title: PICS-Ord: unlimited coding of ambiguous regions by pairwise identity and cost scores ordination.
abstract: BACKGROUND
  We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method.
  
  
  RESULTS
  Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model.
  
  
  CONCLUSIONS
  Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED.
  
  
  BACKGROUND
  An implementation of the PICS-Ord algorithm is available from http://scit.us/projects/ngila/wiki/PICS-Ord. It requires both the statistical software, R http://www.r-project.org and the alignment software Ngila http://scit.us/projects/ngila.
authors.affiliation: Department of Botany, The Field Museum, 1400 South Lake Shore Drive, Chicago, IL 60605-2496, USA. rlucking@fieldmuseum.org
authors.forename: Robert
authors.initials: R
authors.lastname: Lücking
authors.forename: Brendan P
authors.initials: BP
authors.lastname: Hodkinson
authors.forename: Alexandros
authors.initials: A
authors.lastname: Stamatakis
authors.forename: Reed A
authors.initials: RA
authors.lastname: Cartwright
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004247:DNA / Q000235:genetics; D008011:Lichens / Q000235:genetics; D016013:Likelihood Functions; D010802:Phylogeny; D025341:Principal Component Analysis; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004247:DNA