IntroductionFasciolopsiasis, a food-borne intestinal disease is most common in Asia and the Indian subcontinent. Pigs are the reservoir host, and fasciolopsiasis is most widespread in locations where pigs are reared and aquatic plants are widely consumed. Human infection has been most commonly documented in China, Bangladesh, Southeast Asia, and parts of India. It predominates in school-age children, and significant worm burdens are not uncommon. The causal organism is Fasciolopsis buski, a giant intestinal fluke that infects humans and causes diarrhoea, fever, ascites, and intestinal blockage. The increasing prevalence of medication resistance and the necessity for an effective vaccination make controlling these diseases challenging.MethodsOver the last decade, we have achieved major advances in our understanding of intestinal fluke biology by in-depth interrogation and analysis of evolving F. buski omics datasets. The creation of large omics datasets for F. buski by our group has accelerated the discovery of key molecules involved in intestinal fluke biology, toxicity, and virulence that can be targeted for vaccine development. Finding successful vaccination antigen combinations from these huge number of genes/proteins in the available omics datasets is the key in combating these neglected tropical diseases. In the present study, we developed an in silico workflow to select antigens for composing a chimeric vaccine, which could be a significant technique for developing a fasciolopsiasis vaccine that prevents the parasite from causing serious harm.Results and discussionThis chimeric vaccine can now be tested experimentally and compared to other vaccine candidates to determine its potential influence on human health. Although the results are encouraging, additional validation is needed both in vivo and in vitro. Considering the extensive genetic data available for intestinal flukes that has expanded with technological advancements, we may need to reassess our methods and suggest a more sophisticated technique in the future for identifying vaccine molecules.

Introduction: In the realm of next-generation sequencing datasets, various characteristics can be extracted through k-mer based analysis. Among these characteristics, genome size (GS) is one that can be estimated with relative ease, yet achieving satisfactory accuracy, especially in the context of heterozygosity, remains a challenge.

Methods: In this study, we introduce a high-precision genome size estimator, GSET (Genome Size Estimation Tool), which is based on k-mer histogram correction.

Results: We have evaluated GSET on both simulated and real datasets. The experimental results demonstrate that this tool can estimate genome size with greater precision, even surpassing the accuracy of state-of-the-art tools. Notably, GSET also performs satisfactorily on heterozygous datasets, where other tools struggle to produce useable results.

Discussion: The processing model of GSET diverges from the popular data fitting models used by similar tools. Instead, it is derived from empirical data and incorporates a correction term to mitigate the impact of sequencing errors on genome size estimation. GSET is freely available for use and can be accessed at the following URL: https://github.com/Xingyu-Liao/GSET.