即密集数据+人工智能。
材料基因工程的数据驱动模式正是“第四范式”的体现。
应该看到,实验和计算驱动模式的实质是基于事实的判断或基于物理规律的推演,并未从根本上改变材料科学的既有思维模式与工作套路。
生物信息学论文

人类角蛋白14(KRT14)基因的生物信息学分析前言:角蛋白14(Keratin 14 , K14)是角蛋白家族中一员,与角蛋白5 组成一组角蛋白对。
在正常胎儿和成人皮肤内,K14 的转录和翻译主要在表皮基底层和毛囊进行。
角蛋白(keratin )系硬蛋白之一,是一类具有结缔和保护功能的纤维状蛋白质。
由处于α-螺旋或β-折叠构象的平行的多肽链组成不溶于水的起着保护或结构作用蛋白质。
角蛋白(Keratin)是以各种动物的毛发、鳞片、羽毛、蹄、角为主要形式广泛存在于自然界中的一种结构蛋白。
胶原(Collagen)主要存在于动物的皮、骨、软骨、肌腱、韧带和血管中,是结缔组织重要的结构物质。
角蛋白和胶原均为很好的动物蛋白资源[9],已被广泛的运用到医药、食品等方面,但由于两者特殊的化学结构,性质较稳定,动物难以大量直接吸收,这局限了其在饲料方面的发展[10,11]。
而微生物来源的蛋白酶尽管活性很强[12],可以水解多种难降解的纤维蛋白,如角蛋白和胶原等,但往往不是安全的菌株。
关键词:KRT14、序列、引物、进化树、图谱、基因一、材料与方法1.材料:数据来源NCBI的GenBank数据库。
获得KRT14相应的mRNA序列及其注释。
2.方法2.1人类角蛋白14的序列分析:利用NCBI的ORF Find预测开放阅读框ORF;利用DNAstar 的genquest程序对序列进行转录因子结合位点、限制性内切酶图谱分析;用clustalX进行同源序列比对并构建进化树,用NCBI中的primer-blast设计引物。
2.2人类角蛋白14(KRT14)基因的生物信息学分析二、结果与分析1. 从NCBI中获得人类角蛋白14(KRT14)基因全长序列。
其全长:1653 bp 登录号:NM_000526 ,更新时间:2011年10月16日。
基因来源菌种分类为:Homo sapiens、Eukaryota; Metazoa; Chordata; Craniata; V ertebrata; Euteleostomi;Mammalia; Eutheria; Euarchontoglires; Primates; Haplorrhini; Catarrhini; Hominidae; Homo.作者分别为:Natsuga,K., Nishie,W., Smith,B.J., Shinkuma,S., Smith,T.A., Parry,D.A.,Oiso,N., Kawada,A., Y oneda,K., Akiyama,M. 和Shimizu,H.2.mRNA序列如下:1 acccgagcac cttctcttca ctcagccaac tgctcgctcg ctcacctccc tcctctgcac61 catgaccacc tgcagccgcc agttcacctc ctccagctcc atgaagggct cctgcggcat121 cgggggcggc atcgggggcg gctccagccg catctcctcc gtcctggccg gagggtcctg 181 ccgcgccccc agcacctacg ggggcggcct gtctgtctca tcctcccgct tctcctctgg241 gggagcctac gggctggggg gcggctatgg cggtggcttc agcagcagca gcagcagctt301 tggtagtggc tttgggggag gatatggtgg tggccttggt gctggcttgg gtggtggctt361 tggtggtggc tttgctggtg gtgatgggct tctggtgggc agtgagaagg tgaccatgca421 gaacctcaat gaccgcctgg cctcctacct ggacaaggtg cgtgctctgg aggaggccaa481 cgccgacctg gaagtgaaga tccgtgactg gtaccagagg cagcggcctg ctgagatcaa541 agactacagt ccctacttca agaccattga ggacctgagg aacaagattc tcacagccac601 agtggacaat gccaatgtcc ttctgcagat tgacaatgcc cgtctggccg cggatgactt661 ccgcaccaag tatgagacag agttgaacct gcgcatgagt gtggaagccg acatcaatgg721 cctgcgcagg gtgctggacg aactgaccct ggccagagct gacctggaga tgcagattga781 gagcctgaag gaggagctgg cctacctgaa gaagaaccac gaggaggaga tgaatgccct841 gagaggccag gtgggtggag atgtcaatgt ggagatggac gctgcacctg gcgtggacct901 gagccgcatt ctgaacgaga tgcgtgacca gtatgagaag atggcagaga agaaccgcaa961 ggatgccgag gaatggttct tcaccaagac agaggagctg aaccgcgagg tggccaccaa 1021 cagcgagctg gtgcagagcg gcaagagcga gatctcggag ctccggcgca ccatgcagaa 1081 cctggagatt gagctgcagt cccagctcag catgaaagca tccctggaga acagcctgga1141 ggagaccaaa ggtcgctact gcatgcagct ggcccagatc caggagatga ttggcagcgt 1201 ggaggagcag ctggcccagc tccgctgcga gatggagcag cagaaccagg agtacaagat 1261 cctgctggac gtgaagacgc ggctggagca ggagatcgcc acctaccgcc gcctgctgga 1321 gggcgaggac gcccacctct cctcctccca gttctcctct ggatcgcagt catccagaga 1381 tgtgacctcc tccagccgcc aaatccgcac caaggtcatg gatgtgcacg atggcaaggt 1441 ggtgtccacc cacgagcagg tccttcgcac caagaactga ggctgcccag ccccgctcag 1501 gcctaggagg ccccccgtgt ggacacagat cccactggaa gatcccctct cctgcccaag 1561 cacttcacag ctggaccctg cttcaccctc accccctcct ggcaatcaat acagcttcat 1621 tatctgagtt gcataaaaaa aaaaaaaaaa aaa3.该基因所翻译的氨基酸序列为:MTTCSRQFTSSSSMKGSCGIGGGIGGGSSRISSVLAGGSCRAPS TYGGGLSVSSSRFSSGGAYGLGGGYGGGFSSSSSSFGSGFGGGYGGGLGAGLGGGFGG GFAGGDGLLVGSEKVTMQNLNDRLASYLDKVRALEEANADLEVKIRDWYQRQRPAEIK DYSPYFKTIEDLRNKILTATVDNANVLLQIDNARLAADDFRTKYETELNLRMSVEADI NGLRRVLDELTLARADLEMQIESLKEELAYLKKNHEEEMNALRGQVGGDVNVEMDAAP GVDLSRILNEMRDQYEKMAEKNRKDAEEWFFTKTEELNREVATNSELVQSGKSEISEL RRTMQNLEIELQSQLSMKASLENSLEETKGRYCMQLAQIQEMIGSVEEQLAQLRCEME QQNQEYKILLDVKTRLEQEIATYRRLLEGEDAHLSSSQFSSGSQSSRDVTSSSRQIRT KVMDVHDGKVVSTHEQVLRTKN4. KRT14基因的开放阅读框ORF序列如下:62 atgaccacctgcagccgccagttcacctcctccagctccatgaagM T T C S R Q F T S S S S M K107 ggctcctgcggcatcgggggcggcatcgggggcggctccagccgcG S C G I G G G I G G G S S R152 atctcctccgtcctggccggagggtcctgccgcgcccccagcaccI S S V L A G G S C R A P S T197 tacgggggcggcctgtctgtctcatcctcccgcttctcctctgggY G G G L S V S S S R F S S G242 ggagcctacgggctggggggcggctatggcggtggcttcagcagcG A Y G L G G G Y G G G F S S287 agcagcagcagctttggtagtggctttgggggaggatatggtggtS S S S F G S G F G G G Y G G332 ggccttggtgctggcttgggtggtggctttggtggtggctttgctG L G A G L G G G F G G G F A377 ggtggtgatgggcttctggtgggcagtgagaaggtgaccatgcagG G D G L L V G S E K V T M Q422 aacctcaatgaccgcctggcctcctacctggacaaggtgcgtgctN L N D R L A S Y L D K V R A467 ctggaggaggccaacgccgacctggaagtgaagatccgtgactggL E E A N A D L E V K I R D W512 taccagaggcagcggcctgctgagatcaaagactacagtccctacY Q R Q R P A E I K D Y S P Y557 ttcaagaccattgaggacctgaggaacaagattctcacagccacaF K T I E D L R N K I L T A T602 gtggacaatgccaatgtccttctgcagattgacaatgcccgtctgV D N A N V L L Q I D N A R L 647 gccgcggatgacttccgcaccaagtatgagacagagttgaacctgA A D D F R T K Y E T E L N L692 cgcatgagtgtggaagccgacatcaatggcctgcgcagggtgctgR M S V E A D I N G L R R V L 737 gacgaactgaccctggccagagctgacctggagatgcagattgagD E L T L A R A D L E M Q I E782 agcctgaaggaggagctggcctacctgaagaagaaccacgaggagS L K E E L A Y L K K N H E E 827 gagatgaatgccctgagaggccaggtgggtggagatgtcaatgtgE M N A L R G Q V G G D V N V872 gagatggacgctgcacctggcgtggacctgagccgcattctgaacE M D A A P G V D L S R I L N917 gagatgcgtgaccagtatgagaagatggcagagaagaaccgcaagE M R D Q Y E K M A E K N R K962 gatgccgaggaatggttcttcaccaagacagaggagctgaaccgcD AE E WF F T K T E E L N R1007 gaggtggccaccaacagcgagctggtgcagagcggcaagagcgagE V A T N S E L V Q S G K S E1052 atctcggagctccggcgcaccatgcagaacctggagattgagctgI S E L R R T M Q N L E I E L1097 cagtcccagctcagcatgaaagcatccctggagaacagcctggagQ S Q L S M K A S L E N S L E 1142 gagaccaaaggtcgctactgcatgcagctggcccagatccaggagE T K G R Y C M Q L A Q I Q E1187 atgattggcagcgtggaggagcagctggcccagctccgctgcgagM I G S V E E Q L A Q L R C E 1232 atggagcagcagaaccaggagtacaagatcctgctggacgtgaagM E Q Q N Q E Y K I L L D V K 1277 acgcggctggagcaggagatcgccacctaccgccgcctgctggagT R L E Q E I A T Y R R L L E 1322 ggcgaggacgcccacctctcctcctcccagttctcctctggatcgG E D A H L S S S Q F S S G S1367 cagtcatccagagatgtgacctcctccagccgccaaatccgcaccQ S S R D V T S S S R Q I R T 1412 aaggtcatggatgtgcacgatggcaaggtggtgtccacccacgagK V M D V H D G K V V S T H E 1457 caggtccttcgcaccaagaactga 1480Q V L R T K N *5.引物设计如下。
生物大数据_福建农林大学中国大学mooc课后章节答案期末考试题库2023年

生物大数据_福建农林大学中国大学mooc课后章节答案期末考试题库2023年1.Bioinformatics的含义是()答案:生物信息学2.利用PubMed文献数据查找论文“Transgenic plants of Petunia hybridaharboring the CYP2E1 gene efficiently remove benzene and toluenepollutants and improve resistance to formaldehyde”的第一作者是答案:Zhang D3.被誉为“生物信息学之父”的科学家是()答案:林华安4.Proteomics的含义是()答案:蛋白质组学5.生物信息学主要是利用哪种工具实现对生命科学研究中生物信息的存储、检索和分析的?()答案:计算机6.HGP是()答案:人类基因组计划7.下列哪些方法不能用于遗传育种()答案:自然选择8.Genbank数据库中,mRNA的获取号可以以()字母开头答案:NM_9.下列那个数据库不属于NCBI()答案:ArrayExpress10.大数据处理遇到的瓶颈不包括()答案:数据量11.可以用来做数据库搜索的比对算法是()答案:BLAST12.下列哪个方法最可能在基因组组装过程中留下空缺()答案:鸟枪法建库13.“一旦空位,永远空位”描述的是()答案:渐进比对算法14.下列不属于分子生物学数据库的特点的是()答案:版本不更新15.GenBank中具有唯一性的字段是()答案:Accession16.哪个基因组序列还完全未被破解()答案:恐龙17.下面哪个工具可以用来检验原始读段的质量?()答案:Fastqc18.基于边合成边测序的测序方法是()答案:Illumina/Solexa19.比较成熟的三代测序技术是()答案:PacBio20.不采用荧光标记核苷酸的测序技术是()答案:NanoPore21.靶向测序使用的测序文库是()答案:Amplicon22.RNA-seq从头组装的常用工具是()答案:Trinity23.RNA-Seq技术用途不包括()答案:基因组测序24.重测序数据分析的最后一步是()答案:功能注释25.影响基因组组装效果的因素不包括()答案:测序时间26.组装基因组时,由重复序列导致的错误类型不包括()答案:基因融合27.重复序列是在基因组中出现次数大于1的DNA片段,不包括()答案:调控序列28.研究蛋白质与DNA相互作用的是()答案:ChIP-seq29.在线的染色体可视化工具是()答案:Genome browser30.下列属于最不易突变的氨基酸()答案:半胱氨酸31.常用的2个全基因组测序策略是答案:鸟枪法逐步克隆法32.二代测序数据分析中经常使用的2种比对工具是答案:BowtieBWA33.20世纪70年代,出现的2种DNA测序方法是答案:链终止测序法化学降解测序法34.关于C值悖论的描述正确的有哪些答案:亲缘关系相近的物种间C值差异很大C值远远超过了遗传信息量的需要进化程度低的生物C值反而更高35.基因组重测序技术可被用于哪些检测领域答案:皮草的真伪检测中草药的产区检测食品掺假检测宠物疾病检测36.fastaq文件中,Q值越小,测序质量越高()答案:错误37.基因组从头组装的本质是寻找重叠区域()答案:正确38.读段长于重复序列的长度才可能填补空缺()答案:正确39.Contig越长基因组拼接效果越好()答案:正确40.N50可以作为评估基因组组装效果的一个指标()答案:正确41.RNA-seq基因对应的读段数量和基因长度及测序深度有关()答案:正确42.进行有参考基因组的二代测序数据比对时,只需要基因组序列文件即可()答案:错误43.FPKM是单端RNA-seq基因表达量的表示方法()答案:错误44.对于复杂基因组,一般一种测序文库就足够了()答案:错误45.测序文库构建很大程度决定了测序数据的好坏()答案:正确46.二代测序的核心技术是循环芯片测序法()答案:正确47.测序深度越高,从头组装的质量一定越好()答案:错误48.测序深度越高,测序数据量越大()答案:正确49.二代测序数据文件的后缀是.fa或.fastq()答案:正确50.基于焦磷酸合成测序的方法是SOLiD/ABI()答案:错误51.Sanger测序发现时间早于K.Mullis的PCR()答案:正确52.DNA测序和蛋白质测序相关技术都获得过诺贝尔奖()答案:正确53.大规模基因组测序主要有逐步克隆和鸟枪法2种策略()答案:正确54.多数遗传性状是由单个基因决定的()答案:错误55.人类基因组计划是中国人主持的第一个国际项目()答案:错误56.相同长度序列,蛋白质组的复杂度低于基因的复杂度()答案:错误57.一个氨基酸的性质主要由它的侧链决定()答案:正确58.大数据必然会造福人类答案:错误59.大数据已经成为我国国家战略答案:正确60.双端测序与单端测序的区别在于,前者需要在DNA片段的两端分别加上引物和连接子答案:正确61.配对测序方式可以用来解决重复序列长度超过read长度,无法拼接易形成断点的问题答案:正确62.配对测序是一种特殊的双端测序方式答案:正确63.读段文件除了文本格式之外,还可以用图象表示答案:错误64.测序深度即测序得到的碱基总量(bp)与基因组大小(Genome)的比值,它是评价测序量的指标之一答案:正确65.二代测序数据文件的后缀是.fa或.fastq答案:正确66.常见的三大核酸数据库中,位于欧洲的是答案:EMBL##%_YZPRLFH_%##embl##%_YZPRLFH_%##Embl67.列举二代测序数据分析中经常使用的1种短序列比对工具。
生物信息学分析骨关节炎滑膜炎症相关基因和分子途径

收稿日期:2020-07-22;网络出版时间:2021-03-31 15:55:54 网络出版地址:http://kns_/kcms/detail/37.1390.R.20210331.1018.004.html 基金项目:国家自然科学基金(814710〇7 ) 通信作者:辛玮. . E-mail:weixin@
2021年 3 月 Mar. 2021
生物信息学分析骨关节炎滑膜炎症 相关基因和分子途径
华 芳 h2,张 薇 薇 u ,吕 波 u ,辛 玮 K2
( 山 东 大 学 附 属 省 立 医 院 1.检 验 科 ; 2 .中 心 实 验 室 ,山 东 济 南 250021)
摘 要 :日 的 旨 在 利 用 生 物 信 息 学 分 析 鉴 定 与 骨 关 节 炎 滑 膜 炎 症 进 展 相 关 的 差 异 表 达 基 因 (DEGs) 。 方 法 从 基
因 表 达 总 览 (G E O )数 据 库 下 栽 GSE55457、GSE55235和 GSE12021基 因 表 达 谱 ,筛 选 骨 关 节 炎 发 生 发 展 过 程 中 的
相 关 基 因 。 对 筛 选 出 的 差 异 表 达 基 因 (D E G s)进 行 热 图 绘 制 、基 因 本 体 论 (G O ) 和 京 都 基 因 与 基 因 组 百 科 全 书
A bstract:Objective To identify the differentially expressed genes ( DEGs) associated with the progression of osteoar thritis synovitis by bioinformatic analysis. Methods The gene expression profiles of GSE55457,GSE55235 and GSE12021 were downloaded from the Gene Expression Omnibus ( GEO) to screen related genes in the pathogenesis of osteoarthritis. After the DEGs were identified, heatmaps were drawn, and functional enrichment of GO and KEGG was analyzed. The protein-protein interaction network ( PPI) was constructed with STRING and Cytoscape, top module was screened with MCODE plug-in unit, and hub genes were screened with cytoHubba plug-in unit. Results There were 72 upregulated genes and 151 downregulated genes in the GSE55457, GSE55235 and GSE 12021 gene expression profiles. GO analysis showed DEGs were involved in leukocyte migration, response to glucocorticoid, glycosaminoglycan binding, endoplasmic reticulum lumen, and nuclear outer membrane. KEGG analysis revealed DEGs were involved in MAPK signaling pathway, osteoclast differentiation and TNF signaling pathway. The cytoHubba screened out 10 key genes, including IL6, TLR7, SELE, VEGFA, LDLR, JUN, MYC, CD44, SNAI1 and hnRNAl. Conclusion Bioinfor matic analysis can help to discover the molecular mechanism and key genes of synovitis in patients with osteoarthritis. Key w ords:Osteoarthritis;Bioinformatics;Differentially expressed genes
- 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。
392Genome Informatics14:392–393(2003) Conservation of DNA Curvature Signals in GeneRegulatory RegionsRuy Jauregui1Enrique Merino2ruy@kuicr.kyoto-u.ac.jp merino@ibt.unam.mx1Bioinformatics Group,Institute of Chemical Research,Kyoto University,Uji,Kyoto 611-0011,Japan2Biotechnology Institute,National Autonomous University of Mexico,Cuernavaca Mor.62210,MexicoKeywords:DNA curvature,transcriptional regulation,orthologous groups.1IntroductionDNA curvature has been studied for more than20years and has been related to a broad spectrum of biological functions such as DNA replication,transcriptional regulation,and recombination.Studies on the relationship between DNA curvature and transcriptional regulation have been conducted for a relatively small number of genes and discrete loci,such as Sigma-54dependent glnAp2and glnHp2 genes[1],and IHF and CRP regulatory proteins[5,8].In this work we extend the previous notions about the biological relevance of DNA curvature as an element of transcriptional regulation by evalu-ating the conservation of DNA static curvature signals in the regulatory regions of orthologous groups of genes in99eubacterial and archaeal genomes.2Method and ResultsDNA sequence was derived from the complete bacterial genomes available in the Entrez Genome Database.A250nt.window,containing200bases upstream and50bases downstream of the start codon of each coding sequence(CDS),was chosen as our analysis window,since more than90%of the regulatory signals are found within this range in E.coli K12.Operons were predicted based on intergenic distances and genomic context as described by Moreno-Hagelsieb and Collado-Vides[6], and the regulatory region of each gene was considered as the upstream region of thefirst gene in the operon and was called the Minimal Upstream Regions set,(MURs).DNA curvature was calculated using the algorithm BEND[2].A curvature profile was obtained by assigning to each nucleotide of the genome a curvature value,expressed as a deviation angle from the helical axis per helical turn.Since each genome presents a distinctive curvature profile,a cutoffvalue of3SD from the genomic curvature mean of each organism was used to identify statistically significant signals in the set of MURs.Each gene selected this way was sorted into its corresponding orthologous group.Our orthologous groups were mainly those found in the COGs database[9].Sixty-eight of the4391COGs studied presented a statistically significant number of curvature signals(at least3SD above the expected mean),including genes coding for DNA binding regulatory proteins such as HU,IHF and FIS,transposases,ribosomal proteins and aminoacil tRNA synthetases, translation factors,cell division,andflagellum biosynthesis among others.These COGs were classified according to their global functional characterization.Experimental data supporting a role of DNA curvature in transcriptional regulation for these genes was searched for in the literature,and in several cases our observations were supported by previous analysis of discrete loci.Conservation of DNA Curvature Signals in Gene Regulatory Regions393 3DiscussionAmong the relevant groups that were found in our analysis,COGs with proteins coding for the global regulators HU,IHF and FIS were found to present a significant number of genes with curvature signals in a broad spectrum of phylogenetically unrelated organisms,this results are coherent with previous experiments that demonstrate that these proteins bind to curved DNA and are autogenous transcriptional regulators of their own genes[3,5,7].The two subunits of DNA topoisomerase IV (DNA Gyrase)were also found to present conserved curvature signals in their regulatory regions.DNA gyrase is known to be a homeostatic regulator of the nucleus supercoiling state,and to be regulated by FIS[4].Ourfinding of several COGs related to cell division andflagellum biosynthesis was unexpected since there is no experimental evidence relating the regulation of these genes with DNA curvature;even tough their transcription is known to be dependent on global regulatory proteins such as HU and FIS. Our work demonstrates the prevalence of curvature signals as conserved elements in transcriptional regulation and extends the previous knowledge described for unique genes in single organisms into a genomic context.References[1]Carmona,M.and Magasanik,B.,Activation of transcription at Sigma54-dependent promoters onlinear templates requires intrinsic or induced bending of the DNA,J.Mol.Biol.,261(3):348–356, 1996.[2]Goodsell,D.S.and Dickerson,R.E.,Bending and curvature calculations in B-DNA,Nucleic AcidsRes.,22(24):5497–5503,1994.[3]Kohno,K.,Wada,M.,Kano,Y.,and Imamoto,F.,Promoters and autogenous control of theEscherichia coli hupA and hupB genes,J.Mol.Biol.,213(1):27–36,1990.[4]Menzel,R.and Gellert,M.,Regulation of the genes for E.coli DNA gyrase:homeostatic controlof DNA supercoiling,Cell,34(1):105–113,1983.[5]Miller,H.I.,Kirk,M.,and Echols,H.,SOS induction and autoregulation of the himA gene forsite-specific recombination in Escherichia coli,A,78(11):6754–6758, 1981.[6]Moreno-Hagelsieb,G.and Collado-Vides,J.,A powerful non-homology method for the predictionof operons in prokaryotes,Bioinformatics,18(Suppl.1):329–336,2002.[7]Ninnemann,O.,Koch,C.,and Kahmann,R.,The E.colifis promoter is subject to stringentcontrol and autorregulation,EMBO J.,11(3):1075–1083,1992.[8]Perez-Martin,J.and De Lorenzo,V.,Clues and consequences of DNA bending in transcription,Annu.Rev.Microbiol.,51:593–628,1997.[9]Tatusov,R.L.,Galperin,M.Y.,Natale,D.A.,and Koonin,E.V.,The COG database:a tool forgenome-scale analysis of protein functions and evolution,Nucleic Acids Res.,28(1):33–36,2000.。