Evidence map›Paper›PMID 39130747›Full record

ArticleFrontiers in genetics2024

AsmMix: an efficient haplotype-resolved hybrid

Chao Liu, Pei Wu, Xue Wu, Xia Zhao, Fang Chen, Xiaofang Cheng, Hongmei Zhu, Ou Wang, Mengyang Xu

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Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Chao Liu *BGI, Tianjin, China.
Pei Wu *BGI, Tianjin, China.
Xue Wu *BGI Research, Shenzhen, China.
Xia ZhaoMGI Tech, Shenzhen, China.
Fang ChenMGI Tech, Shenzhen, China.
Xiaofang ChengMGI Tech, Shenzhen, China.
Hongmei ZhuBGI, Tianjin, China.
Ou WangBGI Research, Shenzhen, China.
Mengyang XuBGI Research, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate haplotyping facilitates distinguishing allele-specific expression, identifying cis-regulatory elements, and characterizing genomic variations, which enables more precise investigations into the relationship between genotype and phenotype. Recent advances in third-generation single-molecule long read and synthetic co-barcoded read sequencing techniques have harnessed long-range information to simplify the assembly graph and improve assembly genomic sequence. However, it remains methodologically challenging to reconstruct the complete haplotypes due to high sequencing error rates of long reads and limited capturing efficiency of co-barcoded reads. We here present a pipeline, AsmMix, for generating both contiguous and accurate diploid genomes. It first assembles co-barcoded reads to generate accurate haplotype-resolved assemblies that may contain many gaps, while the long-read assembly is contiguous but susceptible to errors. Then two assembly sets are integrated into haplotype-resolved assemblies with reduced misassembles. Through extensive evaluation on multiple synthetic datasets, AsmMix consistently demonstrates high precision and recall rates for haplotyping across diverse sequencing platforms, coverage depths, read lengths, and read accuracies, significantly outperforming other existing tools in the field. Furthermore, we validate the effectiveness of our pipeline using a human whole genome dataset (HG002), and produce highly contiguous, accurate, and haplotype-resolved assemblies. These assemblies are evaluated using the GIAB benchmarks, confirming the accuracy of variant calling. Our results demonstrate that AsmMix offers a straightforward yet highly efficient approach that effectively leverages both long reads and co-barcoded reads for haplotype-resolved assembly.

Indexed as

bioinformaticsde novogenome assemblyhaplotypehybridlong reads

Identifiers

PMID39130747
PMCPMC11310137

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.