DIPER: Detection and Identification of Pathogens using Edit distance-tolerant Resistive CAM

Itay Merlin, Esteban Garzon, Alex Fish, Leonid Yavits

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We propose a novel resistive edit distance-tolerant content addressable memory for computational genomics applications, particularly for detection and identification of pathogens of pandemic importance. Unlike state-of-the-art approximate search solutions that tolerate small number of replacements between the query pattern and the stored data, DIPER tolerates insertions and deletions, ubiquitous in genomics. DIPER achieves up to 1.7&#x00D7; higher <italic>F</italic>1 score for high-quality DNA reads and up to 6.2&#x00D7; higher <italic>F</italic>1 score for DNA reads with 15% error rate, compared to state-of-the-art DNA classification tool Kraken2. Simulated at 500MHz, DIPER provides 910&#x00D7; average speedup over Kraken2.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Computers
DOIs
StateAccepted/In press - 2023

Bibliographical note

Publisher Copyright:
Author

Keywords

  • Bioinformatics
  • DNA
  • DNA detection and classification
  • Genomics
  • Hamming distances
  • Pandemics
  • Pathogens
  • Sequential analysis
  • approximate search
  • content addressable memory
  • memristors
  • resistive memory

Fingerprint

Dive into the research topics of 'DIPER: Detection and Identification of Pathogens using Edit distance-tolerant Resistive CAM'. Together they form a unique fingerprint.

Cite this