Multiple sequence alignment is a classical and challenging task. The problem is NP-hard. The full dynamic programming takes too much time. The progressive alignment heuristics adopted by most state-of-the-art works suffer from the "once a gap, always a gap" phenomenon. Is there a radically new way to do multiple sequence alignment? In this paper, we introduce a novel and orthogonal multiple sequence alignment method, using both multiple optimized spaced seeds and new algorithms to handle these seeds efficiently. Our new algorithm processes information of all sequences as a whole and tries to build the alignment vertically, avoiding problems caused by the popular progressive approaches. Because the optimized spaced seeds have proved significantly more sensitive than the consecutive k-mers, the new approach promises to be more accurate and reliable. To validate our new approach, we have implemented MANGO: Multiple Alignment with N Gapped Oligos. Experiments were carried out on large 16S RNA benchmarks, showing that MANGO compares favorably, in both accuracy and speed, against state-of-the-art multiple sequence alignment methods, including ClustalW 1.83, MUSCLE 3.6, MAFFT 5.861, ProbConsRNA 1.11, Dialign 2.2.1, DIALIGN-T 0.2.1, T-Coffee 4.85, POA 2.0, and Kalign 2.0. We have further demonstrated the scalability of MANGO on very large datasets of repeat elements. MANGO can be downloaded at http://www.bioinfo.org.cn/mango/ and is free for academic usage.