A benchmark of multiple sequence alignment programs upon structural RNAs

By Paul P. Gardner, Andreas Wilm, Stefan Washietl. Nucleic Acids Research, 2005

302 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cece26b94bfa0508985f88e290992fad7b16f750

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Citations in the corpus

Recent developments in the MAFFT multiple sequence alignment program (2008, methodology): “RNA structural alignment methods are listed below the dashed line.The names of MAFFT options are shaded.The benchmark dataset in the MASTR paper [87] was used.The alignment accuracies were assessed with two criteria, SPS and SCI [ 110 ,111], using the compalign and scif programs distributed with the”
Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement (2021, unknown): “38 .”
A High-Throughput DNA Sequence Aligner for Microbial Ecology Studies (2009, background): “The secondary structure is an important feature to consider in generating the alignment because it increases the likelihood that the alignment conserves positional homology between sequences [13].”
Clustal Omega for making accurate alignments of many protein sequences (2018, methodology): “Initial pseudo-count transfer and gap parameters for RNA were determined using BRaliBase II.(19)”
Improved accuracy of multiple ncRNA alignment by incorporating structural information into a MAFFT-based framework (2008, methodology): “We also chose LaRA as an alternative pairwise alignment method, because it shows a characteristic pattern of the SPS score for pairwise alignment (N = 2) and it has a remarkably high structure conservation index [9,46,47].”
A fast structural multiple alignment method for long RNA sequences (2008, methodology): “Computational Experiments Datasets To test the empirical performance of MXSCARNA, we used three datasets for the benchmark multiple alignments: an original multiple alignment dataset, the BRAlibaseII multiple alignment dataset [33], and Kiryu et al.”
Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering (2007, background): “structured RNAs fail at pairwise sequence identities below about 60% [19].”
The impact of rRNA secondary structure consideration in alignment and tree reconstruction: simulated data and a case study on the phylogeny of hexapods. (2010, methodology): “A common approach to test the performance of alignment programs on structured RNA sequences are the BRALIBASE benchmarks (Gardner et al. 2005).”
Muscle5: High-accuracy alignment ensembles enable unbiased assessments of sequence homology and phylogeny (2021, unknown): “Despite decades of research into automated alignment, current algorithms predict >30% columns incorrectly on structure-based benchmarks 4,5 .”