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Licensed Unlicensed Requires Authentication Published by De Gruyter June 16, 2021

Automated capture-based NGS workflow: one thousand patients experience in a clinical routine framework

  • Elena Tenedini , Fabio Celestini , Pierluigi Iapicca , Marco Marino , Sara Castellano , Lucia Artuso , Fiammetta Biagiarelli , Laura Cortesi , Angela Toss , Elena Barbieri , Luca Roncucci , Monica Pedroni , Rossella Manfredini , Mario Luppi , Tommaso Trenti and Enrico Tagliafico ORCID logo EMAIL logo
From the journal Diagnosis



The Next Generation Sequencing (NGS) based mutational study of hereditary cancer genes is crucial to design tailored prevention strategies in subjects with different hereditary cancer risk. The ease of amplicon-based NGS library construction protocols contrasts with the greater uniformity of enrichment provided by capture-based protocols and so with greater chances for detecting larger genomic rearrangements and copy-number variations. Capture-based protocols, however, are characterized by a higher level of complexity of sample handling, extremely susceptible to human bias. Robotics platforms may definitely help dealing with these limits, reducing hands-on time, limiting random errors and guaranteeing process standardization.


We implemented the automation of the CE-IVD SOPHiA Hereditary Cancer Solution™ (HCS) libraries preparation workflow by SOPHiA GENETICS on the Hamilton’s STARlet platform. We present the comparison of results between this automated approach, used for more than 1,000 DNA patients’ samples, and the performances of the manual protocol evaluated by SOPHiA GENETICS onto 240 samples summarized in their HCS evaluation study.


We demonstrate that this automated workflow achieved the same expected goals of manual setup in terms of coverages and reads uniformity, with extremely lower standard deviations among samples considering the sequencing reads mapped onto the regions of interest.


This automated solution offers same reliable and affordable NGS data, but with the essential advantages of a flexible, automated and integrated framework, minimizing possible human errors and depicting a laboratory’s walk-away scenario.

Corresponding author: Professor Enrico Tagliafico, Department of Laboratory Medicine and Pathology, Diagnostic Hematology and Clinical Genomics Unit, Modena University Hospital, Modena, Italy; Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Via del Pozzo 71, 41124 Modena, Italy; and Center for Genome Research, University of Modena and Reggio Emilia, Modena, Italy, Phone: +390594225971, E-mail:

  1. Research funding: Not applicable.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: The local Institutional Review Board deemed the study exempt from review.


1. Morganti, S, Tarantino, P, Ferraro, E, D’Amico, P, Viale, G, Trapani, D, et al.. Complexity of genome sequencing and reporting: next generation sequencing (NGS) technologies and implementation of precision medicine in real life. Crit Rev Oncol Hematol 2019;133:171–82. in Google Scholar PubMed

2. Giani, AM, Gallo, GR, Gianfranceschi, L, Formenti, G. Long walk to genomics: history and current approaches to genome sequencing and assembly. Comput Struct Biotechnol J 2020;18:9–19. in Google Scholar PubMed PubMed Central

3. Petersen, BS, Fredrich, B, Hoeppner, MP, Ellinghaus, D, Franke, A. Opportunities and challenges of whole-genome and -exome sequencing. BMC Genet 2017;18:14. in Google Scholar PubMed PubMed Central

4. Arboleda, VA, Xian, RR. An overview of DNA analytical methods. Methods Mol Biol 2019;1897:385–402. in Google Scholar PubMed

5. Xu, J, Yang, P, Xue, S, Sharma, B, Sanchez-Martin, M, Wang, F, et al.. Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives. Hum Genet 2019;138:109–24. in Google Scholar PubMed PubMed Central

6. Ellingford, JM, Campbell, C, Barton, S, Bhaskar, S, Gupta, S, Taylor, RL, et al.. Validation of copy number variation analysis for next-generation sequencing diagnostics. Eur J Hum Genet 2017;25:719–24. in Google Scholar PubMed PubMed Central

7. Tagliafico, E, Bernardis, I, Grasso, M, D’Apice, MR, Lapucci, C, Botta, A, et al.. Workload measurement for molecular genetics laboratory: a survey study. PloS One 2018;13:e0206855. in Google Scholar PubMed PubMed Central

8. Katsanis, SH, Katsanis, N. Molecular genetic testing and the future of clinical genomics. Nat Rev Genet 2013;14:415–26. in Google Scholar PubMed PubMed Central

9. Greaves, RF, Bernardini, S, Ferrari, M, Fortina, P, Gouget, B, Gruson, D, et al.. Key questions about the future of laboratory medicine in the next decade of the 21st century: a report from the IFCC-Emerging Technologies Division. Clin Chim Acta 2019;495:570–89. in Google Scholar PubMed

10. Neben, CL, Zimmer, AD, Stedden, W, van den Akker, J, O’Connor, R, Chan, RC, et al.. Multi-gene panel testing of 23,179 individuals for hereditary cancer risk identifies pathogenic variant carriers missed by current genetic testing guidelines. J Mol Diagn 2019;21:646–57. in Google Scholar PubMed

11. Susswein, LR, Marshall, ML, Nusbaum, R, Vogel Postula, KJ, Weissman, SM, Yackowski, L, et al.. Pathogenic and likely pathogenic variant prevalence among the first 10,000 patients referred for next-generation cancer panel testing. Genet Med 2016;18:823–32. in Google Scholar PubMed PubMed Central

12. Crawford, B, Adams, SB, Sittler, T, van den Akker, J, Chan, S, Leitner, O, et al.. Multi-gene panel testing for hereditary cancer predisposition in unsolved high-risk breast and ovarian cancer patients. Breast Canc Res Treat 2017;163:383–90. in Google Scholar PubMed PubMed Central

13. Stern, B, McGarrity, T, Baker, M. Incorporating colorectal cancer genetic risk assessment into gastroenterology practice. Curr Treat Options Gastroenterol 2019;17:702–15. in Google Scholar PubMed

14. Cohen, SA, Pritchard, CC, Jarvik, GP. Lynch syndrome: from screening to diagnosis to treatment in the era of modern molecular oncology. Annu Rev Genom Hum Genet 2019;20:293–307. in Google Scholar PubMed

15. Kozarewa, I, Armisen, J, Gardner, AF, Slatko, BE, Hendrickson, CL. Overview of target enrichment strategies. Curr Protoc Mol Biol 2015;112:7.21.1–23. in Google Scholar PubMed

16. Samorodnitsky, E, Jewell, BM, Hagopian, R, Miya, J, Wing, MR, Lyon, E, et al.. Evaluation of hybridization capture versus amplicon-based methods for whole-exome sequencing. Hum Mutat 2015;36:903–14. in Google Scholar PubMed PubMed Central

17. Muscarella, LA, Fabrizio, FP, De Bonis, M, Mancini, MT, Balsamo, T, Graziano, P, et al.. Automated workflow for somatic and germline next generation sequencing analysis in routine clinical cancer diagnostics. Cancers 2019;11:1691. in Google Scholar PubMed PubMed Central

18. Wang, K, Li, M, Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010;38:e164. in Google Scholar PubMed PubMed Central

19. McLaren, W, Gil, L, Hunt, SE, Riat, HS, Ritchie, GR, Thormann, A, et al.. The ensembl variant effect predictor. Genome Biol 2016;17:122. in Google Scholar PubMed PubMed Central

20. Richards, S, Aziz, N, Bale, S, Bick, D, Das, S, Gastier-Foster, J, et al.. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015;17:405–24. in Google Scholar PubMed PubMed Central

21. Mu, W, Lu, HM, Chen, J, Li, S, Elliott, AM. Sanger confirmation is required to achieve optimal sensitivity and specificity in next-generation sequencing panel testing. J Mol Diagn 2016;18:923–32. in Google Scholar PubMed

22. Hess, JF, Kohl, TA, Kotrova, M, Ronsch, K, Paprotka, T, Mohr, V, et al.. Library preparation for next generation sequencing: a review of automation strategies. Biotechnol Adv 2020;41:107537. in Google Scholar PubMed

23. Quail, MA, Smith, M, Coupland, P, Otto, TD, Harris, SR, Connor, TR, et al.. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genom 2012;13:341. in Google Scholar PubMed PubMed Central

24. Plebani, M. Harmonization in laboratory medicine: more than clinical chemistry? Clin Chem Lab Med 2018;56:1579–86. in Google Scholar PubMed

Supplementary Material

The online version of this article offers supplementary material (

Received: 2021-04-09
Accepted: 2021-05-12
Published Online: 2021-06-16

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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