Search

A new algorithm for the calculation of the isotopic distribution is described here. The extreme levels of detail, the coarser structures (~1 Da) and the sharper irregular details (~millidaltons), get split in separate dimensions. Consequently, dense sampling can be concentrated in the close informative surrounding of the isotopic peaks. The one-dimensional isotopic distribution is reconstructed by allotting the abundances along the mass axis. Performance is evaluated with small and relatively large compounds (300 Da to 90 kDa) of diverse composition including challenging polyisotopic elements. The superiority over well-established methods in terms of accuracy, speed, and memory resources is clearly demonstrated for high and ultrahigh resolution ranges by a program implementing the algorithm. 10.1021/ac101039x

Fine isotopic structure patterns resolvable by ultrahigh-resolution mass spectrometers are diagnostic of the elemental composition of moderately large compounds. Despite the proven performance of Fourier transforms algorithms to calculate accurate high resolution isotopic distribution, its application to finer ultrahigh resolving power exhibits limited performance. Fast Fourier transforms algorithm requires sampling the relevant range at equally spaced mass values, but ultrahigh resolution mass spectrum displays highly localized complex patterns (peaks) separated in between by relatively large unstructured intervals. Computational efforts consumed on those uninformative intervals are a waste of resources. A fast and memory efficient procedure is introduced in this paper to calculate the isotopic distribution of a single relatively high-mass molecule at ultrahigh resolution by Fourier transforms approaches. The whole isotopic distribution is packed closer to the monoisotopic peak without distorting the actual scale of the peak fine structure. This packing procedure reduced 8 to 32 times the computation resources in comparison to the same calculation performed without packing. The procedure can be readily implemented in existing software. 10.1021/ac9023523

Abstract Background The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics. Results BisoGenet is a multi-tier application for visualization and analysis of biomolecular relationships. The system consists of three tiers. In the data tier, an in-house database stores genomics information, protein-protein interactions, protein-DNA interactions, gene ontology and metabolic pathways. In the middle tier, a global network is created at server startup, representing the whole data on bioentities and their relationships retrieved from the database. The client tier is a Cytoscape plugin, which manages user input, communication with the Web Service, visualization and analysis of the resulting network. Conclusion BisoGenet is able to build and visualize biological networks in a fast and user-friendly manner. A feature of Bisogenet is the possibility to include coding relations to distinguish between genes and their products. This feature could be instrumental to achieve a finer grain representation of the bioentities and their relationships. The client application includes network analysis tools and interactive network expansion capabilities. In addition, an option is provided to allow other networks to be converted to BisoGenet. This feature facilitates the integration of our software with other tools available in the Cytoscape platform. BisoGenet is available at http://bio.cigb.edu.cu/bisogenet-cytoscape/.

18O-labeling of peptides is a technique widely and routinely applied in the protein chemistry laboratories. The rate of 18O incorporation at the carboxyl terminus of peptides via enzymecatalyzed oxygen exchange fluctuates from peptide to peptide. This fluctuation is mostly attributed to enzyme-substrate different affinity. The final distributions of the 18Oo, 18O1, and 18O2-tagged peptides remain unpredictable though usually constrained to binomial proportions. It is proved here that this constraint can sometimes be a poor model. A more general model is then derived which predicts linear paths for digestion in H2 18O-enriched water while confining binomial proportions to postdlgestion labeling. born subderived models are simple in structure and relevant for the current software development in the analysis of quantitative shotgun proteomics data. Accuracy and time dependency are examined and compared with actual labeled-digest data. [ABSTRACT FROM AUTHOR]

Background: The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics. Results: BisoGenet is a multi-tier application for visualization and analysis of biomolecular relationships. The system consists of three tiers. In the data tier, an in-house database stores genomics information, protein-protein interactions, protein-DNA interactions, gene ontology and metabolic pathways. In the middle tier, a global network is created at server startup, representing the whole data on bioentities and their relationships retrieved from the database. The client tier is a Cytoscape plugin, which manages user input, communication with the Web Service, visualization and analysis of the resulting network. Conclusion: BisoGenet is able to build and visualize biological networks in a fast and user-friendly manner. A feature of Bisogenet is the possibility to include coding relations to distinguish between genes and their products. This feature could be instrumental to achieve a finer grain representation of the bioentities and their relationships. The client application includes network analysis tools and interactive network expansion capabilities. In addition, an option is provided to allow other networks to be converted to BisoGenet. This feature facilitates the integration of our software with other tools available in the Cytoscape platform. BisoGenet is available at http://bio.cigb.edu.cu/ bisogenet-cytoscape/. [ABSTRACT FROM AUTHOR]

(18)O-labeling of peptides is a technique widely and routinely applied in the protein chemistry laboratories. The rate of (18)O incorporation at the carboxyl terminus of peptides via enzyme-catalyzed oxygen exchange fluctuates from peptide to peptide. This fluctuation is mostly attributed to enzyme-substrate different affinity. The final distributions of the (18)O(0)-, (18)O(1)-, and (18)O(2)-tagged peptides remain unpredictable though usually constrained to binomial proportions. It is proved here that this constraint can sometimes be a poor model. A more general model is then derived which predicts linear paths for digestion in H(2)(18)O-enriched water while confining binomial proportions to postdigestion labeling. Both subderived models are simple in structure and relevant for the current software development in the analysis of quantitative shotgun proteomics data. Accuracy and time dependency are examined and compared with actual labeled-digest data., (© 2011 American Chemical Society)