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Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data.

ABSTRACT: High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the 5 alternative methods, also evaluated by their capacity to retain meaningful features of biologic samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.

SUBMITTER: Borisov N

PROVIDER: S-EPMC5628641 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data.

Borisov Nicolas N Suntsova Maria M Sorokin Maxim M Garazha Andrew A Kovalchuk Olga O Aliper Alexander A Ilnitskaya Elena E Lezhnina Ksenia K Korzinkin Mikhail M Tkachev Victor V Saenko Vyacheslav V Saenko Yury Y Sokov Dmitry G DG Gaifullin Nurshat M NM Kashintsev Kirill K Shirokorad Valery V Shabalina Irina I Zhavoronkov Alex A Mishra Bhubaneswar B Cantor Charles R CR Buzdin Anton A

Cell cycle (Georgetown, Tex.) 20170821 19

High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherw ...[more]

PMID: 28825872

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Project description:The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline, it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organism-specific composite data sets.

Dataset Information

Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data.

Publications

Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data.

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