ASTRAL is a fast, statistically consistent method for estimating species trees from multiple unrooted gene trees under the multi-species coalescent model. It outperforms existing methods like MP-EST and BUCKy-pop in accuracy and can handle genome-scale datasets efficiently. ASTRAL uses dynamic programming to solve the Maximum Quartet Support Species Tree (MQSST) problem, which involves finding the species tree that agrees with the largest number of quartet trees induced by the gene trees. It runs in polynomial time and can analyze large datasets in minutes. ASTRAL is more accurate than concatenation under moderate levels of incomplete lineage sorting (ILS), but concatenation is more accurate when ILS is low. ASTRAL is statistically consistent under the multi-species coalescent model and can handle large datasets with thousands of genes. It was evaluated on biological and simulated datasets, showing superior accuracy compared to other methods. ASTRAL's performance was tested on mammalian, plant, and amniota datasets, where it provided accurate species trees. It was also compared to other methods like MRP, Greedy Consensus, and CA-ML, showing better accuracy in most cases. ASTRAL's running time is significantly faster than other coalescent-based methods, making it suitable for genome-scale analyses. The study concludes that ASTRAL is a valuable tool for species tree estimation, especially in the presence of high ILS, and highlights the importance of choosing appropriate methods based on the data and model conditions.ASTRAL is a fast, statistically consistent method for estimating species trees from multiple unrooted gene trees under the multi-species coalescent model. It outperforms existing methods like MP-EST and BUCKy-pop in accuracy and can handle genome-scale datasets efficiently. ASTRAL uses dynamic programming to solve the Maximum Quartet Support Species Tree (MQSST) problem, which involves finding the species tree that agrees with the largest number of quartet trees induced by the gene trees. It runs in polynomial time and can analyze large datasets in minutes. ASTRAL is more accurate than concatenation under moderate levels of incomplete lineage sorting (ILS), but concatenation is more accurate when ILS is low. ASTRAL is statistically consistent under the multi-species coalescent model and can handle large datasets with thousands of genes. It was evaluated on biological and simulated datasets, showing superior accuracy compared to other methods. ASTRAL's performance was tested on mammalian, plant, and amniota datasets, where it provided accurate species trees. It was also compared to other methods like MRP, Greedy Consensus, and CA-ML, showing better accuracy in most cases. ASTRAL's running time is significantly faster than other coalescent-based methods, making it suitable for genome-scale analyses. The study concludes that ASTRAL is a valuable tool for species tree estimation, especially in the presence of high ILS, and highlights the importance of choosing appropriate methods based on the data and model conditions.