High throughput detection and genetic epidemiology of SARS-CoV-2 using COVIDSeq next generation sequencing

  1. Rahul C. Bhoyar
  2. Abhinav Jain
  3. Paras Sehgal
  4. Mohit Kumar Divakar
  5. Disha Sharma
  6. Mohamed Imran
  7. Bani Jolly
  8. Gyan Ranjan
  9. Mercy Rophina
  10. Sumit Sharma
  11. Sanjay Siwach
  12. Kavita Pandhare
  13. Swayamprabha Sahoo
  14. Maheswata Sahoo
  15. Ananya Nayak
  16. Jatindra Nath Mohanty
  17. Jayashankar Das
  18. Sudhir Bhandari
  19. Sandeep K Mathur
  20. Anshul Kumar
  21. Rahul Sahlot
  22. Pallavali Rojarani
  23. Juturu Vijaya Lakshmi
  24. Araveti Surekha
  25. Pulala Chandra Sekhar
  26. Shelly Mahajan
  27. Shet Masih
  28. Pawan Singh
  29. Vipin Kumar
  30. Blessy Jose
  31. Vidur Mahajan
  32. Vivek Gupta
  33. Rakesh Gupta
  34. Prabhakar Arumugam
  35. Anjali Singh
  36. Ananya Nandy
  37. P.V. Raghavendran
  38. Rakesh Mohan Jha
  39. Anupama Kumari
  40. Sheetal Gandotra
  41. Vivek Rao
  42. Mohammed Faruq
  43. Sanjeev Kumar
  44. G Betsy Reshma
  45. G Narendra Varma
  46. Shuvra Shekhar Roy
  47. Antara Sengupta
  48. Sabyasachi Chattopadhyay
  49. Khushboo Singhal
  50. Shalini Pradhan
  51. Diksha Jha
  52. Salwa Naushin
  53. Saruchi Wadhwa
  54. Nishu Tyagi
  55. Mukta Poojary
  56. Vinod Scaria
  57. Sridhar Sivasubbu
2 evaluations Published on
Read the full article Related papers
This article on Sciety

Abstract

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance and for determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for detection of SARS-CoV-2, with an additional advantage of enabling genetic epidemiology of SARS-CoV-2.

Related articles

Related articles are currently not available for this article.