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A next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions.
SimExTargId / Simultaneous metabolomic MS1-profiling Experiment and statistically relevant MS2 Target Identification
Permits users to realize autonomous and real-time analysis of metabolomic data. SimExTargId is an open source R package that provides an autonomous workflow that can also calculate data preprocessing in real-time, thereby alerting the user to signal degradation or loss. This method also facilitates real-time monitoring of liquid chromatography-mass spectrometry (LC-MS) data acquisition.
Integrates algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. CAMERA integrates multiple methods for grouping related features, and uses a dynamic rule table for the annotation of ion species. It is designed to post-process XCMS feature lists, and to collect all features related to a compound into a compound spectrum. For this, a set of algorithms has been implemented in CAMERA, such as the fast retention time-based grouping, but also a graph-based algorithm to integrate the peak shape analysis, isotopic information and intensity correlation across samples. The automatic sample selection avoids poor results if compounds have a low intensity (or are absent) in some samples. The ion species annotation uses a dynamic rule set, and a new strategy to combine spectral information from samples measured in positive and negative ion mode.
MSClust / Multi-Seeds based Clustering algorithm
An adaptive multi-seeds based heuristic clustering method that avoids the large memory need for storing seeds and/or distance matrix. MSClust uses a greedy heuristic strategy to build one cluster at a time. Each cluster is expanded from a limited initial set with multi-seeds, where the initial multi-seeds are generated based on an adaptive strategy. Unassigned sequences are then compared to the seeds sequentially. A new sequence is added to the current cluster and removed from the input if the average distance between the sequence and seeds is smaller than the user-defined threshold; otherwise, the sequence is marked as unassigned.
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