Long-Read Transcriptome Complexity and Cell-Type Regulatory Signatures in ENCODE4

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ENCODE4 sequences 216 long‑read RNA libraries (about 1 billion CCS reads) across 60 human and mouse samples and reports >100,000 full‑length transcripts with roughly one third novel, detecting 94.4% of GENCODE protein‑coding genes and 42.6% of known coding transcripts. Data processing uses Minimap, TranscriptClean, TALON and peak‑based end correction LAPA, then groups isoforms by CERBERUS triplets (TSS, intron chain, TES) and visualizes regulatory bias with SWAN; main‑select isoforms often differ from major isoforms and only ~40% of genes share the same isoform‑mode in human and mouse.

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1. 0:00Start
2. 5:05ENCODE Assays
3. 5:29Studying full-length isoforms using Long-read RNA seq
4. 6:30Long-read sequencing platforms
5. 9:27Annotating transcripts can be complicated
6. 10:08Isoform novelty categories
7. 11:20The ENCODE Annotation Pipeline
8. 12:19LAPA identifies supported TSS and TES
9. 13:30Cerberus annotates transcript isoforms as triplets to identify isoform commonalities
10. 15:16The triplet annotation captures 3 different classes of transcript isoform diversity
11. 16:06Swan is a Python library designed for the analysis and visualization of full-length transcriptomes
12. 16:19Triplet annotation quickly shows shared isoform features
13. 20:22Defining the Gene Isoform Simplex
14. 21:18Gene isoform diversity is generally constant across tissues
15. 22:03Isoform diversity can change when considering only the most highly expressed isoforms
16. 22:55How often is the MANE Select Isoform the major isoform ?
17. 25:33We detect changes in splicing regime between human and mouse