Beyond Annotation: Leveraging Raw RNA‑seq Reads via
<p dir="ltr">The poster presents a new foundation-model approach that bypasses traditional RNA-seq preprocessing and instead learns directly from raw reads to detect early-stage cancers across multiple tissue types. The work reframes early detection as a sequence-level inference prob...
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2025
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| _version_ | 1852014397598203904 |
|---|---|
| author | Sang Lee (22750319) |
| author2 | Ryan Kim (22750338) |
| author2_role | author |
| author_facet | Sang Lee (22750319) Ryan Kim (22750338) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sang Lee (22750319) Ryan Kim (22750338) |
| dc.date.none.fl_str_mv | 2025-12-01T05:45:55Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.30749084.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/poster/Beyond_Annotation_Leveraging_Raw_RNA_seq_Reads_via/30749084 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Genomics and transcriptomics Gene expression (incl. microarray and other genome-wide approaches) AI Cancer Oncology RNA transcriptomics approach uncovers novel roles cfRNA RNA-seq approach |
| dc.title.none.fl_str_mv | Beyond Annotation: Leveraging Raw RNA‑seq Reads via |
| dc.type.none.fl_str_mv | Image Poster info:eu-repo/semantics/publishedVersion image |
| description | <p dir="ltr">The poster presents a new foundation-model approach that bypasses traditional RNA-seq preprocessing and instead learns directly from raw reads to detect early-stage cancers across multiple tissue types. The work reframes early detection as a sequence-level inference problem rather than a gene-expression problem. Instead of relying on annotated transcripts or fixed reference genomes, the model ingests fragment-level patterns, error signatures, splicing signals, and coverage irregularities that typically get discarded during alignment.</p><p dir="ltr">The visual layout highlights three layers of innovation:</p><ol><li>an architecture trained end-to-end on billions of raw read tokens;</li><li>a representation space that captures cancer-specific perturbations invisible to conventional pipelines;</li><li>multi-cancer classifiers demonstrating improved sensitivity on early-stage disease, particularly in low-tumor-fraction settings.</li></ol><p dir="ltr">Figures show the contrast between aligned vs raw-read signal loss, the model’s latent space separating tumor vs non-tumor samples, and cross-cohort performance benchmarks. The final section outlines translational pathways—from early liquid biopsy screening to integration with cfRNA-based multi-omics signals.</p><p dir="ltr">The overall message is clear: annotated RNA-seq has reached its ceiling, and treating raw reads as a high-dimensional language unlocks a new direction for early cancer detection.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_50e123b3dd32db3e808237a2931836da |
| identifier_str_mv | 10.6084/m9.figshare.30749084.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30749084 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Beyond Annotation: Leveraging Raw RNA‑seq Reads viaSang Lee (22750319)Ryan Kim (22750338)Genomics and transcriptomicsGene expression (incl. microarray and other genome-wide approaches)AICancerOncologyRNAtranscriptomics approach uncovers novel rolescfRNARNA-seq approach<p dir="ltr">The poster presents a new foundation-model approach that bypasses traditional RNA-seq preprocessing and instead learns directly from raw reads to detect early-stage cancers across multiple tissue types. The work reframes early detection as a sequence-level inference problem rather than a gene-expression problem. Instead of relying on annotated transcripts or fixed reference genomes, the model ingests fragment-level patterns, error signatures, splicing signals, and coverage irregularities that typically get discarded during alignment.</p><p dir="ltr">The visual layout highlights three layers of innovation:</p><ol><li>an architecture trained end-to-end on billions of raw read tokens;</li><li>a representation space that captures cancer-specific perturbations invisible to conventional pipelines;</li><li>multi-cancer classifiers demonstrating improved sensitivity on early-stage disease, particularly in low-tumor-fraction settings.</li></ol><p dir="ltr">Figures show the contrast between aligned vs raw-read signal loss, the model’s latent space separating tumor vs non-tumor samples, and cross-cohort performance benchmarks. The final section outlines translational pathways—from early liquid biopsy screening to integration with cfRNA-based multi-omics signals.</p><p dir="ltr">The overall message is clear: annotated RNA-seq has reached its ceiling, and treating raw reads as a high-dimensional language unlocks a new direction for early cancer detection.</p>2025-12-01T05:45:55ZImagePosterinfo:eu-repo/semantics/publishedVersionimage10.6084/m9.figshare.30749084.v1https://figshare.com/articles/poster/Beyond_Annotation_Leveraging_Raw_RNA_seq_Reads_via/30749084CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307490842025-12-01T05:45:55Z |
| spellingShingle | Beyond Annotation: Leveraging Raw RNA‑seq Reads via Sang Lee (22750319) Genomics and transcriptomics Gene expression (incl. microarray and other genome-wide approaches) AI Cancer Oncology RNA transcriptomics approach uncovers novel roles cfRNA RNA-seq approach |
| status_str | publishedVersion |
| title | Beyond Annotation: Leveraging Raw RNA‑seq Reads via |
| title_full | Beyond Annotation: Leveraging Raw RNA‑seq Reads via |
| title_fullStr | Beyond Annotation: Leveraging Raw RNA‑seq Reads via |
| title_full_unstemmed | Beyond Annotation: Leveraging Raw RNA‑seq Reads via |
| title_short | Beyond Annotation: Leveraging Raw RNA‑seq Reads via |
| title_sort | Beyond Annotation: Leveraging Raw RNA‑seq Reads via |
| topic | Genomics and transcriptomics Gene expression (incl. microarray and other genome-wide approaches) AI Cancer Oncology RNA transcriptomics approach uncovers novel roles cfRNA RNA-seq approach |