Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls.
Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls.
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DOI：
10.1186/1741-7007-8-58
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发表时间：
2010-05-11
期刊：
影响因子：
5.4
通讯作者：
Lui WO
中科院分区：
文献类型：
--
作者：
Witten D;Tibshirani R;Gu SG;Fire A;Lui WO
超高通量测序技术为发现新分子种类和详细比较基因表达模式提供了机会。小 RNA 群体特别适合这种分析，因为许多不同的小 RNA 可以在一次仪器运行中完全测序。我们从 29 对肿瘤/正常人宫颈组织样本中制备了小 RNA 文库。对所得序列（总共 4200 万个）的分析确定了 64 个新的人类 microRNA (miRNA) 基因。在 23 个新鉴定的 miRNA 候选物中观察到了发夹前体的两个臂。我们测试了几种用于分析高通量测序数据集之间的类别差异的计算方法，并描述了对数线性模型的新颖应用，该模型为该数据提供了最有效的分析。该方法以小于 0.001 的错误发现率鉴定了 67 个在肿瘤和正常样本之间差异表达的 miRNA。这种方法可以潜在地应用于任何类型的 RNA 测序数据，以分析生物样本集之间的差异序列表示。
Ultra-high throughput sequencing technologies provide opportunities both for discovery of novel molecular species and for detailed comparisons of gene expression patterns. Small RNA populations are particularly well suited to this analysis, as many different small RNAs can be completely sequenced in a single instrument run. We prepared small RNA libraries from 29 tumour/normal pairs of human cervical tissue samples. Analysis of the resulting sequences (42 million in total) defined 64 new human microRNA (miRNA) genes. Both arms of the hairpin precursor were observed in twenty-three of the newly identified miRNA candidates. We tested several computational approaches for the analysis of class differences between high throughput sequencing datasets and describe a novel application of a log linear model that has provided the most effective analysis for this data. This method resulted in the identification of 67 miRNAs that were differentially-expressed between the tumour and normal samples at a false discovery rate less than 0.001. This approach can potentially be applied to any kind of RNA sequencing data for analysing differential sequence representation between biological sample sets.
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影响因子：
11.2
作者：
Bracken, Cameron P.;Gregory, Philip A.;Goodall, Gregory J.
通讯作者：
Goodall, Gregory J.
影响因子：
11.2
作者：
Iorio, MV;Ferracin, M;Croce, CM
通讯作者：
Croce, CM
影响因子：
12.3
作者：
Blow MJ;Grocock RJ;van Dongen S;Enright AJ;Dicks E;Futreal PA;Wooster R;Stratton MR
通讯作者：
Stratton MR
影响因子：
8.8
作者：
Chen, H-C;Chen, G-H;Chen, Y-H;Liao, W-L;Liu, C-Y;Chang, K-P;Chang, Y-S;Chen, S-J
通讯作者：
Chen, S-J
DOI：
10.1073/pnas.0800334105
发表时间：
2008-06-10
影响因子：
11.1
作者：
Azuma-Mukai, Asuka;Oguri, Hideo;Siomi, Mikiko C.
通讯作者：
Siomi, Mikiko C.