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Related Experiment Video

Updated: Mar 29, 2026

ATAC-Seq Optimization for Cancer Epigenetics Research

ATAC-Seq Optimization for Cancer Epigenetics Research

Published on: June 30, 2022

Optimizing cancer genome sequencing and analysis.

Malachi Griffith¹, Christopher A Miller², Obi L Griffith³

¹The McDonnell Genome Institute, Washington University, St. Louis, MO, USA, 63108 ; Department of Genetics, Washington University, St. Louis, MO, USA, 63108 ; Siteman Cancer Center, Washington University, St. Louis, MO, USA, 63108.

|December 9, 2015

Summary

This summary is machine-generated.

Standard tumor sequencing is insufficient for complex cancers. Ultra-deep sequencing reveals critical insights into tumor heterogeneity, improving cancer characterization and treatment strategies.

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

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Last Updated: Mar 29, 2026

ATAC-Seq Optimization for Cancer Epigenetics Research

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Published on: June 30, 2022

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Area of Science:

Genomics
Cancer Biology
Bioinformatics

Background:

Current tumor sequencing depths (75-100× exome, 30-50× whole genome) may be inadequate for impure, aneuploid, or clonally heterogeneous tumors.
Characterizing complex tumor genomes requires advanced sequencing strategies to accurately identify genetic alterations.

Purpose of the Study:

To reassess optimal sequencing strategies for complex tumors.
To evaluate the impact of sequencing depth and analysis methods on tumor characterization.
To provide a comprehensive genomic dataset as a community resource.

Main Methods:

Performed ultra-deep whole genome sequencing (WGS) up to ~312× and exome capture up to ~433× on acute myeloid leukemia (AML) samples (primary, relapse) and matched normal tissue.
Tested various alignment and variant calling algorithms.
Validated over 200,000 single nucleotide variants (SNVs) at ~1,000× depth, with additional targeted sequencing (>10,000×) and digital droplet PCR (ddPCR) assays (up to ~250,000×).

Main Results:

Demonstrated that current sequencing paradigms are inadequate for complex tumor types.
Ultra-deep sequencing significantly enhances the ability to characterize tumor purity, aneuploidy, and clonal heterogeneity.
The comprehensive dataset provides unprecedented resolution for genomic analysis.

Conclusions:

Ultra-deep sequencing is essential for accurate characterization of complex and heterogeneous tumors.
Optimized sequencing and analysis strategies are crucial for advancing cancer genomics.
The publicly available dataset (dbGaP phs000159) will facilitate future cancer research.