Utility analyses of AVITI sequencing chemistry.
AVITI
NextSeq
Nucleic acid sequencing
Journal
BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258
Informations de publication
Date de publication:
10 Aug 2024
10 Aug 2024
Historique:
received:
31
01
2024
accepted:
02
08
2024
medline:
11
8
2024
pubmed:
11
8
2024
entrez:
10
8
2024
Statut:
epublish
Résumé
DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.
Sections du résumé
BACKGROUND
BACKGROUND
DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist.
RESULTS
RESULTS
Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate.
CONCLUSION
CONCLUSIONS
These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.
Identifiants
pubmed: 39127634
doi: 10.1186/s12864-024-10686-4
pii: 10.1186/s12864-024-10686-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
778Subventions
Organisme : NIH HHS
ID : UL1TR001857 and S10OD028483
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30- DK120531-01
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30- DK120531-01
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30- DK120531-01
Pays : United States
Organisme : Innovation in Cancer Informatics
ID : N/A
Organisme : NCI NIH HHS
ID : 1R56CA229262-01
Pays : United States
Organisme : NCI NIH HHS
ID : 1R56CA229262-01
Pays : United States
Organisme : National Cancer Institute, United States
ID : 1R56CA229262-01
Organisme : National Cancer Institute, United States
ID : 1R56CA229262-01
Organisme : University of Pittsburgh Clinical and Translational Science Institute
ID : N/A
Informations de copyright
© 2024. The Author(s).
Références
Nyren P, Pettersson B, Uhlen M. Solid phase DNA minisequencing by an enzymatic luminometric inorganic pyrophosphate detection assay. Anal Biochem. 1993;208:171–5.
doi: 10.1006/abio.1993.1024
pubmed: 8382019
Ronaghi M, Karamohamed S, Pettersson B, Uhlen M, Nyren P. Real-time DNA sequencing using detection of pyrophosphate release. Anal Biochem. 1996;242:84–9.
doi: 10.1006/abio.1996.0432
pubmed: 8923969
Ronaghi M, Uhlen M, Nyren P. A sequencing method based on real-time pyrophosphate. Science. 1998;281:363.
doi: 10.1126/science.281.5375.363
pubmed: 9705713
Liu L, Li Y, Li S, Hu N, He Y, Pong R, Lin D, Lu L, Law M. Comparison of next-generation sequencing systems. J Biomed Biotechnol. 2012;2012:251364.
doi: 10.1155/2012/251364
pubmed: 22829749
pmcid: 3398667
McCombie WR, McPherson JD, Mardis ER. Next-generation sequencing technologies. Cold Spring Harb Perspect Med 2019, 9.
Pervez MT, Hasnain MJU, Abbas SH, Moustafa MF, Aslam N, Shah SSM. A Comprehensive Review of Performance of Next-Generation Sequencing Platforms. Biomed Res Int 2022, 2022:3457806.
Pollie R. Genomic sequencing costs set to Head Down again. Engineering. 2023;23:3–6.
doi: 10.1016/j.eng.2023.02.002
Arslan S, Garcia FJ, Guo M, Kellinger MW, Kruglyak S, LeVieux JA, Mah AH, Wang H, Zhao J, Zhou C et al. Sequencing by avidity enables high accuracy with low reagent consumption. Nat Biotechnol 2023.
Senabouth A, Andersen S, Shi Q, Shi L, Jiang F, Zhang W, Wing K, Daniszewski M, Lukowski SW, Hung SSC et al. Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing. NAR Genomics Bioinf 2020, 2.
Stoler N, Nekrutenko A. Sequencing error profiles of Illumina sequencing instruments. NAR Genomics Bioinf. 2021;3:lqab019.
doi: 10.1093/nargab/lqab019
Leggett RM, Ramirez-Gonzalez RH, Clavijo BJ, Waite D, Davey RP. Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics. Front Genet. 2013;4:288.
doi: 10.3389/fgene.2013.00288
pubmed: 24381581
pmcid: 3865868
Baker SC, Bauer SR, Beyer RP, Brenton JD, Bromley B, Burrill J, Causton H, Conley MP, Elespuru R, Fero M, et al. The external RNA controls Consortium: a progress report. Nat Methods. 2005;2:731–4.
doi: 10.1038/nmeth1005-731
pubmed: 16179916
External RNACC. Proposed methods for testing and selecting the ERCC external RNA controls. BMC Genomics. 2005;6:150.
doi: 10.1186/1471-2164-6-150
Finnis M, Dayan S, Hobson L, Chenevix-Trench G, Friend K, Ried K, Venter D, Woollatt E, Baker E, Richards RI. Common chromosomal fragile site FRA16D mutation in cancer cells. Hum Mol Genet. 2005;14:1341–9.
doi: 10.1093/hmg/ddi144
pubmed: 15814586
Pushkarev D, Neff NF, Quake SR. Single-molecule sequencing of an individual human genome. Nat Biotechnol. 2009;27:847–50.
doi: 10.1038/nbt.1561
pubmed: 19668243
pmcid: 4117198
Amarasinghe SL, Su S, Dong X, Zappia L, Ritchie ME, Gouil Q. Opportunities and challenges in long-read sequencing data analysis. Genome Biol. 2020;21:30.
doi: 10.1186/s13059-020-1935-5
pubmed: 32033565
pmcid: 7006217
Logsdon GA, Vollger MR, Eichler EE. Long-read human genome sequencing and its applications. Nat Rev Genet. 2020;21:597–614.
doi: 10.1038/s41576-020-0236-x
pubmed: 32504078
pmcid: 7877196
van Dijk EL, Jaszczyszyn Y, Naquin D, Thermes C. The third revolution in sequencing technology. Trends Genet. 2018;34:666–81.
doi: 10.1016/j.tig.2018.05.008
pubmed: 29941292
Warburton PE, Sebra RP. Long-read DNA sequencing: recent advances and remaining challenges. Annu Rev Genomics Hum Genet. 2023;24:109–32.
doi: 10.1146/annurev-genom-101722-103045
pubmed: 37075062
Callahan BJ, Grinevich D, Thakur S, Balamotis MA, Yehezkel TB. Ultra-accurate microbial amplicon sequencing with synthetic long reads. Microbiome. 2021;9:130.
doi: 10.1186/s40168-021-01072-3
pubmed: 34090540
pmcid: 8179091
Liu S, Wu I, Yu YP, Balamotis M, Ren B, Ben Yehezkel T, Luo JH. Targeted transcriptome analysis using synthetic long read sequencing uncovers isoform reprograming in the progression of colon cancer. Commun Biol. 2021;4:506.
doi: 10.1038/s42003-021-02024-1
pubmed: 33907296
pmcid: 8079361
Yu T, Cheng L, Liu Q, Wang S, Zhou Y, Zhong H, Tang M, Nian H, Lian T. Effects of Waterlogging on soybean Rhizosphere Bacterial Community using V4, LoopSeq, and PacBio 16S rRNA sequence. Microbiol Spectr. 2022;10:e0201121.
doi: 10.1128/spectrum.02011-21
pubmed: 35171049
Liu S, Yu YP, Ren BG, Ben-Yehezkel T, Obert C, Smith M, Wang W, Ostrowska A, Soto-Gutierrez A, Luo JH. Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells. Elife 2024, 12.
Silvia Liu Y-PY, Ren B-G, Ben-Yehezkel T, Obert C, Smith M, Wang W. Alina Ostrowska, Alejandro Soto-Gutierrez, Jian-Hua Luo: Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells. bioRxiv 2023.
Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, Ginhoux F, Newell EW. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol 2018.
Lim HS, Qiu P. Quantifying cell-type-specific differences of single-cell datasets using uniform Manifold Approximation and Projection for Dimension reduction and Shapley Additive exPlanations. J Comput Biol. 2023;30:738–50.
doi: 10.1089/cmb.2022.0366
pubmed: 37093052
Carroll A, Kolesnikov A, Cook DE, Brambrink L, Wiseman KN, Billings SM, Kruglyak S, Lajoie BR, June, Zhao SE et al. Levy, : Accurate human genome analysis with Element Avidity sequencing. BioRxiv 2024.
Koboldt DC. Best practices for variant calling in clinical sequencing. Genome Med. 2020;12:91.
doi: 10.1186/s13073-020-00791-w
pubmed: 33106175
pmcid: 7586657
Singer J, Irmisch A, Ruscheweyh HJ, Singer F, Toussaint NC, Levesque MP, Stekhoven DJ, Beerenwinkel N. Bioinformatics for precision oncology. Brief Bioinform. 2019;20:778–88.
doi: 10.1093/bib/bbx143
pubmed: 29272324
Liu S, Nalesnik MA, Singhi A, Wood-Trageser MA, Randhawa P, Ren BG, Humar A, Liu P, Yu YP, Tseng GC, et al. Transcriptome and Exome Analyses of Hepatocellular Carcinoma reveal patterns to Predict Cancer recurrence in liver transplant patients. Hepatol Commun. 2022;6:710–27.
doi: 10.1002/hep4.1846
pubmed: 34725972
Luo JH, Liu S, Tao J, Ren BG, Luo K, Chen ZH, Nalesnik M, Cieply K, Ma T, Cheng SY, et al. Pten-NOLC1 fusion promotes cancers involving MET and EGFR signalings. Oncogene. 2021;40:1064–76.
doi: 10.1038/s41388-020-01582-8
pubmed: 33323972
Yu YP, Ding Y, Chen Z, Liu S, Michalopoulos A, Chen R, Gulzar ZG, Yang B, Cieply KM, Luvison A, et al. Novel fusion transcripts associate with progressive prostate cancer. Am J Pathol. 2014;184:2840–9.
doi: 10.1016/j.ajpath.2014.06.025
pubmed: 25238935
pmcid: 4188871
Yu YP, Ding Y, Chen R, Liao SG, Ren BG, Michalopoulos A, Michalopoulos G, Nelson J, Tseng GC, Luo JH. Whole-genome methylation sequencing reveals distinct impact of Differential methylations on Gene transcription in prostate Cancer. Am J Pathol 2013.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.
doi: 10.1093/bioinformatics/btu170
pubmed: 24695404
pmcid: 4103590
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.
doi: 10.1093/bioinformatics/btp324
pubmed: 19451168
pmcid: 2705234
Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27:2987–93.
doi: 10.1093/bioinformatics/btr509
pubmed: 21903627
pmcid: 3198575
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
doi: 10.1093/bioinformatics/bts635
pubmed: 23104886
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.
doi: 10.1089/cmb.2012.0021
pubmed: 22506599
pmcid: 3342519
Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094–100.
doi: 10.1093/bioinformatics/bty191
pubmed: 29750242
pmcid: 6137996
Tardaguila M, de la Fuente L, Marti C, Pereira C, Pardo-Palacios FJ, Del Risco H, Ferrell M, Mellado M, Macchietto M, Verheggen K et al. SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification. Genome Res 2018.
Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184:3573–e35873529.
doi: 10.1016/j.cell.2021.04.048
pubmed: 34062119
pmcid: 8238499