Automatic extraction of reliable regions from multiple sequence alignments

Multiple alignments are of key importance in transferring annotation from model organism to humans [1]. The importance is reflected by the number of alignment methods that have emerged recently [2–5]. The development of alignment programs is governed by achieving ever increasing levels of accuracy on several commonly used benchmark sets [6, 7]. The accuracy is usually measured by calculating the number of identically aligned residues divided by the number of aligned residues in a reference alignment. Essentially, this reflects the extent to which an alignment method managed to reconstruct a reference alignment. Misaligned residues in the test alignment are completely ignored. Therefore alignment programs that tend to align more residues, usually global methods, appear to perform well.

It is often more desirable in practice to create alignments in which only reliable regions are aligned and unreliable regions remain unaligned. Misaligned regions can give the impression of conservation where in fact there is none. This is particularly true in multi-domain cases where alignment programs often fail to insert large gaps corresponding to the loss, or gain, of entire domains. In phylogenetic reconstruction such misaligned regions essentially contribute noise that can lead to false tree topologies [8–10]. Previously, we made the simple observation that regions aligned similarly by several alignment methods are usually more reliable than those aligned differently [11]. Our method Mumsa takes advantage of this observation by using cross-alignment conservation as an indicator of alignment accuracy and overall case difficulty.

Here we present a new algorithm that extracts identically aligned regions from several multiple alignments and creates new alignments out of them. Within these alignments, unreliably aligned residues from the input alignments are disentangled and therefore we term them relaxed alignments.

The input of our method is a set of multiple alignments of the same sequences generated either by different alignment methods or by a few methods employing different parameter settings.

An important concept in our algorithm are pairs-of-aligned residues (POARS) as defined in Lassmann and Sonnhammer (2005). Briefly, within an alignment two residues form a POAR when they occur in different sequences (rows) but within the same column.

The previous version of Mumsa required that all input alignments to be in the Fasta format and contain the sequences in the same order. Both of these limitations were removed in this version to make it easier for users to use several different alignment methods.

Mumsa can produce output alignments in Fasta, Clustal and Macsim [12] alignment formats. When using the Macsim format, the residues in each sequence obtain a reliability score. For residue A this reliability score is the sum of the occurrences of all POARs, that A is involved in, normalized by the maximum reliability score attainable (the score if residue A would be aligned identically in all input alignments). These reliability scores can be visualized by Kalignvu [13] to aid users in distinguish between reliable and unreliable regions.

Alignment accuracy is measured by comparing a test alignment to a reference alignment. The sum-of-pairs score (SPS) [14] and Q-score [15] reflect how many residues are aligned identically in both test and reference alignment. In a sense this reflect how much of the reference alignment was reconstructed. However, no attention is being paid on the number of misaligned residues in the test alignment. For example a SPS score of 0.5 could either mean that 50 percent of the residues in the test alignment are simply not aligned but also that 50 percent are misaligned. We therefore introduce a per-residue accuracy (PRA) score that also has the number of shared POARs as the nominator but the total number of POARs of the test alignment as the denominator. Essentially, this is equivalent to asking how many residues are aligned correctly in the test alignment.

In addition, we calculated the percentage of aligned residues, or the number of aligned residues divided by the total number of possible aligned residues.

The relaxed alignments created by Mumsa have a much higher per-residue accuracy than those of the input alignment. Depending on the overall difficulty of the subset of benchmark alignments, the increase in accuracy is dramatic. This is especially true for the first Balibase reference set where the accuracy is almost doubled. It is particularly striking that the stringency cutoff f does not have to be high to give good accuracy gains. Residues occurring in more than 25 percent, here three or more, of input alignments are reliable and lead to good relaxed alignments.

As expected, the alignments generated by Mumsa contain fewer aligned residues than the input alignments. Moreover, the higher the cutoff f the fewer residues are aligned. Another observation is that the difficulty of the alignment cases affects the number of aligned residues in the merged alignments. For example, fewer aligned residues are present in the Mumsa alignments for the set 11, the most difficult one, compared to the other ones. In fact, the Pearson correlation coefficient between the average accuracy of input alignments (a measure of alignment difficulty) and the percentage of aligned residues of the most relaxed alignments is 0.98. This supports the notion that alignment programs usually disagree in difficult cases – more precisely in regions that are difficult to align.

The alignment viewer Kalignvu can be used to display the output alignments of Mumsa (Figure 1). A heat-map color scheme highlights the more reliable regions in red tones and less reliable regions in blue tones.

The running time of Mumsa is very low in comparison to that required by the input alignments (Figure 2). We ran Mumsa using a stringent, moderate and relaxed parameter setting for f. It is clear that the running time is influenced by the choice of f. Nevertheless, the running time of Mumsa remains two to three orders of magnitude lower than that required by the input alignment methods.

Much emphasis has been given to alignment methods that focus on aligning a large fractions of residues because this often gives high scores on benchmark sets. We argue that it is equally relevant to increase the ration of correctly aligned residues within generated alignments. For this purpose we have introduced an algorithm to extract identically aligned parts from several multiple alignments and have shown that those are very reliable. Our method operates on pairs of aligned residues rather than columns. In addition to being able to identify blocks of high conservation, our method can therefore also identify correctly aligned regions which only few input sequences share.

In contrast to alignments generated by most popular direct alignment method, the relaxed alignments generated by Mumsa contain more gaps and are therefore less compact. As such they are inherently different from traditional alignments and users will have to decide whether these are suitable to be used directly in their specific studies. However, relaxed alignments can always be used to get an overview of conservation, a sense of how trustworthy alignments are and, more importantly, which regions are reliable. Users may wish to examine Mumsa alignments in a hierarchical manner, starting from the most reliable (high stringency cutoff f) to more compact but less reliable alignments (low f). Due to the good computationally properties of Mumsa little extra computing time is required to perform such an analysis. A direct application of relaxed alignments is in phylogenetics where alignment accuracy is of prime importance. An alignment in which 60 percent of the residues are aligned with an accuracy of more than 95 percent is clearly more desirable than an alignment where 90 percent of the residues are aligned, but incorrectly so in a quarter of cases.

The Mumsa program is freely available at http://msa.cgb.ki.se or by request from T. Lassmann.