Measuring the Evolution of Ontology Complexity: The Gene Ontology Case Study

Dameron, Olivier; Bettembourg, Charles; Meur, Nolwenn Le

doi:10.1371/journal.pone.0075993

Cited by 9 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For instance, the strength of selection on gene expression varies with the functional categories a gene annotates to in nematodes (Denver et al 2005); however, no independent information on pleiotropy is used to define these categorical divisions, and such comparisons are difficult to interpret if genes annotate to multiple categories (Rhee et al 2008). Finally, it is not clear how metrics describing connections among genes based on analyses of GO-directed acyclic graphs (Dameron et al 2013) relate to real interactions among genes, given the parent-child nature of these graphs (Rhee et al 2008). There has been considerable debate in the literature about the effect of pleiotropy (inferred in many ways) on the rate of protein sequence evolution (Pal et al 2006).…”

Section: Discussionmentioning

confidence: 99%

Pleiotropic Mutations Are Subject to Strong Stabilizing Selection

et al. 2014

View full text Add to dashboard Cite

The assumption that pleiotropic mutations are more deleterious than mutations with more restricted phenotypic effects is an important premise in models of evolution. However, empirical evidence supporting this assumption is limited. Here, we estimated the strength of stabilizing selection on mutations affecting gene expression in male Drosophila serrata. We estimated the mutational variance (V M ) and the standing genetic variance (V G ) from two well-matched panels of inbred lines: a panel of mutation accumulation (MA) lines derived from a single inbred ancestral line and a panel of inbred lines derived from an outbred population. For 855 geneexpression traits, we estimated the strength of stabilizing selection as s = V M /V G . Selection was observed to be relatively strong, with 17% of traits having s . 0.02, a magnitude typically associated with life-history traits. Randomly assigning expression traits to five-trait sets, we used factor analytic mixed modeling in the MA data set to identify covarying traits that shared pleiotropic mutations. By assigning traits to the same trait sets in the outbred line data set, we then estimated s for the combination of traits affected by pleiotropic mutation. For these pleiotropic combinations, the median s was three times greater than s acting on the individual component traits, and 46% of the pleiotropic trait combinations had s . 0.02. Although our analytical approach was biased toward detecting mutations with relatively large effects, likely overestimating the average strength of selection, our results provide widespread support for the prediction that stronger selection can act against mutations with pleiotropic effects.

show abstract

Section: Discussionmentioning

confidence: 99%

Pleiotropic Mutations Are Subject to Strong Stabilizing Selection

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Además, son importantes en la regulación génica, ya que fueron identificados a partir de los genes diferencialmente expresados (DERs) en el tejido del xilema secundario del tallo. Debido a que las funciones particulares de una proteína se determinan analizando su secuencia de residuos de aminoácidos, la evaluación de dominios conservados funcionales mediante genes ortólogos de T. grandis en A. thaliana es útil para identificar posibles funciones del gen (Consortium, 2000;Dameron et al, 2013), siendo fundamental hacer uso de esta especie modelo, ya que tiene abundante recurso bioinformático disponible. También, la base de datos PubMed fue útil para obtener publicaciones que documentan evidencia experimental encontradas con el programa pubtator para filtrar la información y optimizar la sistematización de datos, la predicción de genes y el soporte experimental.…”

Section: Conclusionesunclassified

“…Por otro lado, la tecnología de RNAseq es una técnica cuantitativa que ayuda a determinar niveles de expresión de RNA y su aplicación directa puede direccionarse a la construcción de bases de datos a gran escala para hacer estudios de redes de coexpresión (Dameron et al, 2013;Mizrachi et al, 2010). Dentro de las largas listas de genes detectadas por el RNAseq en diferentes condiciones ambientales, de edad o de tejidos, se pueden analizar por bioinformática patrones de expresión similares entre sí (también llamado coexpresión); genes con patrones y funciones biológicas similares pueden ser anotados por ontologías génicas y luego ser agrupados (Consortium, 2000;Dameron et al, 2013). Además, la sistematización y agrupación de los genes coexpresados, pueden estar apoyados con resultados experimentales (Consortium, 2000;Dameron et al, 2013), y de ese modo poder elucidar rutas moleculares que rigen a los organismos vivos (Fröhlich et al, 2007;Jin et al, 2014).…”

unclassified

“…Dentro de las largas listas de genes detectadas por el RNAseq en diferentes condiciones ambientales, de edad o de tejidos, se pueden analizar por bioinformática patrones de expresión similares entre sí (también llamado coexpresión); genes con patrones y funciones biológicas similares pueden ser anotados por ontologías génicas y luego ser agrupados (Consortium, 2000;Dameron et al, 2013). Además, la sistematización y agrupación de los genes coexpresados, pueden estar apoyados con resultados experimentales (Consortium, 2000;Dameron et al, 2013), y de ese modo poder elucidar rutas moleculares que rigen a los organismos vivos (Fröhlich et al, 2007;Jin et al, 2014). Actualmente, se conocen 58 familias de FTs en plantas (Jin et al, 2014;Naika et al, 2013), de las cuales algunas de ellas cumplen funciones importantes en la regulación de la expresión génica, especialmente al inicio de la transcripción.…”

unclassified

See 1 more Smart Citation

RED DE COEXPRESIÓN DE 320 GENES DE Tectona grandis RELACIONADOS CON PROCESOS DE ESTRÉS ABIÓTICO Y XILOGÉNESIS

Camel

Galeano

Carrer

2017

TIP

View full text Add to dashboard Cite

show abstract

“…As a result, interpretation is frequently based on either an implicit threshold (for example: “a similarity of 0.83 is high enough to consider that two genes are similar”) or an arbitrary one (typically 0.5 for measures in [0;1] even though no mathematical property of the measures supports this choice). Moreover, the value of these thresholds may vary over time, as both GO and GOA evolve [ 10 ]. Here, we propose a method to define suitable thresholds based on analysis of the distributions of similarity values.…”

Section: Introductionmentioning

confidence: 99%

Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI

2015

Self Cite

View full text Add to dashboard Cite

BackgroundThe analysis of gene annotations referencing back to Gene Ontology plays an important role in the interpretation of high-throughput experiments results. This analysis typically involves semantic similarity and particularity measures that quantify the importance of the Gene Ontology annotations. However, there is currently no sound method supporting the interpretation of the similarity and particularity values in order to determine whether two genes are similar or whether one gene has some significant particular function. Interpretation is frequently based either on an implicit threshold, or an arbitrary one (typically 0.5). Here we investigate a method for determining thresholds supporting the interpretation of the results of a semantic comparison.ResultsWe propose a method for determining the optimal similarity threshold by minimizing the proportions of false-positive and false-negative similarity matches. We compared the distributions of the similarity values of pairs of similar genes and pairs of non-similar genes. These comparisons were performed separately for all three branches of the Gene Ontology. In all situations, we found overlap between the similar and the non-similar distributions, indicating that some similar genes had a similarity value lower than the similarity value of some non-similar genes. We then extend this method to the semantic particularity measure and to a similarity measure applied to the ChEBI ontology. Thresholds were evaluated over the whole HomoloGene database. For each group of homologous genes, we computed all the similarity and particularity values between pairs of genes. Finally, we focused on the PPAR multigene family to show that the similarity and particularity patterns obtained with our thresholds were better at discriminating orthologs and paralogs than those obtained using default thresholds.ConclusionWe developed a method for determining optimal semantic similarity and particularity thresholds. We applied this method on the GO and ChEBI ontologies. Qualitative analysis using the thresholds on the PPAR multigene family yielded biologically-relevant patterns.

show abstract

Measuring the Evolution of Ontology Complexity: The Gene Ontology Case Study

Cited by 9 publications

References 43 publications

Pleiotropic Mutations Are Subject to Strong Stabilizing Selection

Pleiotropic Mutations Are Subject to Strong Stabilizing Selection

RED DE COEXPRESIÓN DE 320 GENES DE Tectona grandis RELACIONADOS CON PROCESOS DE ESTRÉS ABIÓTICO Y XILOGÉNESIS

Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI

Contact Info

Product

Resources

About