iMonDB: Mass Spectrometry Quality Control through Instrument Monitoring

Abstract

Over the past few years, awareness has risen that for mass-spectrometry-based proteomics methods to mature into everyday analytical and clinical practices, extensive quality assessment is mandatory. A currently overlooked source of qualitative information originates from the mass spectrometer itself. Apart from the actual mass spectral data, raw-data objects also contain parameter settings and sensory information about the mass instrument. This information gives a detailed account of the operation of the instrument, which eventually can be related to observations in mass spectral data. The advantage of instrument information at the lowest level is the high sensitivity to detect emerging defects in a timely fashion. To this end, we introduce the Instrument MONitoring DataBase (iMonDB), which allows us to automatically extract, store, and manage the instrument parameters from raw-data objects into a highly efficient database structure. This enables us to monitor the instrument parameters over a considerable time period. Time course information about the instrument performance is necessary to define the normal range of operation and to detect anomalies that may correlate with instrument failure. The proposed tools foster an additional handle on quality control and are released as open source under the permissive Apache 2.0 license. The tools can be downloaded from https://bitbucket.org/proteinspector/imondb.

QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories

“…Later, the release of SIMPATIQCO [ 22 ], Metriculator [ 21 ], and others [ 25 ], introduced web-based interfaces, interactive plots and comparison capabilities to assist instrument operators in monitoring quality control metric. Other tools, such as OpenMS [ 26 ], iMonDB [ 27 ] and AutoQC [ 23 ], have also contributed to automate the extraction of quality metrics from raw files and, thus, to generate automatic pipelines for quality control. In parallel, methods for evaluating multivariate quality control metrics have emerged [ 28 – 30 ], and tools from the statistical process control framework have been introduced to evaluate instrument performance and to improve the quality of the process [ 31 ], such as SproCoP [ 32 ] and MsstatsQC [ 33 ].…”

Computational quality control tools for mass spectrometry proteomics

“…Unlike the previous tools the Instrument MONitoring DataBase (iMonDB) does not compute metrics from the spectral results, but extracts instrument metrics from the raw files. The iMonDB uses a MySQL database to store its information.…”

“…To compare different types of metrics we used the set of instrument metrics computed by the iMonDB , the set of ID‐free metrics computed by QuaMeter , and the set of ID‐based metrics as identified by Rudnick et al. .…”

Designing biomedical proteomics experiments: state-of-the-art and future perspectives

“…In particular, one should analyze which assumptions can and cannot be made with respect to the independence of variables, and what the distributions are from which the observations are drawn. The instrument's maintenance status [7], instrument settings, the ambient temperature, and the perfume of choice of the lab technician can all influence the results. Some of those variables may affect the results more strongly when a particular combination of multiple variables occurs -an effect that requires many more sample runs if we wish to be able to discover it rather than the simple main effects of single variables.…”