Ultra-fast sample-to-sequencing workflow for clinical diagnostics using micropillars
Bisogni, A. J.; Bastuzel, I.; Rashed, M.; Goffena, J.; Storz, S. H. R.; Anderson, Z. B.; Park, M. S.; Prall, T.; Zalusky, M. P. G.; Crotty, E. E.; Cole, B.; Stevens, J.; Lin, D. M.; Tian, H.; Miller, D. E.
Show abstract
We present a streamlined, solid-phase workflow for Oxford Nanopore sequencing that integrates DNA extraction, purification, and library preparation within a single microfluidic cartridge. By eliminating tube transfers and performing all enzymatic steps directly on captured DNA, the method minimizes sample loss, reduces hands-on time, and simplifies library generation for long-read sequencing. Starting from volumes as small as a single drop of blood, this integrated approach produces high-quality sequencing libraries from cell lines, whole blood, and tissue. The workflow achieves robust recovery of high-molecular-weight DNA and high pore occupancy, enabling rapid, low-complexity sample preparation suitable for clinical, field, and decentralized sequencing applications.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Molecular counting enables accurate and precise quantification of methylated ctDNA for tumor-naive cancer therapy response monitoring 94%
- Short and long-read genome sequencing methodologies for somatic variant detection; genomic analysis of a patient with diffuse large B-cell lymphoma 92%
- A flexible microfluidic system for single-cell transcriptome profiling elucidates phased transcriptional regulators of cell cycle 92%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- 3-hour genome sequencing and targeted analysis to rapidly assess genetic risk 94%
- Genetic Diagnosis of Facioscapulohumeral Muscular Dystrophy Type 1 Using Rare Variant Linkage Analysis and Long Read Genome Sequencing 89%
- Combining rare and common genetic variants improves population risk stratification for breast cancer 87%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.