Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls

BMC Biology

Research Article

Sam Guoping Gu¹  Andrew Fire²  Weng-Onn Lui³  Daniela Witten⁴  Robert Tibshirani⁵ 

[1] Department of Pathology, Stanford University School of Medicine, 94305-5324, Stanford, California, USA;Department of Pathology, Stanford University School of Medicine, 94305-5324, Stanford, California, USA;Department of Genetics, Stanford University School of Medicine, 94305-5324, Stanford, California, USA;Department of Pathology, Stanford University School of Medicine, 94305-5324, Stanford, California, USA;Department of Molecular Medicine and Surgery, Karolinska University Hospital-Solna, 17176, Stockholm, Sweden;Department of Statistics, Stanford University, 94305-4065, Stanford, California, USA;Department of Statistics, Stanford University, 94305-4065, Stanford, California, USA;Department of Health Research and Policy, Stanford University, 94305-5405, Stanford, California, USA;

关键词: Cervical Cancer;    miRNA Gene;    miRNA Cluster;    Cervical Cancer Tissue;    Hairpin Precursor;   

DOI  :  10.1186/1741-7007-8-58

 received in 2010-04-21, accepted in 2010-05-11,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundUltra-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.ResultsWe 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.ConclusionsThis approach can potentially be applied to any kind of RNA sequencing data for analysing differential sequence representation between biological sample sets.

【 授权许可】

Unknown   
© Witten et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.