This framework is open-source and designed for mass spectrometry purposes.
If you're looking to manage and analyze LC/MS data, then check out OpenMS - a C++ library that's both free and open-source. One of the things that sets OpenMS apart is its excellent infrastructure for developing mass spectrometry software. OpenMS is packed with features designed to streamline the analysis of protein separation and mass spectrometry data.
One of the key aspects of signal processing is peak detection. OpenMS offers a Wavelet-based scheme that handles the unique challenges posed by applications in proteomics. Raw data can also be preprocessed with baseline reduction and noise filtering.
Peak picking is just the beginning - OpenMS's feature finding functionality further reduces the data by determining the chemical entities each peak belongs to, such as a peptide charge variant. These features can then be used for label-free and isotope-labeled quantitation.
OpenMS is also excellent for visualization. HPLC-MS data can be displayed in a variety of different views, from 2D to 3D. The images on the right showcase TOPPView, which is a MS data viewer included with TOPP.
Map mapping is another critical feature of OpenMS. It allows users to align several HPLC-MS runs to account for issues in chromatography, and determine combinatorial matching and extract groups of corresponding features for differential analysis.
And of course, in proteomics research, identification is key. Like most high-end proteomics software, OpenMS can read and write data formats for identification engines like Sequest, Mascot, and X!Tandem.
Finally, OpenMS also offers database support through the Qt SQL module, allowing users to analyze and store annotated and analyzed data easily. OpenMS is a fantastic option for anyone looking for a robust, open-source suite of proteomic data analysis tools.
One of the key aspects of signal processing is peak detection. OpenMS offers a Wavelet-based scheme that handles the unique challenges posed by applications in proteomics. Raw data can also be preprocessed with baseline reduction and noise filtering.
Peak picking is just the beginning - OpenMS's feature finding functionality further reduces the data by determining the chemical entities each peak belongs to, such as a peptide charge variant. These features can then be used for label-free and isotope-labeled quantitation.
OpenMS is also excellent for visualization. HPLC-MS data can be displayed in a variety of different views, from 2D to 3D. The images on the right showcase TOPPView, which is a MS data viewer included with TOPP.
Map mapping is another critical feature of OpenMS. It allows users to align several HPLC-MS runs to account for issues in chromatography, and determine combinatorial matching and extract groups of corresponding features for differential analysis.
And of course, in proteomics research, identification is key. Like most high-end proteomics software, OpenMS can read and write data formats for identification engines like Sequest, Mascot, and X!Tandem.
Finally, OpenMS also offers database support through the Qt SQL module, allowing users to analyze and store annotated and analyzed data easily. OpenMS is a fantastic option for anyone looking for a robust, open-source suite of proteomic data analysis tools.
What's New
Version 1.4.0 Build 1: N/A