Quantitative proteomics using uniform 15N-labeling, mascot and the trans-proteomic pipeline

Stable isotope labeling combined with MS is a powerful method for measuring relative protein abundances, for instance, by differential metabolic labeling of some or all amino acids with 14N and 15N in cell culture or hydroponic media. These and most other types of quantitative proteomics experiments using high-throughput technologies, such as LC-MS/MS, generate large amounts of raw MS data. This data needs to be processed efficiently and automatically, from the mass spectrometer to statistically evaluated protein identifications and abundance ratios. This paper describes in detail an approach to the automated analysis of uniformly 14N/15N-labeled proteins using MASCOT peptide identification in conjunction with the trans-proteomic pipeline (TPP) and a few scripts to integrate the analysis workflow. Two large proteomic datasets from uniformly labeled Arabidopsis thaliana were used to illustrate the analysis pipeline. The pipeline can be fully automated and uses only common or freely available software.

Quantitative proteomics using uniform N-15-labeling, MASCOT, and the trans-proteomic pipeline

Stable isotope labeling combined with MS is a powerful method for measuring relative protein abundances, for instance, by differential metabolic labeling of some or all amino acids with N-14 and N-15 in cell culture or hydroponic media. These and most other types of quantitative proteomics experiments using high-throughput technologies, such as LC-MS/MS, generate large amounts of raw MS data. This data needs to be processed efficiently and automatically, from the mass spectrometer to statistically evaluated protein identifications and abundance ratios. This paper describes in detail an approach to the automated analysis of Uniformly N-14/N-15-labeled proteins using MASCOT peptide identification in conjunction with the trans-proteomic pipeline (TPP) and a few scripts to integrate the analysis workflow. Two large proteomic datasets from uniformly labeled Arabidopsis thaliana were used to illustrate the analysis pipeline. The pipeline can be fully automated and uses only common or freely available software.

Error, reproducibility and sensitivity: a pipeline for data processing of Agilent oligonucleotide expression arrays

Background: Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples. Results: We describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2 of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log(2) units (6 of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators. Conclusions: This study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells.

Transmathematical basis of infinitely scalable pipeline machines

We describe infinitely scalable pipeline machines with perfect parallelism, in the sense that every instruction of an inline program is executed, on successive data, on every clock tick. Programs with shared data effectively execute in less than a clock tick. We show that pipeline machines are faster than single or multi-core, von Neumann machines for sufficiently many program runs of a sufficiently time consuming program. Our pipeline machines exploit the totality of transreal arithmetic and the known waiting time of statically compiled programs to deliver the interesting property that they need no hardware or software exception handling.