SPARK: in silico simulations for benchmarking nascent RNA sequencing experiments
Calvo-Roitberg, E.; Lehman, J. W.; Tam, E.; Elhajjajy, S.; Engelhardt, B. E.; Pai, A. A.
Show abstract
Nascent RNA sequencing offers profound insights into transcriptional dynamics, yet there are substantial challenges to analyzing these data. The development of proper computational tools necessitates realistic benchmarking datasets that reflect biological variability and technical biases. We present simulated pre-mRNA and RNA kinetics (SPARK), a versatile in silico framework for generating reads across nascent RNA sequencing approaches. SPARK simulates the process of transcription -- allowing for variable elongation rates and pausing events -- and key experimental features. SPARK provides a comprehensive platform for computational development and benchmarking in nascent RNA genomics.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Differential Analysis of RNA Structure Probing Experiments at Nucleotide Resolution: Uncovering Regulatory Functions of RNA Structure 96%
- Generative and predictive neural networks for the design of functional RNA molecules 95%
- Optimizing 5'UTRs for mRNA-delivered gene editing using deep learning 95%
Similar papers in this journal
- CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms 96%
- Evaluating the representational power of pre-trained DNA language models for regulatory genomics 96%
- An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced TF binding 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.