The accuracy of several multiple sequence alignment programs for proteins

By Paulo A. S. Nuin, Zhouzhi Wang, Elisabeth R. M. Tillier. 2006

372 citations recorded by the source corpus.

BackgroundThere have been many algorithms and software programs implemented for the inference of multiple sequence alignments of protein and DNA sequences. The "true" alignment is usually unknown due to the incomplete knowledge of the evolutionary history of the sequences, making it difficult to gauge the relative accuracy of the programs.ResultsWe tested nine of the most often used protein alignment programs and compared their results using sequences generated with the simulation software Simprot which creates known alignments under realistic and controlled evolutionary scenarios. We have simulated more than 30000 alignment sets using various evolutionary histories in order to define strengths and weaknesses of each program tested. We found that alignment accuracy is extremely dependent on the number of insertions and deletions in the sequences, and that indel size has a weaker effect. We also considered benchmark alignments from the latest version of BAliBASE and the results relative to BAliBASE- and Simprot-generated data sets were consistent in most cases.ConclusionOur results indicate that employing Simprot's simulated sequences allows the creation of a more flexible and broader range of alignment classes than the usual methods for alignment accuracy assessment. Simprot also allows for a quick and efficient analysis of a wider range of possible evolutionary histories that might not be present in currently available alignment sets. Among the nine programs tested, the iterative approach available in Mafft (L-INS-i) and ProbCons were consistently the most accurate, with Mafft being the faster of the two.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Decapod phylogenetics and molecular evolution (2009, positive): “These programs produce more accurate alignments than ClustalW with increasing evolutionary distance (e.g., MAFFT, Nuin et al. 2006 ) or when gaps are present (indels) in the resulting alignment of sequence data (e.g., DIALIGN-T and MAFFT, Golubchik et al. 2007).”
Protein networks in disease. (2008, background): “In addition to experimentation, several efforts have attempted to build large networks of human protein interactions curated from prior studies in the literature (Ramani et al. 2005; Gandhi et al. 2006) (for review, see Mathivanan et al. 2006 ).”
ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules (2016, methodology): “This was suggested to be the upper boundary of the ‘twilight zone’ for protein structures (30); (iii) the MAFFT-LINSi procedure, suggested to be one of the most accurate MSA methodologies (31), is used to align the homologous sequences.”
An alignment confidence score capturing robustness to guide tree uncertainty. (2010, background): “A recent evaluation of SPs across the BAliBASE benchmark concluded that the best alignment programs to date achieve only 76% average accuracy, that is, a quarter of all residue pairs are incorrectly aligned (Nuin et al. 2006).”
Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. (2007, methodology): “We have selected ClustalW as the currently most used progressive alignment method (Thompson et al., 1994) and Mafft (Katoh et al., 2005) and Probcons (Do et al., 2005) as examples of more recently developed methods that have been shown to obtain very high scores in terms of alignment accuracy (Black”
GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters (2015, background): “It was previously shown that alignment programs often err (12).”
INDELible: A Flexible Simulator of Biological Sequence Evolution (2009, methodology): “…to investigate the accuracy and efficiency of phylogenetic reconstructionmethods(e.g.,GautandLewis1995;Huelsenbeck 1995), ancestral sequence reconstruction methods (e.g., Blanchette et al. 2004), or methods of sequence alignment (e.g., Nuin et al. 2006).”
Consensus protein design (2016, background): “Unfortunately, MSA methods tend to vary significantly, and there is currently no quantitative measure for the quality of alignment (Nuin et al., 2006; Kemena and Notredame, 2009; Pearson, 2013).”
ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids (2010, methodology): “MAFFT and PRANK were shown to be among the leading sequence alignment algorithms (34,37).”
Data Mining Techniques for the Life Sciences (2010, background): “This can be seen in the large numbers of PDB atlases and fold comparison servers.”
Recent developments in the MAFFT multiple sequence alignment program (2008, background): “Recent benchmark studies under various conditions [26–30] consistently concluded that MAFFT is one of the best choices.”
A Bioinformatic Analysis of NAC Genes for Plant Cell Wall Development in Relation to Lignocellulosic Bioenergy Production (2009, methodology): “603 [28] was used in the alignment employing L-INS-I, which is considered to be one of the most accurate MSA methods [29, 30].”