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Updated: May 5, 2026
06:40
G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
Published on: March 22, 2018
5.0K
Direct determination of diploid genome sequences
Neil I Weisenfeld1, Vijay Kumar1, Preyas Shah1
110x Genomics, Pleasanton, California 94566, USA.
Genome Research
|April 7, 2017
Summary
This study presents a low-cost method for true diploid de novo genome assembly. This approach overcomes reference bias and accurately represents individual human genomes, advancing genomic biology and medicine.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Human genome sequencing is crucial for biomedical research but often relies on reference genomes, introducing bias and missing novel sequences.
- Existing de novo assemblies struggle to represent diploid biology accurately, with true diploid assemblies being prohibitively expensive.
- Current methods fail to capture the full spectrum of genomic variation within individuals.
Purpose of the Study:
- To develop a straightforward and low-cost method for true diploid de novo genome assembly.
- To enable accurate representation of an individual's diploid genome.
- To provide a scalable solution for genomic analysis.
Main Methods:
- Utilized a single library from approximately 1 ng of high molecular weight DNA.
- Employed the 10x Genomics microfluidic platform for genome partitioning.
- Generated low-cost HiSeq X data and assembled using the Supernova algorithm.
Main Results:
- Successfully generated true diploid de novo assemblies for seven human samples.
- Achieved contigs >100 kb, phase blocks >2.5 Mb, and scaffolds >15 Mb.
- Assembly computation completed in 2 days on a single server.
Conclusions:
- The developed method offers a scalable and cost-effective approach to determining diploid genome sequences.
- This technique overcomes limitations of reference-based assemblies and expensive diploid assemblies.
- Enables new avenues in genomic biology and personalized medicine by accurately capturing individual genomic diversity.
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