PubMed · 15455412
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
Doug Redelman. 2004. CytometryML.. https://doi.org/10.1002/cyto.a.10093
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Only recently fast-paced developments in computer technology allowed for the digitization of complete histologic slides. The resulting virtual slides may be viewed via webbrowser by any number of pathologists or students independent of time and location. Usage of a virtual microscope simply requires a computer workstation with a fast internet connection, which opens this technology to a broad public. A virtual microscopy system consists of three components: acquisition, server and client. Such systems are under development by different commercial and academic bodies worldwide. We have developed a virtual microscope system called vMic (http://www.vmic.unibas.ch) which provides virtual slides of very high image quality. Several successfully held online slide seminars and a histology course for students in dentistry are freely accessible in the internet. With the commercial availability of ultra rapid and easy-to-use slide scanners and the fast improvements of technology virtual microscopy will offer many applications in teaching, research and diagnostics. Thanks to additional functionalities, real microscopes will most likely be replaced by computer workstations in a couple of years.
Reliable identification of posttranslational modifications is key to understanding various cellular regulatory processes. We describe a tool, InsPecT, to identify posttranslational modifications using tandem mass spectrometry data. InsPecT constructs database filters that proved to be very successful in genomics searches. Given an MS/MS spectrum S and a database D, a database filter selects a small fraction of database D that is guaranteed (with high probability) to contain a peptide that produced S. InsPecT uses peptide sequence tags as efficient filters that reduce the size of the database by a few orders of magnitude while retaining the correct peptide with very high probability. In addition to filtering, InsPecT also uses novel algorithms for scoring and validating in the presence of modifications, without explicit enumeration of all variants. InsPecT identifies modified peptides with better or equivalent accuracy than other database search tools while being 2 orders of magnitude faster than SEQUEST, and substantially faster than X!TANDEM on complex mixtures. The tool was used to identify a number of novel modifications in different data sets, including many phosphopeptides in data provided by Alliance for Cellular Signaling that were missed by other tools.