Whole-Genome Annotation by Using Evidence Integration in Functional-Linkage Networks

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 101; no. 9; pp. 2888 - 2893
• Main Authors: Karaoz, Ulas, Murali, T. M., Letovsky, Stan, Zheng, Yu, Ding, Chunming, Cantor, Charles R., Kasif, Simon
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 02.03.2004; National Acad Sciences
• Subjects: Bioinformatics; Biological Sciences; Catalysis; Cellular biology; Chromosome Mapping; Computational Biology; Diagrams; Genes; Genetic linkage; Genetic screening; Genomes; Genomics; Models, Biological; Neighborhoods; Neural Networks (Computer); Oligonucleotide Array Sequence Analysis; Open reading frames; Reproducibility of Results; Ribosomes; Software; Yeasts
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.0307326101

The advent of high-throughput biology has catalyzed a remarkable improvement in our ability to identify new genes. A large fraction of newly discovered genes have an unknown functional role, particularly when they are specific to a particular lineage or organism. These genes, currently labeled "hypothetical," might support important biological cell functions and could potentially serve as targets for medical, diagnostic, or pharmacogenomic studies. An important challenge to the scientific community is to associate these newly predicted genes with a biological function that can be validated by experimental screens. In the absence of sequence or structural homology to known genes, we must rely on advanced biotechnological methods, such as DNA chips and protein-protein interaction screens as well as computational techniques to assign putative functions to these genes. In this article, we propose an effective methodology for combining biological evidence obtained in several high-throughput experimental screens and integrating this evidence in a way that provides consistent functional assignments to hypothetical genes. We use the visualization method of propagation diagrams to illustrate the flow of functional evidence that supports the functional assignments produced by the algorithm. Our results contain a number of predictions and furnish strong evidence that integration of functional information is indeed a promising direction for improving the accuracy and robustness of functional genomics.