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Definitive demonstration by synthesis of genome annotation completeness.

ABSTRACT: We develop a method for completing the genetics of natural living systems by which the absence of expected future discoveries can be established. We demonstrate the method using bacteriophage øX174, the first DNA genome to be sequenced. Like many well-studied natural organisms, closely related genome sequences are available-23 Bullavirinae genomes related to øX174. Using bioinformatic tools, we first identified 315 potential open reading frames (ORFs) within the genome, including the 11 established essential genes and 82 highly conserved ORFs that have no known gene products or assigned functions. Using genome-scale design and synthesis, we made a mutant genome in which all 11 essential genes are simultaneously disrupted, leaving intact only the 82 conserved but cryptic ORFs. The resulting genome is not viable. Cell-free gene expression followed by mass spectrometry revealed only a single peptide expressed from both the cryptic ORF and wild-type genomes, suggesting a potential new gene. A second synthetic genome in which 71 conserved cryptic ORFs were simultaneously disrupted is viable but with ?50% reduced fitness relative to the wild type. However, rather than finding any new genes, repeated evolutionary adaptation revealed a single point mutation that modulates expression of gene H, a known essential gene, and fully suppresses the fitness defect. Taken together, we conclude that the annotation of currently functional ORFs for the øX174 genome is formally complete. More broadly, we show that sequencing and bioinformatics followed by synthesis-enabled reverse genomics, proteomics, and evolutionary adaptation can definitely establish the sufficiency and completeness of natural genome annotations.

SUBMITTER: Jaschke PR

PROVIDER: S-EPMC6883844 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Definitive demonstration by synthesis of genome annotation completeness.

Jaschke Paul R PR Dotson Gabrielle A GA Hung Kay S KS Liu Diane D Endy Drew D

Proceedings of the National Academy of Sciences of the United States of America 20191112 48

We develop a method for completing the genetics of natural living systems by which the absence of expected future discoveries can be established. We demonstrate the method using bacteriophage øX174, the first DNA genome to be sequenced. Like many well-studied natural organisms, closely related genome sequences are available-23 <i>Bullavirinae</i> genomes related to øX174. Using bioinformatic tools, we first identified 315 potential open reading frames (ORFs) within the genome, including the 11 e ...[more]

PMID: 31719208

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Project description:BackgroundBiological knowledge, and therefore Gene Ontology annotation sets, for human genes is incomplete. Recent studies have reported that biases in available GO annotations result in biased estimates of functional similarities of genes, but it is still unclear what the effect of incompleteness itself may be, even in the absence of bias. Pairwise gene similarities are used in a number of contexts, including gene "functional similarity" clustering and the related problem of functional ontology structure inference, but it is not known how different similarity measures or clustering methods perform on this task, and how the clusters are affected by annotation completeness.ResultsWe developed representations of both "complete" and "incomplete" GO annotation datasets based on experimentally-supported annotations from the GO database-specifically designed to model the incompleteness of human gene annotations-and computed semantic similarities for each set using a variety of different published measures. We then assessed the clusters derived from these measures using two different clustering methods: hierarchical clustering, and the CliXO algorithm. We find the CliXO algorithm, combined with appropriate measures, performs better than hierarchical clustering in reconstructing GO both when the data are complete, and incomplete. Some measures, particularly those that create a pairwise gene similarity by averaging over all pairwise annotation similarities, had consistently poor performance, and a few measures, such as Lin best-matched average and Relevance maximum, were generally among the best performers for a broad range in annotation completeness and types of GO classes. Finally, we show that for semantic similarity-based clustering, the multicellular organism process branch of the GO biological process ontology is more challenging to represent than the cellular process branch.ConclusionsWe assessed the effects of annotation completeness on the distribution of pairwise gene semantic similarity scores, and subsequent effects on the clusters derived from these scores. Our results suggest combinations of semantic similarity measures, gene-level scoring methods and clustering method that perform best for functional gene clustering using annotation sets of varying completeness. Overall, our results underscore the importance of increasing the completeness of GO annotations to for supporting computational analyses of gene function.

Dataset Information

Definitive demonstration by synthesis of genome annotation completeness.

Publications

Definitive demonstration by synthesis of genome annotation completeness.

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