NGS Workflow Optimization: From Sample Input to Sequencing-Ready Libraries
NGS workflow optimization starts well before the sequencer. Sample collection, nucleic acid extraction, fragmentation, library preparation, and quality control each influence library complexity, sequencing performance, and downstream analysis. Optimizing these upstream steps can help reduce failed runs, minimize bias, and improve confidence in genomic data across a wide range of sample types.
Why NGS Workflow Optimization Matters for FFPE and Low-Input Samples
FFPE and low-input clinical samples often yield fragmented or limited nucleic acid, so decisions made early in the workflow carry more weight in the final data. Lia covers how to interpret DIN, RIN, and DV200 when assessing these samples, how the choice between mechanical and enzymatic fragmentation affects library quality, and what to check during library QC before committing to a sequencing run. For labs running whole genome, exome, or transcriptome workflows, NGS workflow optimization at these upstream stages can help reduce GC bias and duplicate reads.
Topics Covered in This NGS Workflow Optimization Webinar
Sample quality metrics for successful library preparation
DNA and RNA fragmentation strategies
Mechanical versus enzymatic fragmentation
Library quality control and validation
Optimizing NGS workflows
Working with FFPE and low-input samples
Whole genome, exome, and transcriptome sequencing workflows
Reducing GC bias and duplicate reads