EXQN_KU
Identification and analysis of miRNA in human breast cancer and teratoma samples using deep sequencing
We developed a pipeline combining exact and probabilistic methods to analyse 454 small RNA data for the purpose of identifying putative new miRNAs. This task can be divided into two objectives: finding and quantifying expressed genomic regions giving rise to small RNA reads, and scoring these regions as potential new miRNAs. Our approach to the first part of this problem was to retain as much sequence information as possible, despite possible sequencing errors and redundant mapping, thus increasing the amount of available data. For the second objective, we trained a Support Vector Machine (SVM) for reliable classification of potential miRNAs. The pipeline was used to analyze deep sequencing data generated from four different human tissue samples: Breast cancer, normal adjacent breast, and two teratoma cell lines. We chose to analyze breast cancer associated miRNAs, as these represent an important case for finding miRNA based biomarkers for cancer diagnosis. The discovery of novel miRNAs, as well as understanding the expression of already known miRNAs in these tissues, is therefore of medical interest. The two teratoma cell lines were included in the analysis with the aim of identifying novel miRNAs. Given that teratoma can develop into many different tissue types, we hypothesized that these samples could potentially express different miRNAs than normal samples and thus be a good source of new miRNAs.
Background: MiRNAs play important roles in cellular control and in various disease states such as cancers, where they may serve as markers or possibly even therapeutics. Identifying the whole repertoire of miRNAs and understanding their expression patterns is therefore an important goal. Results: Here we describe the analysis of 454 pyrosequencing of small RNA from four different tissues: Breast cancer, normal adjacent breast, and two teratoma cell lines. We developed a pipeline for identifying new miRNAs, emphasizing extracting and retaining as much data as possible from even noisy sequencing data. We investigated differential expression of miRNAs in the breast cancer and normal adjacent breast samples, and systematically examined the mature sequence end variability of miRNA compared to non-miRNA loci. Conclusions: We identified five novel miRNAs, as well as two putative alternative precursors for known miRNAs. Several miRNAs were differentially expressed between the breast cancer and normal breast samples. The end variability was shown to be significantly different between miRNA and non-miRNA loci, suggesting that this may in the future be incorporated as a discriminatory feature for miRNA discovery.