Table 6 Challenges and solutions in HTS data analysis for plant disease research
Challenges | Description | Implications | Solutions | Reference |
|---|---|---|---|---|
Bioinformatics expertise | HTS data analysis requires specialized bioinformatics expertise | The barrier to fully leveraging HTS is the potential difficulty in data analysis | Training collaboration with bioinformatics experts | Brinkmann et al. (2019) |
Computational tools & pipelines | Development and optimization of HTS data analysis tools can be time-consuming and resource-intensive | Challenge in selecting and adapting tools for specific research questions | Utilize open-source tools and seek guidance from experts | Krewski et al. (2020) |
Data interpretation | Difficulty distinguishing between true pathogen sequences and contaminants or host-derived sequences | False positives/negatives, additional validation required | Validation with traditional methods, complementary omics approaches | Khan et al. (2018) |
Standardization of analysis methods | Lack of standardized methods and protocols for HTS data analysis | Inconsistencies in interpretation, challenging comparisons between studies | Develop standardized methods and best practices for HTS data analysis | Malo et al. (2006) |
Integration of multi-omics data | Complex integration of HTS data with other omics approaches | Requires advanced bioinformatics tools and expertise | Collaboration with experts and development of user-friendly integration tools | Zhou et al. (2021) |