ASTRO: Automated Spatial Whole-Transcriptome RNA-Expression Workflow
I tiakina i:
| I whakaputaina i: | bioRxiv (Feb 5, 2025) |
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| Kaituhi matua: | |
| Ētahi atu kaituhi: | , , , , , |
| I whakaputaina: |
Cold Spring Harbor Laboratory Press
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full text outside of ProQuest |
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Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3160209342 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2692-8205 | ||
| 024 | 7 | |a 10.1101/2025.01.24.634814 |2 doi | |
| 035 | |a 3160209342 | ||
| 045 | 0 | |b d20250205 | |
| 100 | 1 | |a Zhang, Dingyao | |
| 245 | 1 | |a ASTRO: Automated Spatial Whole-Transcriptome RNA-Expression Workflow | |
| 260 | |b Cold Spring Harbor Laboratory Press |c Feb 5, 2025 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Motivation: Despite significant advances in spatial transcriptomics, the analysis of formalin-fixed paraffin-embedded (FFPE) tissues, which constitute most clinically available samples, remains challenging. Additionally, capturing both coding and noncoding RNAs in a spatial context poses significant challenges. We recently introduced Patho-DBiT, a technology designed to address these unmet needs. However, the marked differences between Patho-DBiT and existing spatial transcriptomics protocols necessitate specialized computational tools for comprehensive whole-transcriptome analysis in FFPE samples. Results: Here, we present ASTRO, an automated pipeline developed to process spatial transcriptomics data. In addition to supporting standard datasets, ASTRO is optimized for whole-transcriptome analyses of FFPE samples, enabling the detection of various RNA species, including non-coding RNAs such as miRNAs. To compensate for the reduced RNA quality in FFPE tissues, ASTRO incorporates a specialized filtering step and optimizes spatial barcode calling, increasing the mapping rate. These optimizations allow ASTRO to spatially quantify coding and non-coding RNA species in the entire transcriptome and achieve robust performance in FFPE samples. Availability: Codes are available at GitHub (https://github.com/gersteinlab/ASTRO).Competing Interest StatementThe authors have declared no competing interest.Footnotes* Manuscript resubmitted; Figure 1 quality improved; Figure 2 quality improved; Figure 3 quality improved. | |
| 653 | |a Transcriptomes | ||
| 653 | |a Non-coding RNA | ||
| 653 | |a Transcriptomics | ||
| 653 | |a Automation | ||
| 700 | 1 | |a Chu, Zhiyuan | |
| 700 | 1 | |a Huo, Yiran | |
| 700 | 1 | |a Bai, Zhiliang | |
| 700 | 1 | |a Fan, Rong | |
| 700 | 1 | |a Lu, Jun | |
| 700 | 1 | |a Gerstein, Mark | |
| 773 | 0 | |t bioRxiv |g (Feb 5, 2025) | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3160209342/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://www.biorxiv.org/content/10.1101/2025.01.24.634814v2 |