Smith-Waterman peak alignment for comprehensive two-dimensional gas chromatography-mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful technique which has gained increasing attention over the last two decades. The GC × GC-MS provides much increased separation capacity, chemical selectivity and sensitivity for complex sample analysis and brings more accurate information about compound retention times and mass spectra. Despite these advantages, the retention times of the resolved peaks on the two-dimensional gas chromatographic columns are always shifted due to experimental variations, introducing difficulty in the data processing for metabolomics analysis. Therefore, the retention time variation must be adjusted in order to compare multiple metabolic profiles obtained from different conditions.</p> <p>Results</p> <p>We developed novel peak alignment algorithms for both homogeneous (acquired under the identical experimental conditions) and heterogeneous (acquired under the different experimental conditions) GC × GC-MS data using modified Smith-Waterman local alignment algorithms along with mass spectral similarity. Compared with literature reported algorithms, the proposed algorithms eliminated the detection of landmark peaks and the usage of retention time transformation. Furthermore, an automated peak alignment software package was established by implementing a likelihood function for optimal peak alignment.</p> <p>Conclusions</p> <p>The proposed Smith-Waterman local alignment-based algorithms are capable of aligning both the homogeneous and heterogeneous data of multiple GC × GC-MS experiments without the transformation of retention times and the selection of landmark peaks. An optimal version of the SW-based algorithms was also established based on the associated likelihood function for the automatic peak alignment. The proposed alignment algorithms outperform the literature reported alignment method by analyzing the experiment data of a mixture of compound standards and a metabolite extract of mouse plasma with spiked-in compound standards.</p

Automating Data Analysis for Two-Dimensional Gas Chromatography/Time-of-Flight Mass Spectrometry Non-Targeted Analysis of Comparative Samples

Non-targeted analysis of environmental samples, using comprehensive two‐dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO® ChromaTOF® software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post‐bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post‐bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO® ChromaTOF® software Statistical Compare feature. Based on this list, 63–100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold

Interpretation of comprehensive two-dimensional gas chromatography data using advanced chemometrics

The power of comprehensive two-dimensional gas chromatography (GC × GC) for the study of complex mixtures has been indisputably proved in the past several decades. This review encompasses the whole of GC × GC-related data processing and summarizes relevant applications. We include theoretical introduction to some specific methods and studies to aid readers&#039; understanding of chemometrics strategies for advanced data interpretation

Entwicklung einer flexiblen bioinformatischen Plattform zur Analyse von Massenspektrometriedaten

Sowohl in der Klinischen Labormedizin, der Klinischen Mikrobiologie als auch in der Pathologie ist die Massenspektrometrie (MS) ein bedeutender Bestandteil der Diagnostik geworden. Der Fortschritt in der Gerätetechnik ermöglicht in kurzer Zeit viele, hochaufgelöste Spektren zu generieren. Diese Informationsvielfalt macht die manuelle Auswertung durch den Anwender sehr kompliziert bis unmöglich. Aus diesem Grund ist die Unterstützung durch bioinformatische Programme notwendig. Für die Reproduzierbarkeit der Ergebnisse und die Qualitätskontrolle ist es essentiell, dass die verwendeten Algorithmen transparent und die Programme als Open Source Software (OSS) frei verfügbar sind (Aebersold and Mann, 2003). Das Ziel dieser Arbeit war die Entwicklung von MALDIquant, einer unter der GNU General Public License (GPL) stehenden, flexiblen OSS, die für die o.g. Anwendungsbereiche modernste Algorithmen für die komplette Analyse bietet und in der freien Programmiersprache R (R Core Team, 2014) geschrieben ist. Im Zusammenspiel mit dem dazugehörigen Paket MALDIquantForeign ist MALDIquant in der Lage die üblichen Dateiformate der verschiedenen MS-Geräte zu verarbeiten. Dadurch ist MALDIquant hersteller- und geräteunabhängig und eignet sich nicht nur für MALDI/TOF, sondern für alle zweidimensionalen MS-Daten. Angefangen vom Datenimport über die Prozessierung bis hin zur Analyse der Spektren bietet MALDIquant eine komplette Analyse-Pipeline und implementiert state-of-the-art Methoden. Neben weit verbreiteten Verfahren zur Baseline Correction und Peak Detection zeichnet sich MALDIquant besonders durch ein hervorragendes Peak Alignment aus. Dieses ist sehr genau und aufgrund des Fokus auf die Peaks schneller als die meisten anderen Verfahren und weitestgehend unabhängig von der Qualität der Intensitätenkalibrierung. Eine weitere Stärke von MALDIquant ist die Möglichkeit, eigene Algorithmen zu integrieren, sowie den Ablauf der Analyse den individuellen Bedürfnissen anzupassen. In der beispielhaften Analyse der Daten von Fiedler et al. (2009) konnten durch MALDIquant Peaks gefunden werden, die Patienten mit Pankreaskarzinom von nicht erkrankten Probanden unterscheiden. Einige dieser Peaks wurden bereits in anderen Publikationen beschrieben. Neben diesem Beispiel hat MALDIquant seine Nützlichkeit bereits in verschiedenen Anwendungsbereichen und Publikationen bewiesen, wie etwa in Ouedraogo et al. (2013) oder Jung et al. (2014).:Bibliographische Beschreibung (III) Abbildungsverzeichnis (V) Tabellenverzeichnis (VII) Abkürzungsverzeichnis (IX) 1 Einleitung (1) 1.1 Intention (1) 1.2 Eigene Beiträge (2) 1.3 Übersicht (3) 2 Hintergrund (5) 2.1 Proteomik (5) 2.2 Massenspektrometrie (6) 2.3 Bioinformatik (7) 3 Methoden (9) 3.1 Überblick (9) 3.2 Import der Rohdaten (9) 3.3 Transformation der Intensitäten (11) 3.4 Korrektur der Grundlinie (11) 3.5 Kalibrierung der Intensitäten (13) 3.6 Identifizierung von Merkmalen (15) 3.7 Kalibrierung der m/z-Werte (17) 3.8 Nachbearbeitung (19) 4 Ergebnisse (23) 4.1 Implementierung (23) 4.2 Anwendungsbeispiel Fiedler et al. 2009 (23) 4.3 Vorbehandlung der Daten aus Fiedler et al. 2009 mit MALDIquant (24) 4.4 Multivariate Analyse (24) 4.5 Mögliche Biomarker (26) 5 Diskussion (29) 6 Zusammenfassung (31) 7 Literaturverzeichnis (35) A Publikation (45) B Übersicht Codeumfang (49) C Analyse Fiedler et al. 2009 (51) D Erklärung über die eigenständige Abfassung der Arbeit (69) E Lebenslauf (71) F Danksagung (75

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Optical and sensing properties of various shaped gold nanoplates and highly controlled asymmetric gold nanoplate/nanosphere coupled assemblies.

With the development of a strategy to correlate the dark-field light scattering spectra of individual nanostructures with scanning electron microscopy (SEM) and atomic force microscopy (AFM) images of the same nanostructures, we were able to investigate several interesting optical properties of Au nanoplates (NPs) and asymmetrically-coupled Au nanospheres (NSs) attached to Au NPs with a high level of control. The light scattering spectra of the NP/NS coupled structures depend strongly on the location of NS attachment on the NP. Attachment of multiple NSs at the edge/vertex sites leads to a unique synergistic effect. In contrast to the uniform distribution of NSs, asymmetric distributions of multiple NSs attached to the sides of a NP result in complex, broadened, multi-peaked spectra with larger plasmonic shifts. Simulations using the discrete dipole approximation (DDA) method verified all of the experimental results. The positive shift in the dipolar plasmon mode of the NP/NS assembly relative to the original NP increases with increasing NS size for those attached on the side of the NP in the order of 9±2 nm, 24±4 nm, and 98±16 nm for the 13, 24, and 51 nm average diameter NSs, respectively. For a NS attached to the top terrace of a NP, the shift in the dipolar plasmon mode is 1±1 nm, 3±1 nm, and 14±4 nm for the 13, 24, and 51 nm NS, respectively, and the spectra become more broad. The attachment of a Au NS to a hexagonal or circular Au NP through a cysteamine (Cys) linker shows different light scattering properties compared to attachment through 4-aminothiophenol (4-ATP). The shorter length of Cys leads to stronger dipolar plasmon coupling along the long axis of the NP/NS structure. This leads to a larger red-shift compared to linking with 4-ATP. The geometric shape of the NPs dramatically affects their sensitivity to refractive index changes in the environment and sensitivity to the attachment of a Au NS. The sensitivity of λmax to a change in the refractive index of the environment followed the order of triangles \u3e hexagons \u3e circles. This research provides new fundamental information and a better understanding of shape-dependent optical properties and plasmon coupling of asymmetric metallic nanostructures with potential use in three-dimensional spatial sensing and other plasmonic applications