Comparison of the effects of four library construction methods for whole genome sequencing

doi:10.14088/j.cnki.issn0439-8114.2022.21.035

Abstract

Abstract: Explore a library construction method more suitable for large-scale low-depth resequencing, which had important application value for livestock and poultry genome breeding, four different methods were used to construct the library, and the performance of manual and automated low-coverage data was compared. The results showed that the database based on MGIcare, PCRfree and TN5 digestion methods had similar data performance with the classical mechanical disruption method, and the operation process was simpler and faster. Automated database construction had better consistency than manual sequencing data. Using these methods, 3 million to 7 million SNPs （corresponding to sequencing depth 0.5×-1×） can be obtained.

Key words: library preparation, automation, low-coverage resequencing

CLC Number:

Q343

YANG Man-man, CHEN Tao, SHEN Jun-ran, WANG Ran, LI Yong. Comparison of the effects of four library construction methods for whole genome sequencing[J]. HUBEI AGRICULTURAL SCIENCES, 2022, 61(21): 189-192.

References

[1] LI J H, MAZUR C A, BERISA T, et al.Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays[J]. Genome Res, 2021,31(4):529-537.
[2] FALKER-GIESKE C, BLAJ I, PREUSS S, et al.GWAS for meat and carcass traits using imputed sequence level genotypes in pooled F₂-designs in pigs[J]. G3 (Bethesda), 2019,9(9):2823-2834.
[3] ZHANG W,LI W,LIU G, et al.Evaluation for the effect of low-coverage sequencing on genomic selection in large yellow croaker[J]. Aquaculture, 2021,534:736323.
[4] HOMBURGER J R, NEBEN C L, MISHNE G, et al.Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores[J]. Genome Med, 2019,11(1):74.
[5] JONES M B, HIGHLANDER S K, ANDERSON E L, et al.Library preparation methodology can influence genomic and functional predictions in human microbiome research[J]. Proc Natl Acad Sci USA, 2015,112(45):14024-14029.
[6] 喻东, 郭瀛军. 高通量测序临床应用中数据质量控制和分析若干问题的探讨[J]. 检验医学, 2017,32(4):255-261.
[7] KEBSCHULL J M, ZADOR A M.Sources of PCR-induced distortions in high-throughput sequencing data sets[J]. Nucleic Acids Res, 2015,43(21):e143.
[8] CHEN Y C, LIU T, YU C H, et al.Effects of GC bias in next-generation-sequencing data on de novo genome assembly[J]. PLoS One, 2013,8(4):e62856.
[9] DOLZHENKO E,VAN VUGT J, SHAW R J, et al.Detection of long repeat expansions from PCR-free whole-genome sequence data[J]. Genome Res, 2017,27(11):1895-1903.
[10] YAMAGUCHI I, WATANABE T, OHARA O, et al.PCR-free whole exome sequencing: Cost-effective and efficient in detecting rare mutations[J]. PLoS One, 2019,14(9):e222562.
[11] FARIAS-HESSON E, ERIKSON J, ATKINS A, et al.Semi-automated library preparation for high-throughput DNA sequencing platforms[J]. J Biomed Biotechnol, 2010,2010:617469.
[12] PICELLI S, BJORKLUND A K, REINIUS B, et al.Tn5 transposase and tagmentation procedures for massively scaled sequencing projects[J]. Genome Res, 2014,24(12):2033-2040.
[13] ZAN Y, PAYEN T, LILLIE M, et al.Genotyping by low-coverage whole-genome sequencing in intercross pedigrees from outbred founders: A cost-efficient approach[J]. Genet Sel Evol, 2019,51(1):44.
[14] GREEN B, BOUCHIER C, FAIRHEAD C, et al.Insertion site preference of Mu, Tn5, and Tn7 transposons[J]. Mob DNA, 2012,3(1):3.
[15] KIM H M, JEON S, CHUNG O, et al.Comparative analysis of 7 short-read sequencing platforms using the Korean Reference Genome: MGI and Illumina sequencing benchmark for whole-genome sequencing[J]. Gigascience, 2021,10(3):14.
[16] HENNIG B P, VELTEN L, RACKE I, et al.Large-scale low-cost NGS library preparation using a robust Tn5 purification and tagmentation protocol[J]. G3 (Bethesda), 2018,8(1):79-89.
[17] PEI Y F, LI J, ZHANG L, et al.Analyses and comparison of accuracy of different genotype imputation methods[J]. PLoS One, 2008,3(10):e3551.
[18] ZHANG Z, DRUET T.Marker imputation with low-density marker panels in Dutch Holstein cattle[J]. J Dairy Sci, 2010,93(11):5487-5494.