{"doi":"10.1101/2025.07.25.666829","title":"Tranquillyzer: A Flexible Neural Network Framework for Structural Annotation and Demultiplexing of Long-Read Transcriptomes","abstract":"Abstract Long-read single-cell RNA sequencing using platforms such as Oxford Nanopore Technologies (ONT) enables full-length transcriptome profiling at single-cell resolution. However, high sequencing error rates, diverse library architectures, and increasing dataset scale introduce major challenges for accurately identifying cell barcodes (CBCs) and unique molecular identifiers (UMIs) - key prerequisites for reliable demultiplexing and deduplication, respectively. Existing pipelines rely on hard-coded heuristics or local transition rules that cannot fully capture this broader structural context and often fail to robustly interpret reads with indel-induced shifts, truncated segments, or non-canonical element ordering. We introduce Tranquillyzer (TRANscript QUantification In Long reads-anaLYZER), a flexible, architecture-aware deep learning framework for processing long-read single-cell RNA-seq data. Tranquillyzer employs a hybrid neural network architecture and a global, context-aware design, and enables precise identification of structural elements - even when elements are shifted, partially degraded, or repeated due to sequencing noise or library construction variability. In addition to supporting established single-cell protocols, Tranquillyzer accommodates custom library formats through rapid, one-time model training on user-defined label schemas, typically completed within a few hours on standard GPUs. Additional features such as scalability across large datasets and comprehensive visualization capabilities further position Tranquillyzer as a flexible and scalable framework solution for processing long-read single-cell transcriptomic datasets.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":558618,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9519,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":638458,"name":"Jacob Morrison","orcid":"0000-0001-8592-4744","position":1,"is_corresponding":false},{"id":496127,"name":"Ian Beddows","orcid":"0000-0001-7403-0016","position":2,"is_corresponding":false},{"id":706085,"name":"Theron Palmer","orcid":"0000-0002-8806-4062","position":3,"is_corresponding":false},{"id":1459011,"name":"Mary Rhodes","orcid":"0000-0002-7293-3171","position":4,"is_corresponding":false},{"id":280738,"name":"H. Josh Jang","orcid":"0000-0001-9654-4448","position":5,"is_corresponding":false},{"id":486008,"name":"Benjamin K. Johnson","orcid":"0000-0002-1482-1032","position":6,"is_corresponding":false},{"id":14040,"name":"Hui Shen","orcid":"0000-0001-9767-4084","position":7,"is_corresponding":false},{"id":1028106,"name":"Ayush Semwal","orcid":"0000-0002-7598-6834","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-19T02:55:25.969263Z","pmid":"40766630","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}