Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Deconvolution and phylogeny inference of diverse variant types integrating bulk DNA-seq with single-cell RNA-seq.

Bioinformatics advances·2025

Same author

Biological databases in the age of generative artificial intelligence.

Bioinformatics advances·2025

Same author

Marker selection strategies for circulating tumor DNA guided by phylogenetic inference.

Bioinformatics (Oxford, England)·2025

Same author

Sc-TUSV-Ext: Single-Cell Clonal Lineage Inference from Single Nucleotide Variants, Copy Number Alterations, and Structural Variants.

Journal of computational biology : a journal of computational molecular cell biology·2025

Same author

CITEgeist: Cellular Indexing of Transcriptomes and Epitopes for Guided Exploration of Intrinsic Spatial Trends.

bioRxiv : the preprint server for biology·2025

Same author

Deconvolution and Phylogeny Inference of Diverse Variant Types Integrating Bulk DNA-seq with Single-cell RNA-seq.

bioRxiv : the preprint server for biology·2025

Same journal

Correction to: Pathogenicity patterns in cytochrome P450 family.

Bioinformatics advances·2026

Same journal

Region-aware bridge modeling enables interpretable mesoscale representation of spatial transcriptomic tissue sections.

Bioinformatics advances·2026

Same journal

Microbiome differential abundance methodologies to detect relevant taxa associated with chemotherapy toxicity rate in colorectal cancer.

Bioinformatics advances·2026

Same journal

maldipickr dereplicates microbial MALDI-TOF spectra to facilitate multiplexed isolation.

Bioinformatics advances·2026

Same journal

RAM-MSA: an anytime memory-bounded method for exact multiple sequence alignment using path finding.

Bioinformatics advances·2026

Same journal

Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response.

Bioinformatics advances·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 5, 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, United States.

Bioinformatics Advances

|December 16, 2024

Summary

This summary is machine-generated.

This study introduces a Bayesian optimization framework to create optimal genomic study designs. It efficiently allocates resources for genomic analyses, particularly for somatic variation in cancer genomics, balancing information recovery with cost.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 5, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

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:

Genomic biotechnology
Single-cell genomics
Cancer genomics

Background:

Rapid advancements in genomic biotechnology enable single-cell level genetic and epigenetic analysis.
Designing effective genomic studies, especially for somatic variation in heterogeneous cell populations, presents challenges.
Optimizing study designs requires balancing data utility, cost, and sampling protocols.

Purpose of the Study:

To develop a principled framework for optimizing genomic study designs.
To address the challenge of efficiently deploying genomic technologies for somatic variation studies.
To create study designs that maximize desired genomic information while minimizing costs.

Main Methods:

Formulated the study design problem as a stochastic constrained nonlinear optimization problem.
Introduced a Bayesian optimization framework utilizing surrogate modeling.
Employed pattern and gradient search for iterative objective function optimization.

Main Results:

Demonstrated a procedure for deriving optimized resource and study design allocations.
Showcased efficient optimization across diverse scenarios for genomic study design.
Validated the framework on several test cases for cancer genomics applications.

Conclusions:

The proposed Bayesian optimization framework provides an efficient method for optimizing genomic study designs.
This approach is particularly valuable for complex studies involving somatic variation and cancer genomics.
The methodology facilitates principled resource allocation to maximize information gain within cost constraints.