A new estimation of protein-level false discovery rate.

BACKGROUND: In mass spectrometry-based proteomics, protein identification is an essential task. Evaluating the statistical significance of the protein identification result is critical to the success of proteomics studies. Controlling the false discovery rate (FDR) is the most common method for assuring the overall quality of the set of identifications. Existing FDR estimation methods either rely on specific assumptions or rely on the two-stage calculation process of first estimating the error rates at the peptide-level, and then combining them somehow at the protein-level. We propose to estimate the FDR in a non-parametric way with less assumptions and to avoid the two-stage calculation process. RESULTS: We propose a new protein-level FDR estimation framework. The framework contains two major components: the Permutation+BH (Benjamini-Hochberg) FDR estimation method and the logistic regression-based null inference method. In Permutation+BH, the null distribution of a sample is generated by searching data against a large number of permuted random protein database and therefore does not rely on specific assumptions. Then, p-values of proteins are calculated from the null distribution and the BH procedure is applied to the p-values to achieve the relationship of the FDR and the number of protein identifications. The Permutation+BH method generates the null distribution by the permutation method, which is inefficient for online identification. The logistic regression model is proposed to infer the null distribution of a new sample based on existing null distributions obtained from the Permutation+BH method. CONCLUSIONS: In our experiment based on three public available datasets, our Permutation+BH method achieves consistently better performance than MAYU, which is chosen as the benchmark FDR calculation method for this study. The null distribution inference result shows that the logistic regression model achieves a reasonable result both in the shape of the null distribution and the corresponding FDR estimation result.

The Pathway Analysis of Micrornas Regulated Drug-Resistant Responses in HeLa Cells.

Chemotherapy is the main strategy in the treatment of cancer; however, the development of drug-resistance is the obstacle in long-term treatment of cervical cancer. Cisplatin is one of the most common drugs used in cancer therapy. Recently, accumulating evidence suggests that miRNAs are involved in various bioactivities in oncogenesis. It is not unexpected that miRNAs play a key role in acquiring of drug-resistance in the progression of tumor. In this study, we induced and maintained four levels of cisplatin-resistant HeLa cell lines (HeLa/CR1, HeLa/CR2, HeLa/CR3, and HeLa/CR4). According to the previous studies and existing evidence, we selected five miRNAs (miR-183, miR-182, miR-30a, miR-15b, and miR-16) and their potential target mRNAs as our research targets. The real-time RT-PCR was adopted to detect the relative expression of miRNAs and their mRNAs. The results show that miR-182 and miR-15b were up-regulated in resistant cell lines, while miR-30a was significantly down-regulated. At the same time, their targets are related to drug resistance. Compared to their parent HeLa cell line, the expression of selected miRNAs in resistant cell lines altered. The alteration suggests that HeLa cell drug resistance is associated with distinct miRNAs, which indicates that miRNAs may be one of the therapy targets in the treatment of cervical cancer by sensitizing cell to chemotherapy. We suggested a possible network diagram based on the existing theory and the preliminary results of candidate miRNAs and their targets in HeLa cells during development of drug resistance.