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Benefits of purePlex:

  • Fast, flexible workflow with no requirement for full plate processing
  • Auto-normalization reduces QC burden, improves data consistency
  • Early pooling for easier sample handling
  • Reduced GC bias compared to other transposase-based methods
  • Significant cost and plastics savings

      Streamlined Workflow

Product Highlights

Auto-Normalization of Insert Size and Read Depth:

The insert size within a pool of samples is consistent regardless of input (panel A, left) or GC content (panel B, lower left).  In contrast, Nextera XT libraries have varied fragment distributions from GC content even after normalizing the sample input. 

 

Auto-normalization of Read Depth

Samples were normalized to inputs of 3, 5, 10, and 30 ng then underwent purePlex library prep with (+) and without () normalization reagent. Read counts for each sample are equal, regardless of input, when normalization reagent is used. In contrast, without normalization reagent, sample read count scales with input.

Specifications

Specs Description
Sample Type Amplicons, Plasmids, Genomic DNA, cDNA
DNA Input Range 5 – 50 ng
Number of Unique Index Combinations 96 (more in development)
Supported Paired Reads (Clusters)/Sample ≤ 20 million
Output Fragment Range 400 – 1,200 bp
Applications Synthetic construct sequencing (amplicons, plasmids, etc.), Low-pass whole genome sequencing, Whole small genome sequencing (<50 Mb), scRNA-seq, Metagenomics/Microbiome screening
Sequencer Compatibility All Illumina SequencerspurePlex DNA Library Prep Kit Includes:
Reactions per Kit 96
Reactions per Batch All Illumina SequencerspurePlex DNA Library Prep Kit Includes:

 

purePlex DNA Library Prep Kit Includes:

  • i7 Tagging Reagent Plate
  • i5 Tagging Reagent Plate
  • Coding Buffer (3X)
  • X Solution
  • MAGwise Paramagnetic Beads
  • Normalization Reagent
  • Library Primer Mix
Caught in the Act: Tracking the Emergence and Divergence of SARS-CoV-2 through Statewide Testing and SequencingUPCOMING WEBINAR

Date:  March 23, 2022
Time: 11:00am (PDT),  2:00pm (EDT), 8:00pm (CEDT)

Learning Objectives

  • Understand the utility of full-genome viral sequence analysis for public health
  • Recognize how viral load impacts the ability to detect and sequence intact SARS-CoV-2
  • Identify the similarities and differences in host responses to SARS-CoV-2 depending on strain and immunologic history

Speakers

Frank A. Middleton, Ph.D.
Professor Director, SUNY Molecular Analysis CoreState University of New York (SUNY) Upstate Medical University

Joseph C. Mellor, Ph.D.
Co-founder and Chief Scientific Officer of seqWell Inc.