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Toward an unbiased evolutionary platform for unraveling Xenopus developmental gene networks.

ABSTRACT: The availability of both the Xenopus tropicalis genome and the soon to be released Xenopus laevis genome provides a solid foundation for Xenopus developmental biologists. The Xenopus community has presently amassed expression data for ∼2,300 genes in the form of published images collected in the Xenbase, the principal Xenopus research database. A few of these genes have been examined in both X. tropicalis and X. laevis and the cross-species comparison has been proven invaluable for studying gene function. A recently published work has yielded developmental expression profiles for the majority of Xenopus genes across fourteen developmental stages spanning the blastula, gastrula, neurula, and the tail-bud. While this data was originally queried for global evolutionary and developmental principles, here we demonstrate its general use for gene-level analyses. In particular, we present the accessibility of this dataset through Xenbase and describe biases in the characterized genes in terms of sequence and expression conservation across the two species. We further indicate the advantage of examining coexpression for gene function discovery relating to developmental processes conserved across species. We suggest that the integration of additional large-scale datasets--comprising diverse functional data--into Xenbase promises to provide a strong foundation for researchers in elucidating biological processes including the gene regulatory programs encoding development.

SUBMITTER: Beer R

PROVIDER: S-EPMC8128080 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundInferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications.ResultsHere we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study.ConclusionsOur study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.

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Toward an unbiased evolutionary platform for unraveling Xenopus developmental gene networks.

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