SUMMARY: Many factors can influence results in clinical research, in particular bias in the distribution of samples prior to biochemical preparation. Well Plate Maker is a user-friendly application to design single- or multiple-well plate assays. It allows multiple group experiments to be randomized and therefore helps to reduce possible batch effects. Although primarily fathered to optimize the design of clinical sample analysis by high throughput mass spectrometry (e.g. proteomics or metabolomics), it includes multiple options to limit edge-of-plate effects, to incorporate control samples or to limit cross-contamination. It thus fits the constraints of many experimental fields. AVAILABILITY AND IMPLEMENTATION: Well Plate Maker is implemented in R and available at Bioconductor repository (https://bioconductor.org/packages/wpm) under the open source Artistic 2.0 license. In addition to classical scripting, it can be used through a graphical user interface, developed with Shiny technology.

BACKGROUND: The clustering of data produced by liquid chromatography coupled to mass spectrometry analyses (LC-MS data) has recently gained interest to extract meaningful chemical or biological patterns. However, recent instrumental pipelines deliver data which size, dimensionality and expected number of clusters are too large to be processed by classical machine learning algorithms, so that most of the state-of-the-art relies on single pass linkage-based algorithms. RESULTS: We propose a clustering algorithm that solves the powerful but computationally demanding kernel k-means objective function in a scalable way. As a result, it can process LC-MS data in an acceptable time on a multicore machine. To do so, we combine three essential features: a compressive data representation, Nyström approximation and a hierarchical strategy. In addition, we propose new kernels based on optimal transport, which interprets as intuitive similarity measures between chromatographic elution profiles. CONCLUSIONS: Our method, referred to as CHICKN, is evaluated on proteomics data produced in our lab, as well as on benchmark data coming from the literature. From a computational viewpoint, it is particularly efficient on raw LC-MS data. From a data analysis viewpoint, it provides clusters which differ from those resulting from state-of-the-art methods, while achieving similar performances. This highlights the complementarity of differently principle algorithms to extract the best from complex LC-MS data.

MOTIVATION: The proteomics field requires the production and publication of reliable mass spectrometry-based identification and quantification results. Although many tools or algorithms exist, very few consider the importance of combining, in a unique software environment, efficient processing algorithms and a data management system to process and curate hundreds of datasets associated with a single proteomics study. RESULTS: Here, we present Proline, a robust software suite for analysis of MS-based proteomics data, which collects, processes and allows visualization and publication of proteomics datasets. We illustrate its ease of use for various steps in the validation and quantification workflow, its data curation capabilities and its computational efficiency. The DDA label-free quantification workflow efficiency was assessed by comparing results obtained with Proline to those obtained with a widely used software using a spiked-in sample. This assessment demonstrated Proline's ability to provide high quantification accuracy in a user-friendly interface for datasets of any size. AVAILABILITY AND IMPLEMENTATION: Proline is available for Windows and Linux under CECILL open-source license. It can be deployed in client-server mode or in standalone mode at http://proline.profiproteomics.fr/#downloads. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser. AVAILABILITY AND IMPLEMENTATION: DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project (http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages. CONTACT: [email protected], [email protected], [email protected].