Analysis and exploration of the use of rule-based algorithms and consensus methods for the inferral of haplotypes.

The difficulty of experimental determination of haplotypes from phase-unknown genotypes has stimulated the development of nonexperimental inferral methods. One well-known approach for a group of unrelated individuals involves using the trivially deducible haplotypes (those found in individuals with zero or one heterozygous sites) and a set of rules to infer the haplotypes underlying ambiguous genotypes (those with two or more heterozygous sites). Neither the manner in which this "rule-based" approach should be implemented nor the accuracy of this approach has been adequately assessed. We implemented eight variations of this approach that differed in how a reference list of haplotypes was derived and in the rules for the analysis of ambiguous genotypes. We assessed the accuracy of these variations by comparing predicted and experimentally determined haplotypes involving nine polymorphic sites in the human apolipoprotein E (APOE) locus. The eight variations resulted in substantial differences in the average number of correctly inferred haplotype pairs. More than one set of inferred haplotype pairs was found for each of the variations we analyzed, implying that the rule-based approach is not sufficient by itself for haplotype inferral, despite its appealing simplicity. Accordingly, we explored consensus methods in which multiple inferrals for a given ambiguous genotype are combined to generate a single inferral; we show that the set of these "consensus" inferrals for all ambiguous genotypes is more accurate than the typical single set of inferrals chosen at random. We also use a consensus prediction to divide ambiguous genotypes into those whose algorithmic inferral is certain or almost certain and those whose less certain inferral makes molecular inferral preferable.

Neuroanatomical term generation and comparison between two terminologies.

An approach and software tools are described for identifying and extracting compound terms (CTs), acronyms and their associated contexts from textual material that is associated with neuroanatomical atlases. A set of simple syntactic rules were appended to the output of a commercially available part of speech (POS) tagger (Qtag v 3.01) that extracts CTs and their associated context from the texts of neuroanatomical atlases. This "hybrid" parser. appears to be highly sensitive and recognized 96% of the potentially germane neuroanatomical CTs and acronyms present in the cat and primate thalamic atlases. A comparison of neuroanatomical CTs and acronymsbetween the cat and primate atlas texts was initially performed using exact-term matching. The implementation of string-matching algorithms significantly improved the identification of relevant terms and acronyms between the two domains. The End Gap Free string matcher identified 98% of CTs and the Needleman Wunsch (NW) string matcher matched 36% of acronyms between the two atlases. Combining several simple grammatical and lexical rules with the POS tagger ("hybrid parser") (1) extracted complex neuroanatomical terms and acronyms from selected cat and primate thalamic atlases and (2) and facilitated the semi-automated generation of a highly granular thalamic terminology. The implementation of string-matching algorithms (1) reconciled terminological errors generated by optical character recognition (OCR) software used to generate the neuroanatomical text information and (2) increased the sensitivity of matching neuroanatomical terms and acronyms between the two neuroanatomical domains that were generated by the "hybrid" parser.