计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 285-291.doi: 10.11896/jsjkx.201100195
陈伟, 李杭, 李维华
CHEN Wei, LI Hang, LI Wei-hua
摘要： 核小体定位指DNA双螺旋相对于组蛋白的位置,并在DNA的转录阶段起着重要的调节作用。依靠生物实验的手段测得核小体定位会消耗大量的时间和资源,因此基于计算方法利用DNA序列进行核小体定位预测成为了一个重要的研究方向。针对核小体定位预测中单一模型和单一编码在DNA序列特征表示和学习方面的不足,文中提出了一种端到端的集成深度学习模型FuseENup,利用3种编码方式从多个维度表示DNA数据,利用不同的模型从不同维度提取数据中隐含的关键特征,构造了一种全新的DNA序列表征模型。在4种数据集上进行20倍交叉验证,相比当前针对核小体定位预测问题综合性能最优的模型CORENup,FuseENup的准确度(Accuracy)和精度(Precision)在HS数据集上提高了3%和9%,在DM数据集上提高了2%和6%,在E数据集上提高了1%和4%,相比其他的机器学习和深度学习基准模型,FuseENup具有更好的性能。实验结果表明,FuseENup能提高核小体定位的预测准确度,说明了该方法的有效性和科学性。
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| [1]RIDGWAY P,ALMOUZNI G.Chromatin assembly and organization[J].Journal of Cell Science,2001,114(15):2711-2712. [2]MASKELL D P,RENAULT L,SERRAO E,et al.Structuralbasis for retroviral integration into nucleosomes[J].Nature,2015,523(7560):366-369. [3]TABERLAY P C,STATHAM A L,KELLY T K,et al.Reconfiguration of nucleosome-depleted regions at distal regulatory elementsaccompanies DNA methylation of enhancers and insulators in cancer[J].Genome Research,2014,24(9):1421-1432. [4]COLE H A,CUI F,OCAMPO J,et al.Novel nucleosomal particles containing core histones and linker DNA but no histone H1[J].Nucleic Acids Research,2016,44(2):573-581. [5]SHAHBAZIAN M D,GRUNSTEIN M.Functions of Site-Specific Histone Acetylation and Deacetylation[J].Annual Review of Biochemistry,2007,76:75-100. [6]SCHNITZLER G R.Control of nucleosome positions by DNAsequence and remodeling machines[J].Cell Biochemistry and Biophysics,2008,51(2/3):67-80. [7]ZHENG D S,TRYNDA J,SUN Z F,et al.NUCLIZE for quantifying epigenome:generating histone modification data at single-nucleosome resolution using genuine nucleosome positions[J].Bmc Genomics,2019,20(1):541-544. [8]BUITRAGO D,CODO L,ILLA R,et al.Nucleosome Dynamics:a new tool for the dynamic analysis of nucleosome positioning[J].Nucleic Acids Research,2019,47(18):9511-9523. [9]SATCHWELL S C,DREW H R,TRAVERS A A.Sequence Periodicities in Chicken Nucleosome Core DNA[J].Journal of Molecular Biology,1986,191(4):659-675. [10]DREW H R,TRAVERS A A.DNA Bending and Its Relation to Nucleosome Positioning[J].Journal of Molecular Biology,1985,186(4):773-790. [11]LOWMAN H,BINA M.Correlation between Dinucleotide Pe-riodicities and Nucleosome Positioning on Mouse Satellite DNA[J].Biopolymers,1990,30(9/10):861-876. [12]ZIVKOVIĆ V,STANKOVIĆ A,TATJANA C,et al.Anti-dsDNA,Anti-Nucleosome and Anti-C1q Antibodies as Disease Activity Markers in Patients with Systemic Lupus Erythematosus[J].Srpski Arhiv za Celokupno Lekarstvo,2014,142:431-436. [13]YANG J F,XU Z Z,SUI M S,et al.Co-Positivity for Anti-dsDNA,-Nucleosome and-Histone Antibodies in Lupus Nephritis is Indicative of High Serum Levels and Severe Nephropathy[J].Plos One,2015,10(10):0140441. [14]OLIVEIRA R C,OLIVEIRA I S,SANTIAGO M B,et al.High Avidity dsDNA Autoantibodies in Brazilian Women with Systemic Lupus Erythematosus:Correlation with Active Disease and Renal Dysfunction[J].Journal of Immunology Research,2015,2015:814748. [15]CHENG J,CHEN H,MEN J L.Correlation between anti-nucleosome antibodies and systemic lupus erythematosus[J].Anhui Medicine,2019,23(1):83-86. [16]ZHANG D F,MA Q Y,YIN T M.Third-generation sequencing technology and its application[J].Chinese Journal of Bioengineering,2013,33(5):125-131. [17]XING Y Q,LIU G Q,ZHAO X J,et al.An analysis and prediction of nucleosome positioning based on information content[J].Chromosome Research,2013,21(1):63-74. [18]STRUHL K,SEGAL E.Determinants of nucleosome positio-ning[J].Nature Structural & Molecular Biology,2013,20(3):267-273. [19]LIELEG C,KRIETENSTEIN N,WALKER M,et al.Nucleo-some positioning in yeasts:methods,maps,and mechanisms[J].Chromosoma,2015,124(2):131-151. [20]MEHER P K,SAHU T K,RAO A R.Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier[J].Gene,2016,592(2):316-324. [21]CHEN W,FENG P M,DING H,et al.Using deformation energy to analyze nucleosome positioning in genomes[J].Genomics,2016,107(2/3):69-75. [22]BOSCO G L,RIZZO R,FIANNACA A,et al.A Deep Learning Model for Epigenomic Studies[C]//International Conference on Signal-image Technology & Internet-based Systems.2017. [23]GANGI M A D,GAGLIO S,BUA C L,et al.A Deep Learning Network for Exploiting Positional Information in Nucleosome Related Sequences[J].Bioinformatics and Biomedical Enginee-ring,2017,10209(4):524-533. [24]DI G M,LO B G,RIZZO R.Deep learning architectures for prediction of nucleosome positioning from sequences data[J].BMC Bioinformatics,2018,19(Suppl 14):418. [25]ZHANG J,PENG W,WANG L.LeNup:learning nucleosome positioning from DNA sequences with improved convolutional neural networks[J].Bioinformatics,2018,34(10):1705-1712. |
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