AlleleID: a pathogen detection and identification system.

Efficient clinical diagnosis of pathogens is important for the management of infectious diseases. Conventional methods have longer turnaround time and, in most cases, lower sensitivity. Nucleic acid-based methods for the detection of microorganisms are rapid, sensitive and are generally successful even when the culturing of microorganisms fails. Sequence-based molecular methods such as real-time PCR, microarrays, and band biosensors provide high sensitivity, rapid diagnostics, and higher specificity allowing differentiation between related strains. Although numerous chemistries are available for the molecular level identification of pathogens, the most common are qPCR and DNA microarrays. Both of these techniques have a high accuracy when used with specific primers and probes. Manual design of these primer and probes is both tedious and results in lower quality of results because of the inability to simultaneously handle multiple criteria for design. Here, we describe a program AlleleID that designs qPCR and microarray assays to identify and detect pathogens.

Automating mass spectrometry-based quantitative glycomics using aminoxy tandem mass tag reagents with SimGlycan.

Protein glycosylation is a common post-translational modification, which serves critical roles in the biological processes of organisms. Monitoring of changes in the abundance and structure of glycans may be necessary to explain the correlations between protein glycosylation and various diseases. Hence, the growing importance of glycoproteomics necessitates in-depth qualitative and quantitative studies of glycans. One of the emerging trends in glycomics research is the innovation related to accurate mass spectrometry based quantitative analysis of glycans. Recently, we have introduced aminoxyTMT reagents, which enable efficient relative quantitation of carbohydrates, improved glycan ionization efficiency and increased analytical throughput. These reagents can be used for quantitative analysis of N-glycans by direct infusion or liquid chromatography (LC)-coupled to electrospray ionization mass spectrometry (ESI-MS). However, unlike in proteomics, one of the major challenges left unaddressed is the lack of informatics tools to automate the qualitative and quantitative analysis of generated data. This analysis typically includes identification/quantitation of glycans using MS/MS data and differential analysis across biological samples. We have developed software modules to streamline such protocols for quantitative analysis of aminoxyTMT labeled-glycans derived from complex mixtures. This article is part of a Special Issue entitled: Proteomics in India.

Bioinformatics in glycomics: glycan characterization with mass spectrometric data using SimGlycan.

Mass spectrometry (MS), with its low sample requirement and high sensitivity, has been the predominantly used methodology for characterization and elucidation of glycan structures. However, manual interpretation of MS data is complex and tedious due to large number of product ions observed and also due to the variation in their m/z values under various experimental conditions. We present an automated tool, SimGlycan, for this purpose, which accepts raw/standard MS data files as input and characterizes the associated glycan structure with high accuracy using database searching and scoring techniques. Not only does it predict the glycan structure using an MS/MS database searching technique, but it also facilitates predicting novel glycans by drawing a glycan and mapping it onto an experimental spectrum to check the degree of proximity between the theoretical and the experimental glycans. It serves as a platform for developing advanced tools that may be used for glycopeptide identification using MS data and 3D structural analysis of glycans with a few improvements in the existing features.