AI Article Synopsis

  • The domestic chicken is used in developmental biology and livestock research, leading to the creation of a comprehensive mRNA expression atlas by integrating various RNA-seq datasets.
  • This integration involved standardizing data through random down-sampling and using tools like Kallisto and Graphia to identify co-expressed gene clusters related to specific tissues and developmental stages.
  • The study emphasizes that RNA-seq datasets from different labs can be compared effectively, allowing further expansion and application of this method to other species.

Article Abstract

Background: The domestic chicken (Gallus gallus) is widely used as a model in developmental biology and is also an important livestock species. We describe a novel approach to data integration to generate an mRNA expression atlas for the chicken spanning major tissue types and developmental stages, using a diverse range of publicly-archived RNA-seq datasets and new data derived from immune cells and tissues.

Results: Randomly down-sampling RNA-seq datasets to a common depth and quantifying expression against a reference transcriptome using the mRNA quantitation tool Kallisto ensured that disparate datasets explored comparable transcriptomic space. The network analysis tool Graphia was used to extract clusters of co-expressed genes from the resulting expression atlas, many of which were tissue or cell-type restricted, contained transcription factors that have previously been implicated in their regulation, or were otherwise associated with biological processes, such as the cell cycle. The atlas provides a resource for the functional annotation of genes that currently have only a locus ID. We cross-referenced the RNA-seq atlas to a publicly available embryonic Cap Analysis of Gene Expression (CAGE) dataset to infer the developmental time course of organ systems, and to identify a signature of the expansion of tissue macrophage populations during development.

Conclusion: Expression profiles obtained from public RNA-seq datasets - despite being generated by different laboratories using different methodologies - can be made comparable to each other. This meta-analytic approach to RNA-seq can be extended with new datasets from novel tissues, and is applicable to any species.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6081845PMC
http://dx.doi.org/10.1186/s12864-018-4972-7DOI Listing

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