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Optimizing NGS Library Preparation for Reliable Sequencing Data
Technique Talks

Optimizing NGS Library Preparation for Reliable Sequencing Data

Learn practical strategies for optimizing sample preparation, fragmentation, library construction, and quality control to generate high-quality next-generation sequencing (NGS) data across a range of sample types.

Brought to you byCovaris

This webinar, brought to you by Covaris, will be hosted live and available on-demand.

Tuesday, September 15, 2026
11:00 AM - 12:00 PM ET 

Generating high-quality next-generation sequencing (NGS) data depends on more than sequencing alone. Sample collection, nucleic acid extraction, fragmentation, library preparation, and quality control each influence library complexity, sequencing performance, and downstream analyses. Optimizing these upstream workflows can help reduce failed runs, minimize bias, and improve confidence in genomic data generated from a wide range of sample types.

In this webinar, Lia Abarzua, Applications Scientist at Covaris, will discuss practical strategies for optimizing NGS library preparation workflows from sample input through sequencing-ready libraries. The session will examine key quality metrics—including DNA Integrity Number (DIN), RNA Integrity Number (RIN), and DV200—along with fragmentation approaches, library quality assessment, and workflow optimization for challenging specimens such as formalin-fixed paraffin-embedded (FFPE) and low-input clinical samples. Attendees will also learn how integrated collection, extraction, and library preparation workflows can improve sequencing consistency across whole genome, whole exome, and whole transcriptome applications.

Topics to be covered

  • 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


Lia Abarzua


Lia Abarzua
Applications Scientist
Covaris

Sponsored by

  • Covaris

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