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Squire, D. M.

Publications and source records attributed to Squire, D. M..

2 recordsLinked to original sources

TIRE-seq: an Integrated Sample Extraction and Transcriptomics Workflow for High Throughput Perturbation Studies

RNA sequencing (RNA-seq) is widely used in biomedical research, advancing our understanding of gene expression across biological systems. Traditional methods require upstream RNA extraction from biological inputs, adding time and expense to workflows. We developed TIRE-seq (Turbocapture Integrated RNA Expression Sequencing) to address these challenges. TIRE-seq integrates mRNA purification directly into library preparation, eliminating the need for a separate extraction step. This streamlined approach reduces turnaround time, minimizes sample loss, and improves data quality. A comparative study with the widely used Prime-seq protocol demonstrates TIRE-seqs superior sequencing efficiency with crude cell lysates as inputs. TIRE-seqs utility was demonstrated across three biological applications. It captured transcriptional changes in stimulated human T cells, revealing activation-associated gene expression profiles. It also identified key genes driving murine dendritic cell differentiation, providing insights into lineage commitment. Lastly, TIRE-seq analyzed the dose-response and time-course effects of temozolomide on patient-derived neurospheres, identifying differentially expressed genes and enriched pathways linked to the drugs mechanism of action. With its simplified workflow and high sequencing efficiency, TIRE-seq offers a cost-effective solution for large-scale gene expression studies across diverse biological systems.

genomics↗

MHC*IMP - Imputation of Alleles for Genes in the Major Histocompatibility Complex

We report the development of MHC*IMP, a method for imputing non-classical HLA and other genes in the human Major Histocompatibility Complex (MHC). We created a reference panel for 25 genes in the MHC using allele calls from Whole Genome Sequencing data, combined with SNP data for the same individuals. We used this to construct an allele imputation model, MHC*IMP, for each gene. Cross-validation showed that MHC*IMP performs very well, with allele prediction accuracy 93% or greater for all but two of the genes, and greater than 95% for all but four.

genomics↗