ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples
Autoři:
Ardi Tampuu aff001; Zurab Bzhalava aff002; Joakim Dillner aff002; Raul Vicente aff001
Působiště autorů:
Computational Neuroscience Lab, Institute of Computer Science, University of Tartu, Tartu, Estonia
aff001; Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
aff002; Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden
aff003
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222271
Souhrn
Despite its clinical importance, detection of highly divergent or yet unknown viruses is a major challenge. When human samples are sequenced, conventional alignments classify many assembled contigs as “unknown” since many of the sequences are not similar to known genomes. In this work, we developed ViraMiner, a deep learning-based method to identify viruses in various human biospecimens. ViraMiner contains two branches of Convolutional Neural Networks designed to detect both patterns and pattern-frequencies on raw metagenomics contigs. The training dataset included sequences obtained from 19 metagenomic experiments which were analyzed and labeled by BLAST. The model achieves significantly improved accuracy compared to other machine learning methods for viral genome classification. Using 300 bp contigs ViraMiner achieves 0.923 area under the ROC curve. To our knowledge, this is the first machine learning methodology that can detect the presence of viral sequences among raw metagenomic contigs from diverse human samples. We suggest that the proposed model captures different types of information of genome composition, and can be used as a recommendation system to further investigate sequences labeled as “unknown” by conventional alignment methods. Exploring these highly-divergent viruses, in turn, can enhance our knowledge of infectious causes of diseases.
Klíčová slova:
Biology and life sciences – Genetics – Genomics – Metagenomics – Viral genomics – Viral genome – Microbiology – Microbial genomics – Virology – Molecular biology – Molecular biology techniques – Molecular biology assays and analysis techniques – DNA filter assay – Neuroscience – Neural networks – Research and analysis methods – Database and informatics methods – Bioinformatics – Sequence analysis – Sequence alignment – BLAST algorithm – DNA sequence analysis – Computer and information sciences – Artificial intelligence – Machine learning
Zdroje
1. Wylie KM, Mihindukulasuriya KA, Sodergren E, Weinstock GM, Storch GA. Sequence analysis of the human virome in febrile and afebrile children. PLoS One. 2012;7(6):e27735. doi: 10.1371/journal.pone.0027735 22719819
2. Willner D, Furlan M, Haynes M, Schmieder R, Angly FE, Silva J, et al. Metagenomic analysis of respiratory tract DNA viral communities in cystic fibrosis and non-cystic fibrosis individuals. PLoS One. 2009;4(10):e7370. doi: 10.1371/journal.pone.0007370 19816605
3. Wylie KM, Weinstock GM, Storch GA. Emerging view of the human virome. Transl Res. 2012;160(4):283–90. doi: 10.1016/j.trsl.2012.03.006 22683423
4. Lecuit M, Eloit M. The human virome: new tools and concepts. Trends Microbiol. 2013;21(10):510–5. doi: 10.1016/j.tim.2013.07.001 23906500
5. Bzhalava D, Ekström J, Lysholm F, Hultin E, Faust H, Persson B, et al. Phylogenetically diverse TT virus viremia among pregnant women. Virology. 2012;432(2):427–434. https://doi.org/10.1016/j.virol.2012.06.022 22819835
6. Bzhalava D, Hultin E, Arroyo Muhr LS, Ekstrom J, Lehtinen M, de Villiers EM, et al. Viremia during pregnancy and risk of childhood leukemia and lymphomas in the offspring: Nested case-control study. Int J Cancer. 2016;138(9):2212–20. doi: 10.1002/ijc.29666 26132655
7. Bzhalava D, Johansson H, Ekstrom J, Faust H, Moller B, Eklund C, et al. Unbiased approach for virus detection in skin lesions. PLoS One. 2013;8(6):e65953. doi: 10.1371/journal.pone.0065953 23840382
8. Bzhalava D, Muhr LS, Lagheden C, Ekstrom J, Forslund O, Dillner J, et al. Deep sequencing extends the diversity of human papillomaviruses in human skin. Sci Rep. 2014;4:5807. doi: 10.1038/srep05807 25055967
9. Ekstrom J, Bzhalava D, Svenback D, Forslund O, Dillner J. High throughput sequencing reveals diversity of Human Papillomaviruses in cutaneous lesions. Int J Cancer. 2011;129(11):2643–50. doi: 10.1002/ijc.26204 21630257
10. Feng H, Shuda M, Chang Y, Moore PS. Clonal integration of a polyomavirus in human Merkel cell carcinoma. Science. 2008;319(5866):1096–100. doi: 10.1126/science.1152586 18202256
11. Mercalli A, Lampasona V, Klingel K, Albarello L, Lombardoni C, Ekström J, et al. No evidence of enteroviruses in the intestine of patients with type 1 diabetes. Diabetologia. 2012;55(9):2479–2488. doi: 10.1007/s00125-012-2591-4 22684312
12. Sundström P, Juto P, Wadell G, Hallmans G, Svenningsson A, Nyström L, et al. An altered immune response to Epstein-Barr virus in multiple sclerosis: A prospective study. vol. 62; 2004.
13. Meiring TL, Salimo AT, Coetzee B, Maree HJ, Moodley J, Hitzeroth I, et al. Next-generation sequencing of cervical DNA detects human papillomavirus types not detected by commercial kits. Virol J. 2012;9:164. doi: 10.1186/1743-422X-9-164 22897914
14. Foulongne V, Sauvage V, Hebert C, Dereure O, Cheval J, Gouilh MA, et al. Human skin microbiota: high diversity of DNA viruses identified on the human skin by high throughput sequencing. PLoS One. 2012;7(6):e38499. doi: 10.1371/journal.pone.0038499 22723863
15. Towner JS, Sealy TK, Khristova ML, Albarino CG, Conlan S, Reeder SA, et al. Newly discovered ebola virus associated with hemorrhagic fever outbreak in Uganda. PLoS Pathog. 2008;4(11):e1000212. doi: 10.1371/journal.ppat.1000212 19023410
16. Willner D, Haynes MR, Furlan M, Hanson N, Kirby B, Lim YW, et al. Case studies of the spatial heterogeneity of DNA viruses in the cystic fibrosis lung. Am J Respir Cell Mol Biol. 2012;46(2):127–31. doi: 10.1165/rcmb.2011-0253OC 21980056
17. Johansson H, Bzhalava D, Ekstrom J, Hultin E, Dillner J, Forslund O. Metagenomic sequencing of “HPV-negative” condylomas detects novel putative HPV types. Virology. 2013;440(1):1–7. doi: 10.1016/j.virol.2013.01.023 23522725
18. Labonte JM, Suttle CA. Previously unknown and highly divergent ssDNA viruses populate the oceans. ISME J. 2013;7(11):2169–77. doi: 10.1038/ismej.2013.110 23842650
19. Thomas T, Gilbert J, Meyer F. Metagenomics—a guide from sampling to data analysis. Microb Inform Exp. 2012;2(1):3. doi: 10.1186/2042-5783-2-3 22587947
20. Mistry J, Finn RD, Eddy SR, Bateman A, Punta M. Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions. Nucleic Acids Res. 2013;41(12):e121. doi: 10.1093/nar/gkt263 23598997
21. Skewes-Cox P, Sharpton TJ, Pollard KS, DeRisi JL. Profile hidden Markov models for the detection of viruses within metagenomic sequence data. PLoS One. 2014;9(8):e105067. doi: 10.1371/journal.pone.0105067 25140992
22. Bzhalava Z, Hultin E, Dillner J. Extension of the viral ecology in humans using viral profile hidden Markov models. PLOS ONE. 2018;13(1):e0190938. doi: 10.1371/journal.pone.0190938 29351302
23. Amgarten D, Braga LPP, da Silva AM, Setubal JC. MARVEL, a Tool for Prediction of Bacteriophage Sequences in Metagenomic Bins. Frontiers in Genetics. 2018;9(304). doi: 10.3389/fgene.2018.00304 30131825
24. Ren J, Ahlgren NA, Lu YY, Fuhrman JA, Sun F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome. 2017;5:69.
25. Ren J, Song K, Deng C, Ahlgren NA, Fuhrman JA, Li Y, et al. Identifying viruses from metagenomic data by deep learning. In: Conference Proceedings;.
26. Vervier K, Mahé P, Tournoud M, Veyrieras JB, Vert JP. Large-scale machine learning for metagenomics sequence classification. Bioinformatics (Oxford, England). 2016;32(7):1023–1032. doi: 10.1093/bioinformatics/btv683
27. Chaudhary N, Sharma AK, Agarwal P, Gupta A, Sharma VK. 16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets. PLOS ONE. 2015;10(2):e0116106. doi: 10.1371/journal.pone.0116106 25646627
28. Bzhalava Z, Tampuu A, Bała P, Vicente R, Dillner J. Machine Learning for detection of viral sequences in human metagenomic datasets. BMC Bioinformatics. 2018;19(1):336. doi: 10.1186/s12859-018-2340-x 30249176
29. Huang W, Li L, Myers JR, Marth GT. ART: a next-generation sequencing read simulator. Bioinformatics. 2011;28(4):593–594. doi: 10.1093/bioinformatics/btr708 22199392
30. Smelov V, Bzhalava D, Arroyo Muhr LS, Eklund C, Komyakov B, Gorelov A, et al. Detection of DNA viruses in prostate cancer. Sci Rep. 2016;6:25235. doi: 10.1038/srep25235 27121729
31. Arroyo Mühr LS, Bzhalava D, Lagheden C, Eklund C, Johansson H, Forslund O, et al. Does human papillomavirus-negative condylomata exist? Virology. 2015;485:283–288. https://doi.org/10.1016/j.virol.2015.07.023 26318260
32. Arroyo Muhr LS, Hultin E, Bzhalava D, Eklund C, Lagheden C, Ekstrom J, et al. Human papillomavirus type 197 is commonly present in skin tumors. Int J Cancer. 2015;136(11):2546–55. doi: 10.1002/ijc.29325 25388227
33. Bzhalava D, Dillner J. Bioinformatics for Viral Metagenomics. J Data Mining Genomics Proteomics. 2013;4(3). doi: 10.4172/2153-0602.1000134
34. Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754–1760. doi: 10.1093/bioinformatics/btp324 19451168
35. Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28(11):1420–1428. doi: 10.1093/bioinformatics/bts174 22495754
36. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nature biotechnology. 2011;29(7):644–652. doi: 10.1038/nbt.1883 21572440
37. Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience. 2012;1(1):18. doi: 10.1186/2047-217X-1-18 23587118
38. Nowicki M, Bzhalava D, BaŁa P. Massively Parallel Implementation of Sequence Alignment with Basic Local Alignment Search Tool Using Parallel Computing in Java Library. Journal of Computational Biology. 2018;25(8):871–881. doi: 10.1089/cmb.2018.0079 30004240
39. Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I. Spark: Cluster computing with working sets. HotCloud. 2010;10(10-10):95.
40. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, et al. Backpropagation applied to handwritten zip code recognition. Neural computation. 1989;1(4):541–551. doi: 10.1162/neco.1989.1.4.541
41. Goodfellow I, Bengio Y, Courville A. Deep learning. MIT press; 2016.
42. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cognitive Science; 1985.
43. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems; 2012. p. 1097–1105.
44. LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks. 1995;3361(10):1995.
45. Hinton G, Deng L, Yu D, Dahl G, Mohamed Ar, Jaitly N, et al. Deep neural networks for acoustic modeling in speech recognition. IEEE Signal processing magazine. 2012;29. doi: 10.1109/MSP.2012.2205597
46. Kelley DR, Snoek J, Rinn JL. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome research. 2016;26(7):990–999. doi: 10.1101/gr.200535.115 27197224
47. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning–based sequence model. Nature methods. 2015;12(10):931. doi: 10.1038/nmeth.3547 26301843
48. Angermueller C, Pärnamaa T, Parts L, Stegle O. Deep learning for computational biology. Molecular systems biology. 2016;12(7):878. doi: 10.15252/msb.20156651 27474269
49. Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980. 2014;.
50. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. Journal of machine learning research. 2011;12(Oct):2825–2830.
51. Chollet F, et al. Keras; 2015. https://keras.io.
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