Detection of Novel BEST1 Variations in Autosomal Recessive Bestrophinopathy Using Third-generation Sequencing

Objective:Autosomal recessive bestrophinopathy(ARB),a retinal degenerative disease,is characterized by central visual loss,yellowish multifocal diffuse subretinal deposits,and a dramatic decrease in the light peak on electrooculogram.The potential pathogenic mechanism involves mutations in the BEST1 gene,which encodes Ca2+-activated Cl−channels in the retinal pigment epithelium(RPE),resulting in degeneration of RPE and photoreceptor.In this study,the complete clinical characteristics of two Chinese ARB families were summarized.Methods:Pacific Biosciences(PacBio)single-molecule real-time(SMRT)sequencing was performed on the probands to screen for disease-causing gene mutations,and Sanger sequencing was applied to validate variants in the patients and their family members.Results:Two novel mutations,c.202T>C(chr11:61722628,p.Y68H)and c.867+97G>A,in the BEST1 gene were identified in the two Chinese ARB families.The novel missense mutation BEST1 c.202T>C(p.Y68H)resulted in the substitution of tyrosine with histidine in the N-terminal region of transmembrane domain 2 of bestrophin-1.Another novel variant,BEST1 c.867+97G>A(chr11:61725867),located in intron 7,might be considered a regulatory variant that changes allele-specific binding affinity based on motifs of important transcriptional regulators.Conclusion:Our findings represent the first use of third-generation sequencing(TGS)to identify novel BEST1 mutations in patients with ARB,indicating that TGS can be a more accurate and efficient tool for identifying mutations in specific genes.The novel variants identified further broaden the mutation spectrum of BEST1 in the Chinese population.

Diagnosis and treatment of refractory infectious diseases using nanopore sequencing technology:Three case reports

BACKGROUND Infectious diseases are still one of the greatest threats to human health,and the etiology of 20%of cases of clinical fever is unknown;therefore,rapid identification of pathogens is highly important.Traditional culture methods are only able to detect a limited number of pathogens and are time-consuming;serologic detection has window periods,false-positive and false-negative problems;and nucleic acid molecular detection methods can detect several known pathogens only once.Three-generation nanopore sequencing technology provides new options for identifying pathogens.CASE SUMMARY Case 1:The patient was admitted to the hospital with abdominal pain for three days and cessation of defecation for five days,accompanied by cough and sputum.Nanopore sequencing of the drainage fluid revealed the presence of orallike bacteria,leading to a clinical diagnosis of bronchopleural fistula.Cefoperazone sodium sulbactam treatment was effective.Case 2:The patient was admitted to the hospital with fever and headache,and CT revealed lung inflammation.Antibiotic treatment for Streptococcus pneumoniae,identified through nanopore sequencing of cerebrospinal fluid,was effective.Case 3:The patient was admitted to our hospital with intermittent fever and an enlarged neck mass that had persisted for more than six months.Despite antibacterial treatment,her symptoms worsened.The nanopore sequencing results indicate that voriconazole treatment is effective for Aspergillus brookii.The patient was diagnosed with mixed cell type classical Hodgkin's lymphoma with infection.CONCLUSION Three-generation nanopore sequencing technology allows for rapid and accurate detection of pathogens in human infectious diseases.

Breed identification using breed‑informative SNPs and machine learning based on whole genome sequence data and SNP chip data

Background Breed identification is useful in a variety of biological contexts.Breed identification usually involves two stages,i.e.,detection of breed-informative SNPs and breed assignment.For both stages,there are several methods proposed.However,what is the optimal combination of these methods remain unclear.In this study,using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project,we compared the combinations of three methods(Delta,FST,and In)for breed-informative SNP detection and five machine learning methods(KNN,SVM,RF,NB,and ANN)for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs.In addition,we evaluated the accuracy of breed identification using SNP chip data of different densities.Results We found that all combinations performed quite well with identification accuracies over 95%in all scenarios.However,there was no combination which performed the best and robust across all scenarios.We proposed to inte-grate the three breed-informative detection methods,named DFI,and integrate the three machine learning methods,KNN,SVM,and RF,named KSR.We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99%in most cases and was very robust in all scenarios.The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.Conclusions The current study showed that the combination of DFI and KSR was the optimal strategy.Using sequence data resulted in higher accuracies than using chip data in most cases.However,the differences were gener-ally small.In view of the cost of genotyping,using chip data is also a good option for breed identification.

Surveillance of emerging SARS-CoV-2 variants by nanopore technology-based genome sequencing

Objective:To surveill emerging variants by nanopore technology-based genome sequencing in different COVID-19 waves in Sri Lanka and to examine the association with the sample characteristics,and vaccination status.Methods:The study analyzed 207 RNA positive swab samples received to sequence laboratory during different waves.The N gene cut-off threshold of less than 30 was considered as the major inclusion criteria.Viral RNA was extracted,and elutes were subjected to nanopore sequencing.All the sequencing data were uploaded in the publicly accessible database,GISAID.Results:The Omicron,Delta and Alpha variants accounted for 58%,22%and 4%of the variants throughout the period.Less than 1%were Kappa variant and 16%of the study samples remained unassigned.Omicron variant was circulated among all age groups and in all the provinces.Ct value and variants assigned percentage was 100%in Ct values of 10-15 while only 45%assigned Ct value over 25.Conclusions:The present study examined the emergence,prevalence,and distribution of SARS-CoV-2 variants locally and has shown that nanopore technology-based genome sequencing enables whole genome sequencing in a low resource setting country.

Examining heterogeneity of stromal cells in tumor microenvironment based on pan-cancer single-cell RNA sequencing data

Tumor tissues contain both tumor and non-tumor cells,which include infiltrated immune cells and stromal cells,collectively called the tumor microenvironment(TME).Single-cell RNA sequencing(sc RNAseq)enables the examination of heterogeneity of tumor cells and TME.In this review,we examined sc RNAseq datasets for multiple cancer types and evaluated the heterogeneity of major cell type composition in different cancer types.We further showed that endothelial cells and fibroblasts/myofibroblasts in different cancer types can be classified into common subtypes,and the subtype composition is clearly associated with cancer characteristic and therapy response.

Generating barcodes for nanopore sequencing data with PRO

DNA barcodes,short and unique DNA sequences,play a crucial role in sample identification when processing many samples simultaneously,which helps reduce experimental costs.Nevertheless,the low quality of long-read sequencing makes it difficult to identify barcodes accurately,which poses significant challenges for the design of barcodes for large numbers of samples in a single sequencing run.Here,we present a comprehensive study of the generation of barcodes and develop a tool,PRO,that can be used for selecting optimal barcode sets and demultiplexing.We formulate the barcode design problem as a combinatorial problem and prove that finding the optimal largest barcode set in a given DNA sequence space in which all sequences have the same length is theoretically NP-complete.For practical applications,we developed the novel method PRO by introducing the probability divergence between two DNA sequences to expand the capacity of barcode kits while ensuring demultiplexing accuracy.Specifically,the maximum size of the barcode kits designed by PRO is 2,292,which keeps the length of barcodes the same as that of the official ones used by Oxford Nanopore Technologies(ONT).We validated the performance of PRO on a simulated nanopore dataset with high error rates.The demultiplexing accuracy of PRO reached 98.29%for a barcode kit of size 2,922,4.31%higher than that of Guppy,the official demultiplexing tool.When the size of the barcode kit generated by PRO is the same as the official size provided by ONT,both tools show superior and comparable demultiplexing accuracy.

High-throughput Sequencing Technology and Its Application

Gene sequencing is a great way to interpret life, and high-throughput sequencing technology is a revolutionary technological innovation in gene sequencing researches. This technology is characterized by low cost and high-throughput data. Currently, high-throughput sequencing technology has been widely applied in multi-level researches on genomics, transcriptomics and epigenomics. And it has fundamentally changed the way we approach problems in basic and translational researches and created many new possibilities. This paper presented a general description of high-throughput sequencing technology and a comprehensive review of its application with plain, concisely and precisely. In order to help researchers finish their work faster and better, promote science amateurs and understand it easier and better.

Incorporating genomic annotation into single-step genomic prediction with imputed whole-genome sequence data

Single-step genomic best linear unbiased prediction(ss GBLUP) is now intensively investigated and widely used in livestock breeding due to its beneficial feature of combining information from both genotyped and ungenotyped individuals in the single model. With the increasing accessibility of whole-genome sequence(WGS) data at the population level, more attention is being paid to the usage of WGS data in ss GBLUP. The predictive ability of ss GBLUP using WGS data might be improved by incorporating biological knowledge from public databases. Thus, we extended ss GBLUP, incorporated genomic annotation information into the model, and evaluated them using a yellow-feathered chicken population as the examples. The chicken population consisted of 1 338 birds with 23 traits, where imputed WGS data including 5 127 612 single nucleotide polymorphisms(SNPs) are available for 895 birds. Considering different combinations of annotation information and models, original ss GBLUP, haplotype-based ss GHBLUP, and four extended ss GBLUP incorporating genomic annotation models were evaluated. Based on the genomic annotation(GRCg6a) of chickens, 3 155 524 and 94 837 SNPs were mapped to genic and exonic regions, respectively. Extended ss GBLUP using genic/exonic SNPs outperformed other models with respect to predictive ability in 15 out of 23 traits, and their advantages ranged from 2.5 to 6.1% compared with original ss GBLUP. In addition, to further enhance the performance of genomic prediction with imputed WGS data, we investigated the genotyping strategies of reference population on ss GBLUP in the chicken population. Comparing two strategies of individual selection for genotyping in the reference population, the strategy of evenly selection by family(SBF) performed slightly better than random selection in most situations. Overall, we extended genomic prediction models that can comprehensively utilize WGS data and genomic annotation information in the framework of ss GBLUP, and validated the idea that properly handling the genomic annotation information and WGS data increased the predictive ability of ss GBLUP. Moreover, while using WGS data, the genotyping strategy of maximizing the expected genetic relationship between the reference and candidate population could further improve the predictive ability of ss GBLUP. The results from this study shed light on the comprehensive usage of genomic annotation information in WGS-based single-step genomic prediction.

Analysis on the Influence of Automatic Station Temperature Data on the Sequence Continuity of Historical Meteorological Data

[Objective] The research aimed to study the influence of automatic station data on the sequence continuity of historical meteorological data. [Method] Based on the temperature data which were measured by the automatic meteorological station and the corresponding artificial observation data during January-December in 2001, the monthly average, maximum and minimum temperatures in the automatic station were compared with the corresponding artificial observation temperature data in the parallel observation period by using the contrast difference and the standard deviation of difference value. The difference between the automatic station and the artificial data, the variation characteristics were understood. Meanwhile, the significance test and analysis of annual average value were carried out by the data sequence during 1990-2009. The influence of automatic station replacing the artificial observation on the sequence continuity of historical temperature data was discussed. [Result] Although the two temperature data in the parallel observation period had the certain difference, the difference was in the permitted range of automatic station difference value on average. The difference of individual month surpassed the permitted range of automatic station difference value. The significance test showed that the annual average temperature and the annual average minimum temperature which were observed in the automatic station had the difference with the historical data. It had the certain influence on the annual temperature sequence, but the difference wasn’t significant as a whole. When the automatic observation combined with the artificial observation to use, the sequence needed carry out the homogeneous test and correction. [Conclusion] The research played the important role on guaranteeing the monorail running of automatic station, optimizing the meteorological surface observation system, improving the climate sequence continuity of meteorological element and the reliability of climate statistics.

Sequence detection data fusion with distributed multisensor

This Paper presents a data fusion method with distributed sequence detection for on hypothasis testingtheory including the data fusion algorithm of sequence detection based on least error probability rule, the decision ruleand the calcation formula of the detction times and the simulation result of system performance as well.

Logging Data High-Resolution Sequence Stratigraphy

The recognition and contrast of bed sets in parasequence is difficult in terrestrial basin high-resolution sequence stratigraphy. This study puts forward new methods for the boundary identification and contrast of bed sets on the basis of manifold logging data. The formation of calcareous interbeds, shale resistivity differences and the relation of reservoir resistivity to altitude are considered on the basis of log curve morphological characteristics, core observation, cast thin section, X-ray diffraction and scanning electron microscopy. The results show that the thickness of calcareous interbeds is between 0.5 m and 2 m, increasing on weathering crusts and faults. Calcareous interbeds occur at the bottom of a distributary channel and the top of a distributary mouth bar. Lower resistivity shale （4-5 Ω · m） and higher resistivity shale （〉 10Ω·m） reflect differences in sediment fountain or sediment microfacies. Reservoir resistivity increases with altitude. Calcareous interbeds may be a symbol of recognition for the boundary of bed sets and isochronous contrast bed sets, and shale resistivity differences may confirm the stack relation and connectivity of bed sets. Based on this, a high-resolution chronostratigraphic frame- work of Xi-1 segment in Shinan area, Junggar basin is presented, and the connectivity of bed sets and oil-water contact is confirmed. In this chronostratigraphic framework, the growth order, stack mode and space shape of bed sets are qualitatively and quantitatively described.

Clinical and ethical considerations of massively parallel sequencing in transplantation science

Massively parallel sequencing(MPS), alias next-generation sequencing, is making its way from research laboratories into applied sciences and clinics. MPS is a framework of experimental procedures which offer possibilities for genome research and genetics which could only be dreamed of until around 2005 when these technologies became available. Sequencing of a transcriptome, exome, even entire genomes is now possible within a time frame and precision that we could only hope for 10 years ago. Linking other experimental procedures with MPS enables researchers to study secondary DNA modifications across the entire genome, and protein binding sites, to name a few applications. How the advancements of sequencing technologies can contribute to transplantation science is subject of this discussion: immediate applications are in graft matching via human leukocyte antigen sequencing, as part of systems biology approaches which shed light on gene expression processes during immune response, as biomarkers of graft rejection, and to explore changes of microbiomes as a result of transplantation. Of considerable importance is the socio-ethical aspect of data ownership, privacy, informed consent, and result report to the study participant. While the technology is advancing rapidly, legislation is lagging behind due to the globalisation of data requisition, banking and sharing.

Next generation sequencing for profiling expression of miRNAs: technical progress and applications in drug development

miRNAs are non-coding RNAs that play a regulatory role in expression of genes and are associated with diseases. Quantitatively measuring expression levels of miRNAs can help understanding the mechanisms of human diseases and discovering new drug targets. There are three major methods that have been used to measure the expression levels of miRNAs: real-time reverse transcription PCR (qRT-PCR), microarray, and the newly introduced next-generation sequencing (NGS). NGS is not only suitable for profiling of known miRNAs that qRT-PCR and microarray can do too but also able to detect unknown miRNAs that the other two methods are incapable. Profiling of miRNAs by NGS has been progressed rapidly and is a promising field for applications in drug development. This paper will review the technical advancement of NGS for profiling miRNAs, including comparative analyses between different platforms and software packages for analyzing NGS data. Examples and future perspectives of applications of NGS profiling miRNAs in drug development will be discussed.

DISCUSSION ON AVERAGE GENERATION OF TWODIMENSIONAL DATA SEQUENCE IN GREY SYSTEM THEORY

An unequal time interval sequence or a sequence with blanks is usually completed with average generation in grey system theory. This paper discovers that there exists obvious errors when using average generation to generate internal points of non-consecutive neighbours. The average generation and the preference generation of the sequence are discussed, the concave and convex properties show the status of local sequence and propose a new idea for using the status to build up the criteria of choosing generation coefficient. Compared with the general average method of the one-dimensional data sequence, the two-dimensional data sequence is defined and its average generation is discussed, and the coefficient decision method for the preference generation is presented.

Sequence-To-Sequence Learning for Online Imputation of Sensory Data

Online sensing can provide useful information in monitoring applications,for example,machine health monitoring,structural condition monitoring,environmental monitoring,and many more.Missing data is generally a significant issue in the sensory data that is collected online by sensing systems,which may affect the goals of monitoring programs.In this paper,a sequence-to-sequence learning model based on a recurrent neural network(RNN)architecture is presented.In the proposed method,multivariate time series of the monitored parameters is embedded into the neural network through layer-by-layer encoders where the hidden features of the inputs are adaptively extracted.Afterwards,predictions of the missing data are generated by network decoders,which are one-step-ahead predictive data sequences of the monitored parameters.The prediction performance of the proposed model is validated based on a real-world sensory dataset.The experimental results demonstrate the performance of the proposed RNN-encoder-decoder model with its capability in sequence-to-sequence learning for online imputation of sensory data.

Next-generation sequencing for clinical HLA typing

下一代测序(NGS)已经被证明可有效的减少人类白细胞抗原(HLA)分型的不准确性和检测成本,同时还可以检测出之前未测序的HLA基因的详细信息。本研究介绍了在Illumina公司的Mi Seq平台上使用NGS开发的HLA分型测定的性能要求。共纳入288个样品,其之前以HLA-A,HLA-B,HLA-C,HLA-DRB1,HLA-DQA/B和HLA-DPA/B为特征,其使用Sanger测序、序列特异性引物和序列特异性寡核苷酸技术进行高分辨率的分型。这些样本携带高比例HLA特异性的等位基因。测序数据使用Omixon的HLA TwinTM进行分析。评估等位基因平衡、敏感性、特异性、精确性、准确性和不准确性。这些结果证明了NGS对HLA分型的可行性和获益处,因为这项技术非常准确,几乎排除了所有的不确定性,为HLA基因长度提供了完整的测序信息,并形成了利用单一方法进行HLA分型的基础免疫遗传学实验室。

基于生成对抗Transformer的电力负荷数据异常检测

电力负荷异常数据将给电力系统规划、负荷预测以及用能分析等带来较大的负面影响,因此亟须对负荷数据异常进行检测与识别。首先,针对电力负荷数据异常分类、原因及其特征开展分析。其次,改进传统Transformer编码器结构,采用多头注意力层代替掩码多头注意力层,同时移除前馈网络,以提高模型对负荷时序序列的全局注意力。基于生成对抗网络(generative adversarial networks,GAN)生成器与判别器的博弈结构,提出一种改进的GAN-Transformer模型,以更好地捕捉趋势性特征并加速模型收敛。然后,引入多阶段映射与训练方法,综合焦点分数打分机制,通过分阶段负荷序列重构帮助模型更好地提取负荷数据异常特征。最后,算例分析结果表明,GAN-Transformer模型在负荷数据异常检测精确率、召回率、F_(1)值以及训练时间方面均具有更优的性能,验证了所提方法的有效性和优越性。文中研究工作为基于深度学习进一步实现电力负荷数据异常分类与数据修复提供了有益参考。

纳米孔测序技术在动物疫病中应用的研究进展

第三代纳米孔测序是一种便携、方便、相对快速且经济有效的技术,具有长读长、高通量、实时提供测序数据等特点,在现场检测、临床诊断、流行病学调查和微生物鉴别等方面都有巨大的发展潜力。本文主要比较了下一代测序技术和第三代测序技术的优缺点,讨论了纳米孔测序数据的相关分析,以及该技术在动物疫病临床应用的例子,展望了纳米孔测序技术的发展以及在动物疫病诊断与防控领域的潜在前景。

基于傅里叶变换与近端采样的序列推荐算法

传统推荐算法比较注重于模型本身对于推荐效果的提升,实际上数据质量对于算法的影响更为重要,但目前在推荐算法领域对于数据的科学处理方法比较零散,没有形成一个系统的框架。针对以上问题,基于傅里叶变换与近端序列采样的数据预处理,结合SASRec提出可以普遍应用的序列推荐框架FTRRec。首先通过傅里叶变换将序列数据在时域和频域中相互转换,并根据序列数据的特点,过滤序列中的无用信息,其次使用近端序列采样替换传统的滑动窗口采样法,加速样本采样的同时,提升模型对于序列的特征捕获能力。通过在5个公开数据集上的实验,将框架应用于三个不同的主流推荐算法时,每种模型均有3%-5%的提升。

基于混沌序列和AES算法的数据库外层数据传输同步加密方法

为保障数据库外层数据的安全传输,提出一种基于混沌序列和AES算法的数据库外层数据传输同步加密方法。该方法结合AES算法的加密安全性和混沌序列的加密速度优势,通过加密代理服务器和应用服务器对数据库外层数据进行处理,利用混沌序列logistic映射参数初始化、混沌序列二进制量化、AES动态加密和尾端处理实现数据库外层数据传输同步加密。实验结果表明,该方法能够成功地对文本和图像数据进行加密和隐藏,同时提高加密效率,并兼顾了数据的安全性和传输效率。