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Precision indexing for cell-free DNA (cfDNA) methylation analysis
Maximize epigenetic study resolution with adapters built for low-diversity cfDNA methylation libraries. Fully compatible with the Twist Methylation Detection System and target enrichment workflows, this system enables accurate deduplication by preserving fragment integrity and library complexity for reliable liquid biopsy research.
Improve library complexity
Preserve fragment length and diversity to ensure high-resolution mapping of methylated sites across the entire genome.
Increase sequencing efficiency
Minimize false duplicate calls with UMIs, allowing for more productive sequencing runs.
Improve data reproducibility
Achieve higher mean target coverage and consistent performance across samples for more reliable epigenetic biomarker discovery.
Product data
UMI informed duplicate calling reduces library duplicates by over 15%, leading to an increase in library complexity (25–35%) and mean target coverage (6–8%) compared to standard positional based duplicate calling.
UMI Performance at Variable cfDNA Mass Inputs 5, 10, or 15 ng of cfDNA were used to generate EM-seq converted libraries with Twist Methylated UMI Adapters following the NEBNext EM-Seq Library Preparation. Libraries were captured with the Twist Alliance Pan-cancer Methylation Panel - 1.5MB using the Targeted Methylation Sequencing Protocol and sequenced on an Illumina Nextseq 550 to 1000X raw coverage. Duplicates were called using GATK MarkDuplicates tool (standard positional method) or called using UMIs with GATK UmiAwareMarkDuplicatesWithMateCigar tool.
EM-seq libraries were generated from 10 ng of human cfDNA using either NEB EM-Seq adapters or Twist Methylated UMI Adapters. Libraries were captured with the Twist Alliance Pan-Cancer Methylation Panel (1.5 Mb) using the Targeted Methylation Sequencing Protocol and sequenced on an Illumina NextSeq 550 to ~1000X raw coverage.
During apoptosis, cfDNA is released into the bloodstream. Although endonucleases fragment DNA randomly, nucleosome positioning creates biased fragment start and end sites, limiting fragment diversity and causing distinct molecules to appear as duplicates in sequencing data. UMIs, added during library preparation, label individual molecules and enable accurate discrimination between true duplicates and unique fragments.
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Superior performance across low-input cfDNA
UMI informed duplicate calling reduces library duplicates by over 15%, leading to an increase in library complexity (25–35%) and mean target coverage (6–8%) compared to standard positional based duplicate calling.
UMI Performance at Variable cfDNA Mass Inputs 5, 10, or 15 ng of cfDNA were used to generate EM-seq converted libraries with Twist Methylated UMI Adapters following the NEBNext EM-Seq Library Preparation. Libraries were captured with the Twist Alliance Pan-cancer Methylation Panel - 1.5MB using the Targeted Methylation Sequencing Protocol and sequenced on an Illumina Nextseq 550 to 1000X raw coverage. Duplicates were called using GATK MarkDuplicates tool (standard positional method) or called using UMIs with GATK UmiAwareMarkDuplicatesWithMateCigar tool.
High-coverage targeted sequencing using 10 ng cfDNA inputs
EM-seq libraries were generated from 10 ng of human cfDNA using either NEB EM-Seq adapters or Twist Methylated UMI Adapters. Libraries were captured with the Twist Alliance Pan-Cancer Methylation Panel (1.5 Mb) using the Targeted Methylation Sequencing Protocol and sequenced on an Illumina NextSeq 550 to ~1000X raw coverage.
Schematic of apoptotic cfDNA fragmentation to sequencing coverage.
During apoptosis, cfDNA is released into the bloodstream. Although endonucleases fragment DNA randomly, nucleosome positioning creates biased fragment start and end sites, limiting fragment diversity and causing distinct molecules to appear as duplicates in sequencing data. UMIs, added during library preparation, label individual molecules and enable accurate discrimination between true duplicates and unique fragments.
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Superior performance across low-input cfDNA
UMI informed duplicate calling reduces library duplicates by over 15%, leading to an increase in library complexity (25–35%) and mean target coverage (6–8%) compared to standard positional based duplicate calling.
High-coverage targeted sequencing using 10 ng cfDNA inputs
EM-seq libraries were generated from 10 ng of human cfDNA using either NEB EM-Seq adapters or Twist Methylated UMI Adapters. Libraries were captured with the Twist Alliance Pan-Cancer Methylation Panel (1.5 Mb) using the Targeted Methylation Sequencing Protocol and sequenced on an Illumina NextSeq 550 to ~1000X raw coverage.
Schematic of apoptotic cfDNA fragmentation to sequencing coverage.
During apoptosis, cfDNA is released into the bloodstream. Although endonucleases fragment DNA randomly, nucleosome positioning creates biased fragment start and end sites, limiting fragment diversity and causing distinct molecules to appear as duplicates in sequencing data. UMIs, added during library preparation, label individual molecules and enable accurate discrimination between true duplicates and unique fragments.
Intended Use: For Research Use Only. Not for use in diagnostic procedures.
Performance Disclaimer: Performance claims and duplicate reduction metrics are based on internal validation data using 5–15 ng cfDNA inputs, EM-seq library preparation, and specific bioinformatics tools (GATK UmiAwareMarkDuplicatesWithMateCigar). Individual library complexity and coverage yields may vary depending on sample quality, input mass, protocol modifications, and platform instrumentation.
Trademarks: All trademarks, service marks, and trade names referenced herein are the property of their respective owners. Illumina® and NextSeq® are registered trademarks of Illumina, Inc. NEBNext® is a registered trademark of New England Biolabs, Inc. GATK is a product of the Broad Institute. Reference to third-party products or workflows does not constitute or imply an endorsement, affiliation, or sponsorship by Twist Bioscience or the respective mark owners.
