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Related Experiment Video

Updated: Sep 19, 2025

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Pseudoassembly of k-mers.

Delaney K Sullivan^1,2, Mayuko Boffelli³, Lior Pachter^1,4

¹Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.

Biorxiv : the Preprint Server for Biology

|June 4, 2025

Summary

This summary is machine-generated.

We developed a new pseudoassembly method using colored de Bruijn graphs to find genetic variations. This approach successfully identified cell-type specific de novo variants from single-cell RNA sequencing data.

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Last Updated: Sep 19, 2025

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

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Published on: August 20, 2021

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Published on: January 8, 2019

Examining Proteasome Assembly with Recombinant Archaeal Proteasomes and Nondenaturing PAGE: The Case for a Combined Approach

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Published on: December 17, 2016

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Identifying genetic variations is crucial for understanding diseases.
Single-cell RNA sequencing (scRNA-seq) offers high resolution for cellular studies.
Existing methods face challenges in detecting novel variants from complex genomic data.

Purpose of the Study:

To introduce a novel pseudoassembly approach for genomic variation identification.
To implement the method in a user-friendly program called klue.
To demonstrate the utility of this approach for discovering cell-type specific variants in scRNA-seq data.

Main Methods:

Utilized colored de Bruijn graphs for sequence analysis.
Developed a pseudoassembly strategy compatible with pseudoalignment.
Applied the klue program to assemble k-mers into variant-aware sequences.

Main Results:

Successfully identified cell-type specific de novo variants.
Demonstrated the method's effectiveness on single-cell RNA-seq data from a mouse melanoma model.
Validated the pseudoassembly approach for variant detection in complex datasets.

Conclusions:

The pseudoassembly approach provides an effective strategy for identifying genomic variations.
The klue program facilitates the discovery of novel, cell-type specific variants.
This method advances the analysis of single-cell genomics data for variant discovery.