{"database":"GEO","file_versions":[],"scores":null,"additional":{"omics_type":["Other"],"species":["Schmidtea mediterranea"],"gds_type":["Other"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE289299"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"Syrah: a pipeline to maximize spatial transcriptomics data output","description":"Spatial analysis of gene expression patterns has been a key technique for revealing the functions of genes. Traditionally, these analyses were constrained to examining only a few candidate genes per sample. However, the advent of spatial transcriptomic techniques like Slide-seqV2 has transformed this field, enabling unbiased and massively parallel exploration of gene expression patterns within their tissue contexts through RNA sequencing. Despite its potential, Slide-seq datasets often suffer from low read counts, loss of data points, and poor quality. We have identified that a significant source of these errors stems from the chemical synthesis of mRNA capture oligonucleotides used in Slide-seqV2. To address this issue, we have developed \"Syrah,\" an analysis pipeline designed to correct these errors. Syrah can dramatically enhance Slide-seqV2 datasets by recovering nearly 30% more reads. Unlike other dataset improvement methods that rely on complex mathematical imputation or single-cell RNA-seq references, Syrah operates independently, requiring no additional datasets or intricate calculations. This innovative technique promises to salvage previously unusable Slide-seq datasets by restoring valuable reads that were inadvertently discarded during analysis.","dates":{"publication":"2026/04/21"},"accession":"GSE289299","cross_references":{"GSM":["GSM8788641","GSM8788640"],"GPL":["21689"],"GSE":["289299"],"taxon":["Schmidtea mediterranea"]}}