This paper presents a new software package, STAMP, for comparative metagenomics that supports best practices in analysis and reporting. The software provides a user-friendly graphical environment for performing statistical techniques discussed in the article. The authors demonstrate that deeper biological insights can be gained using statistical techniques available in their software by examining a pair of iron mine metagenomes. They also analyze the functional potential of 'Candidatus Accumulibacter phosphatis' in two enhanced biological phosphorus removal metagenomes, identifying several subsystems that differ between the A.phosphatis stains in these related communities, including phosphate metabolism, secretion and metal transport. The paper discusses key statistical concerns in metagenomic data analysis, including statistical hypothesis tests, effect size, confidence intervals, and multiple test correction. It highlights the importance of distinguishing biologically relevant results from statistically significant ones, emphasizing the need for effect size statistics and confidence intervals in addition to P-values. The authors compare the results of statistical tests using STAMP with those from other software packages, such as XIPE-TOTEC, ShotgunFunctionalizeR, MEGAN, and IMG/M, and discuss the advantages of using STAMP for comparative metagenomic analysis. The paper also discusses the implementation of STAMP, which is implemented in Python and provides a user-friendly graphical interface for statistical analysis. It includes various statistical tests, effect size statistics, confidence interval methods, and multiple hypothesis test correction techniques. The software can be used for comparing multiple metagenomes and provides publication-quality plots for visualizing results. The results section presents an analysis of the Soudan iron mine metagenomes, where STAMP was used to compare the statistical results reported by Edwards et al. (2006) using XIPE-TOTEC. The results show that STAMP identifies fewer statistically significant subsystems than XIPE-TOTEC, but provides more detailed information about effect sizes and confidence intervals. The analysis also supports the hypothesis that the 'red' aerobic and 'black' anaerobic communities predominantly utilize different respiratory pathways. In the second part of the results, the authors compare the functional profiles of A.phosphatis strains from two lab-scale WWTP in Australia and the US. They identify several statistically significant features, including 'phosphate metabolism', 'general secretion pathway', and 'transport proteins related to metals'. The results suggest that these features may indicate endemic strains of phage and mobile elements in the communities. The paper concludes that STAMP is a valuable tool for comparative metagenomic analysis, providing a user-friendly graphical interface and statistical techniques for interpreting and communicating results.This paper presents a new software package, STAMP, for comparative metagenomics that supports best practices in analysis and reporting. The software provides a user-friendly graphical environment for performing statistical techniques discussed in the article. The authors demonstrate that deeper biological insights can be gained using statistical techniques available in their software by examining a pair of iron mine metagenomes. They also analyze the functional potential of 'Candidatus Accumulibacter phosphatis' in two enhanced biological phosphorus removal metagenomes, identifying several subsystems that differ between the A.phosphatis stains in these related communities, including phosphate metabolism, secretion and metal transport. The paper discusses key statistical concerns in metagenomic data analysis, including statistical hypothesis tests, effect size, confidence intervals, and multiple test correction. It highlights the importance of distinguishing biologically relevant results from statistically significant ones, emphasizing the need for effect size statistics and confidence intervals in addition to P-values. The authors compare the results of statistical tests using STAMP with those from other software packages, such as XIPE-TOTEC, ShotgunFunctionalizeR, MEGAN, and IMG/M, and discuss the advantages of using STAMP for comparative metagenomic analysis. The paper also discusses the implementation of STAMP, which is implemented in Python and provides a user-friendly graphical interface for statistical analysis. It includes various statistical tests, effect size statistics, confidence interval methods, and multiple hypothesis test correction techniques. The software can be used for comparing multiple metagenomes and provides publication-quality plots for visualizing results. The results section presents an analysis of the Soudan iron mine metagenomes, where STAMP was used to compare the statistical results reported by Edwards et al. (2006) using XIPE-TOTEC. The results show that STAMP identifies fewer statistically significant subsystems than XIPE-TOTEC, but provides more detailed information about effect sizes and confidence intervals. The analysis also supports the hypothesis that the 'red' aerobic and 'black' anaerobic communities predominantly utilize different respiratory pathways. In the second part of the results, the authors compare the functional profiles of A.phosphatis strains from two lab-scale WWTP in Australia and the US. They identify several statistically significant features, including 'phosphate metabolism', 'general secretion pathway', and 'transport proteins related to metals'. The results suggest that these features may indicate endemic strains of phage and mobile elements in the communities. The paper concludes that STAMP is a valuable tool for comparative metagenomic analysis, providing a user-friendly graphical interface and statistical techniques for interpreting and communicating results.