Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from http://seamass.net/viz/

Pre-processing of tandem mass spectra using machine learning methods

Protein identification has been more helpful than before in the diagnosis and treatment of many diseases, such as cancer, heart disease and HIV. Tandem mass spectrometry is a powerful tool for protein identification. In a typical experiment, proteins are broken into small amino acid oligomers called peptides. By determining the amino acid sequence of several peptides of a protein, its whole amino acid sequence can be inferred. Therefore, peptide identification is the first step and a central issue for protein identification. Tandem mass spectrometers can produce a large number of tandem mass spectra which are used for peptide identification. Two issues should be addressed to improve the performance of current peptide identification algorithms. Firstly, nearly all spectra are noise-contaminated. As a result, the accuracy of peptide identification algorithms may suffer from the noise in spectra. Secondly, the majority of spectra are not identifiable because they are of too poor quality. Therefore, much time is wasted attempting to identify these unidentifiable spectra. The goal of this research is to design spectrum pre-processing algorithms to both speedup and improve the reliability of peptide identification from tandem mass spectra. Firstly, as a tandem mass spectrum is a one dimensional signal consisting of dozens to hundreds of peaks, and majority of peaks are noisy peaks, a spectrum denoising algorithm is proposed to remove most noisy peaks of spectra. Experimental results show that our denoising algorithm can remove about 69% of peaks which are potential noisy peaks among a spectrum. At the same time, the number of spectra that can be identified by Mascot algorithm increases by 31% and 14% for two tandem mass spectrum datasets. Next, a two-stage recursive feature elimination based on support vector machines (SVM-RFE) and a sparse logistic regression method are proposed to select the most relevant features to describe the quality of tandem mass spectra. Our methods can effectively select the most relevant features in terms of performance of classifiers trained with the different number of features. Thirdly, both supervised and unsupervised machine learning methods are used for the quality assessment of tandem mass spectra. A supervised classifier, (a support vector machine) can be trained to remove more than 90% of poor quality spectra without removing more than 10% of high quality spectra. Clustering methods such as model-based clustering are also used for quality assessment to cancel the need for a labeled training dataset and show promising results

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Optimized data processing algorithms for biomarker discovery by LC-MS

This thesis reports techniques and optimization of algorithms to analyse label-free LC-MS data sets for clinical proteomics studies with an emphasis on time alignment algorithms and feature selection methods. The presented work is intended to support ongoing medical and biomarker research. The thesis starts with a review of important steps in a data processing pipeline of label-free Liquid Chromatography – Mass Spectrometry (LC-MS) data. The first part of the thesis discusses an optimization strategy for aligning complex LC-MS chromatograms. It explains the combination of time alignment algorithms (Correlation Optimized Warping, Parametric Time Warping and Dynamic Time Warping) with a Component Detection Algorithm to overcome limitations of the original methods that use Total Ion Chromatograms when applied to highly complex data. A novel reference selection method to facilitate the pre-alignment process and an approach to globally compare the quality of time alignment using overlapping peak area are introduced and used in the study. The second part of this thesis highlights an ongoing challenge faced in the field of biomarker discovery where improvements in instrument resolution coupled with low sample numbers has led to a large discrepancy between the number of measurements and the number of measured variables. A comparative study of various commonly used feature selection methods for tackling this problem is presented. These methods are applied to spiked urine data sets with variable sample size and class separation to mimic typical conditions of biomarker research. Finally, the summary and the remaining challenges in the data processing field are summarized at the end of this thesis.

Statistical Methods in Metabolomics

Metabolomics lies at the fulcrum of the system biology ‘omics’. Metabolic profiling offers researchers new insight into genetic and environmental interactions, responses to pathophysi- ological stimuli and novel biomarker discovery. Metabolomics lacks the simplicity of a single data capturing technique; instead, increasingly sophisticated multivariate statistical techniques are required to tease out useful metabolic features from various complex datasets. In this work, two major metabolomics methods are examined: Nuclear Magnetic Resonance (NMR) Spec- troscopy and Liquid Chromatography-Mass Spectrometry (LC-MS). MetAssimulo, an 1H-NMR metabolic-profile simulator, was developed in part by this author and is described in the Chap- ter 2. Peak positional variation is a phenomenon occurring in NMR spectra that complicates metabolomic analysis so Chapter 3 focuses on modelling the effect of pH on peak position. Analysis of LC-MS data is somewhat more complex given its 2-D structure, so I review existing pre-processing and feature detection techniques in Chapter 4 and then attempt to tackle the issue from a Bayesian viewpoint. A Bayesian Partition Model is developed to distinguish chro- matographic peaks representing useful features from chemical and instrumental interference and noise. Another of the LC-MS pre-processing problems, data binning, is also explored as part of H-MS: a pre-processing algorithm incorporating wavelet smoothing and novel Gaussian and Exponentially Modified Gaussian peak detection. The performance of H-MS is compared alongside two existing pre-processing packages: apLC-MS and XCMS.Open Acces