ABSTRACT
The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describe the incorporation of three new data sets that provide expression, function, protein-protein binary interaction, post-translational modifications (PTM) and variant information. New SPARQL query examples illustrating uses of the new data were added. neXtProt has continued to develop tools for proteomics. We have improved the peptide uniqueness checker and have implemented a new protein digestion tool. Together, these tools make it possible to determine which proteases can be used to identify trypsin-resistant proteins by mass spectrometry. In terms of usability, we have finished revamping our web interface and completely rewritten our API. Our SPARQL endpoint now supports federated queries. All the neXtProt data are available via our user interface, API, SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally, the data on our FTP site is now CC BY 4.0 to promote its reuse.
Subject(s)
Databases, Protein , Knowledge Bases , Humans , Internet , Mass Spectrometry , Peptides/chemistry , Protein Kinases/chemistry , Protein Kinases/metabolism , Protein Processing, Post-Translational , Proteins/chemistry , Proteins/genetics , Proteins/metabolism , Sequence Analysis, RNA , Software , Trypsin , User-Computer InterfaceABSTRACT
BACKGROUND: The brain generates signals in a wide frequency range (â¼2840 Hz). Existing magnetoencephalography (MEG) methods typically detect brain activity in a median-frequency range (1-70 Hz). The objective of the present study was to develop a new method to utilize the frequency signatures for source imaging. NEW METHOD: Morlet wavelet transform and two-step beamforming were integrated into a systematic approach to estimate magnetic sources in time-frequency domains. A grid-frequency kernel (GFK) was developed to decode the correlation between each time-frequency representation and grid voxel. Brain activity was reconstructed by accumulating spatial- and frequency-locked signals in the full spectral data for all grid voxels. To test the new method, MEG data were recorded from 20 healthy subjects and 3 patients with verified epileptic foci. RESULTS: The experimental results showed that the new method could accurately localize brain activation in auditory cortices. The epileptic foci localized with the new method were spatially concordant with invasive recordings. COMPARISON WITH EXISTING METHODS: Compared with well-known existing methods, the new method is objective because it scans the entire brain without making any assumption about the number of sources. The novel feature of the new method is its ability to localize high-frequency sources. CONCLUSIONS: The new method could accurately localize both low- and high-frequency brain activities. The detection of high-frequency MEG signals can open a new avenue in the study of the human brain function as well as a variety of brain disorders.