Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics

<p>Abstract</p> <p>Background</p> <p>High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data.</p> <p>Results</p> <p>We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a <it>p</it>-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly.</p> <p>Conclusion</p> <p>The proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a <it>p</it>-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.</p

BASECOL2023 scientific content

Context. The global context of making numerous data produced by researchers available requires collecting and organising the data, assigning meaningful metadata, and presenting the data in a meaningful and homogeneous way. The BASECOL database, which collects inelastic rate coefficients for application to the interstellar medium and to circumstellar and cometary atmospheres, meets those requirements. Aims. We aim to present the scientific content of the BASECOL2023 edition. Methods. While the previous versions relied on finding rate coefficients in the literature, the current version is populated with published results sent by the producers of data. The paper presents the database, the type of data that can be found, the type of metadata that are used, and the Virtual Atomic and Molecular Data Centre (VAMDC) standards that are used for the metadata. Finally, we present the different datasets species by species. Results. As the BASECOL database, interconnected with the VAMDC e-infrastructure, uses the VAMDC standards, the collisional data can be extracted with tools using VAMDC standards and can be associated with spectroscopic data extracted from other VAMDC connected databases such as the Cologne database for molecular spectroscopy (CDMS), the jet propulsion laboratory molecular spectroscopy database (JPL), and the high-resolution transmission molecular absorption database (HITRAN)

Measuring persistence of implementation: QUERI Series

As more quality improvement programs are implemented to achieve gains in performance, the need to evaluate their lasting effects has become increasingly evident. However, such long-term follow-up evaluations are scarce in healthcare implementation science, being largely relegated to the "need for further research" section of most project write-ups. This article explores the variety of conceptualizations of implementation sustainability, as well as behavioral and organizational factors that influence the maintenance of gains. It highlights the finer points of design considerations and draws on our own experiences with measuring sustainability, framed within the rich theoretical and empirical contributions of others. In addition, recommendations are made for designing sustainability analyses