title: A new statistical framework to assess structural alignment quality using information compression.
abstract: BACKGROUND
  Progress in protein biology depends on the reliability of results from a handful of computational techniques, structural alignments being one. Recent reviews have highlighted substantial inconsistencies and differences between alignment results generated by the ever-growing stock of structural alignment programs. The lack of consensus on how the quality of structural alignments must be assessed has been identified as the main cause for the observed differences. Current methods assess structural alignment quality by constructing a scoring function that attempts to balance conflicting criteria, mainly alignment coverage and fidelity of structures under superposition. This traditional approach to measuring alignment quality, the subject of considerable literature, has failed to solve the problem. Further development along the same lines is unlikely to rectify the current deficiencies in the field.
  
  
  RESULTS
  This paper proposes a new statistical framework to assess structural alignment quality and significance based on lossless information compression. This is a radical departure from the traditional approach of formulating scoring functions. It links the structural alignment problem to the general class of statistical inductive inference problems, solved using the information-theoretic criterion of minimum message length. Based on this, we developed an efficient and reliable measure of structural alignment quality, I-value. The performance of I-value is demonstrated in comparison with a number of popular scoring functions, on a large collection of competing alignments. Our analysis shows that I-value provides a rigorous and reliable quantification of structural alignment quality, addressing a major gap in the field.
  
  
  BACKGROUND
  http://lcb.infotech.monash.edu.au/I-value.
  
  
  BACKGROUND
  Online supplementary data are available at http://lcb.infotech.monash.edu.au/I-value/suppl.html.
authors.affiliation: Clayton School of Information Technology, Monash University, Clayton, VIC 3800, Australia and Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: James H
authors.initials: JH
authors.lastname: Collier
authors.affiliation: Clayton School of Information Technology, Monash University, Clayton, VIC 3800, Australia and Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Lloyd
authors.initials: L
authors.lastname: Allison
authors.affiliation: Clayton School of Information Technology, Monash University, Clayton, VIC 3800, Australia and Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Arthur M
authors.initials: AM
authors.lastname: Lesk
authors.affiliation: Clayton School of Information Technology, Monash University, Clayton, VIC 3800, Australia and Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Maria
authors.initials: M
authors.lastname: Garcia de la Banda
authors.affiliation: Clayton School of Information Technology, Monash University, Clayton, VIC 3800, Australia and Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Arun S
authors.initials: AS
authors.lastname: Konagurthu
mesh_terms: D000465:Algorithms; D044962:Data Compression; D003627:Data Interpretation, Statistical; D040681:Structural Homology, Protein