Optimisation of Milk Protein Top-Down Sequencing Using In-Source Collision-Induced Dissociation in the Maxis Quadrupole Time-of-Flight Mass Spectrometer.

Top-down sequencing in proteomics has come of age owing to continuous progress in LC-MS. With their high resolution and broad mass range, Quadrupole Time-of-Flight (Q-ToF) hybrid mass spectrometers equipped with electrospray ionisation source and tandem MS capability by collision-induced dissociation (CID) can be employed to analyse intact proteins and retrieve primary sequence information. To our knowledge, top-down proteomics methods with Q-ToF have only been evaluated using samples of relatively low complexity. Furthermore, the in-source CID (IS-CID) capability of Q-ToF instruments has been under-utilised. This study aimed at optimising top-down sequencing of intact milk proteins to achieve the greatest sequence coverage possible from samples of increasing complexity, assessed using nine known proteins. Eleven MS/MS methods varying in their IS-CID and conventional CID parameters were tested on individual and mixed protein standards as well as raw milk samples. Top-down sequencing results from the nine most abundant proteoforms of caseins, alpha-lactalbumin and beta-lactoglubulins were compared. Nine MS/MS methods achieved more than 70% sequence coverage overall to distinguish between allelic proteoforms, varying only by one or two amino acids. The optimal methods utilised IS-CID at low energy. This experiment demonstrates the utility of Q-ToF systems for top-down proteomics and that IS-CID could be more frequently employed.

Case Study III: The Construction of a Nanotoxicity Database - The MOD-ENP-TOX Experience.

The amount of experimental studies on the toxicity of nanomaterials is growing fast. Interpretation and comparison of these studies is a complex issue due to the high amount of variables possibly determining the toxicity of nanomaterials.Qualitative databases providing a structured combination, integration and quality evaluation of the existing data could reveal insights that cannot be seen from different studies alone. A few database initiatives are under development but in practice very little data is publicly available and collaboration between physicists, toxicologists, computer scientists and modellers is needed to further develop databases, standards and analysis tools.In this case study the process of building a database on the in vitro toxicity of amorphous silica nanoparticles (NPs) is described in detail. Experimental data were systematically collected from peer reviewed papers, manually curated and stored in a standardised format. The result is a database in ISA-Tab-Nano including 68 peer reviewed papers on the toxicity of 148 amorphous silica NPs. Both the physicochemical characterization of the particles and their biological effect (described in 230 in vitro assays) were stored in the database. A scoring system was elaborated in order to evaluate the reliability of the stored data.

Qupe--a Rich Internet Application to take a step forward in the analysis of mass spectrometry-based quantitative proteomics experiments.

MOTIVATION: The goal of present -omics sciences is to understand biological systems as a whole in terms of interactions of the individual cellular components. One of the main building blocks in this field of study is proteomics where tandem mass spectrometry (LC-MS/MS) in combination with isotopic labelling techniques provides a common way to obtain a direct insight into regulation at the protein level. Methods to identify and quantify the peptides contained in a sample are well established, and their output usually results in lists of identified proteins and calculated relative abundance values. The next step is to move ahead from these abstract lists and apply statistical inference methods to compare measurements, to identify genes that are significantly up- or down-regulated, or to detect clusters of proteins with similar expression profiles. RESULTS: We introduce the Rich Internet Application (RIA) Qupe providing comprehensive data management and analysis functions for LC-MS/MS experiments. Starting with the import of mass spectra data the system guides the experimenter through the process of protein identification by database search, the calculation of protein abundance ratios, and in particular, the statistical evaluation of the quantification results including multivariate analysis methods such as analysis of variance or hierarchical cluster analysis. While a data model to store these results has been developed, a well-defined programming interface facilitates the integration of novel approaches. A compute cluster is utilized to distribute computationally intensive calculations, and a web service allows to interchange information with other -omics software applications. To demonstrate that Qupe represents a step forward in quantitative proteomics analysis an application study on Corynebacterium glutamicum has been carried out. AVAILABILITY AND IMPLEMENTATION: Qupe is implemented in Java utilizing Hibernate, Echo2, R and the Spring framework. We encourage the usage of the RIA in the sense of the 'software as a service' concept, maintained on our servers and accessible at the following location: http://qupe.cebitec.uni-bielefeld.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An evaluation framework for statistical tests on microarray data.

Microarray analysis has become a popular and routine method in functional genomics. It is typical for such experiments to involve a small number of replicates, which causes unreliable estimates of the sample variance. Microarrays have fostered the development of new statistical methods to analyze data resulting from experiments with small sample sizes. In this study, we tackle the problem of evaluating the performance of statistical tests for generating ranked gene lists from two-channel direct comparisons. We propose an evaluation method based on a oligonucleotide microarray with a large number of replicate spots yielding a maximum of 400 replicates per gene. We apply Spearman's rank correlation coefficient to ranked gene-lists generated by eight widely used microarray specific test statistics, which are applied to small random samples. We could show that variance stabilizing methods such as Cyber-T, SAM, and LIMMA can be beneficial for very small sample sizes and that SAM and the t-test provide stronger control of the type I error rate than the other methods. Specifically, we report that for four replicates all methods reach a high to very high correlation with our reference standard.

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data.

BACKGROUND: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. RESULTS: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. CONCLUSION: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.