July 31, 2025Open Access

Tranquillyzer: A Flexible Neural Network Framework for Structural Annotation and Demultiplexing of Long-Read Transcriptomes

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Tranquillyzer enables precise structural annotation in long-read single-cell RNA sequencing data.
It efficiently identifies cell barcodes and unique molecular identifiers, critical for accurate demultiplexing.
Combining deep learning with context-aware design, it overcomes limitations of existing methods.
The flexible framework supports various library formats, emphasizing scalability and visualization for larger datasets.

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Deep learning enhances demultiplexing accuracy in single-cell RNA sequencing, suggesting improved data analysis.

Semwal et al. (2025) studied this question.

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