# Deep learning reveals determinants of transcriptional infidelity at nucleotide resolution in the allopolyploid line by goldfish and common carp hybrids

## TL;DR

This study uses deep learning to uncover the causes of transcriptional errors in hybrid fish at the nucleotide level.

## Contribution

A novel deep learning model identifies transcriptional infidelity determinants in goldfish and common carp hybrids.

## Key findings

- The model accurately identifies transcriptional infidelity sequences at nucleotide resolution.
- Position-specific motifs and transcription factors were found to influence transcriptional infidelity.
- The study reveals the distribution of transcription factor families linked to transcriptional errors.

## Abstract

During DNA transcription, the central dogma states that DNA generates corresponding RNA sequences based on the principle of complementary base pairing. However, in the allopolyploid line by goldfish and common carp hybrids, there is a significant level of transcriptional infidelity. To explore deeper into the causes of transcriptional infidelity in this line, we developed a deep learning model to explore its underlying determinants. First, our model can accurately identify transcriptional infidelity sequences at the nucleotide resolution and effectively distinguish transcriptional infidelity regions at the subregional level. Subsequently, we utilized this model to quantitatively assess the importance of position-specific motifs. Furthermore, by integrating the relationship between transcription factors and their recognition motifs, we unveiled the distribution of position-specific transcription factor families and classes that influence transcriptional infidelity in this line. In summary, our study provides new insights into the deeper determinants of transcriptional infidelity in this line.