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Updated: Jun 29, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

Optimizing Design of Genomics Studies for Clonal Evolution Analysis.

Arjun Srivatsa¹, Russell Schwartz^1,2

¹Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh PA 15213, USA.

Biorxiv : the Preprint Server for Biology

|April 1, 2024

Summary

This summary is machine-generated.

This study introduces an optimization method for designing genomic studies, particularly for cancer genomics research. It helps researchers effectively allocate resources and select sequencing strategies for analyzing somatic variations at the single-cell level.

Keywords:

Cancer Genomics Optimization Somatic Variation Study Design

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Last Updated: Jun 29, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood

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Published on: July 11, 2019

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

Area of Science:

Genomics
Biotechnology
Computational Biology

Background:

Genomic biotechnologies enable single-cell genetic and epigenetic analysis, offering potential for research, diagnostics, and treatment.
Effective study design is crucial for leveraging these advanced genomic tools, considering various sequencing modalities and sampling protocols.
Challenges exist in optimizing study designs for genomic analyses, especially concerning somatic variation in cancer genomics.

Conclusions:

The proposed optimization procedure effectively derives optimal study designs for genomic analyses.
The method provides valuable insights for resource allocation and strategy selection in cancer genomics research.
This approach facilitates more effective deployment of genomic biotechnologies at the single-cell level.