A benchmark of multiple sequence alignment programs upon structural RNAs

Abstract

To date, few attempts have been made to benchmark the alignment algorithms upon nucleic acid sequences. Frequently, sophisticated PAM or BLOSUM like models are used to align proteins, yet equivalents are not considered for nucleic acids; instead, rather ad hoc models are generally favoured. Here, we systematically test the performance of existing alignment algorithms on structural RNAs. This work was aimed at achieving the following goals: (i) to determine conditions where it is appropriate to apply common sequence alignment methods to the structural RNA alignment problem. This indicates where and when researchers should consider augmenting the alignment process with auxiliary information, such as secondary structure and (ii) to determine which sequence alignment algorithms perform well under the broadest range of conditions. We find that sequence alignment alone, using the current algorithms, is generally inappropriate <50-60% sequence identity. Second, we note that the probabilistic method ProAlign and the aging Clustal algorithms generally outperform other sequence-based algorithms, under the broadest range of applications.

Figure 1

An overview of alignment programs used in this work. Programs were classified into the categories described in detail in Alignment algorithms section.

Figure 2

Both measures of structural RNA alignment correctness, SCI (A) and SPS (B), are plotted as functions of the mean pairwise sequence identity (calculated using the reference alignments). The curves are fit to dataset 1 (see text for details) using lowess (local weighted regression) smoothing. At most, two curves are plotted for each alignment package—one corresponding to the default parameters, the other corresponds to the best parameter combination we could identify.

Figure 3

SCI (A) and SPS (B) as functions of the sequence identity for dataset 2 (see text for details). Five structural algorithms are shown: the Sankoff-based methods Dynalign, Foldalign, PMcomp and Stemloc, and the base pair probability profile alignment method implemented in PMcomp (fast). These are in contrast with the hand-curated structural alignments and six of the better sequence-based alignment algorithms (ClustalW, MUSCLE, PCMA, POA (gp), ProAlign and Prrn).