Abstract Researchers have traditionally used bibliographic databases to search out information. Today, the full‐text of resources is increasingly available for searching, and more researchers are performing full‐text searches. This study compares differences in the number of articles discovered between metadata and full‐text searches of the same literature cohort when searching for gene names in two biomedical literature domains. Three reviewers additionally ranked 100 articles in each domain. Significantly more articles were discovered via full‐text searching; however, the precision of full‐text searching also is significantly lower than that of metadata searching. Certain features of articles correlated with higher relevance ratings. A significant feature measured was the number of matches of the search term in the full‐text of the article, with a larger number of matches having a statistically significant higher usefulness (i.e., relevance) rating. By using the number of hits of the search term in the full‐text to rank the importance of the article, performance of full‐text searching was improved so that both recall and precision were as good as or better than that for metadata searching. This suggests that full‐text searching alone may be sufficient, and that metadata searching as a surrogate is not necessary.

UNLABELLED: Investigators conducting studies of the molecular genetics of complex traits in humans often need rationally to select a set of single nucleotide polymorphisms (SNPs) from the hundreds or thousands available for a candidate gene. Accomplishing this requires integration of genomic data from distributed databases and is both time-consuming and error-prone. We developed the TAMAL (Technology And Money Are Limiting) web site to help identify promising SNPs for further investigation. For a given list of genes, TAMAL identifies SNPs that meet user-specified criteria (e.g. haplotype tagging SNPs or SNP predicted to lead to amino acid changes) from current versions of online resources (i.e. HapMap, Perlegen, Affymetrix, dbSNP and the UCSC genome browser). AVAILABILITY: TAMAL is a platform independent web-based application available free of charge at http://neoref.ils.unc.edu/tamal. SUPPLEMENTARY INFORMATION: http://neoref.ils.unc.edu/tamal/.