EST分析技术在鸡基因组学研究中的应用

表达序列标签（EST）是由大量随机取出的cDNA库克隆经测序得到的组织或细胞基因组的一段cDNA序列,一个EST代表生物体某种组织某一时期的一个表达基因。综述了EST分析技术在鸡基因组研究中的应用。如用于鉴定、发现和预测鸡的新基因,用于基因图谱的绘制,用于筛选基因的单核苷酸多态性（SNP）位点,用于基因表达分析和基因芯片制作等。EST数据库和生物信息学的联合分析技术在推动家鸡后基因组的研究中发挥着重要的作用。     

静电纺丝的主要参数对PLGA纤维支架形貌和纤维直径的影响

EST(expressed sequence tags,EST)是一段长约150～500bp基因表达的外源序列片段,是由大规模随机挑取的cDNA克隆测序得到的组织或细胞基因组的表达序列标签。一个EST代表生物某一时期的某种组织或细胞的一个表达基因。主要综述了EST技术的原理方法,哺乳动物早期胚胎研究的理论基础以及EST技术在早期胚胎研究方面的应用,并讨论了利用EST进行研究分析的发展趋势。     

EST技术及其在哺乳动物早期胚胎研究中的应用

EST(expressed sequence tags ,EST) 是一段长约150～500 bp的基因表达的外源序列片段,是由大规模随机挑取的cDNA克隆测序得到的组织或细胞基因组的表达序列标签。一个EST代表生物某一时期的某种组织或细胞的一个表达基因。本文主要综述了EST技术的原理方法,哺乳动物早期胚胎研究的理论基础以及EST技术在早期胚胎研究方面的应用,并讨论了利用EST进行研究分析的发展趋势。     

一种新的EST聚类方法

该研究发展了一种EST（expressed sequence tag）聚类方法（ESTClustering）,用于分析大规模EST测序中所产生的大量数据,以获得高质量,非重复表达序列,该方法在聚类过程中采用MEGABLAST工具对一致序列进行序列同源比较,并用phrap程序对每一EST簇进行拼接检验。这一聚类策略能降低测序错误带来的影响,有效识别基因家族成员,并避免选择性剪接的干扰,与NCB（National Center for Biotechnology Information）的UniGene clustering）方法相比,ESTClustering的聚类结果可以更好地反映表达序列的多样性,用ESTClustering对112256条拟南芥EST聚类测试,产生23581个EST簇,其中13597个EST簇有对应拟南芥基因组编码序列,与该基因组中有EST作为依据的预测基因数目接近。应用该方法对收集的147191条水稻EST序列进行聚类,形成33896个EST簇。     

表达序列标签及其应用

表达序列标签（EST）在基因组作图,克隆基因,新基因的识别,蛋白质组研究等许多方面具有重要的用途。本介绍了EST的制备方法,以及构建均一化cDNA库的方法,并介绍了EST在以上各方面的应用。     

植物基因组表达序列标签(EST)计划研究进展

植物表达序列标签(EST)计划是随机挑选cDNA克隆,并对其3′或5′端进行大规模一次性测序,将得到的150～500 bp长度的DNA片段与数据库中的序列进行比较,获得对基因组结构、组织、表达等认识的基因组研究策略.就近年来国际植物EST计划的实施情况、植物EST计划的研究范围、生物信息学在EST研究中的应用、EST数据库及查询、植物EST研究中遇到的问题等方面内容进行了综述.     

应用生物信息学技术对植物表达序列标签(EST)的大规模分析

目前 ,一些基因组较小的植物 (如拟南芥 ,水稻等 )的全基因组已经基本完成测序 ,较大基因组的测序工作则主要集中在基因组中表达基因的测序上 ,表达序列标签 (EST)计划由此产生。研究表明 ,对EST进行大规模研究已成为功能基因组学研究的最佳途经之一。本文着重介绍和讨论应用生物信息学技术对植物EST数据的大规模分析。     

盐芥（Arabidopsi salsuginea）cDNA文库的随机测序

实验以盐生植物盐芥为材料,提取经盐处理的盐芥总RNA,分离mRNA后,构建cDNA文库。从cDNA文库中随机挑取克隆进行测序。结果共测得53个表达序列标记（EST）,有37个EST（69．8％）,与拟南芥的基因在多肽水平上有较高同源性;21个EST（39．6％）的功能未知。     

球毛壳菌循环肽HC-毒素基因的序列分析

为了验证Phrap软件是否适合在EST分析中应用,对球毛壳菌循环肽HC-毒素基因进行了序列分析。根据EST分析的结果,从cDNA文库中挑取循环肽HC-毒素基因的克隆进行了测序并序列分析。结果表明cDNA文库中循环肽HC-毒素基因的克隆插入片断大小为1217bp;用Phrap软件拼接出来的循环肽HC-毒素的表达序列标签拼接序列与实际序列不完全一致,因此Phrap软件不适合在EST分析中应用。     

杨树基因组中染色体基因丰度分化及EST序列对基因覆盖度的检测(英文)

高等植物基因组中,大部分序列为非表达序列,基因序列所占的比例很小,了解基因在基因组中的分布是研究基因组结构的一个重要方面。在美国能源部资助下,一个毛果杨无性系的基因组测序已经完成并对公众发布。杨树全基因组序列的完成,为我们了解林木基因组中基因的分布提供了一个特例。在本文中,我们利用泊松分析对杨树基因组中基因在各个染色体上的密度进行了检测,结果表明杨树基因组中各条染色体的基因含量存在显著差异。杨树全基因组测序项目揭示现代杨树基因组起源于一次古全基因组复制事件(称为杨柳科基因组复制),所以杨树基因组不同染色体间存在很大的同源复制片段。但是我们的研究显示,杨树基因组中大多数高度同源的染色体上基因的密度与染色体间的同源性没有明显关系,这说明杨柳科全基因组复制事件后,各个高度同源染色体上的基因发生了流失,且基因流失的速率是不一样的。同时本文还对近九万条毛果杨EST序列进行了比对分析,结果显示这些EST序列覆盖的基因仅占杨树基因组中基因总数的16.8%左右。EST测序虽然是发现基因的一个重要手段,但小规模EST测序对基因的覆盖度很低,所以小规模EST测序的应用价值是有限的。     

Expressed sequence tags and their application for plant research

Expressed Sequence Tags (ESTs) are short, usually unedited sequences obtained by single-pass sequencing of cDNA clones from any cDNA library. Analyzing and comparing ESTs can provide information on gene expression, function and evolution. Large-scale EST sequencing has become an attractive alternative to plant genome sequencing. Currently, plant EST collections comprise over 3.8 million sequences from about 200 species. They have proved to be a valuable tool for gene discovery and plant metabolism analysis. Several plant-specific EST databases have been created which provide access to sequence data and bioinformatics-based tools for data mining. Searching EST collections allows pre-selection of genes for preparing cDNA arrays, targeted to bring maximum information on specialized processes, like stress response, symbiotic nitrogen fixation etc. Also, ESt-based molecular markers such as SNP, SSR, and indels are fast developing tools for breeders and researchers.     

Gene expression profile of human bone marrow stromal cells: high-throughput expressed sequence tag sequencing analysis

Human bone marrow stromal cells (HBMSC) are pluripotent cells with the potential to differentiate into osteoblasts, chondrocytes, myelosupportive stroma, and marrow adipocytes. We used high-throughput DNA sequencing analysis to generate 4258 single-pass sequencing reactions (known as expressed sequence tags, or ESTs) obtained from the 5' (97) and 3' (4161) ends of human cDNA clones from a HBMSC cDNA library. Our goal was to obtain tag sequences from the maximum number of possible genes and to deposit them in the publicly accessible database for ESTs (dbEST of the National Center for Biotechnology Information). Comparisons of our EST sequencing data with nonredundant human mRNA and protein databases showed that the ESTs represent 1860 gene clusters. The EST sequencing data analysis showed 60 novel genes found only in this cDNA library after BLAST analysis against 3.0 million ESTs in NCBI's dbEST database. The BLAST search also showed the identified ESTs that have close homology to known genes, which suggests that these may be newly recognized members of known gene families. The gene expression profile of this cell type is revealed by analyzing both the frequency with which a message is encountered and the functional categorization of expressed sequences. Comparing an EST sequence with the human genomic sequence database enables assignment of an EST to a specific chromosomal region (a process called digital gene localization) and often enables immediate partial determination of intron/exon boundaries within the genomic structure. It is expected that high-throughput EST sequencing and data mining analysis will greatly promote our understanding of gene expression in these cells and of growth and development of the skeleton.     

Using mtDNA Sequences to Estimate SNP Parameters in ESTs

Discovery of single nucleotide polymorphisms (SNPs) requires analysis of redundant sequences such as those available in large public databases. The ability to detect SNPs, especially those of low frequency, is dependent on the depth and scale of the discovery effort. Large numbers of SNPs have been identified by mining large-scale EST surveys and whole genome sequencing projects. These surveys however are subject to ascertainment bias and the inherent errors in large-scale single pass sequencing efforts. For example, the number of steps involved in the construction and sequencing of cDNA libraries make ESTs highly error prone, resulting in an increased frequency of nonvalid SNPs obtained in these surveys. Sequences of mtDNA genes are often incorporated into cDNA libraries as an artifact of the library construction process and are typically either subtracted from cDNA libraries or are considered superfluous when evaluating the information content of EST datasets. Sequences of mtDNA genes provide a unique resource for the analysis of SNP parameters in EST projects. This study uses sequences from four turkey muscle cDNA libraries to demonstrate how mtDNA sequences gleaned from collections of ESTs can be used to estimate SNP parameters and thus help predict the validity of SNPs.     

Using mtDNA sequences to estimate SNP parameters in ESTs

Discovery of single nucleotide polymorphisms (SNPs) requires analysis of redundant sequences such as those available in large public databases. The ability to detect SNPs, especially those of low frequency, is dependent on the depth and scale of the discovery effort. Large numbers of SNPs have been identified by mining large-scale EST surveys and whole genome sequencing projects. These surveys however are subject to ascertainment bias and the inherent errors in large-scale single pass sequencing efforts. For example, the number of steps involved in the construction and sequencing of cDNA libraries make ESTs highly error prone, resulting in an increased frequency of nonvalid SNPs obtained in these surveys. Sequences of mtDNA genes are often incorporated into cDNA libraries as an artifact of the library construction process and are typically either subtracted from cDNA libraries or are considered superfluous when evaluating the information content of EST datasets. Sequences of mtDNA genes provide a unique resource for the analysis of SNP parameters in EST projects. This study uses sequences from four turkey muscle cDNA libraries to demonstrate how mtDNA sequences gleaned from collections of ESTs can be used to estimate SNP parameters and thus help predict the validity of SNPs.     

Establishment of a high throughput EST sequencing system using poly(A) tail-removed cDNA libraries and determination of 36 000 bovine ESTs

We determined 36 310 bovine expressed sequence tag (EST) sequences using 10 different cDNA libraries. For massive EST sequencing, we devised a new system with two major features. First, we constructed cDNA libraries in which the poly(A) tails were removed using nested deletion at the 3′-ends. This permitted high quality reading of sequences from the 3′-end of the cDNA, which is otherwise difficult to do. Second, we increased throughput by sequencing directly on templates generated by colony PCR. Using this system, we determined 600 cDNA sequences per day. The read-out length was >450 bases in >90% of the sequences. Furthermore, we established a data management system for analyses, storage and manipulation of the sequence data. Finally, 16 358 non-redundant ESTs were derived from ~6900 independent genes. These data will facilitate construction of a precise comparative map across mammalian species and isolate the functional genes that govern economic traits. This system is applicable to other organisms, including livestock, for which EST data are limited.