Understanding the proximal cis-regulatory architecture of allotetraploid Brassica napus using a deep sequence-to-expression model
This study demonstrates the applicability of deep convolutional neural networks for understanding the proximal cis-regulatory architecture of polyploid crop plants. For this we leveraged an existing genome assembly of the Brassica napus cultivar Express617, paired with a wide range of RNA-Seq samples of the same cultivar from various tissues and developmental stages. These included samples from leaves and apices between 7 and 98 days after planting, as well as samples of floral organs between Sanders stages 6 and 13. A tissue-agnostic model was trained to predict median expression across samples, in order to learn general regulatory features. The model was then probed to identify the patterns it had learned, revealing positional dependencies relative to the transcription start site for many transcription factor families, in particular TCP and C2C2-GATA.
This study demonstrates the applicability of deep convolutional neural networks for understanding the proximal cis-regulatory architecture of polyploid crop plants. For this we leveraged an existing genome assembly of the Brassica napus cultivar Express617, paired with a wide range of RNA-Seq samples of the same cultivar from various tissues and developmental stages. These included samples from leaves and apices between 7 and 98 days after planting, as well as samples of floral organs between Sanders stages 6 and 13. A tissue-agnostic model was trained to predict median expression across samples, in order to learn general regulatory features. The model was then probed to identify the patterns it had learned, revealing positional dependencies relative to the transcription start site for many transcription factor families, in particular TCP and C2C2-GATA.
John Innes Centre