Kalign2: high-performance multiple alignment of protein and nucleotide sequences allowing external features - PubMed (original) (raw)

Kalign2: high-performance multiple alignment of protein and nucleotide sequences allowing external features

Timo Lassmann et al. Nucleic Acids Res. 2009 Feb.

Abstract

In the growing field of genomics, multiple alignment programs are confronted with ever increasing amounts of data. To address this growing issue we have dramatically improved the running time and memory requirement of Kalign, while maintaining its high alignment accuracy. Kalign version 2 also supports nucleotide alignment, and a newly introduced extension allows for external sequence annotation to be included into the alignment procedure. We demonstrate that Kalign2 is exceptionally fast and memory-efficient, permitting accurate alignment of very large numbers of sequences. The accuracy of Kalign2 compares well to the best methods in the case of protein alignments while its accuracy on nucleotide alignments is generally superior. In addition, we demonstrate the potential of using known or predicted sequence annotation to improve the alignment accuracy. Kalign2 is freely available for download from the Kalign web site (http://msa.sbc.su.se/).

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Figures

Figure 1.

Figure 1.

Running time of several multiple alignment methods on four scenarios with simulated alignments of varying evolutionary distance (PAM = 100 and PAM = 250), increasing sequence length (L = 10–2000), and number (N = 10–1500). For each case one parameter was varied (_x_-axis) while two parameters were kept constant (plot heading). Kalign2 scales much better than most of the methods, especially with increasing number of sequences. All tests were carried out on an AMD64 3200+ processor with 2GB of RAM running Linux.

Figure 2.

Figure 2.

Accuracy on RNA alignments using the SPS score. Boxplots for the accuracy measured using the Bralibase2.1 benchmark set. (A) Alignments with an average pairwise sequence identity (APSI) <40%. (**B**) Alignments with an APSI >40%. Kalign2 was the most accurate method, especially in regions with low APSI.

Figure 3.

Figure 3.

External feature alignment using protein secondary structure generally improves accuracy on the Balibase benchmark. An increase in the SPS score is seen mostly for cases with high structural coverage.

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