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There is a growing appreciation for the diverse and important roles RNA molecules play in cellular function. RNAMAT is an approach based on matrix representation of all potential base-pairing of a set of sequences to reveal common secondary-structure features. When the RNA sequences come from one class, proper summation of these matrices exposes common structural features as demonstrated for tRNA and HACA-RNA. For C/D-RNA, a novel structural motif is suggested. Furthermore, it is demonstrated, in the case of tmRNA that the method can detect pseudo-knots which are structural motifs that are difficult to detect in other methods. When the sequences come from diverse sources, a specific clustering algorithm is suggested that is capable of detecting the common motifs. The algorithm is demonstrated in a case of a simulated example and in a real case derived from trypanosomes comparative RNomics study.