Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection.
Masateru TaniguchiShohei MinamiChikako OnoRina HamajimaAyumi MorimuraShigeto HamaguchiYukihiro AkedaYuta KanaiTakeshi KobayashiWataru KamitaniYutaka TeradaKoichiro SuzukiNobuaki HatoriYoshiaki YamagishiNobuei WashizuHiroyasu TakeiOsamu SakamotoNorihiko NaonoKenji TatematsuTakashi WashioYoshiharu MatsuuraKazunori TomonoPublished in: Nature communications (2021)
High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.
Keyphrases
- sars cov
- artificial intelligence
- machine learning
- single molecule
- solid state
- big data
- respiratory syndrome coronavirus
- real time pcr
- high throughput
- high speed
- atomic force microscopy
- deep learning
- loop mediated isothermal amplification
- label free
- high resolution
- small molecule
- transcription factor
- sensitive detection