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Bioinformatics in reproductive biology--functional annotation based on comparative sequence analysis.

Abstract

Recent studies of the genomes of a variety of model organisms have provided an unprecedented opportunity to identify and characterize all signaling molecules in the human genome. Regardless of the approaches used to decipher gene characteristics and their role in physiology, pairwise sequence comparison represents the fundamental bioinformatic tool for initial functional annotation of newly identified genes. Because genes evolved from duplication and adapted to different evolutionary niches for each organism during speciation, detailed sequence analysis could provide additional information on the biochemical and biological characteristics of novel genes. In addition, the integration of sequence-based gene discovery with phylogeny-based function prediction leads to a more complete understanding of the signaling pathways. For example, detailed pairwise sequence analysis has led to the identification of (1) stresscopin (SCP) and stresscopin-related peptides (SRP) as the CRH-related genes, (2) multiple relaxin-like factor genes, and (3) the novel glycoprotein hormone subunit family genes, alpha2 and beta5. Furthermore, based on the understanding that ligands and receptors coevolved during evolution, we have identified a variety of novel extracellular signaling polypeptides including (1) stresscopin and stresscopin-related peptides as selective ligands for the type 2 CRH receptor, (2) the pregnancy hormone, relaxin, and related peptides that activate two orphan G protein-coupled receptors (GPCRs), LGR7 and LGR8, and (3) alpha2 and beta5 that form a heterodimer capable of activating the TSH receptor. Thus, detailed studies on the characteristics and evolution of gene sequences have provided an inroad to the elucidation of novel signaling polypeptides and the associated signal transduction pathways.

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BibTeXRIS

Sheau Yu Teddy Hsu. 2004. Bioinformatics in reproductive biology--functional annotation based on comparative sequence analysis.. https://doi.org/10.1016/j.jri.2004.01.008

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