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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
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The technique...
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Related Experiment Video

Updated: Mar 28, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

806

Identifying prognosis-associated spatial patterns by integrating bulk RNA-seq and spatial transcriptomic data.

Yuxin Miao, Yanhong Wu, Xinqi Li

    IEEE Transactions on Computational Biology and Bioinformatics
    |March 26, 2026
    PubMed
    Summary
    This summary is machine-generated.

    We developed stSurvTrans, a novel deep transfer learning framework. It links spatial transcriptomics data with survival information to identify prognostic spatial patterns in tumors, like bile duct tumor thrombus in hepatocellular carcinoma.

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    Area of Science:

    • Computational biology
    • Cancer research
    • Bioinformatics

    Background:

    • Spatial transcriptomics (ST) reveals intra-tumor heterogeneity.
    • Bulk RNA sequencing (RNA-seq) data contains valuable clinical phenotype information.
    • Linking spatial features with survival data is crucial for cancer prognosis.

    Purpose of the Study:

    • To develop a deep transfer learning framework, stSurvTrans, for identifying prognosis-associated spatial patterns.
    • To harmonize ST and bulk RNA-seq data.
    • To transfer clinical survival information from bulk samples to ST data.

    Main Methods:

    • Conditional variational autoencoder (CVAE) for data harmonization.
    • Weibull module for survival information transfer.
    • Benchmarking on simulated datasets and hepatocellular carcinoma (HCC) ST data.

    Main Results:

    • stSurvTrans demonstrated accuracy and superiority on simulated datasets.
    • The framework successfully linked spatial patterns with survival information.
    • Identified bile duct tumor thrombus in HCC ST data as a spatial structure associated with worse prognosis.

    Conclusions:

    • stSurvTrans is an effective deep transfer learning framework for integrating ST and bulk RNA-seq data.
    • It enables the identification of spatial patterns linked to patient survival.
    • This approach has potential for improving cancer prognosis and understanding tumor biology.