NanoTAX: Nanogözenek DNA Dizileme Verisi Üzerinden Hızlı Patojen Tanıma

Bu çalışmada gerçek zamanlı DNA dizilemesi sağlayan, düşük maliyetli ve taşınabilir bir yeni nesil dizileme teknolojisi olan nanogözenek dizileme teknolojisini kullanarak gerçek zamanlı ve düşük maliyetli patojen/etken tespit algoritmaları sunulmaktadır. Çalışma kapsamında Oxford Nanopore MinION DNA dizileyicisi ile sekanslanan bakterileri gerçek zamanlı tanıyabilecek bilgi kuramı temelli biyoinformatik teşhis algoritmaları geliştirilmiş ve performansları gerçek veri üzerinde test edilmiştir. Yeni nesil dizileme verisi üzerinde hız, doğruluk ve dayanıklılık açısından başarılı olduğu raporlanan Bağıl Bolluk Endeksleri (ing: Relative Abundance Index-RAI) ve nanogözenek dizilemesinde olduğu gibi hatalı DNA okumaları üzerinde başarılı olduğu raporlanmış olan Ortalama Karşılıklı Bilgi (OKB) yöntemi ile DNA karakterizasyonu yapılarak patojen tanıma algoritmaları geliştirilmiştir. Tasarlanan simülasyonlar ile ortalama teşhis koyma süreleri ve doğrulukları hakkındaki istatistikler elde edilerek bu yönde oluşturulacak sistemlerin rutin kullanım için fizibilitesi ortaya konmuştur. Önerilen OKB profili ve RAI tabanlı algoritmaların hızlı patojen tanıma konusunda yeterli doğruluk seviyesinde ve kısa sürede tanıma yapabilecek hızda olduğu ve mevcut programlarla rekabet edebilen performansta olduğu nanogözenek dizilemesi yapılan patojen paneli üzerinde gösterilmiştir.

Anahtar Kelimeler:

Biyoinformatik, Hızlı patojen tanıma, Makine öğrenme, Yeni nesil dizileme, Nanogözenek dizileme

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Spížek J., Novotná J., Rezanka T., Demain A. L. 2010. Do we need new antibiotics? The search for new targets and new compounds. J. Ind. Microbiol. Biotechnol., 37, 1241–1248.
Fauci A. S., Touchette N. A., Folkers G. K. 2005. Emerging infectious diseases: a 10-year perspective from the National institute of allergy and infectious diseases. Emerging Infect. Dis. 11, 519–525.
Maurer, J. J. 2011. Rapid detection and limitations of molecular techniques. Annual Review of Food Science and Technology, 2, 259-279.
Lazcka, O., Del Campo, F. J., Munoz, F. X. 2007. Pathogen detection: A perspective of traditional methods and biosensors. Biosensors and bioelectronics, 22(7), 1205-1217.
Erlich Y. 2015. A vision for ubiquitous sequencing. Genome Res., 25, 1411-1416.
Frank C., Werber D., Cramer J. P., Askar M., Faber M., an der Heiden M., Bernard H., Fruth A., Prager R., Spode A., Wadl M., Zoufaly A., Jordan S., Kemper M. J., Follin P., Müller L., King L. A., Rosner B., Buchholz U., Stark K., Krause G., HUS Investigation Team. 2011. Epidemic profile of Shiga-toxin-producing Escherichia coli O104:H4 outbreak in Germany. N Engl J Med, 365, 1771-1780.
Hong C., Manimaran S., Shen Y., Perez-Rogers J. F., Byrd A. L., Castro-Nallar E., vd. 2014. PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples. Microbiome, 2:33.
Quail M., Smith M. E., Coupland P., Otto T. D., Harris S. R., Connor T. R., Bertoni A., Swerdlow H. P., Gu Y. 2012. A tale of three next generation sequencing platforms: comparison of Ion torrent, pacific biosciences and illumina MiSeq sequencers. BMC Genomics, 13:341.
Mardis E. R. 2008. Next-generation DNA sequencing methods. Annu Rev Genomics Hum Genet, 9, 387–402.
Stoddart D., Heron A. J., Mikhailova E. vd. 2009. Single-Nucleotide Discrimination in Immobilized DNA Oligonucleotides with a Biological Nanopore. Proceedings of the National Academy of Sciences of the United States of America, 106, 7702–7707.
Kasianowicz J. J, Brandin E., Branton D., Deamer D. W. 1996. Characterization of individual polynucleotide molecules using a membrane chanel. Proc Natl Acad Sci, 93(24), 13770–13773.
Hayden E. C. 2015. Pint-sized DNA sequencer impresses first users. Nature 521, 15–16.
Feng Y., Zhang Y., Ying C., Wang D., Du C. 2015. Nanopore-based fourth-generation DNA sequencing technology. Genomics Proteomics Bioinformatics, 13, 4–16.
Quick J., Quinlan A., Loman N. 2014. A reference bacterial genome dataset generated on the MinION(TM) portable single-molecule nanopore sequencer. GigaScience, 3:22.
Loman N. J., Quick J., Simpson J. T. 2015. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat. Methods, 12, 733–735.
Karlsson E., Lärkeryd A., Sjödin A., vd. 2015. Scaffolding of a bacterial genome using MinION nanopore sequencing. Sci Rep., 5: 11996.
Kilianski A., Haas J. L., Corriveau E. J., Liem A. T., Willis K. L., Kadavy D. R., vd. 2015. Bacterial and viral identification and differentiation by amplicon sequencing on the MinION nanopore sequencer. Gigascience 4, 12.
Benitez-Paez A., Portune K., Sanz Y. 2015. Species level resolution of 16S rRNA gene amplicons sequenced through MinIONTM portable nanopore sequencer. BioRxiv, 021758.
Madoui M. A., Engelen S., Cruaud C., Belser C., Bertrand L., Alberti A., vd. 2015. Genome assembly using Nanopore-guided long and error-free DNA reads. BMC Genom., 16:327.
Warren R. L., Yang C., Vandervalk B. P., Behsaz B., Lagman A., Jones S. J., Birol, I. 2015. LINKS: Scalable, alignmentfree scaffolding of draft genomes with long reads. GigaScience, 4(1), 1–11.
Bradley P., Gordon N. C., Walker T. M. vd. 2015. Rapid antibiotic resistance predictions from genome sequence data for S aureus and M tuberculosis. BioRxiv, 018564.
Bolisetty M. T., Rajadinakaran G., Graveley B. R. 2015. Determining exon connectivity in complex mRNAs by nanopore sequencing. Genome Biology, 16:204.
Ammar R., Paton T. A., Torti D., Shlien A., Bader G. D. 2015. Long read nanopore sequencing for detection of HLA and CYP2D6 variants and haplotypes. F1000Res, 4, 17.
Norris A. L., Workman R. E., Fan Y., Eshleman J. R., Timp W. 2015. Nanopore sequencing detects structural variants in cancer. BioRxiv, 028290.
Wei, S., Williams, Z. 2016. Rapid short-read sequencing and aneuploidy detection using MinION nanopore technology. Genetics, 202(1), 37-44.
Mikheyev A. S., Tin M. M. 2014. A first look at the Oxford Nanopore MinION sequencer. Mol. Ecol. Resour., 14, 1097–1102.
Greninger A. L., vd. 2015. Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis. Genome Medicine, 7 (1), 99.
Bull, R. A., Adikari, T. N., Ferguson, J. M., Hammond, J. M., Stevanovski, I., Beukers, A. G., Deveson, I. W. 2020. Analytical validity of nanopore sequencing for rapid SARS-CoV-2 genome analysis. Nature communications, 11(1), 1-8.
Li, J., Wang, H., Mao, L., Yu, H., Yu, X., Sun, Z., Wang, X. 2020. Rapid genomic characterization of SARS-CoV-2 viruses from clinical specimens using nanopore sequencing. Scientific Reports, 10(1), 1-10.
Kielbasa S. M, Wan R., Sato K., Horton P., Frith M. C. 2011. Adaptive seeds tame genomic sequence comparison. Genome Res., 21(3), 487–93.
Wang J., Moore N., Deng Y., Eccles D., Hall R. 2015. MinION nanopore sequencing of an influenza genome. Front Microbiol, 6: 766.
Judge K., Harris S. R., Reuter S., Parkhill J., Peacock S. J. 2015. Early insights into the potential of the Oxford Nanopore MinION for the detection of antimicrobial resistance genes. J. Antimicrob. Chemother., 70, 2775–2778.
Risse J., Thomson M., Blakely G., Koutsovoulos G., Blaxter M., Watson M. 2015. A single chromosome assembly of Bacteroides fragilis strain BE1 from illumina and MinION nanopore sequencing data. BioRxiv, 024323.
Jain M. vd., 2015. Improved data analysis for the MinION nanopore sequencer. Nat. Methods, 12, 351–356.
Nalbantoglu O. U., Sayood K. 2011. Computational Genomics Signatures. Synthesis Lectures on Biomedical Engineering, 6(2), 1-129. Morgan-Claypool Publishers, 129 sayfa.
Brady S. S. vd. 2009. Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated markov models. Nat Methods, 6, 673–676.
Nalbantoglu O. U., 2011. Computational Genomic Signatures and Metagenomics. Ph.D. Dissertation, University of Nebraska-Lincoln, 201 s, Lincoln, Nebraska, ABD.
Nalbantoglu O. U., Way S., Hinrichs S., Sayood K. 2011.“RAIphy: Phylogenetic classification of metagenomics samples using iterative refinement of relative abundance index profiles. BMC Bioinformatics, 12(41).
Otu H., Sayood K. 2003. A divide and conquer approach to sequence assembly. Bioinformatics, 19, 22–29.
Bauer M., Schuster S. M., Sayood K. 2008. The average mutual information profile as a genomic signature. BMC Bioinformatics, 9 , p. 48.
Bazinet A. L., Cummings M. P. 2012. A comparative evaluation of sequence classification programs. BMC Bioinformatics 13: 92.
Rish I. 2001. An empirical study of the naive Bayes classifier”, IJCAI Workshop on Empirical Methods in AI. 4-6 Ağustos 2001, Seattle, ABD. Vol. 3, No. 22, pp. 41-46 .
Vapnik, V. 1995. Support-vector networks. Machine Learning. 20 (3): 273–297
Altman, N. S. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician. 46 (3): 175–185.
Utgoff, P. E., 1989. Incremental induction of decision trees. Machine learning, 4(2), 161-186.
Leo B., 2001. Random Forests. Machine Learning. 45 (1): 5–32.
Giordano, F., vd., 2017. De novo yeast genome assemblies from MinION, PacBio and MiSeq platforms. Scientific reports, 7.
Li, H., 2016. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics, 32(14), 2103-2110.
Wood, D. E., Salzberg, S. L., 2014. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome biology, 15(3), R46.