Phylogeny-Aware Gap Placement Prevents Errors in Sequence Alignment and Evolutionary Analysis

By Ari Löytynoja, Nick Goldman. Science, 2008

812 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/db0d824617daf65453c92b2f445d08da64d8bf4d

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Citations in the corpus

Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo (2010, positive): “A tree that is more consistent with our binding specificity data was also obtained using the Prank algorithm (Supplementary Figure S6), which uses phylogeny-aware gap placement and scores insertions and deletions more accurately than other alignment programs (Löytynoja and Goldman, 2005, 2008).”
MACSE: Multiple Alignment of Coding SEquences Accounting for Frameshifts and Stop Codons (2011, methodology): “A wide range of molecular analyses rely on multiple sequence alignments (MSA), e.g., motif detection within genes and genomes [1], prediction of tridimensional structures [2], phylogenetic inference [3] and detection of positive selection [4].”
Accurate extension of multiple sequence alignments using a phylogeny-aware graph algorithm (2012, background): “Yet, they ignore the phylogeny during the remainder of the alignment process and produce alignments whose gap patterns are not evolutionarily meaningful (Löytynoja and Goldman, 2008).”
ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids (2010, methodology): “MAFFT-LINSi is much faster than PRANK and thus was chosen to be the default alignment algorithm in ConSurf.”
Aligning short reads to reference alignments and trees (2011, methodology): “This type of a tree-derived gap signal is based on similar ideas as used in PRANK+F (Loytynoja and Goldman, 2008), which has been designed for de novo MSA.”
An alignment confidence score capturing robustness to guide tree uncertainty. (2010, methodology): “In the last decade, considerable efforts have been made to improve alignment accuracy (e.g., Notredame et al. 2000; Edgar 2004; Katoh et al. 2005; Loytynoja and Goldman 2008).”
Ensembl comparative genomics resources (2016, background): “The ncRNA gene sequences are aligned with Infernal (48), while the flanking regions are aligned with PRANK (49).”
A simple method to control over-alignment in the MAFFT multiple sequence alignment program (2016, methodology): “In simulation-based benchmarks that take into consideration over-alignment, PRANK and BAli-Phy consistently outperform other methods (L € oytynoja and Goldman, 2008; Redelings, 2014).”
Advances and pitfalls of protein structural alignment. (2009, methodology): “In sequence comparison, this is typically achieved by modeling amino acid substitution as well as insertion and deletion probabilities [5].”
webPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser (2010, methodology): “The alignments generated using the algorithm have been found to perform well in phylogenetic alignment benchmarks based on both real [4] and simulated [2] data, and give the most accurate inference of selection on codon sequences [5].”
Ensembl comparative genomics resources (2016, background): “The ncRNA gene sequences are aligned with Infernal (48), while the flanking regions are aligned with PRANK (49).”
Fast Statistical Alignment (2009, background): “Thus, it is important that alignments be as ‘‘evolutionarily correct’’ as possible [47], which is the purpose of the accuracy criterion.”
Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega (2011, background): “The weakest program here, is Clustal W (Larkin et al, 2007) followed by PRANK (Löytynoja and Goldman, 2008).”
Legume phylogeny and classification in the 21st century: Progress, prospects and lessons for other species-rich clades (2013, methodology): “To explore this alignment issue, M.J. Sanderson and M.F. Wojciechowski sampled 1000 legume ITS sequences randomly across those available from GenBank and built multiple sequence alignments using the progressive aligner MUSCLE (Edgar, 2004) and PRANK (Loytynoja & Goldman, 2008), which attempts to inf”
Phylogenetic assessment of alignments reveals neglected tree signal in gaps (2010, background): “Using simulation, Löytynoja and Gold-man, the authors of Prank, have recently argued that other alignment programs infer less phylogenetically plausible alignments [41].”