Motivation: Glycosylation is one of the most heterogeneous and complex protein post-translational modifications. Liquid chromatography coupled mass spectrometry (LC-MS) is a common high throughput method for analyzing complex biological samples. Accurate study of glycans require high resolution mass spectrometry. Mass spectrometry data contains intricate sub-structures that encode mass and abundance, requiring several transformations before it can be used to identify biological molecules, requiring automated tools to analyze samples in a high throughput setting. Existing tools for interpreting the resulting data do not take into account related glycans when evaluating individual observations, limiting their sensitivity. Results: We developed an algorithm for assigning glycan compositions from LC-MS data by exploring biosynthetic network relationships among glycans. Our algorithm optimizes a set of likelihood scoring functions based on glycan chemical properties but uses network Laplacian regularization and optionally prior information about expected glycan families to smooth the likelihood and thus achieve a consistent and more representative solution. Our method was able to identify as many, or more glycan compositions compared to previous approaches, and demonstrated greater sensitivity with regularization. Our network definition was tailored to N-glycans but the method may be applied to glycomics data from other glycan families like O-glycans or heparan sulfate where the relationships between compositions can be expressed as a graph. Availability and implementation Built Executable: http://www.bumc.bu.edu/msr/glycresoft/ and Source Code: https://github.com/BostonUniversityCBMS/glycresoft. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: In the new release of pymzML (v2.0), we have optimized the speed of this established tool for mass spectrometry data analysis to adapt to increasing amounts of data in mass spectrometry. Thus, we integrated faster libraries for numerical calculations, improved data retrieving algorithms and have optimized the source code. Importantly, to adapt to rapidly growing file sizes, we developed a generalizable compression scheme for very fast random access and applied this concept to mzML files to retrieve spectral data. Results: pymzML performs at par with established C programs when it comes to processing times. However, it offers the versatility of a scripting language, while adding unprecedented fast random access to compressed files. Additionally, we designed our compression scheme in such a general way that it can be applied to any field where fast random access to large data blocks in compressed files is desired. Availability and implementation: pymzML is freely available on https://github.com/pymzML/pymzML under GPL license. pymzML requires Python3.4+ and optionally numpy. Documentation available on http://pymzml.readthedocs.io.