Background: An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for proteinfolding, gene-finding and multiple-sequence-alignment algorithms. Results: Here we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance. Conclusions: We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms of both sensitivity and selectivity across different lengths and homologies. Furthermore, we outline some directions for future research. Background Motivation RNA, once considered a passive carrier of genetic information, is now known to play a more active role in nature. Many recently discovered RNAs are catalytic, for example RNase P which is involved in tRNA maturation and the self-s...
Paul P. Gardner, Robert Giegerich