title: Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments.
abstract: Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for testing these types of alignments, as test cases either contain a very small number of sequences or are based purely on simulation rather than empirical data.
  
  
  
  We take advantage of recent developments in protein structure prediction methods to create a benchmark (ContTest) for protein MSAs containing many thousands of sequences in each test case and which is based on empirical biological data. We rank popular MSA methods using this benchmark and verify a recent result showing that chained guide trees increase the accuracy of progressive alignment packages on datasets with thousands of proteins.
  
  
  
  Benchmark data and scripts are available for download at http://www.bioinf.ucd.ie/download/ContTest.tar.gz
  
  
  
  des.higgins@ucd.ie
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Conway Institute of Biomolecular and Biomedical Research, and UCD School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland.
authors.forename: Gearóid
authors.initials: G
authors.lastname: Fox
authors.affiliation: Conway Institute of Biomolecular and Biomedical Research, and UCD School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland.
authors.forename: Fabian
authors.initials: F
authors.lastname: Sievers
authors.affiliation: Conway Institute of Biomolecular and Biomedical Research, and UCD School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland.
authors.forename: Desmond G
authors.initials: DG
authors.lastname: Higgins
mesh_terms: D000465:Algorithms; D011506:Proteins; D016415:Sequence Alignment; D012984:Software
chemical_list: D011506:Proteins