MS2Compound: A User-Friendly Compound Identification Tool for LC-MS/MS-Based Metabolomics Data.

Metabolomics is a leading frontier of systems science and biomedical innovation. However, metabolite identification in mass spectrometry (MS)-based global metabolomics investigations remains a formidable challenge. Moreover, lack of comprehensive spectral databases hinders accurate identification of compounds in global MS-based metabolomics. Creating experiment-derived metabolite spectral libraries tailored to each experiment is labor-intensive. Therefore, predicted spectral libraries could serve as a better alternative. User-friendly tools are much needed, as the currently available metabolomic analysis tools do not offer adequate provision for users to create or choose context-specific databases. Here, we introduce the MS2Compound, a metabolite identification tool, which can be used to generate a custom database of predicted spectra using the Competitive Fragmentation Modeling-ID (CFM-ID) algorithm, and identify metabolites or compounds from the generated database. The database generator can create databases of the model/context/species used in the metabolomics study. The MS2Compound is also powered with mS-score, a scoring function for matching raw fragment spectra to a predicted spectra database. We demonstrated that mS-score is robust in par with dot product and hypergeometric score in identifying metabolites using benchmarking datasets. We evaluated and highlight here the unique features of the MS2Compound by a re-analysis of a publicly available metabolomic dataset (MassIVE id: MSV000086784) for a complex traditional drug formulation called Triphala. In conclusion, we believe that the omics systems science and biomedical research and innovation community in the field of metabolomics will find the MS2Compound as a user-friendly analysis tool of choice to accelerate future metabolomic analyses.

Proteogenomic examination of esophageal squamous cell carcinoma (ESCC): new lines of inquiry.

Introduction: Esophageal squamous cell carcinoma (ESCC), a histopathologic subtype of esophageal cancer is a major cause of cancer-related morbidity and mortality worldwide. This is primarily because patients are diagnosed at an advanced stage by the time symptoms appear. The genomics and mass spectrometry-based proteomics continue to provide important leads toward biomarker discovery for ESCC. However, such leads are yet to be translated into clinical utilities. Areas covered: We gathered information pertaining to proteomics and proteogenomics efforts in ESCC from the literature search until 2020. An overview of omics approaches to discover the candidate biomarkers for ESCC were highlighted. We present a summary of recent investigations of alterations in the level of gene and protein expression observed in biological samples including body fluids, tissue/biopsy and in vitro-based models. Expert opinion: A large number of protein-based biomarkers and therapeutic targets are being used in cancer therapy. Several candidates are being developed as diagnostics and prognostics for the management of cancers. High-resolution proteomic and proteogenomic approaches offer an efficient way to identify additional candidate biomarkers for diagnosis, monitoring of disease progression, prediction of response to chemo and radiotherapy. Some of these biomarkers can also be developed as therapeutic targets.

Plant Proteome Databases and Bioinformatic Tools: An Expert Review and Comparative Insights.

Historically, plant biology studies have lagged behind systems biology studies in animals and humans. However, there are signs of positive change as evidenced by the rise of big data in plant proteomics, and the availability of data science tools and next-generation sequencing technologies. Currently, the sequence information on nearly 300 plant species is available although they are curated to varying degrees of sophistication. This has led to significant enrichment of representations in the corresponding plant proteome databases. Analysis of the proteome component of an organism offers structural, functional, and network scale insights. Moreover, the development of high-throughput mass spectrometric techniques has augmented our understanding of proteins and their expression patterns under various conditions. Several thousand proteins can now be identified from a single mass spectrometric analysis. In this expert review, we provide an in-depth analysis on plant proteome databases, how to access them, and, importantly, the biological, research, and application contexts in which each database is significant, their comparative strengths, and limitations. We aimed in this analysis to reach out to young scholars embarking on plant biology and proteomic research as well as to those already established in the field so as to provide integrated critical analyses of plant proteome databases and bioinformatics tools in this nascent field of systems sciences. In conclusion, plant proteome research is an emerging and exciting frontier of integrative biology scholarship and innovation. Our future efforts must also be invested in integrating the available databases to allow for multiomics data analysis, research, and development.