Integrated BSI bacteria identifier-on-chip using approximate k-mer matching

Posted on
integrated-bsi-bacteria-identifier-on-chip-using-approximate-k-mer-matching
Integrated BSI bacteria identifier-on-chip using approximate k-mer matching

References

  1. MacMillan, M. L. et al. A refined risk score for acute graft-versus-host disease that predicts response to initial therapy, survival, and transplant-related mortality. Biol. Blood Marrow Transplant. 21(4), 761–767 (2015).

    Google Scholar 

  2. Kakihana, K. et al. Fecal microbiota transplantation for patients with steroid-resistant acute graft-versus-host disease of the gut. Blood J. Am. Soc. Hematol. 128(16), 2083–2088 (2016).

    Google Scholar 

  3. Eshel, A. et al. Origins of bloodstream infections following fecal microbiota transplantation: a strain-level analysis. Blood Adv. 6(2), 568–573 (2022).

    Google Scholar 

  4. Seymour, C. W. et al. Time to treatment and mortality during mandated emergency care for sepsis. N. Engl. J. Med. 376(23), 2235–2244 (2017).

    Google Scholar 

  5. Ho, Y.-P. & Reddy, P. M. Identification of pathogens by mass spectrometry. Clin. Chem. 56(4), 525–536 (2010).

    Google Scholar 

  6. Reller, L. B., Weinstein, M. P. & Petti, C. A. Detection and identification of microorganisms by gene amplification and sequencing. Clin. Infect. Dis. 44(8), 1108–1114 (2007).

    Google Scholar 

  7. Poppert, S. et al. Rapid diagnosis of bacterial meningitis by real-time pcr and fluorescence in situ hybridization. J. Clin. Microbiol. 43(7), 3390–3397 (2005).

    Google Scholar 

  8. Clarridge, J. E. III. Impact of 16s rrna gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin. Microbiol. Rev. 17(4), 840–862 (2004).

    Google Scholar 

  9. Yamamoto, S. & Harayama, S. Pcr amplification and direct sequencing of gyrb genes with universal primers and their application to the detection and taxonomic analysis of pseudomonas putida strains. Appl. Environ. Microbiol. 61(3), 1104–1109 (1995).

    Google Scholar 

  10. Serapide, F. et al. Impact of multiplex pcr on diagnosis of bacterial and fungal infections and choice of appropriate antimicrobial therapy. Diagnostics 15(8), 1044 (2025).

    Google Scholar 

  11. Maurin, M. Real-time pcr as a diagnostic tool for bacterial diseases. Expert Rev. Mol. Diagn. 12(7), 731–754 (2012).

    Google Scholar 

  12. Rodriguez-Mateos, P., Azevedo, N. F., Almeida, C. & Pamme, N. Fish and chips: A review of microfluidic platforms for fish analysis. Med. Microbiol. Immunol. 209(3), 373–391 (2020).

    Google Scholar 

  13. Amann, R. & Fuchs, B. M. Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat. Rev. Microbiol. 6(5), 339–348 (2008).

    Google Scholar 

  14. Huang, X. X., Urosevic, N. & Inglis, T. J. Accelerated bacterial detection in blood culture by enhanced acoustic flow cytometry (afc) following peptide nucleic acid fluorescence in situ hybridization (pna-fish). PLoS ONE 14(2), 0201332 (2019).

    Google Scholar 

  15. Almeida, C., Azevedo, N. F., Santos, S., Keevil, C. W. & Vieira, M. J. Discriminating multi-species populations in biofilms with peptide nucleic acid fluorescence in situ hybridization (pna fish). PLoS ONE 6(3), 14786 (2011).

    Google Scholar 

  16. Wang, M. et al. Enzyme-assisted nucleic acid amplification in molecular diagnosis: a review. Biosensors 13(2), 160 (2023).

    Google Scholar 

  17. Pala, L., Sirec, T. & Spitz, U. Modified enzyme substrates for the detection of bacteria: A review. Molecules 25(16), 3690 (2020).

    Google Scholar 

  18. Shelef, O. et al. Enzymatic activity profiling using an ultrasensitive array of chemiluminescent probes for bacterial classification and characterization. J. Am. Chem. Soc. 146(8), 5263–5273 (2024).

    Google Scholar 

  19. Moeckel, C. et al. A survey of k-mer methods and applications in bioinformatics. Comput. Struct. Biotechnol. J. 23, 2289–2303 (2024).

    Google Scholar 

  20. Simon, H. Y., Siddle, K. J., Park, D. J. & Sabeti, P. C. Benchmarking metagenomics tools for taxonomic classification. Cell 178(4), 779–794 (2019).

    Google Scholar 

  21. Tran, Q. & Phan, V. Assembling reads improves taxonomic classification of species. Genes 11(8), 946 (2020).

    Google Scholar 

  22. Alser, M. et al. Accelerating genome analysis: A primer on an ongoing journey. IEEE Micro 40(5), 65–75 (2020).

    Google Scholar 

  23. Wu, L., Sharifi, R., Lenjani, M., Skadron, K. & Venkat, A. Sieve: Scalable in-situ dram-based accelerator designs for massively parallel k-mer matching. In: 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), pp. 251–264 (2021). IEEE

  24. Pagiamtzis, K. & Sheikholeslami, A. Content-addressable memory (CAM) circuits and architectures: a tutorial and survey. IEEE J. Solid-State Circuits 41(3), 712–727 (2006).

    Google Scholar 

  25. Garzón, E., Lanuzza, M., Teman, A. & Yavits, L. AM(^4): MRAM crossbar based CAM/TCAM/ACAM/AP for in-memory computing. IEEE J. Emerg. Select. Top. Circuits Syst. 13(1), 408–421 (2023).

    Google Scholar 

  26. Yin, X. et al. FeCAM: A universal compact digital and analog content addressable memory using ferroelectric. IEEE Trans. Electron Devices 67(7), 2785–2792 (2020).

    Google Scholar 

  27. Yang, R. et al. Ternary content-addressable memory with MoS2 transistors for massively parallel data search. Nat. Electron. 2(3), 108–114 (2019).

    Google Scholar 

  28. Pagiamtzis, K., Azizi, N. & Najm, F.N. A Soft-Error Tolerant Content-Addressable Memory (CAM) Using An Error-Correcting-Match Scheme. In: IEEE Custom Int. Circ. Conf. 2006, pp. 301–304 (2006).

  29. Krishnan, S. C., Panigrahy, R. & Parthasarathy, S. Error-correcting codes for ternary content addressable memories. IEEE Trans. Comput. 58(2), 275–279 (2009).

    Google Scholar 

  30. Ni, K. et al. Ferroelectric ternary content-addressable memory for one-shot learning. Nat. Electron. 2(11), 521–529 (2019).

    Google Scholar 

  31. Riazi, M. S., Samragh, M. & Koushanfar, F. Camsure: Secure content-addressable memory for approximate search. ACM Trans. Embedded Comput. Syst. (TECS) 16(5s), 1–20 (2017).

    Google Scholar 

  32. Garzón, E. et al. Hamming distance tolerant content-addressable memory (HD-CAM) for DNA classification. IEEE Access 10, 28080–28093 (2022).

    Google Scholar 

  33. Bui, T.T. & Shibata, T. A Low-Power Associative Processor with the R-th Nearest-Match Hamming-Distance Search Engine Employing Time-Domain Techniques. In: 2010 Fifth IEEE International Symposium on Electronic Design, Test Applications, pp. 54–57 (2010).

  34. Rahimi, A., Ghofrani, A., Cheng, K.-T., Benini, L. & Gupta, R.K. Approximate associative memristive memory for energy-efficient GPUs. In: 2015 Design, Automation Test in Europe Conference Exhibition (DATE), pp. 1497–1502 (2015).

  35. Imani, M., Rahimi, A., Kong, D., Rosing, T. & Rabaey, J.M. Exploring hyperdimensional associative memory. In: 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 445–456 (2017). IEEE

  36. Hanhan, R., Garzón, E., Jahshan, Z., Teman, A., Lanuzza, M. & Yavits, L. EDAM: Edit Distance tolerant Approximate Matching content addressable memory. In: Proc. of the 49th Annual Int. Symp. on Comp. Arch., pp. 495–507 (2022).

  37. NCBI: Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information (2021). https://www.ncbi.nlm.nih.gov/

  38. Huang, W., Li, L., Myers, J. R. & Marth, G. T. ART: A next-generation sequencing read simulator. Bioinformatics 28(4), 593–594 (2012).

    Google Scholar 

  39. Ono, Y., Asai, K. & Hamada, M. PBSIM2: A simulator for long-read sequencers with a novel generative model of quality scores. Bioinformatics 37(5), 589–595 (2021).

    Google Scholar 

  40. Hackl, T., Hedrich, R., Schultz, J. & Förster, F. proovread: large-scale high-accuracy pacbio correction through iterative short read consensus. Bioinformatics 30(21), 3004–3011 (2014).

    Google Scholar 

  41. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20(1), 1–13 (2019).

    Google Scholar 

  42. Yavits, L. Drama: Commodity dram based content addressable memory. IEEE Computer Architecture Letters (2023).

  43. Firtina, C. et al. RawHash: Enabling fast and accurate real-time analysis of raw nanopore signals for large genomes. Bioinformatics 39, 297–307 (2023).

    Google Scholar 

  44. Fujiki, D., Wu, S., Ozog, N., Goliya, K., Blaauw, D., Narayanasamy, S. & Das, R. Seedex: A genome sequencing accelerator for optimal alignments in subminimal space. In: 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 937–950 (2020). IEEE

  45. Gu, Y., Subramaniyan, A., Dunn, T., Khadem, A., Chen, K.-Y., Paul, S., Vasimuddin, M., Misra, S., Blaauw, D. & Narayanasamy, S. et al. Gendp: A framework of dynamic programming acceleration for genome sequencing analysis. In: Proc. 50th Annual International Symposium on Computer Architecture, pp. 1–15 (2023).

  46. Wu, Y.-C., Chen, Y.-L., Yang, C.-H., Lee, C.-H., Chen, W.-C., Lin, L.-Y., Chang, N.-S., Lin, C.-P., Chen, C.-S., Hung, J.-H. & Yang, C.-H. A 28nm Fully Integrated End-to-End Genome Analysis Accelerator for Next-Generation Sequencing. IEEE Transactions on Biomedical Circuits and Systems, 1–15 (2025).

  47. Dunn, T., Sadasivan, H., Wadden, J., Goliya, K., Chen, K.-Y., Blaauw, D., Das, R. & Narayanasamy, S. SquiggleFilter: An Accelerator for Portable Virus Detection. In: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, pp. 535–549 (2021).

  48. Bao, Y. et al. Squigglenet: real-time, direct classification of nanopore signals. Genome Biol. 22, 1–16 (2021).

    Google Scholar 

  49. Harary, Y. et al. GCOC: A genome classifier-on-chip based on similarity search content addressable memory. IEEE Trans. Biomed. Circuits Syst. 19(3), 484–495 (2025).

    Google Scholar 

Download references

Related Posts