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Similarity network fusion (SNF) is an approach to integrate multiple data types on the basis of similarity between biological samples rather than individual measurements. The authors demonstrate SNF by constructing patient networks to identify disease subtypes with differential survival profiles.
A statistical method, label-sparse quantification, and software tool, SparseQuant, allows targeted proteins to be quantified by selected reaction monitoring mass spectrometry without requiring a full set of isotope-labeled reference peptides.
A microfluidic platform creates nanochannels from collapsed microchannels for multisecond single-molecule FRET measurements of untethered biomolecules by total-internal-reflection fluorescence microscopy.
The first community competition designed to objectively compare the performance of particle tracking algorithms provides valuable practical information for both users and developers.
EC-BLAST is a Web-based tool allowing quantitative similarity searches between enzymes at the levels of bond-change, reaction-center or reaction-structure similarity. The tool may help improve the annotation of enzyme function.
A collection of improved FRET-based calcium biosensors, called Twitch sensors, is described. Twitches have a reduced number of calcium binding sites per sensor and display high sensitivity in in vivo imaging experiments in mouse brain and lymph node.
A system using the human glycine receptor expressed in Xenopus oocytes allows characterization of the photoactivation efficiency of photoactivatable and photoconvertible fluorescent proteins at the single-molecule level, providing crucial data for using these probes for quantitative super-resolution microscopy.