Assessment of human diploid genome assembly with 10x Linked-Reads data
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Abstract
Background
Producing cost-effective haplotype-resolved personal genomes remains challenging. 10x Linked-Read sequencing, with its high base quality and long-range information, has been demonstrated to facilitate de novo assembly of human genomes and variant detection. In this study, we investigate in depth how the parameter space of 10x library preparation and sequencing affects assembly quality, on the basis of both simulated and real libraries.
Results
We prepared and sequenced eight 10x libraries with a diverse set of parameters from standard cell lines NA12878 and NA24385 and performed whole-genome assembly on the data. We also developed the simulator LRTK-SIM to follow the workflow of 10x data generation and produce realistic simulated Linked-Read data sets. We found that assembly quality could be improved by increasing the total sequencing coverage (C) and keeping physical coverage of DNA fragments (CF) or read coverage per fragment (CR) within broad ranges. The optimal physical coverage was between 332× and 823× and assembly quality worsened if it increased to >1,000× for a given C. Long DNA fragments could significantly extend phase blocks but decreased contig contiguity. The optimal length-weighted fragment length (W${\mu _{FL}}$) was ~50–150 kb. When broadly optimal parameters were used for library preparation and sequencing, ~80% of the genome was assembled in a diploid state.
Conclusions
The Linked-Read libraries we generated and the parameter space we identified provide theoretical considerations and practical guidelines for personal genome assemblies based on 10x Linked-Read sequencing.
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Now published in GigaScience doi: 10.1093/gigascience/giz141
Lu Zhang 1Department of Computer Science, Hong Kong Baptist University2Department of Pathology, Stanford University3Department of Computer Science, Stanford UniversityFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteXin Zhou 3Department of Computer Science, Stanford UniversityFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteZiming Weng 2Department of Pathology, Stanford UniversityFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteArend Sidow 2Department of Pathology, Stanford University4Department of Genetics, Stanford UniversityFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence:
Now published in GigaScience doi: 10.1093/gigascience/giz141
Lu Zhang 1Department of Computer Science, Hong Kong Baptist University2Department of Pathology, Stanford University3Department of Computer Science, Stanford UniversityFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteXin Zhou 3Department of Computer Science, Stanford UniversityFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteZiming Weng 2Department of Pathology, Stanford UniversityFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteArend Sidow 2Department of Pathology, Stanford University4Department of Genetics, Stanford UniversityFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: arend@stanford.edu
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giz141 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101987 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.101988
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