A review of deep learning applications in human genomics using next-generation sequencing data

Auffray C, Imbeaud S, Roux-Rouquié M, Hood L. From functional genomics to systems biology: concepts and practices. C R Biol. 2003;326(10–11):879–92.

CAS  PubMed  Article  Google Scholar 

Goldfeder RL, Priest JR, Zook JM, Grove ME, Waggott D, Wheeler MT, et al. Medical implications of technical accuracy in genome sequencing. Genome Med. 2016;8(1):24.

PubMed  PubMed Central  Article  CAS  Google Scholar 

Goodwin S, McPherson JD, McCombie WR. Coming of age: Ten years of next-generation sequencing technologies. Nat Rev Genet. 2016;17(6):333–51.

CAS  PubMed  Article  Google Scholar 

Yue T, Wang H. Deep Learning for Genomics: A Concise Overview. 2018

Honoré B, Østergaard M, Vorum H. Functional genomics studied by proteomics. BioEssays. 2004;26(8):901–15.

PubMed  Article  CAS  Google Scholar 

Talukder A, Barham C, Li X, Hu H. Interpretation of deep learning in genomics and epigenomics. Brief Bioinform. 2020;2:447.

Google Scholar 

Fulco CP, Munschauer M, Anyoha R, Munson G, Grossman SR, Perez EM, et al. Systematic mapping of functional enhancer–promoter connections with CRISPR interference. Science (80-). 2016;354(6313):769–73.

CAS  Article  Google Scholar 

Kulasingam V, Pavlou MP, Diamandis EP. Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer. Nat Rev Cancer. 2010;10(5):371–8.

CAS  PubMed  Article  Google Scholar 

Nariai N, Kolaczyk ED, Kasif S. Probabilistic protein function prediction from heterogeneous genome-wide data. PLoS One. 2007;2(3):e337.

PubMed  PubMed Central  Article  CAS  Google Scholar 

Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype–phenotype interactions. Nat Rev Genet. 2015;16(2):85–97.

CAS  PubMed  Article  Google Scholar 

Koumakis L. Deep learning models in genomics; are we there yet? Comput Struct Biotechnol J. 2020;18:1466–73.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Cao C, Liu F, Tan H, Song D, Shu W, Li W, et al. Deep learning and its applications in biomedicine. Genom Proteom Bioinform. 2018;16(1):17–32.

Article  Google Scholar 

Telenti A, Lippert C, Chang PC, DePristo M. Deep learning of genomic variation and regulatory network data. Hum Mol Genet. 2018;27(R1):R63-71.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Kopp W, Monti R, Tamburrini A, Ohler U, Akalin A. Deep learning for genomics using Janggu. Nat Commun. 2020;11(1):3488.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Deep learning for genomics. Nat Genet. 2019;51(1):1–1.

Singh J, Hanson J, Paliwal K, Zhou Y. RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nat Commun. 2019;10(1):5407.

PubMed  PubMed Central  Article  CAS  Google Scholar 

Hsieh T-C, Mensah MA, Pantel JT, Aguilar D, Bar O, Bayat A, et al. PEDIA: prioritization of exome data by image analysis. Genet Med. 2019;21(12):2807–14.

PubMed  PubMed Central  Article  Google Scholar 

Singh R, Lanchantin J, Robins G, Qi Y. DeepChrome: deep-learning for predicting gene expression from histone modifications. Bioinformatics. 2016;32(17):i639–48.

CAS  PubMed  Article  Google Scholar 

Arloth J, Eraslan G, Andlauer TFM, Martins J, Iurato S, Kühnel B, et al. DeepWAS: multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning. PLOS Comput Biol. 2020;16(2):e1007616.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65(6):386–408.

CAS  PubMed  Article  Google Scholar 

Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):160.

PubMed  PubMed Central  Article  Google Scholar 

Wang C, Tan XP, Tor SB, Lim CS. Machine learning in additive manufacturing: state-of-the-art and perspectives. Addit Manuf. 2020;36:101538.

Google Scholar 

Muzio G, O’Bray L, Borgwardt K. Biological network analysis with deep learning. Brief Bioinform. 2021;22(2):1515–30.

PubMed  Article  Google Scholar 

Maraziotis I, Dragomir A, Bezerianos A. Gene networks inference from expression data using a recurrent neuro-fuzzy approach. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE; 2005. p. 4834–7.

LeCun Y. 1.1 Deep learning hardware: past, present, and future. In: 2019 IEEE International Solid-State Circuits Conference-(ISSCC). IEEE; 2019. p. 12–9.

Kuenzi BM, Park J, Fong SH, Sanchez KS, Lee J, Kreisberg JF, et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell. 2020;38(5):672-684.e6.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y, Kosmicki JA, et al. Predicting the clinical impact of human mutation with deep neural networks. Nat Genet. 2018;50(8):1161–70.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Lanchantin J, Singh R, Wang B, Qi Y. Deep motif dashboard: visualizing and understanding genomic sequences using deep neural networks. World Sci. 2017;3:254–65.

Google Scholar 

Albaradei S, Magana-Mora A, Thafar M, Uludag M, Bajic VB, Gojobori T, et al. Splice2Deep: an ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA. Gene X. 2020;5:100035.

CAS  PubMed  PubMed Central  Google Scholar 

Poplin R, Chang PC, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal snp and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36(10):983.

CAS  PubMed  Article  Google Scholar 

Liu Q, Xia F, Yin Q, Jiang R. Chromatin accessibility prediction via a hybrid deep convolutional neural network. Bioinformatics. 2018;2:1147.

Google Scholar 

Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet. 2019;51(1):12–8.

CAS  PubMed  Article  Google Scholar 

Al-Stouhi S, Reddy CK. Transfer learning for class imbalance problems with inadequate data. Knowl Inf Syst. 2016;48(1):201–28.

PubMed  Article  Google Scholar 

Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 2020;21(1):6.

Article  Google Scholar 

Handelman GS, Kok HK, Chandra RV, Razavi AH, Huang S, Brooks M, et al. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. Am J Roentgenol. 2019;212(1):38–43.

Article  Google Scholar 

England JR, Cheng PM. Artificial intelligence for medical image analysis: a guide for authors and reviewers. Am J Roentgenol. 2019;212(3):513–9.

Article  Google Scholar 

Eraslan G, Avsec Ž, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet. 2019;20(7):389–403.

CAS  PubMed  Article  Google Scholar 

Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet. 2019;51(1):12–8.

CAS  PubMed  Article  Google Scholar 

Pérez-Enciso M, Zingaretti LM. A guide for using deep learning for complex trait genomic prediction. Genes (Basel). 2019;10(7):12258.

Article  CAS  Google Scholar 

Abnizova I, Boekhorst RT, Orlov YL. Computational errors and biases in short read next generation sequencing. J Proteom Bioinform. 2017;10(1):400089.

Article  Google Scholar 

Ma X, Shao Y, Tian L, Flasch DA, Mulder HL, Edmonson MN, et al. Analysis of error profiles in deep next-generation sequencing data. Genome Biol. 2019;20(1):50.

PubMed  PubMed Central  Article  Google Scholar 

Pfeiffer F, Gröber C, Blank M, Händler K, Beyer M, Schultze JL, et al. Systematic evaluation of error rates and causes in short samples in next-generation sequencing. Sci Rep. 2018;8(1):10950.

PubMed  PubMed Central  Article  CAS  Google Scholar 

Horner DS, Pavesi G, Castrignano T, De Meo PD, Liuni S, Sammeth M, et al. Bioinformatics approaches for genomics and post genomics applications of next-generation sequencing. Brief Bioinform. 2010;11(2):181–97.

CAS  PubMed  Article  Google Scholar 

McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

PubMed 

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