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本文引用的文献

1
Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project.模式生物基因组计划对秀丽隐杆线虫基因组的综合分析。
Science. 2010 Dec 24;330(6012):1775-87. doi: 10.1126/science.1196914. Epub 2010 Dec 22.
2
Prediction and characterization of noncoding RNAs in C. elegans by integrating conservation, secondary structure, and high-throughput sequencing and array data.通过整合保守性、二级结构以及高通量测序和芯片数据预测和鉴定秀丽隐杆线虫中的非编码 RNA。
Genome Res. 2011 Feb;21(2):276-85. doi: 10.1101/gr.110189.110. Epub 2010 Dec 22.
3
Receptor-type guanylate cyclase is required for carbon dioxide sensation by Caenorhabditis elegans.受体型鸟苷酸环化酶是秀丽隐杆线虫感知二氧化碳所必需的。
Proc Natl Acad Sci U S A. 2011 Jan 4;108(1):254-9. doi: 10.1073/pnas.1017354108. Epub 2010 Dec 20.
4
Mammalian microRNAs predominantly act to decrease target mRNA levels.哺乳动物的 microRNAs 主要作用是降低靶 mRNA 水平。
Nature. 2010 Aug 12;466(7308):835-40. doi: 10.1038/nature09267.
5
Long noncoding RNA as modular scaffold of histone modification complexes.长非编码 RNA 作为组蛋白修饰复合物的模块化支架。
Science. 2010 Aug 6;329(5992):689-93. doi: 10.1126/science.1192002. Epub 2010 Jul 8.
6
Time-lapse imaging and cell-specific expression profiling reveal dynamic branching and molecular determinants of a multi-dendritic nociceptor in C. elegans.延时成像和细胞特异性表达谱分析揭示了线虫多树突伤害感受器的动态分支和分子决定因素。
Dev Biol. 2010 Sep 1;345(1):18-33. doi: 10.1016/j.ydbio.2010.05.502. Epub 2010 Jun 9.
7
Specific roles for DEG/ENaC and TRP channels in touch and thermosensation in C. elegans nociceptors.DEG/ENaC 和 TRP 通道在秀丽隐杆线虫伤害感受器触压和热敏感觉中的特定作用。
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8
Most "dark matter" transcripts are associated with known genes.大多数“暗物质”转录本与已知基因相关。
PLoS Biol. 2010 May 18;8(5):e1000371. doi: 10.1371/journal.pbio.1000371.
9
Learning "graph-mer" motifs that predict gene expression trajectories in development.学习“graph-mer”基序,以预测发育过程中的基因表达轨迹。
PLoS Comput Biol. 2010 Apr 29;6(4):e1000761. doi: 10.1371/journal.pcbi.1000761.
10
Expression patterns of intronic microRNAs in Caenorhabditis elegans.秀丽隐杆线虫内含子微小RNA的表达模式
Silence. 2010 Feb 1;1(1):5. doi: 10.1186/1758-907X-1-5.

秀丽隐杆线虫基因表达的时空图谱。

A spatial and temporal map of C. elegans gene expression.

机构信息

Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, USA.

出版信息

Genome Res. 2011 Feb;21(2):325-41. doi: 10.1101/gr.114595.110. Epub 2010 Dec 22.

DOI:10.1101/gr.114595.110
PMID:21177967
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3032935/
Abstract

The C. elegans genome has been completely sequenced, and the developmental anatomy of this model organism is described at single-cell resolution. Here we utilize strategies that exploit this precisely defined architecture to link gene expression to cell type. We obtained RNAs from specific cells and from each developmental stage using tissue-specific promoters to mark cells for isolation by FACS or for mRNA extraction by the mRNA-tagging method. We then generated gene expression profiles of more than 30 different cells and developmental stages using tiling arrays. Machine-learning-based analysis detected transcripts corresponding to established gene models and revealed novel transcriptionally active regions (TARs) in noncoding domains that comprise at least 10% of the total C. elegans genome. Our results show that about 75% of transcripts with detectable expression are differentially expressed among developmental stages and across cell types. Examination of known tissue- and cell-specific transcripts validates these data sets and suggests that newly identified TARs may exercise cell-specific functions. Additionally, we used self-organizing maps to define groups of coregulated transcripts and applied regulatory element analysis to identify known transcription factor- and miRNA-binding sites, as well as novel motifs that likely function to control subsets of these genes. By using cell-specific, whole-genome profiling strategies, we have detected a large number of novel transcripts and produced high-resolution gene expression maps that provide a basis for establishing the roles of individual genes in cellular differentiation.

摘要

秀丽隐杆线虫的基因组已经被完全测序,并且该模型生物的发育解剖学已经在单细胞分辨率下进行了描述。在这里,我们利用利用这种精确定义的结构的策略将基因表达与细胞类型联系起来。我们使用组织特异性启动子从特定细胞和每个发育阶段获得 RNA,以通过 FACS 标记细胞进行分离,或通过 mRNA 标记方法提取 mRNA。然后,我们使用 tiling arrays 生成了 30 多种不同细胞和发育阶段的基因表达谱。基于机器学习的分析检测到与已建立的基因模型相对应的转录本,并在非编码区域中揭示了新的转录活性区域 (TARs),这些区域至少占秀丽隐杆线虫基因组的 10%。我们的结果表明,在发育阶段和细胞类型之间,大约 75%的可检测表达转录本存在差异表达。对已知的组织和细胞特异性转录本的检查验证了这些数据集,并表明新鉴定的 TARs 可能发挥细胞特异性功能。此外,我们使用自组织映射来定义共调控转录本组,并应用调控元件分析来识别已知的转录因子和 miRNA 结合位点,以及可能用于控制这些基因子集的新基序。通过使用细胞特异性、全基因组分析策略,我们已经检测到大量新的转录本,并生成了高分辨率的基因表达图谱,为确定单个基因在细胞分化中的作用提供了基础。