MR-Tandem: parallel X!Tandem using Hadoop MapReduce on Amazon Web Services.

SUMMARY: MR-Tandem adapts the popular X!Tandem peptide search engine to work with Hadoop MapReduce for reliable parallel execution of large searches. MR-Tandem runs on any Hadoop cluster but offers special support for Amazon Web Services for creating inexpensive on-demand Hadoop clusters, enabling search volumes that might not otherwise be feasible with the compute resources a researcher has at hand. MR-Tandem is designed to drop in wherever X!Tandem is already in use and requires no modification to existing X!Tandem parameter files, and only minimal modification to X!Tandem-based workflows. AVAILABILITY AND IMPLEMENTATION: MR-Tandem is implemented as a lightly modified X!Tandem C++ executable and a Python script that drives Hadoop clusters including Amazon Web Services (AWS) Elastic Map Reduce (EMR), using the modified X!Tandem program as a Hadoop Streaming mapper and reducer. The modified X!Tandem C++ source code is Artistic licensed, supports pluggable scoring, and is available as part of the Sashimi project at http://sashimi.svn.sourceforge.net/viewvc/sashimi/trunk/trans_proteomic_pipeline/extern/xtandem/. The MR-Tandem Python script is Apache licensed and available as part of the Insilicos Cloud Army project at http://ica.svn.sourceforge.net/viewvc/ica/trunk/mr-tandem/. Full documentation and a windows installer that configures MR-Tandem, Python and all necessary packages are available at this same URL. CONTACT: brian.pratt@insilicos.com

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection: analysis of amino acid and organic acid trimethylsilyl derivatives, with application to the analysis of metabolites in rye grass samples.

First, standard mixtures of trimethylsilyl (TMS) derivatives of amino acid and organic acid are analyzed by comprehensive two-dimensional (2D) gas chromatography (GC) coupled to time-of-flight mass spectrometry (GC x GC/TOFMS) in order to illustrate important issues regarding application of the technique. Specifically of interest is the extent to which the peak capacity of the 2D separation space has been utilized and the procedure by which the derivative standards are identified in the 2D separations using the mass spectral information. The resulting 2D separation is found to make extensive use of the GC x GC separation space provided by the complementary stationary phases employed. Second, in order to demonstrate GC x GC/TOFMS on two real sample types, trimethylsilyl metabolite derivatives were analyzed from extracts of common lawn grass samples (i.e., perennial rye grass), as a means to provide insight into both the pre and post harvest physiology. Various chemical components in the two rye grass extract samples were found to either emerge or disappear in relation to the trauma response. For example, a significant difference in the peak for the TMS derivative of malic acid was found. The successful analysis of various components was readily facilitated by the 2D separation, while a 1D separation would have produced too much peak overlap, thus impeding the analysis. The importance of using a GC x GC separation approach for the analysis of complex samples, such as metabolite extracts, is therefore demonstrated. The real-time analysis capability of GC x GC/TOFMS for multidimensional metabolite analysis makes this technique well suited to the high-throughput analysis of metabolomic samples, especially compared to slower, stopped-flow type separation approaches.

Multivariate selectivity as a metric for evaluating comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry subjected to chemometric peak deconvolution.

Two-dimensional gas chromatography (GC x GC) coupled to time-of-flight mass spectrometry (TOFMS) [GC x GC-TOFMS)] is a highly selective technique well suited to analyzing complex mixtures. The data generated is information-rich, making it applicable to multivariate quantitative analysis and pattern recognition. One separation on a GC x GC-TOFMS provides retention times on two chromatographic columns and a complete mass spectrum for each component within the mixture. In this report, we demonstrate how GC x GC-TOFMS combined with trilinear chemometric techniques, specifically parallel factor analysis (PARAFAC) initiated by trilinear decomposition (TLD), results in a powerful analytical methodology for multivariate deconvolution. Using PARAFAC, partially resolved components in complex mixtures can be deconvoluted and identified without requiring a standard data set, signal shape assumptions or any fully selective mass signals. A set of four isomers (iso-butyl, sec-butyl, tert-butyl, and n-butyl benzenes) is used to investigate the practical limitations of PARAFAC for the deconvolution of isomers at varying degrees of chromatographic resolution and mass spectral selectivity. In this report, multivariate selectivity was tested as a metric for evaluating GC x GC-TOFMS data that is subjected to PARAFAC peak deconvolution. It was found that deconvolution results were best with multivariate selectivities over 0.18. Furthermore, the application of GC x GC-TOFMS followed by TLD/PARAFAC is demonstrated for a plant metabolite sample. A region of GC x GC-TOFMS data from a complex natural sample of a derivatized metabolic plant extract from Huilmo (Sisyrinchium striatum) was analyzed using TLD/PARAFAC, demonstrating the utility of this analytical technique on a natural sample containing overlapped analytes without selective ions or peak shape assumptions.

Algorithm for locating analytes of interest based on mass spectral similarity in GC x GC-TOF-MS data: analysis of metabolites in human infant urine.

The developed algorithm reported herein, referred to as "DotMap," addresses the need to rapidly identify analyte peak locations in gas chromatography x gas chromatography-time of flight mass spectrometry (GC x GC-TOF-MS) data. The third-order structure of GC x GC-TOF-MS data is such that at each point in the GC x GC chromatogram, a complete mass spectrum is measured. DotMap utilizes this third-order structure to search for the location of a given spectrum of interest in a complete data set, or in a user selected portion of the complete data set. The algorithm returns a contour plot indicating the location of signal(s) with the most similar mass spectra to the analyte of interest. A spectrum from the region indicated is then subjected to an automated mass spectral search to give immediate feedback on the accuracy of the analysis. This algorithm was investigated with a trimethylsilyl (TMS) derivatized human infant urine sample that contained organic acid metabolites. One hundred percent of 12 selected TMS derivatized organic acid metabolites in human infant urine were located with the DotMap algorithm. A typical automated DotMap analysis takes 30 s on a 1.6 GHz PC with 1024 MB of RAM. Vanillic acid (TMS) was located by DotMap, but also exhibited overlap with other organic acids. The presence of vanillic acid (TMS) was confirmed by subjecting the appropriate GC x GC region to chemometric signal deconvolution by PARAFAC to yield pure component information suitable for subsequent quantification.