MOTIVATION: Full-length DNA and protein sequences that span the entire length of a gene are ideally used for multiple sequence alignments (MSAs) and the subsequent inference of their relationships. Frequently, however, MSAs contain a substantial amount of missing data. For example, expressed sequence tags (ESTs), which are partial sequences of expressed genes, are the predominant source of sequence data for many organisms. The patterns of missing data typical for EST-derived alignments greatly compromise the accuracy of estimated phylogenies. RESULTS: We present a statistical method for inferring phylogenetic trees from EST-based incomplete MSA data. We propose a class of hierarchical models for modeling pairwise distances between the sequences, and develop a fully Bayesian approach for estimation of the model parameters. Once the distance matrix is estimated, the phylogenetic tree may be constructed by applying neighbor-joining (or any other algorithm of choice). We also show that maximizing the marginal likelihood from the Bayesian approach yields similar results to a profile likelihood estimation. The proposed methods are illustrated using simulated protein families, for which the true phylogeny is known, and one real protein family. AVAILABILITY: R code for fitting these models are available from: http://people.bu.edu/gupta/software.htm.

MOTIVATION: Identification of the genetic variation underlying complex traits is challenging. The wealth of information publicly available about the biology of complex traits and the function of individual genes permits the development of informatics-assisted methods for the selection of candidate genes for these traits. RESULTS: We have developed a computational system named CAESAR that ranks all annotated human genes as candidates for a complex trait by using ontologies to semantically map natural language descriptions of the trait with a variety of gene-centric information sources. In a test of its effectiveness, CAESAR successfully selected 7 out of 18 (39%) complex human trait susceptibility genes within the top 2% of ranked candidates genome-wide, a subset that represents roughly 1% of genes in the human genome and provides sufficient enrichment for an association study of several hundred human genes. This approach can be applied to any well-documented mono- or multi-factorial trait in any organism for which an annotated gene set exists. AVAILABILITY: CAESAR scripts and test data can be downloaded from http://visionlab.bio.unc.edu/caesar/

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.