Estimating taxonomic profiles is a central task in microbiome research. Several bioinformatic tools have been developed for this purpose, differing in algorithmic strategy, reference database flexibility, sensitivity parameters, and the type of abundance they estimate. As a result, taxonomic profiles carry an unwanted methodological signal whose driving characteristics remains understudied. While benchmarks have evaluated the performance of some of these tools, they rely on simulated data; little work has been done to compare them using real metagenomes in the presence of noise and uncharacterised diversity. Overall, the impact of taxonomic profiler choice and parameterisation on scientific conclusions remains poorly understood. Here, we leveraged 1,211 shotgun metagenomes from eight datasets to test four taxonomic profilers across 13 methodological designs. Based on diversity indices, we found substantial variability in estimated taxonomic composition depending on methodological features such as reference database and algorithmic strategy. We show that alpha diversity estimates and their associated statistical conclusions varied substantially with tool choice (particularly among k-mer-based tools) and with reference database. Beta diversity showed sensitivity to both database and parameter choices, yet this variability barely affected statistical inference. Our findings highlight the methodological sensitivity of analyses based on diversity indices and the importance for researchers to consider assessing the robustness of their results to their methodological choices. We provide a much-needed summary of tool characteristics to help researchers better understand the available bioinformatic tools and to support their methodological choices justification. This work raises awareness about the bio-informatic causes variability in diversity analyses of metagenomics data. Overall, this study underscores the importance of tool selection and parametrisation, and of conducting sensitivity analyses to support robust and reliable scientific conclusions.
Rondeau-Leclaire, J., Blanchet, G., Jacques, P.-E., Laforest-Lapointe, I.
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