Improved RNA secondary structure prediction by maximizing expected pair accuracy

Abstract

Free energy minimization has been the most popular method for RNA secondary structure prediction for decades. It is based on a set of empirical free energy change parameters derived from experiments using a nearest-neighbor model. In this study, a program, MaxExpect, that predicts RNA secondary structure by maximizing the expected base-pair accuracy, is reported. This approach was first pioneered in the program CONTRAfold, using pair probabilities predicted with a statistical learning method. Here, a partition function calculation that utilizes the free energy change nearest-neighbor parameters is used to predict basepair probabilities as well as probabilities of nucleotides being single-stranded. MaxExpect predicts both the optimal structure (having highest expected pair accuracy) and suboptimal structures to serve as alternative hypotheses for the structure. Tested on a large database of different types of RNA, the maximum expected accuracy structures are, on average, of higher accuracy than minimum free energy structures. Accuracy is measured by sensitivity, the percentage of known base pairs correctly predicted, and positive predictive value (PPV), the percentage of predicted pairs that are in the known structure. By favoring doublestrandedness or single-strandedness, a higher sensitivity or PPV of prediction can be favored, respectively. Using MaxExpect, the average PPV of optimal structure is improved from 66% to 68% at the same sensitivity level (73%) compared with free energy minimization.

ProbKnot: Fast prediction of RNA secondary structure including pseudoknots

“…Previous approaches for predicting maximum expected accuracy structures used dynamic programming algorithms that do not allow pseudoknots (Do et al 2006;Hamada et al 2009;Lu et al 2009), but ProbKnot is not limited in the topology of structures it can predict. Although the partition function algorithm does not account for pseudoknotted structures, each of the helices in the pseudoknot can occur in different structures (Mathews 2004).…”

“…Each was run using default parameters. Additionally, the performance was compared against two other algorithms from RNAstructure, which predicts structures without pseudoknots, free energy minimization , and maximum expected accuracy structure prediction (Lu et al 2009). Overall, ProbKnot had the highest average sensitivity for all methods and the highest PPV among methods that are capable of predicting pseudoknots.…”

“…A more recent approach to predict RNA secondary structures is called maximum expected accuracy structure prediction (Knudsen and Hein 2003;Do et al 2006;Hamada et al 2009;Lu et al 2009). Roughly, maximum expected accuracy structures are structures composed of pairs that provide the maximal sum of pairing probabilities.…”

“…The performance of ProbKnot was benchmarked against other freely available programs that predict pseudoknots: pknotsRG-mfe (Reeder and Giegerich 2004), ILM (Ruan et al 2004), and HotKnots (Ren et al 2005); and programs that do not predict pseudoknots: MaxExpect, a maximum expected accuracy approach (Lu et al 2009) and Free Energy Minimization . ProbKnot was able to predict the largest fraction of known base pairs.…”

Direct Updating of an RNA Base-Pairing Probability Matrix with Marginal Probability Constraints

“…The base-pairing probability matrix (BPPM) of an RNA sequence, which stores all the probabilities for possible base pairs in an RNA sequence (see Section 2 for the detailed definition), has played an essential role in a number of algorithms in RNA informatics, including RNA (common) secondary structure predictions (Seemann et al, 2008,) Lu et al, 2009;Hamada et al, 2009bHamada et al, , 2011cSato et al, 2011, multiple alignment of RNA sequences (Hofacker et al, 2004;Katoh and Toh, 2008;Hamada et al, 2009a;Sahraeian and Yoon, 2011), RNA-RNA interaction predictions (Kato et al, 2010;Seemann et al, 2011), RNA motif search (Hamada et al, 2006), and miRNA gene finding (Terai et al, 2007) (Wei et al, 2011). Estimating accurate base-pairing probabilities, therefore, has the potential to improve those algorithms without modifying them.…”