Fundamentals of massive automatic pairwise alignments of protein sequences: theoretical significance of Z-value statistics.
BACKGROUND
Different automatic methods of sequence alignments are routinely used as a starting point for homology searches and function inference. Confidence in an alignment probability is one of the major fundamentals of massive automatic genome-scale pairwise comparisons, for clustering of putative orthologs and paralogs, sequenced genome annotation or multiple-genomic tree constructions. Extreme value distribution based on the Karlin-Altschul model, usually advised for large-scale comparisons are not always valid, particularly in the case of comparisons of non-biased with nucleotide-biased genomes (such that of Plasmodium falciparum). Z-values estimates based on Monte Carlo technics, can be calculated experimentally for any alignment output, whatever the method used. Empirically, a Z-value higher than approximately 8 is supposed reasonable to assess that an alignment score is significant, but this arbitrary figure was never theoretically justified.


RESULTS
In this paper, we used the Bienaymé-Chebyshev inequality to demonstrate a theorem of the upper limit of an alignment score probability (or P-value). This theorem implies that a computed Z-value is a statistical test, a single-linkage clustering criterion and that 1/Z-value(2) is an upper limit to the probability of an alignment score whatever the actual probability law is. Therefore, this study provides the missing theoretical link between a Z-value cut-off used for an automatic clustering of putative orthologs and/or paralogs, and the corresponding statistical risk in such genome-scale comparisons (using non-biased or biased genomes).
Bastien
Olivier
O
Laboratoire de Physiologie Cellulaire Végétale, Département Réponse et Dynamique Cellulaire, UMR 5168 CNRS-CEA-INRA-Université J. Fourier, CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France.
Aude
Jean-Christophe
JC
Roy
Sylvaine
S
Maréchal
Eric
E
D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry*; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D000596:Amino Acids; D011506:Proteins