Resources
circFL-seq is a full-length circRNA sequencing method to profile ordinary and fusion circRNA at the isoform level. Related articles: Liu Z, Tao C, Li S, Du M, Bai Y, Hu X, Li Y, Chen J, Yang E. circFL-seq reveals full-length circular RNAs with rolling circular reverse transcription and nanopore sequencing. eLife. 2021;10:e69457. [Pubmed] [Article] Liu Z, Yang E. circFL-seq, A Full-length circRNA Sequencing Method. Bio-Protoccol. 2022;12(8):e4384. [Pubmed] [Article] Docs: circFL-seq library construction & circfull Developed by Dr. Zelin LIU at 2021 DEBKS is a toolkit for detecting differentially expressed circRNA (DEC) between two RNA-seq sample groups with replicates. Article: Liu Z, Ding H, She J, Chen C, Zhang W, Yang E. DEBKS: A Tool to Detect Differentially Expressed Circular RNAs. Genomics, Proteomics & Bioinformatics. 2022;20(3):549-556. [Pubmed] [Article] Developed by Dr. Zelin LIU at 2022 ucircFL-seq is an amplification optimized and unique molecular identifier guided full-length circRNA sequencing method to profile high accuracy ordinary and fusion circRNA at the isoform level. Article: Jin Y, Hu X, Zhang Y, She J, Tao C, Yang E. Amplification Optimized and Unique Molecular Identifier Guided High Accuracy Full-length CircRNA Sequencing. Genomics, Proteomics & Bioinformatics. 2026:qzag009 [Pubmed] [Article] Docs: ucircFL-seq library construction & ucircfull Developed by Dr. Yueqi JIN at 2026 SERVE is a pipeline for identifying expressed endogenous retroviruses (ERVs). Article: She J, Du M, Xu Z, Jin Y, Li Y, Zhang D, Tao C, Chen J, Wang J, Yang E. The landscape of hervRNAs transcribed from human endogenous retroviruses across human body sites. Genome Biology. 2022;23(1):231. [Pubmed] [Article] Developed by Dr. Jianqi SHE at 2022 TEIRI is a pipeline to identify TE-initiated RNAs by integrating short-read RNA-seq, long-read RNA-seq, CAGE, and RAMPAGE datasets. Article: Zhang Y, She J, Hu X, Jin Y, Zhao J, Hou S, Tao C, Du M, Yang E. Transposable elements drive species-specific and tissue-specific transcriptomes in human development. Genome Biology. 2025;26(1):379. [Pubmed] [Article] Developed by Dr. Yun ZHANG at 2025 Deep-TEIRI is a pipeline to identify TE-initiated transcripts by integrating DNA sequence and RNA-seq coverage information with a deep-learning model. Article: Zhang Y, She J, Hu X, Jin Y, Zhao J, Hou S, Tao C, Du M, Yang E. Transposable elements drive species-specific and tissue-specific transcriptomes in human development. Genome Biology. 2025;26(1):379. [Pubmed] [Article] Developed by Dr. Yun ZHANG at 2025
MTM (Multi-tissue Transcriptome Mapping) is a unified deep multi-task learning framework that predicts tissue-specific gene expression profiles using any available tissue expression profile from the same donor, such as blood gene expression. Article: He G, Chen M, Bian Y, Yang E. MTM: a multi-task learning framework to predict individualized tissue gene expression profiles. Bioinformatics. 2023;39(6):btad363. [Pubmed] [Article] Developed by Dr. Guangyi HE at 2023 MTGT (Multiscale Text Feature-Guided Transformer) is a deep learning framework for medical image segmentation. It uses multiscale text features to guide a Transformer architecture and improve segmentation performance on medical imaging datasets. Article: Zhao L, Wang T, Zhang X, Chen Y, Yang E, Tong T. MTGT: Multiscale Text Feature-Guided Transformer in medical image segmentation. Image and Vision Computing, 2026;165:105846. [Article] Developed by Longxuan Zhao at 2026circRNA tools and pipelines
circFL-seq [Github] [Docs]
DEBKS [Github] [Docs]
ucircFL-seq [Github] [Docs]
Transposable elements derived RNAs tools and pipelines
SERVE [Github] [Docs]
TEIRI [Github] [Docs]
Deep-TEIRI [Github] [Docs]
General Biomedical AI Models and Pipelines
MTM [Github] [Docs]
MTGT [Github] [Docs]