Computational Methods for Annotation Transfers from Sequence

Cozzetto, Domenico; Jones, David T.

doi:10.1007/978-1-4939-3743-1_5

Cited by 43 publications

(35 citation statements)

References 78 publications

(32 reference statements)

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“…The prediction of gene function generally proceeds by the transfer of function from genes with experimental evidence to unannotated, or less-annotated, genes that are similar by some measure [42]. While several methods use multiple data types to carry out predictions [31,47,11], many solely rely on evolutionary relationships [16,24,6,10] and are the focus of the current study.…”

Section: Introductionmentioning

confidence: 99%

The Ortholog Conjecture Revisited: the Value of Orthologs and Paralogs in Function Prediction

Stamboulian

Guerrero

Hahn

et al. 2019

Preprint

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The computational prediction of gene function is a key step in making full use of newly sequenced genomes. Function is generally predicted by transferring annotations from homologous genes or proteins for which experimental evidence exists. The "ortholog conjecture" proposes that orthologous genes should be preferred when making such predictions, as they evolve functions more slowly than paralogous genes. Previous research has provided little support for the ortholog conjecture, though the incomplete nature of the data cast doubt on the conclusions. Here we use experimental annotations from over 40,000 proteins, drawn from over 80,000 publications, to revisit the ortholog conjecture in two pairs of species: (i) Homo sapiens and Mus musculus and (ii) Saccharomyces cerevisiae and Schizosaccharomyces pombe. By making a distinction between questions about the evolution of function versus questions about the prediction of function, we find strong evidence against the ortholog conjecture in the context of function prediction, though questions about the evolution of function remain difficult to address. In both pairs of species, we quantify the amount of data that must be ignored if paralogs are discarded, as well as the resulting loss in prediction accuracy. Taken as a whole, our results support the view that the types of homologs used for function transfer are largely irrelevant to the task of function prediction. Aiming to maximize the amount of data used for this task, regardless of whether it comes from orthologs or paralogs, is most likely to lead to higher prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

The Ortholog Conjecture Revisited: the Value of Orthologs and Paralogs in Function Prediction

Stamboulian

Guerrero

Hahn

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…4 and 6 [ 8 , 9 ]) and predicted (Chap. 5 [ 10 ]), for all known proteins, noncoding RNA sequences, and cellular components. In other words, carrying out comprehensive functional annotation is what drives the project, not the ontology itself.…”

Section: Database Cross-referencementioning

confidence: 99%

The Vision and Challenges of the Gene Ontology

Lewis

2016

Methods in Molecular Biology

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The overarching goal of the Gene Ontology (GO) Consortium is to provide researchers in biology and biomedicine with all current functional information concerning genes and the cellular context under which these occur. When the GO was started in the 1990s surprisingly little attention had been given to how functional information about genes was to be uniformly captured, structured in a computable form, and made accessible to biologists. Because knowledge of gene, protein, ncRNA, and molecular complex roles is continuously accumulating and changing, the GO needed to be a dynamic resource, accurately tracking ongoing research results over time. Here I describe the progress that has been made over the years towards this goal, and the work that still remains to be done, to make of the Gene Ontology (GO) Consortium realize its goal of offering the most comprehensive and up-to-date resource for information on gene function. Key words Gene Ontology , Gene function , Genomics , Biological modeling MotivationFrom their outset in the early 1990s it was obvious that biological databases demanded a methodical way of describing the function of genes. For one thing, a model system's raison d ' etre was to gain insight into human health and, in the days before entire genomes and proteomes were available, the relevant connections to human biology were largely based on textual descriptions of biological role. In conjunction, as genomes such as yeast were being completed, new laboratory techniques were being developed for surveying the genome, such as microarray expression panels, and these data cried out for systematic description of the voluminous results. Finally, lest we forget, this period also saw the advent of the "World Wide Web." The early pioneers in biological databases were quick to take advantage of the latest technologies for data dissemination (much easier than shipping a copy of GenBank on tape or disk drive as was the norm), but exchanging data in a rational and efficient manner required concomitant syntactic and semantic

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“…Such a dataset should not be used to train the prediction algorithms (refer to Chap. 5 [ 7 ]) and can therefore be used to test them. In the current literature, the validation sets mimic the gold standard dataset, but they are biased:proteins that are prioritized for experimental characterization and curation are often selected for their medical or agricultural relevance, and may not be representative of the full function space that the computational methods address.…”

Section: Challenges Of Assessing Computational Prediction Of Functionmentioning

confidence: 99%

“…Once the pipeline for the computational prediction has been setup-a task which is by no means trivial-it can be relatively straightforward to obtain computational prediction of function across a large number of biological sequences. Chapter 5 [ 7 ] contains a detailed introduction to the methods used in computational annotation.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Computational Gene Ontology Annotations

Škunca

Roberts

Steffen

2016

Methods in Molecular Biology

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Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern.In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.

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Computational Methods for Annotation Transfers from Sequence

Cited by 43 publications

References 78 publications

The Ortholog Conjecture Revisited: the Value of Orthologs and Paralogs in Function Prediction

The Ortholog Conjecture Revisited: the Value of Orthologs and Paralogs in Function Prediction

The Vision and Challenges of the Gene Ontology

Evaluating Computational Gene Ontology Annotations

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