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A team of Vanderbilt researchers has released a new benchmarking study that aims to assist scientists in selecting the most effective methods for analyzing spatial transcriptomics (ST) data.

The study led by Xin Maizie Zhou, assistant professor of biomedical engineering and computer science, evaluates computational tools in spatial transcriptomics (ST), a technology used to map gene expression patterns in tissues while preserving spatial context. It was recently published in Genome Biology.

ST involves slicing a tissue sample and placing it on a specially designed slide with spatially indexed barcodes. When the tissue is processed, the ribonucleic acid (RNA) in each specific location of the tissue is captured by these barcodes. After sequencing the RNA, the data can be mapped back to the original tissue locations, allowing researchers to visualize where certain genes are being expressed within the tissue architecture.

Since its widespread use began in 2020, this groundbreaking sequencing technology has revolutionized the understanding of complex tissues. Applications of ST include cancer research and neuroscience, such as mapping gene expression in parts of the brain to understand regional functions or disease mechanisms.

However, the variety of available tools for analyzing data from ST can be overwhelming, making it difficult to choose the right approach for specific research needs.

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