Computational approaches to phenotyping: high-throughput phenomics

Abstract

The recent completion of the Human Genome Project has made possible a high-throughput "systems approach" for accelerating the elucidation of molecular underpinnings of human diseases, and subsequent derivation of molecular-based strategies to more effectively prevent, diagnose, and treat these diseases. Although altered phenotypes are among the most reliable manifestations of altered gene functions, research using systematic analysis of phenotype relationships to study human biology is still in its infancy. This article focuses on the emerging field of high-throughput phenotyping (HTP) phenomics research, which aims to capitalize on novel high-throughput computation and informatics technology developments to derive genomewide molecular networks of genotype-phenotype associations, or "phenomic associations." The HTP phenomics research field faces the challenge of technological research and development to generate novel tools in computation and informatics that will allow researchers to amass, access, integrate, organize, and manage phenotypic databases across species and enable genomewide analysis to associate phenotypic information with genomic data at different scales of biology. Key state-of-the-art technological advancements critical for HTP phenomics research are covered in this review. In particular, we highlight the power of computational approaches to conduct large-scale phenomics studies.

Figure 1.

Comparison of the number of distinct phenotypes and the number of gene–phenotype relationships in gene–phenotype databases and networks, showing that the PhenoGO is the largest network and that both literature text-mining techniques and OMIM provide the broadest annotations of distinct phenotypes with genes. Solid circle, ontology-anchored database; open circles, database with unstructured phenotypes; solid squares, ontology-anchored high-throughput phenotyping (HTP) phenomics; open square, HTP phenomics with unstructured phenotypes. MGI = Mouse Genome Informatics; OMIM = Online Mendelian Inheritance in Man; QMR = Quick Medical Reference; UMLS-GEO = Unified Medical Language System–Gene Expression Omnibus.

Figure 2.

Descriptions of the conceptual content and expressiveness of relationships in gene-phenotype databases. The figure shows that the current gene-phenotype databases are limited in expressiveness because binary and higher-order relationships are scarce and available only in unstructured form. Solid circle = coded in terminology; thick circle = semistructured text; thin circle = unstructured (free) text; solid square = automated (structured concepts); open square = rate-limiting curation (structured concepts); open diamond = semistructured concepts.