Design of Specific Primer Set for Detection of B.1.1.7 SARS-CoV-2 Variant using Deep Learning

  1. Alejandro Lopez-Rincon
  2. Carmina A. Perez-Romero
  3. Alberto Tonda
  4. Lucero Mendoza-Maldonado
  5. Eric Claassen
  6. Johan Garssen
  7. Aletta D. Kraneveld

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Abstract

The SARS-CoV-2 variant B.1.1.7 lineage, also known as clade GR from Global Initiative on Sharing All Influenza Data (GISAID), Nextstrain clade 20B, or Variant Under Investigation in December 2020 (VUI – 202012/01), appears to have an increased transmissability in comparison to other variants. Thus, to contain and study this variant of the SARS-CoV-2 virus, it is necessary to develop a specific molecular test to uniquely identify it. Using a completely automated pipeline involving deep learning techniques, we designed a primer set which is specific to SARS-CoV-2 variant B.1.1.7 with >99% accuracy, starting from 8,923 sequences from GISAID. The resulting primer set is in the region of the synonymous mutation C16176T in the ORF1ab gene, using the canonical sequence of the variant B.1.1.7 as a reference. Further in-silico testing shows that the primer set’s sequences do not appear in different viruses, using 20,571 virus samples from the National Center for Biotechnology Information (NCBI), nor in other coronaviruses, using 487 samples from National Genomics Data Center (NGDC). In conclusion, the presented primer set can be exploited as part of a multiplexed approach in the initial diagnosis of Covid-19 patients, or used as a second step of diagnosis in cases already positive to Covid-19, to identify individuals carrying the B.1.1.7 variant.

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    SciScore for 10.1101/2020.12.29.424715: (What is this?)

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  2. Version published to 10.1101/2020.12.29.424715 on bioRxiv