Progressive sequence alignment as a prerequisitetto correct phylogenetic trees

By D. Feng, Russell F. Doolittle. 2007

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In Molecular Biology

Citations in the corpus

MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability (2013, methodology): “As listed in table 1, MAFFT version 7 has options for various alignment strategies, including progressive methods (PartTree, FFT-NS-1, and L-INS-1) (Feng and Doolittle 1987; Higgins and Sharp 1988; Katoh and Toh 2007), iterative reﬁnement methods (FFT-NS-i, L-INS-i, E-INS-i, and G-INS-i) (Barton and”
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, background): “Sequence-based protein alignment and homology detection has been extensively studied and widely applied to many biological problems such as homology modeling [1-4], phylogeny inference [5-7] and protein function prediction [8-10].”
A benchmark of multiple sequence alignment programs upon structural RNAs (2005, background): “One common approach is called progressive alignment [36], which builds a multiple alignment from pairwise alignments.”
The protein folding problem. (2008, neutral): “A key advance was the exploitation of evolutionary relationships among sequences through the development of robust sequence alignment methods (32,64,224).”
SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes (2012, positive): “The most prevalent algorithms are based on the progressive alignment (Feng and Doolittle, 1987) technique, which builds the MSA via a series of pairwise alignments of sequences and partial alignments along the branches of a guide tree.”
Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. (2007, methodology): “The most widely used approach has been the progressive method of alignment (Feng and Doolittle, 1987) that, together with enhancements related to the introduction of gap penalties, was implemented in ClustalW (Thompson et al., 1994).”
Multiple sequence alignment. (2006, positive): “The most widely used approach to construct a multiple alignment is ‘progressive alignment’ [2], whereby a set of N proteins are aligned by performing N–1 pairwise alignments of pairs of proteins or pairs of intermediate alignments, guided by a phylogenetic tree connecting the sequences.”
Application of the MAFFT sequence alignment program to large data—reexamination of the usefulness of chained guide trees (2016, methodology): “A progressive method (Feng and Doolittle, 1987; Higgins and Sharp, 1988; Hogeweg and Hesper, 1984) with approximate guide trees.”
Kalign – an accurate and fast multiple sequence alignment algorithm (2005, methodology): “The Kalign algorithm follows a strategy analogous to the standard progressive method for sequence alignment [ 21 ].”
Muscle5: High-accuracy alignment ensembles enable unbiased assessments of sequence homology and phylogeny (2021, methodology): “Most algorithms, including Clustal-Omega, MAFFT and MUSCLE, use progressive alignment according to a guide tree [6] which may cause bias towards the this tree e.”
Recent developments in the MAFFT multiple sequence alignment program (2008, methodology): “The progressive method [48, 49 ] is the most commonly used multiple alignment algorithm.”
Improved accuracy of multiple ncRNA alignment by incorporating structural information into a MAFFT-based framework (2008, background): “(ii) Based on these alignments, an initial multiple alignment is built with the progressive method [35,36].”
MUSTANG: A multiple structural alignment algorithm (2006, background): “The progressive alignment phase involves finding a guide tree along which the final multiple alignment is constructed [8, 62].”
Clustal Omega (2014, methodology): “…papers were published that described heuristic algorithms that could provide MSAs that were not guaranteed to be mathematically optimal but which were good enough to be very useful and save a lot of difﬁcult manual alignment (e.g., Hogeweg and Hesper, 1984; Feng and Doolittle, 1987; Taylor, 1987).”
Hidden Markov Models and their Applications in Biological Sequence Analysis (2009, methodology): “The most widely adopted strategy for constructing a multiple alignment is the progressive alignment approach, where sequences are assembled into one large multiple alignment through consecutive pairwise alignment steps according to a guide tree [53, 54].”