The paper introduces a novel robust rank aggregation (RRA) method for integrating gene lists and performing meta-analysis in genomic data analysis. Standard rank aggregation methods are often inadequate for biological data, which is inherently noisy and contains many irrelevant or unreliable inputs. The RRA method addresses these issues by detecting genes that are consistently ranked higher than expected under a null hypothesis of uncorrelated inputs and assigning significance scores to each gene. This approach is parameter-free, robust to outliers, and noise, making it suitable for various settings. The method is implemented in the GNU R package ROBUSTRankAGGREG, which is freely available. The paper demonstrates the effectiveness of RRA through simulations and real-world applications, showing that it can robustly identify significant genes and amplify biological signals even in the presence of noise. The method is also compared with other rank aggregation methods, highlighting its advantages in terms of robustness, efficiency, and statistical significance.The paper introduces a novel robust rank aggregation (RRA) method for integrating gene lists and performing meta-analysis in genomic data analysis. Standard rank aggregation methods are often inadequate for biological data, which is inherently noisy and contains many irrelevant or unreliable inputs. The RRA method addresses these issues by detecting genes that are consistently ranked higher than expected under a null hypothesis of uncorrelated inputs and assigning significance scores to each gene. This approach is parameter-free, robust to outliers, and noise, making it suitable for various settings. The method is implemented in the GNU R package ROBUSTRankAGGREG, which is freely available. The paper demonstrates the effectiveness of RRA through simulations and real-world applications, showing that it can robustly identify significant genes and amplify biological signals even in the presence of noise. The method is also compared with other rank aggregation methods, highlighting its advantages in terms of robustness, efficiency, and statistical significance.