Exploring the Roles of RNAs in Chromatin Architecture Using Deep Learning

Abstract

Recent studies have highlighted the impact of both transcription and transcripts on 3D genome organization, particularly its dynamics. Here, we propose a deep learning framework, called AkitaR, that leverages both genome sequences and genome-wide RNA-DNA interactions to investigate the roles of chromatin-associated RNAs (caRNAs) on genome folding in HFFc6 cells. In order to disentangle the cis- and trans-regulatory roles of caRNAs, we compared models with nascent transcripts, trans-located caRNAs, open chromatin data, or DNA sequence alone. Both nascent transcripts and trans-located caRNAs improved the models predictions, especially at cell-type-specific genomic regions. Analyses of feature importance scores revealed the contribution of caRNAs at TAD boundaries, chromatin loops and nuclear sub-structures such as nuclear speckles and nucleoli to the models predictions. Furthermore, we identified non-coding RNAs (ncRNAs) known to regulate chromatin structures, such as MALAT1 and NEAT1, as well as several novel RNAs, RNY5, RPPH1, POLG-DT and THBS1-IT, that might modulate chromatin architecture through trans-interactions in HFFc6. Our modeling also suggests that transcripts from Alus and other repetitive elements may facilitate chromatin interactions through trans R-loop formation. Our findings provide new insights and generate testable hypotheses about the roles of caRNAs in shaping chromatin organization.

SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models

“…This value is relevant because when a variant is too close to the chromosome arm end– meaning near centromeres or telomeres– it will be positioned near the edge of the sequence ( Supplementary Fig. S1B ), which can have worse prediction accuracy than the rest of the sequence ( Kuang and Pollard 2023 ). Thus, SuPreMo is a flexible tool for performing ISM that can be applied across sequence-based ML models.…”

Functional identification of cis-regulatory long noncoding RNAs at controlled false-discovery rates

“…Recent studies have mapped RNA:DNA contacts at a global scale and have demonstrated that the majority of contacts occur locally to the RNA transcription, suggesting a mechanism for cis-regulation of genes by 'biochemical' contact of a lncRNA transcript (51)(52)(53). To examine this further, we evaluated the overlap of functional lncRNA-target relationships (defined by LOF experiments) and biochemical relationships (defined by RNA:DNA contacts).…”