Giraffe genome sequence reveals clues to its unique morphology and physiology.

The origins of giraffe's imposing stature and associated cardiovascular adaptations are unknown. Okapi, which lacks these unique features, is giraffe's closest relative and provides a useful comparison, to identify genetic variation underlying giraffe's long neck and cardiovascular system. The genomes of giraffe and okapi were sequenced, and through comparative analyses genes and pathways were identified that exhibit unique genetic changes and likely contribute to giraffe's unique features. Some of these genes are in the HOX, NOTCH and FGF signalling pathways, which regulate both skeletal and cardiovascular development, suggesting that giraffe's stature and cardiovascular adaptations evolved in parallel through changes in a small number of genes. Mitochondrial metabolism and volatile fatty acids transport genes are also evolutionarily diverged in giraffe and may be related to its unusual diet that includes toxic plants. Unexpectedly, substantial evolutionary changes have occurred in giraffe and okapi in double-strand break repair and centrosome functions.

EST clustering error evaluation and correction.

MOTIVATION: The gene expression intensity information conveyed by (EST) Expressed Sequence Tag data can be used to infer important cDNA library properties, such as gene number and expression patterns. However, EST clustering errors, which often lead to greatly inflated estimates of obtained unique genes, have become a major obstacle in the analyses. The EST clustering error structure, the relationship between clustering error and clustering criteria, and possible error correction methods need to be systematically investigated. RESULTS: We identify and quantify two types of EST clustering error, namely, Type I and II in EST clustering using CAP3 assembling program. A Type I error occurs when ESTs from the same gene do not form a cluster whereas a Type II error occurs when ESTs from distinct genes are falsely clustered together. While the Type II error rate is <1.5% for both 5' and 3' EST clustering, the Type I error in the 5' EST case is approximately 10 times higher than the 3' EST case (30% versus 3%). An over-stringent identity rule, e.g., P >/= 95%, may even inflate the Type I error in both cases. We demonstrate that approximately 80% of the Type I error is due to insufficient overlap among sibling ESTs (ISO error) in 5' EST clustering. A novel statistical approach is proposed to correct ISO error to provide more accurate estimates of the true gene cluster profile.