Time course gene expression experiments

Martínez, Octavio

doi:10.1016/b978-0-323-91810-7.00014-5

Cited by 2 publications

(7 citation statements)

References 49 publications

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“…This MN is easier to interpret than genome-wide networks because the researcher can focus on relations between specific processes of interest, with the additional advantage that sub-networks show particular time expression profiles (Figure 7). This approach can be directly applied using the Salsa software to other sets of Capsicum genes during fruit development, grouped by different criteria, or can also be implemented for other organisms or development processes by following the guidelines given in [20].…”

Section: Discussionmentioning

confidence: 99%

“…About the design of future experiments using our methodology, the Gene2Gene and Gene2TF algorithms are only applicable to development processes for which time expression profiles exist in a set of different genotypes. The first question is how many time points need to be fixed to estimate time expression profiles-which can always be transformed to SEPs [20]. In this regard, any case with less than five time points will be too flimsy to obtain useful correlation coefficients, and of course, more time points will give a more precise discrete description of the continuos changes of gene expression through time.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 1 presents standardized expression profiles (SEPs) [19,20] for two genes (Kinesin-4 and HMGB prot. 6) in two different chili pepper accessions, "Ancho San Luis", a domesticated accession, and the wild accession, "Piquín Quéretaro", with keys "AS" and "QU" in the figure legend.…”

Section: Congruent Gene Correlationsmentioning

confidence: 99%

“…The original data were duplicated RNA-Seq libraries from fruits of 12 genotypes (accessions) of chili pepper (Capsicum annuum L.) sampled in each case at seven times of development. These genome-wide data were annotated with GO categories [11], and they were processed to give standardized time expression profiles (SEPs) [19,20]. All data and functions employed here to perform the analyses are implemented in a new version of the publicly available R package "Salsa" [24], and details of the computations and results are presented in supplementary SI.3.…”

Section: Data Analysesmentioning

confidence: 99%

“…Such modules can then be linked together into larger networks by the genes shared between them. The novelty of this approach consists in the design and implementation of an algorithm that uses standardized expression profiles (SEPs) [19,20] from independent experiments. This allows us to judge and tune the robustness of the GFN modules, assuring that they show only relations, which are confirmed in a set of representative and independent genotypes.…”

Section: Introductionmentioning

confidence: 99%

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Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Capsicum annuum L.)

Flores-Díaz

Escoto-Sandoval

Cervantes‐Hernández

et al. 2023

Plants

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Gene co-expression networks are powerful tools to understand functional interactions between genes. However, large co-expression networks are difficult to interpret and do not guarantee that the relations found will be true for different genotypes. Statistically verified time expression profiles give information about significant changes in expressions through time, and genes with highly correlated time expression profiles, which are annotated in the same biological process, are likely to be functionally connected. A method to obtain robust networks of functionally related genes will be useful to understand the complexity of the transcriptome, leading to biologically relevant insights. We present an algorithm to construct gene functional networks for genes annotated in a given biological process or other aspects of interest. We assume that there are genome-wide time expression profiles for a set of representative genotypes of the species of interest. The method is based on the correlation of time expression profiles, bound by a set of thresholds that assure both, a given false discovery rate, and the discard of correlation outliers. The novelty of the method consists in that a gene expression relation must be repeatedly found in a given set of independent genotypes to be considered valid. This automatically discards relations particular to specific genotypes, assuring a network robustness, which can be set a priori. Additionally, we present an algorithm to find transcription factors candidates for regulating hub genes within a network. The algorithms are demonstrated with data from a large experiment studying gene expression during the development of the fruit in a diverse set of chili pepper genotypes. The algorithm is implemented and demonstrated in a new version of the publicly available R package “Salsa” (version 1.0).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Congruent Gene Correlationsmentioning

confidence: 99%

Section: Data Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Capsicum annuum L.)

Flores-Díaz

Escoto-Sandoval

Cervantes‐Hernández

et al. 2023

Plants

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Estimating Transcriptome Diversity and Specialization in Capsicum annuum L.

Ochoa-Alejo,

Reyes-Valdés,

Martínez

2024

Plants

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Chili pepper fruits of the genus Capsicum represent excellent experimental models to study the growth, development, and ripening processes in a non-climacteric species at the physiological, biochemical, and molecular levels. Fruit growth, development, and ripening involve a complex, harmonious, and finely controlled regulation of gene expression. The purpose of this study was to estimate the changes in transcriptome diversity and specialization, as well as gene specificities during fruit development in this crop, and to illustrate the advantages of estimating these parameters. To achieve these aims, we programmed and made publicly available an R package. In this study, we applied these methods to a set of 179 RNA-Seq libraries from a factorial experiment that includes 12 different genotypes at various stages of fruit development. We found that the diversity of the transcriptome decreases linearly from the flower to the mature fruit, while its specialization follows a complex and non-linear behavior during this process. Additionally, by defining sets of genes with different degrees of specialization and applying Gene Ontology enrichment analysis, we identified processes, functions, and components that play a central role in particular fruit development stages. In conclusion, the estimation of diversity, specialization, and specificity summarizes the global properties of the transcriptomes, providing insights that are difficult to achieve by other means.

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Time course gene expression experiments

Cited by 2 publications

References 49 publications

Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Capsicum annuum L.)

Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Capsicum annuum L.)

Estimating Transcriptome Diversity and Specialization in Capsicum annuum L.

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