Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
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...
5.7K
RNA-seq03:21

RNA-seq

9.8K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.8K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.0K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.0K
Next-generation Sequencing03:00

Next-generation Sequencing

87.1K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
87.1K

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

Computationally reconstructing the evolution of cancer progression risk.

bioRxiv : the preprint server for biology·2025
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
See all related articles

Related Experiment Video

Updated: May 27, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.4K

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

Nishat Anjum Bristy1, Russell Schwartz1,2

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

Biorxiv : the Preprint Server for Biology
|February 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces TUSV-int, a novel computational method that integrates bulk DNA sequencing and single-cell RNA sequencing data to reconstruct accurate tumor phylogenetics. TUSV-int enhances the resolution of clonal substructure and mutational history by analyzing single nucleotide variants, copy number alterations, and structural variants.

Keywords:
cancerinteger linear programmingsequencingsingle-cellsomatic evolutiontumor phylogeny

More Related Videos

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

9.4K
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

3.5K

Related Experiment Videos

Last Updated: May 27, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.4K
A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

9.4K
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

3.5K

Area of Science:

  • Cancer Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Reconstructing clonal lineage trees is crucial for understanding cancer genomics.
  • Traditional bulk DNA sequencing (DNA-seq) lacks resolution, while single-cell DNA sequencing (scDNA-seq) is costly and limited.
  • Single-cell RNA sequencing (scRNA-seq) is widely available but has limited genome coverage for detecting structural variations.

Purpose of the Study:

  • To develop a computational method that integrates bulk DNA-seq and scRNA-seq data for improved tumor phylogenetics.
  • To enable the simultaneous analysis of single nucleotide variants (SNVs), copy number alterations (CNAs), and structural variants (SVs).
  • To overcome the limitations of existing methods by combining the strengths of different genomic technologies.

Main Methods:

  • Developed TUSV-int, a method using integer linear programming (ILP).
  • Integrated bulk DNA-seq and scRNA-seq data for deconvolution and phylogenetic inference.
  • Applied to a breast cancer dataset with existing DNA-seq and scRNA-seq data.

Main Results:

  • TUSV-int demonstrated improved deconvolution performance compared to methods using limited data or variant types.
  • The method effectively resolved clonal structure and mutational histories.
  • Successfully applied to a published breast cancer dataset, showcasing enhanced resolution.

Conclusions:

  • TUSV-int offers a powerful approach to cancer phylogenetics by integrating diverse genomic data.
  • The method enhances the resolution of clonal substructure and mutational events.
  • Provides a valuable tool for cancer genomics research, particularly for analyzing complex structural variations.