Machine learning applications in proteomics research: How the past can boost the future

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.acceptedVersio

An unsupervised machine learning method for assessing quality of tandem mass spectra

<p>Abstract</p> <p>Background</p> <p>In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets.</p> <p>Results</p> <p>This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56 % and 62% of database searching time while losing only a small amount of high-quality spectra.</p> <p>Conclusions</p> <p>Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.</p

A decision theory paradigm for evaluating identifier mapping and filtering methods using data integration.

In bioinformatics, we pre-process raw data into a format ready for answering medical and biological questions. A key step in processing is labeling the measured features with the identities of the molecules purportedly assayed: "molecular identification" (MI). Biological meaning comes from identifying these molecular measurements correctly with actual molecular species. But MI can be incorrect. Identifier filtering (IDF) selects features with more trusted MI, leaving a smaller, but more correct dataset. Identifier mapping (IDM) is needed when an analyst is combining two high-throughput (HT) measurement platforms on the same samples. IDM produces ID pairs, one ID from each platform, where the mapping declares that the two analytes are associated through a causal path, direct or indirect (example: pairing an ID for an mRNA species with an ID for a protein species that is its putative translation). Many competing solutions for IDF and IDM exist. Analysts need a rigorous method for evaluating and comparing all these choices. We describe a paradigm for critically evaluating and comparing IDF and IDM methods, guided by data on biological samples. The requirements are: a large set of biological samples, measurements on those samples from at least two high-throughput platforms, a model family connecting features from the platforms, and an association measure. From these ingredients, one fits a mixture model coupled to a decision framework. We demonstrate this evaluation paradigm in three settings: comparing performance of several bioinformatics resources for IDM between transcripts and proteins, comparing several published microarray probeset IDF methods and their combinations, and selecting optimal quality thresholds for tandem mass spectrometry spectral events. The paradigm outlined here provides a data-grounded approach for evaluating the quality not just of IDM and IDF, but of any pre-processing step or pipeline. The results will help researchers to semantically integrate or filter data optimally, and help bioinformatics database curators to track changes in quality over time and even to troubleshoot causes of MI errors

Computational Analysis of Mass Spectrometric Data for Whole Organism Proteomic Studies

In the last decades great breakthroughs have been achieved in the study of the genomes, supplying us with the vast knowledge of the genes and a large number of sequenced organisms. With the availability of genome information, the new systematic studies have arisen. One of the most prominent areas is proteomics. Proteomics is a discipline devoted to the study of the organism’s expressed protein content. Proteomics studies are concerned with a wide range of problems. Some of the major proteomics focuses upon the studies of protein expression patterns, the detection of protein-protein interactions, protein quantitation, protein localization analysis, and characterization of post-translational modifications. The emergence of proteomics shows great promise to furthering our understanding of the cellular processes and mechanisms of life. One of the main techniques used for high-throughput proteomic studies is mass spectrometry. Capable of detecting masses of biological compounds in complex mixtures, it is currently one of the most powerful methods for protein characterization. New horizons are opening with the new developments of mass spectrometry instrumentation, which can now be applied to a variety of proteomic problems. One of the most popular applications of proteomics involves whole organism high-throughput experiments. However, as new instrumentation is being developed, followed by the design of new experiments, we find ourselves needing new computational algorithms to interpret the results of the experiments. As the thresholds of the current technology are being probed, the new algorithmic designs are beginning to emerge to meet the challenges of the mass spectrometry data evaluation and interpretation. This dissertation is devoted to computational analysis of mass spectrometric data, involving a combination of different topics and techniques to improve our understanding of biological processes using high-throughput whole organism proteomic studies. It consists of the development of new algorithms to improve the data interpretation of the current tools, introducing a new algorithmic approach for post-translational modification detection, and the characterization of a set of computational simulations for biological agent detection in a complex organism background. These studies are designed to further the capabilities of understanding the results of high-throughput mass spectrometric experiments and their impact in the field of proteomics

Enabling Data-Guided Evaluation of Bioinformatics Workflow Quality

Bioinformatics can be divided into two phases, the first phase is conversion of raw data into processed data and the second phase is using processed data to obtain scientific results. It is important to consider the first “workflow” phase carefully, as there are many paths on the way to a final processed dataset. Some workflow paths may be different enough to influence the second phase, thereby, leading to ambiguity in the scientific literature. Workflow evaluation in bioinformatics enables the investigator to carefully plan how to process their data. A system that uses real data to determine the quality of a workflow can be based on the inherent biological relationships in the data itself. To our knowledge, a general software framework that performs real data-driven evaluation of bioinformatics workflows does not exist. The Evaluation and Utility of workFLOW (EUFLOW) decision-theoretic framework, developed and tested on gene expression data, enables users of bioinformatics workflows to evaluate alternative workflow paths using inherent biological relationships. EUFLOW is implemented as an R package to enable users to evaluate workflow data. EUFLOW is a framework which also permits user-guided utility and loss functions, which enables the type of analysis to be considered in the workflow path decision. This framework was originally developed to address the quality of identifier mapping services between UNIPROT accessions and Affymetrix probesets to facilitate integrated analysis1. An extension to this framework evaluates Affymetrix probeset filtering methods on real data from endometrial cancer and TCGA ovarian serous carcinoma samples.2 Further evaluation of RNASeq workflow paths demonstrates generalizability of the EUFLOW framework. Three separate evaluations are performed including: 1) identifier filtering of features with biological attributes, 2) threshold selection parameter choice for low gene count features, and 3) commonly utilized RNASeq data workflow paths on The Cancer Genome Atlas data. The EUFLOW decision-theoretic framework developed and tested in my dissertation enables users of bioinformatics workflows to evaluate alternative workflow paths guided by inherent biological relationships and user utility

Desarrollo de nuevas metodologías informáticas aplicadas a la espectrometría de masas y al análisis masivo de datos generados en proyectos de proteómica utilizando técnicas de segunda generación

Tesis doctoral inédita. Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 12-03-201