Oxford Nanopore and Bionano Genomics technologies evaluation for plant structural variation detection.

Arabidopsis thaliana Bionano Genomics optical mapping High molecular weight DNA Oxford Nanopore technologies Structural variations

Journal

BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258

Informations de publication

Date de publication:
21 Apr 2022
Historique:
received: 16 04 2021
accepted: 17 03 2022
entrez: 22 4 2022
pubmed: 23 4 2022
medline: 26 4 2022
Statut: epublish

Résumé

Structural Variations (SVs) are genomic rearrangements derived from duplication, deletion, insertion, inversion, and translocation events. In the past, SVs detection was limited to cytological approaches, then to Next-Generation Sequencing (NGS) short reads and partitioned assemblies. Nowadays, technologies such as DNA long read sequencing and optical mapping have revolutionized the understanding of SVs in genomes, due to the enhancement of the power of SVs detection. This study aims to investigate performance of two techniques, 1) long-read sequencing obtained with the MinION device (Oxford Nanopore Technologies) and 2) optical mapping obtained with Saphyr device (Bionano Genomics) to detect and characterize SVs in the genomes of the two ecotypes of Arabidopsis thaliana, Columbia-0 (Col-0) and Landsberg erecta 1 (Ler-1). We described the SVs detected from the alignment of the best ONT assembly and DLE-1 optical maps of A. thaliana Ler-1 against the public reference genome Col-0 TAIR10.1. After filtering (SV > 1 kb), 1184 and 591 Ler-1 SVs were retained from ONT and Bionano technologies respectively. A total of 948 Ler-1 ONT SVs (80.1%) corresponded to 563 Bionano SVs (95.3%) leading to 563 common locations. The specific locations were scrutinized to assess improvement in SV detection by either technology. The ONT SVs were mostly detected near TE and gene features, and resistance genes seemed particularly impacted. Structural variations linked to ONT sequencing error were removed and false positives limited, with high quality Bionano SVs being conserved. When compared with the Col-0 TAIR10.1 reference genome, most of the detected SVs discovered by both technologies were found in the same locations. ONT assembly sequence leads to more specific SVs than Bionano one, the latter being more efficient to characterize large SVs. Even if both technologies are complementary approaches, ONT data appears to be more adapted to large scale populations studies, while Bionano performs better in improving assembly and describing specificity of a genome compared to a reference.

Sections du résumé

BACKGROUND BACKGROUND
Structural Variations (SVs) are genomic rearrangements derived from duplication, deletion, insertion, inversion, and translocation events. In the past, SVs detection was limited to cytological approaches, then to Next-Generation Sequencing (NGS) short reads and partitioned assemblies. Nowadays, technologies such as DNA long read sequencing and optical mapping have revolutionized the understanding of SVs in genomes, due to the enhancement of the power of SVs detection. This study aims to investigate performance of two techniques, 1) long-read sequencing obtained with the MinION device (Oxford Nanopore Technologies) and 2) optical mapping obtained with Saphyr device (Bionano Genomics) to detect and characterize SVs in the genomes of the two ecotypes of Arabidopsis thaliana, Columbia-0 (Col-0) and Landsberg erecta 1 (Ler-1).
RESULTS RESULTS
We described the SVs detected from the alignment of the best ONT assembly and DLE-1 optical maps of A. thaliana Ler-1 against the public reference genome Col-0 TAIR10.1. After filtering (SV > 1 kb), 1184 and 591 Ler-1 SVs were retained from ONT and Bionano technologies respectively. A total of 948 Ler-1 ONT SVs (80.1%) corresponded to 563 Bionano SVs (95.3%) leading to 563 common locations. The specific locations were scrutinized to assess improvement in SV detection by either technology. The ONT SVs were mostly detected near TE and gene features, and resistance genes seemed particularly impacted.
CONCLUSIONS CONCLUSIONS
Structural variations linked to ONT sequencing error were removed and false positives limited, with high quality Bionano SVs being conserved. When compared with the Col-0 TAIR10.1 reference genome, most of the detected SVs discovered by both technologies were found in the same locations. ONT assembly sequence leads to more specific SVs than Bionano one, the latter being more efficient to characterize large SVs. Even if both technologies are complementary approaches, ONT data appears to be more adapted to large scale populations studies, while Bionano performs better in improving assembly and describing specificity of a genome compared to a reference.

Identifiants

pubmed: 35448948
doi: 10.1186/s12864-022-08499-4
pii: 10.1186/s12864-022-08499-4
pmc: PMC9026655
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

317

Informations de copyright

© 2022. The Author(s).

Références

Gigascience. 2019 Jul 1;8(7):
pubmed: 31289833
Nature. 2000 Dec 14;408(6814):796-815
pubmed: 11130711
PLoS Genet. 2019 Jan 18;15(1):e1007819
pubmed: 30657772
Nat Plants. 2020 Jan;6(1):34-45
pubmed: 31932676
J Exp Bot. 2020 Sep 19;71(18):5313-5322
pubmed: 32459850
Bioinformatics. 2018 Sep 15;34(18):3094-3100
pubmed: 29750242
Mol Plant. 2019 Feb 4;12(2):156-169
pubmed: 30594655
Theor Appl Genet. 2017 Dec;130(12):2479-2490
pubmed: 29043379
Nat Genet. 2018 Oct;50(10):1388-1398
pubmed: 30202056
Nat Genet. 2018 Sep;50(9):1289-1295
pubmed: 30061735
Nat Biotechnol. 2012 Aug;30(8):771-6
pubmed: 22797562
Genome Res. 2017 May;27(5):677-685
pubmed: 27895111
PLoS Genet. 2009 Nov;5(11):e1000734
pubmed: 19956538
BMC Genomics. 2017 Jan 18;18(1):97
pubmed: 28100184
Genome Biol. 2004;5(2):R12
pubmed: 14759262
Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5099-103
pubmed: 11309509
Nat Commun. 2020 Feb 20;11(1):989
pubmed: 32080174
Nat Commun. 2019 Mar 4;10(1):1025
pubmed: 30833565
Genome Res. 2019 May;29(5):870-880
pubmed: 30992303
Plant Cell. 2003 Apr;15(4):809-34
pubmed: 12671079
Brief Funct Genomics. 2014 Jul;13(4):296-307
pubmed: 24907366
Mol Ecol. 2019 Mar;28(6):1476-1490
pubmed: 30270494
Genome Biol. 2019 Dec 16;20(1):277
pubmed: 31842948
F1000Res. 2017 May 31;6:760
pubmed: 28794860
Brief Funct Genomics. 2015 Sep;14(5):305-14
pubmed: 25877305
PLoS One. 2020 Apr 10;15(4):e0219884
pubmed: 32275655
Gigascience. 2021 Feb 16;10(2):
pubmed: 33590861
Nat Commun. 2018 Feb 7;9(1):541
pubmed: 29416032
Proc Natl Acad Sci U S A. 2016 Jul 12;113(28):E4052-60
pubmed: 27354520
Nat Genet. 2019 Jun;51(6):1044-1051
pubmed: 31086351
Trends Plant Sci. 2020 Feb;25(2):148-158
pubmed: 31787539
Nat Genet. 2015 Aug;47(8):944-8
pubmed: 26147619
Genome Res. 2009 Sep;19(9):1639-45
pubmed: 19541911
Theor Appl Genet. 2019 Mar;132(3):733-750
pubmed: 30448864
BMC Genomics. 2021 Aug 6;22(1):599
pubmed: 34362298
Nat Biotechnol. 2010 Jan;28(1):57-63
pubmed: 19997067
Nat Genet. 2020 May;52(5):534-540
pubmed: 32284578
Genome Res. 2017 May;27(5):722-736
pubmed: 28298431
Plant Cell. 2018 Mar;30(3):525-527
pubmed: 29519893
Genomics Proteomics Bioinformatics. 2016 Oct;14(5):265-279
pubmed: 27646134
BMC Biol. 2020 Mar 12;18(1):26
pubmed: 32164699
PLoS One. 2019 May 21;14(5):e0216233
pubmed: 31112551
G3 (Bethesda). 2018 Oct 3;8(10):3247-3253
pubmed: 30111620
Bioinformatics. 2018 Aug 1;34(15):2666-2669
pubmed: 29547981
Front Plant Sci. 2018 Jan 18;8:2199
pubmed: 29403506
Evol Bioinform Online. 2020 Mar 6;16:1176934320911055
pubmed: 32214791
Genome Biol. 2013 Jun 12;14(6):R58
pubmed: 23758725
Genome Biol. 2019 Nov 20;20(1):246
pubmed: 31747936
BMC Genomics. 2019 Nov 13;20(1):848
pubmed: 31722668
Genes (Basel). 2020 Mar 20;11(3):
pubmed: 32245073
Genome Biol. 2019 Jul 31;20(1):149
pubmed: 31366358
Nat Rev Genet. 2020 Mar;21(3):171-189
pubmed: 31729472
Genome Biol. 2016 Mar 01;17:37
pubmed: 26926526
Curr Issues Mol Biol. 2018;27:181-194
pubmed: 28885182
Nat Genet. 2021 Jun;53(6):779-786
pubmed: 33972781
Genes (Basel). 2020 Mar 04;11(3):
pubmed: 32143403
Nat Commun. 2019 Apr 16;10(1):1784
pubmed: 30992455
Genome Res. 2010 Dec;20(12):1689-99
pubmed: 21036921
Front Plant Sci. 2018 Jul 17;9:971
pubmed: 30065731
Nucleic Acids Res. 2019 Jan 8;47(D1):D419-D426
pubmed: 30407594
Genet Mol Res. 2016 Dec 23;15(4):
pubmed: 28081277
Nat Genet. 2019 Jan;51(1):30-35
pubmed: 30455414
Genomics Proteomics Bioinformatics. 2015 Oct;13(5):278-89
pubmed: 26542840
Plant Cell. 2020 Jun;32(6):1797-1819
pubmed: 32265262
Nat Plants. 2018 Nov;4(11):879-887
pubmed: 30390080
Plant J. 2006 Apr;46(2):218-30
pubmed: 16623885
Gigascience. 2014 Dec 30;3(1):34
pubmed: 25671094

Auteurs

Aurélie Canaguier (A)

Université Paris-Saclay, INRAE, Etude du Polymorphisme des Génomes Végétaux EPGV, 91000, Evry-Courcouronnes, France.

Romane Guilbaud (R)

Université Paris-Saclay, INRAE, Etude du Polymorphisme des Génomes Végétaux EPGV, 91000, Evry-Courcouronnes, France.

Erwan Denis (E)

Genoscope, Institut de biologie François-Jacob, Commissariat à l'Energie Atomique CEA, Université Paris-Saclay, Evry, France.

Ghislaine Magdelenat (G)

Genoscope, Institut de biologie François-Jacob, Commissariat à l'Energie Atomique CEA, Université Paris-Saclay, Evry, France.

Caroline Belser (C)

Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France.

Benjamin Istace (B)

Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France.

Corinne Cruaud (C)

Genoscope, Institut de biologie François-Jacob, Commissariat à l'Energie Atomique CEA, Université Paris-Saclay, Evry, France.

Patrick Wincker (P)

Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France.

Marie-Christine Le Paslier (MC)

Université Paris-Saclay, INRAE, Etude du Polymorphisme des Génomes Végétaux EPGV, 91000, Evry-Courcouronnes, France.

Patricia Faivre-Rampant (P)

Université Paris-Saclay, INRAE, Etude du Polymorphisme des Génomes Végétaux EPGV, 91000, Evry-Courcouronnes, France. patricia.faivre-rampant@inrae.fr.

Valérie Barbe (V)

Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France.

Articles similaires

Genome, Chloroplast Phylogeny Genetic Markers Base Composition High-Throughput Nucleotide Sequencing
Coal Metagenome Phylogeny Bacteria Genome, Bacterial
Genome, Bacterial Virulence Phylogeny Genomics Plant Diseases

Classifications MeSH