ResourceLife Sciences
Efficient high-throughput scNMT profiling with the VIAFLO 384
16 Jul 2026INTEGRA Biosciences demonstrates how its VIAFLO 384 platform supports high-throughput scNMT-seq workflows for simultaneous analysis of nucleosome occupancy, DNA methylation, and gene expression at the single-cell level. By combining the VIAFLO 384, MAG module, MAGFLO™ beads, and ASSIST PLUS robot, researchers can improve throughput, reproducibility, and reagent efficiency across library preparation and sequencing workflows.
Resource details:
Resource type: Application note
Page count: 9
Read time: 14 mins
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Next Generation SequencingNext-generation sequencing (NGS), also known as whole-genome sequencing, high-throughput sequencing and massive parallel sequencing, produces and analyses thousands to millions of nucleotide sequences at once. Sequencing systems operate via varying technologies depending on the manufacturer, including sequencing by synthesis, ligation, pyrosequencing, ion semiconductor and single-molecule real-time sequencing. For NGS, library preparation is paramount to successful sequencing. In this section, explore a range of library preparation kits, from targeted, amplicon-based or hybridization-based kits including epigenomic, transcriptomic and genomic workflows to fragmentation kits. Find the best next-generation sequencing products in our peer-reviewed product directory: compare products, check customer reviews and receive pricing direct from manufacturers.GenomicsGenomics is the study of genomes, focusing on the sequencing, analysis, and interpretation of genetic material. It is key in understanding genetic diseases, evolutionary biology, and personalized medicine. Techniques like next-generation sequencing (NGS) are commonly used in genomics research. Browse our peer-reviewed product directory to find the best genomics tools, compare products, check reviews, and get pricing directly from manufacturers.TranscriptomicsHigh ThroughputHigh throughput experiments allow the simultaneous processing of several samples. This parallelization reduces the cost per experiment and increases reproducibility and output volume of data.
