MUSCLE: multiple sequence alignment with high accuracy and high throughput.

By Robert C. Edgar. 2004

43,804 citations recorded by the source corpus.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Local Error Estimates Dramatically Improve the Utility of Homology Models for Solving Crystal Structures by Molecular Replacement (2015, methodology): “However, for comparison, sequence alignments were also calculated with MUSCLE (Edgar, 2004).”
ECOD: An Evolutionary Classification of Protein Domains (2014, methodology): “75 domains were automatically mapped into the ECOD hierarchy using MUSCLE alignments [27].”
Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega (2011, methodology): “Code for fast UPGMA and guide tree handling routines was adopted from MUSCLE (Edgar, 2004).”
Mobility of Plasmids (2010, methodology): “6 (44) was used to make multiple alignments (default settings), and poorly aligned columns of the alignment were removed by visual inspection using Seaview (56).”
Comparison of induced resistance activated by benzothiadiazole, (2R,3R)-butanediol and an isoparaffin mixture against anthracnose of Nicotiana benthamiana (2010, methodology): “Sequences with an e-value lower than 10-10 were collected from N. benthamiana and other Nicotiana species and aligned with MUSCLE (Edgar, 2004), and primers were designed in unique regions using GeneRunner (Hastings Software, Hastings, NY) (Table 2).”
Phylogenetic diversity promotes ecosystem stability (2012, neutral): “MUSCLE: multiple sequence alignment with high accuracy and high throughput.”
Discovery and functional characterization of diverse Class 2 CRISPR-Cas systems (2015, methodology): “[PubMed: 25883141] Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput.”
The influence of soil properties on the structure of bacterial and fungal communities across land-use types (2008, methodology): “6 (Edgar, 2004) and compared to an in house database of 100 fungal 18S sequences derived from the AFTOL data set (Lutzoni et al.”
Rapid Evolution of Enormous, Multichromosomal Genomes in Flowering Plant Mitochondria with Exceptionally High Mutation Rates (2012, methodology): “7 [92] and levels of synonymous (dS) and nonsynonymous (dN) divergence were estimated using PAML v4.”
SAbDab: the structural antibody database (2013, methodology): “In total, 133 are bound to proteins, 38 to peptides and 19 to hapten antigens.”
Kalign – an accurate and fast multiple sequence alignment algorithm (2005, methodology): “Recently two new approaches, Mafft [18] and Muscle [19] were proposed.”
A DNA barcode for land plants (2009, methodology): “We considered discrimination as successful if the minimum uncorrected interspecific p-distance involving a species was larger than its maximum intraspecific distance [all distances were calculated from pairwise global alignments counting unambiguous base substitutions only (24)].”
A subfamily‐level classification of mealybugs (Hemiptera: Pseudococcidae) based on integrated molecular and morphological data (2008, methodology): “2 (Gene Codes Corporation, Ann Arbor, MI) and then aligned using MUSCLE (Edgar, 2004). Alignments were concatenated in MACCLADE (Maddison & Maddison, 2002), andEf-1a intronswere delimited using theGT-AGrule (Rogers & Wall, 1980). Introns and ambiguously aligned ribosomal sequences were excluded prio”
PROMALS3D: a tool for multiple protein sequence and structure alignments (2008, methodology): “PREFAB ( 23 ) consists of 1682 alignments (version 4.0), each of which has two sequences with known structures and up to 24 homologous sequences added from database searches for each structure.”
A heuristic approach to determine an appropriate number of topics in topic modeling (2015, methodology): “The dataset was preprocessed and aligned with the multiple sequence alignment (MSA) algorithm MUSCLE [15].”