Development of a Python-Based Program for ASVs Integration and Deduplication in Microbiome Datasets
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Abstract
Integrating amplicon sequence variant (ASV) datasets from multiple experiments for microbiome analysis often leads to data redundancy, resulting in slow processing, inaccurate taxonomic classification, and an increased burden of manual data management. This study aimed to develop and evaluate the performance of a Python-based program for ASV integration and deduplication. The program was developed under the System Development Life Cycle (SDLC) framework and employs an exact-match method to preserve ASV sequence resolution. It supports flexible operation through a graphical user interface (GUI), Jupyter environment, and command-line interface (CLI).
Technical performance was evaluated using 2 to 10 datasets, comprising 33,445 to 117,678 ASV sequences. The program achieved 100% deduplication accuracy, with average processing times ranging from 4.29 to 35.58 seconds, respectively. User satisfaction with the program was at a good level (mean score = 4.29), with the highest satisfaction reported for the accuracy of the results. Overall, the program effectively reduces ASV redundancy and enhances the reliability of microbiome dataset preparation for downstream bioinformatics analyses.
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References
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