Limitations of other pipelines
Several computational studies have analysed singular genomes or proteomes of individual pathogenic strain or species (1-5). In contrast, many other researchers have attempted to employ pan-genome strategies to identify potential cross-protective antigens using multiple genomes of the same species, such as group B Streptococcus sp. (6-7). In another study, researchers have used the protein-protein interaction dataset and network biology approach to prioritize vaccine targets in B. burgdorferi (8). Currently, there are a number of tools, resources and databases available in the immunoinformatics domain that have contributed to the development of vaccines in recent past (9-14).
Despite the significant advancements in computational proteomics, machine learning and reverse vaccinology domains, an emphasis must be laid upon the fact that generation of lists of vaccine candidates is not a task of major significance. Furthermore, there has been a pressing need for building pipelines or computational frameworks, to integrate diverse algorithms and databases using single input and provide meaningful results for researchers working on vaccine development. There have been numerous efforts made to build such pipelines that combine immunoinformatics programs sequentially or in parallel mode (15-17). In 2019, Dalsass et al. compared 6 open-source standalone RV programs (18) designed for bacterial pathogens: NERVE (19), VaxiJen (20), Vaxign (21), Bowman-Heinson (22-23), Jenner-predict (24), and VacSol (25) and tested them on 11 different bacterial proteomes. These research studies report several limitations in the existing pipelines. For instance, most of the programs and algorithms have been built around bacterial/prokaryotic systems with only a little work being done on eukaryotic pathogens, including T. cruzi. Also, the bioinformatics programs used to determine the characteristics of proteins (in the existing pipelines) are imprecise, leading to propagation of error in the final output.
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