BMC Genomics (Aug 2018)

Combination of novel and public RNA-seq datasets to generate an mRNA expression atlas for the domestic chicken

  • Stephen J. Bush,
  • Lucy Freem,
  • Amanda J. MacCallum,
  • Jenny O’Dell,
  • Chunlei Wu,
  • Cyrus Afrasiabi,
  • Androniki Psifidi,
  • Mark P. Stevens,
  • Jacqueline Smith,
  • Kim M. Summers,
  • David A. Hume

DOI
https://doi.org/10.1186/s12864-018-4972-7
Journal volume & issue
Vol. 19, no. 1
pp. 1 – 19

Abstract

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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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