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


RESULTS
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.


CONCLUSIONS
This approach can potentially be applied to any kind of RNA sequencing data for analysing differential sequence representation between biological sample sets.
Witten
Daniela
D
Department of Statistics, Stanford University, Stanford, California 94305-4065, USA.
Tibshirani
Robert
R
Gu
Sam Guoping
SG
Fire
Andrew
A
Lui
Weng-Onn
WO
D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D018076:DNA, Complementary / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D015723:Gene Library; D016014:Linear Models; D035683:MicroRNAs / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*
D018076:DNA, Complementary; D035683:MicroRNAs
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