Genomics, Proteomics & Bioinformatics最新文献

MethylGenotyper: Accurate Estimation of SNP Genotypes and Genetic Relatedness from DNA Methylation Data MethylGenotyper：从 DNA 甲基化数据准确估计 SNP 基因型和遗传相关性

Genomics, Proteomics & Bioinformatics Pub Date : 2024-06-10 DOI: 10.1093/gpbjnl/qzae044

Yi Jiang, Minghan Qu, Minghui Jiang, Xuan Jiang, Shane Fernandez, T. Porter, Simon M. Laws, Colin L. Masters, Huan Guo, S.-M. Cheng, Chao Wang

{"title":"MethylGenotyper: Accurate Estimation of SNP Genotypes and Genetic Relatedness from DNA Methylation Data","authors":"Yi Jiang, Minghan Qu, Minghui Jiang, Xuan Jiang, Shane Fernandez, T. Porter, Simon M. Laws, Colin L. Masters, Huan Guo, S.-M. Cheng, Chao Wang","doi":"10.1093/gpbjnl/qzae044","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae044","url":null,"abstract":"\u0000 Epigenome-wide association studies (EWAS) are susceptible to widespread confounding caused by population structure and genetic relatedness. Nevertheless, kinship estimation is challenging in EWAS without genotyping data. We proposed MethylGenotyper, a method that for the first time enables accurate genotyping at thousands of single nucleotide polymorphisms (SNPs) directly from commercial DNA methylation microarrays. We modeled the intensities of methylation probes near SNPs with a mixture of three beta distributions corresponding to different genotypes and estimated parameters with an expectation-maximization algorithm. We conducted extensive simulations to demonstrate the performance of the method. When applying MethylGenotyper to Infinium EPIC array data of 4662 Chinese, we obtained genotypes at 4319 SNPs with a concordance rate of 98.26%, enabling the identification of 255 pairs of close relatedness. Furthermore, we showed that MethylGenotyper allows for the estimation of both population structure and cryptic relatedness among 702 Australians of diverse ancestry. We have implemented MethylGenotyper in a publicly available R package (https://github.com/Yi-Jiang/MethylGenotyper) to facilitate future large-scale EWAS.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"101 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141362758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes 利用自裂解核糖酶评估不同 RNA 二级结构预测程序的性能

Genomics, Proteomics & Bioinformatics Pub Date : 2024-06-08 DOI: 10.1093/gpbjnl/qzae043

Fei Qi, Junjie Chen, Yue Chen, Jianfeng Sun, Yiting Lin, Zipeng Chen, Philipp Kapranov

{"title":"Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes","authors":"Fei Qi, Junjie Chen, Yue Chen, Jianfeng Sun, Yiting Lin, Zipeng Chen, Philipp Kapranov","doi":"10.1093/gpbjnl/qzae043","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae043","url":null,"abstract":"\u0000 Accurate identification of the correct, biologically relevant RNA structures is critical to understanding various aspects of RNA biology since proper folding represents the key to functionality of all types of RNA molecules and plays pivotal roles in many essential biological processes. Thus, a plethora of approaches have been developed to predict, identify, or solve RNA structures based on various computational, molecular, genetic, chemical, or physicochemical strategies. Purely computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation, time, speed, cost, and throughput, but they strongly underperform in terms of accuracy that significantly limits their application. Nonetheless, the advantages of these methods led to a steady development of multiple in silico RNA secondary structure prediction approaches including recent deep learning-based programs. Here, we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using 7 in silico RNA folding prediction tools with tasks of varying complexity. We found that while many programs performed well in relatively simple tasks, the performance varied significantly in more complex RNA folding problems. However, in general, a modern deep learning method outperformed the other programs in the complex tasks in predicting the RNA secondary structures, at least based on the specific class of tested sequences, suggesting that it might represent the future of RNA structure prediction algorithms.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"212 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hidden Links Between Skin Microbiome and Skin Imaging Phenome 皮肤微生物组与皮肤成像表型之间的隐秘联系

Genomics, Proteomics & Bioinformatics Pub Date : 2024-06-07 DOI: 10.1093/gpbjnl/qzae040

Mingyue Cheng, Hong Zhou, Haobo Zhang, Xinchao Zhang, Shuting Zhang, Hong Bai, Yugo Zha, Dan Luo, Dan Chen, Siyuan Chen, Kang Ning, Wei Liu

{"title":"Hidden Links Between Skin Microbiome and Skin Imaging Phenome","authors":"Mingyue Cheng, Hong Zhou, Haobo Zhang, Xinchao Zhang, Shuting Zhang, Hong Bai, Yugo Zha, Dan Luo, Dan Chen, Siyuan Chen, Kang Ning, Wei Liu","doi":"10.1093/gpbjnl/qzae040","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae040","url":null,"abstract":"\u0000 Despite the skin microbiome has been linked to skin health and diseases, its role in modulating human skin appearance remains understudied. Using a total of 1244 face imaging phenomes and 246 cheek metagenomes, we first established three skin age indices by machine learning including skin phenotype age (SPA), skin microbiota age (SMA), and skin integration age (SIA) as surrogates of phenotypic aging, microbial aging, and their combination, respectively. Moreover, we found that besides aging and gender as intrinsic factors, skin microbiome might also play a role in shaping skin imaging phenotypes (SIPs). Skin taxonomic and functional α diversity was positively linked to melanin, pore, pigment, and ultraviolet spot levels, but negatively linked to sebum, lightening, and porphyrins levels. Furthermore, certain species were correlated with specific SIPs, such as sebum and lightening levels negatively correlated with Corynebacterium matruchotii, Staphylococcus capitis, and Streptococcus sanguinis. Notably, we demonstrated skin microbial potential in predicting SIPs, among which the lightening level presented the least error of 1.8%. Lastly, we provided a reservoir of potential mechanisms through which skin microbiome adjusted the SIPs, including the modulation of pore, wrinkle, and sebum levels by cobalamin and heme synthesis pathways, predominantly driven by Cutibacterium acnes. This pioneering study unveils the paradigm for the hidden links between skin microbiome and skin imaging phenome, providing novel insights into how skin microbiome shapes skin appearance and its healthy aging.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141373028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AVM: A Manually Curated Database of Aerosol-transmitted Virus Mutations, Human Diseases, and Drugs AVM：人工编辑的气溶胶传播病毒变异、人类疾病和药物数据库

Genomics, Proteomics & Bioinformatics Pub Date : 2024-06-04 DOI: 10.1093/gpbjnl/qzae041

Lan Mei, Yaopan Hou, Jiajun Zhou, Yetong Chang, Yuwei Liu, Di Wang, Yunpeng Zhang, Shangwei Ning, Xia Li

{"title":"AVM: A Manually Curated Database of Aerosol-transmitted Virus Mutations, Human Diseases, and Drugs","authors":"Lan Mei, Yaopan Hou, Jiajun Zhou, Yetong Chang, Yuwei Liu, Di Wang, Yunpeng Zhang, Shangwei Ning, Xia Li","doi":"10.1093/gpbjnl/qzae041","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae041","url":null,"abstract":"\u0000 Aerosol-transmitted viruses possess strong infectivity and can spread over long distances, earning the difficult-to-control title. They cause various human diseases and pose serious threats to human health. Mutations can increase the transmissibility and virulence of the strains, reducing the protection provided by vaccines and weakening the efficacy of antiviral drugs. To this end, in this study, we aimed to establish a manually curated database (AVM) to store information regarding viral mutations (VMs). The current version of the AVM contains 42,041 VMs, including 2613 immune escape mutations, 45 clinical information datasets, and 407 drugs, antibodies, or vaccines. Additionally, we recorded 88 human diseases associated with viruses and found that the same virus can target multiple organs in the body, leading to diverse diseases. Furthermore, the AVM database offers a straightforward user interface for browsing, retrieving, and downloading information. We believe that this database is a comprehensive resource that can provide timely and valuable information regarding the transmission, treatment, and diseases caused by aerosol-transmitted viruses (http://www.bio-bigdata.center/AVM).","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control APIR：聚合通用蛋白质组学数据库搜索算法，利用 FDR 控制进行多肽鉴定

Genomics, Proteomics & Bioinformatics Pub Date : 2024-06-03 DOI: 10.1093/gpbjnl/qzae042

Y. Chen, Xinzhou Ge, Kyla Woyshner, MeiLu McDermott, A. Manousopoulou, S. Ficarro, J. Marto, Kexin Li, Leo David Wang, Jingyi Jessica Li

{"title":"APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control","authors":"Y. Chen, Xinzhou Ge, Kyla Woyshner, MeiLu McDermott, A. Manousopoulou, S. Ficarro, J. Marto, Kexin Li, Leo David Wang, Jingyi Jessica Li","doi":"10.1093/gpbjnl/qzae042","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae042","url":null,"abstract":"\u0000 Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"117 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SuperFeat: Quantitative Features Learning from Single-cell RNAseq Data Facilitates Drug Repurposing SuperFeat：从单细胞 RNAseq 数据中学习定量特征有助于药物再利用

Genomics, Proteomics & Bioinformatics Pub Date : 2024-05-23 DOI: 10.1093/gpbjnl/qzae036

Jianmei Zhong, Junyao Yang, Yinghui Song, Zhihua Zhang, Chunming Wang, Renyang Tong, Chenglong Li, Nanhui Yu, Lianhong Zou, Liu Sulai, Pu Jun, Wei Lin

{"title":"SuperFeat: Quantitative Features Learning from Single-cell RNAseq Data Facilitates Drug Repurposing","authors":"Jianmei Zhong, Junyao Yang, Yinghui Song, Zhihua Zhang, Chunming Wang, Renyang Tong, Chenglong Li, Nanhui Yu, Lianhong Zou, Liu Sulai, Pu Jun, Wei Lin","doi":"10.1093/gpbjnl/qzae036","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae036","url":null,"abstract":"\u0000 In this study, we have devised a computational framework called Supervised Feature Learning and Scoring (SuperFeat) that allows for the training of a machine learning model and evaluates the canonical cellular status/features in pathological tissues that underlie the progression of disease. This framework also enables the identification of potential drugs that target the presumed detrimental cellular features. This framework was constructed on the basis of an artificial neural network with the gene expression profiles serving as input nodes. The training data comprised single-cell RNA sequencing datasets that encompassed the specific cell lineage during the developmental progression of cell features. A few models of the canonical cancer-involved cellular status/features were tested by such framework. Finally, we have illustrated the drug repurposing pipeline, utilizing the training parameters derived from the adverse cellular status/features, which has yielded successful validation results both in vitro and in vivo. SuperFeat is accessible at https://github.com/weilin-genomics/rSuperFeat.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141104301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inter3D: Capture of TAD Reorganization Endows Variant Patterns of Gene Transcription Inter3D：捕捉 TAD 重组赋予基因转录的变异模式

Genomics, Proteomics & Bioinformatics Pub Date : 2024-05-08 DOI: 10.1093/gpbjnl/qzae034

Tianyi Ding, Shaliu Fu, Xiaoyu Zhang, Fan Yang, Jixing Zhang, Haowen Xu, Jiaqi Yang, Chaoqun Chen, Yibing Shi, Yiran Bai, Wannian Li, Xindi Chang, Shanjin Wang, Chao Zhang, Qi Liu, He Zhang

{"title":"Inter3D: Capture of TAD Reorganization Endows Variant Patterns of Gene Transcription","authors":"Tianyi Ding, Shaliu Fu, Xiaoyu Zhang, Fan Yang, Jixing Zhang, Haowen Xu, Jiaqi Yang, Chaoqun Chen, Yibing Shi, Yiran Bai, Wannian Li, Xindi Chang, Shanjin Wang, Chao Zhang, Qi Liu, He Zhang","doi":"10.1093/gpbjnl/qzae034","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae034","url":null,"abstract":"\u0000 Topologically associating domain (TAD) reorganization commonly occurs in the cell nucleus and contributes to gene activation and inhibition through the separation or fusion of adjacent TADs. However, identification of functional genes impacted by TAD alteration and the mechanism of TAD reorganization underlying gene transcription remain to be fully elucidated. Here, we first developed a novel approach termed Inter3D to specifically identify genes regulated by TAD reorganization. Our study revealed that the segregation of TADs led to the disruption of intrachromosomal looping at the myosin light chain 12B (MYL12B) locus, via the meticulous reorganization of TADs mediating epigenomic landscapes within tumor cells, thereby exhibiting a significant correlation with the downregulation of its transcriptional activity. Conversely, the fusion of TADs facilitated intrachromosomal interactions, suggesting a potential association with the activation of cytochrome P450 family 27 subfamily B member 1 (CYP27B1). Our study provides comprehensive insight into the capture of TAD rearrangement-mediated gene loci and moves toward understanding the functional role of TAD reorganization in gene transcription. The Inter3D pipeline used in this study is freely available at https://github.com/bm2-lab/inter3D.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"205 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141001905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Innovative Low-cost Probe Generation Empowers Targeted Long-read RNA Sequencing 创新型低成本探针生成技术助力靶向长读数 RNA 测序

Genomics, Proteomics & Bioinformatics Pub Date : 2024-04-10 DOI: 10.1093/gpbjnl/qzae027

Gang Fang

引用次数: 0

Multi-omic Analyses Shed Light on The Genetic Control of High-altitude Adaptation in Sheep 多组学分析揭示绵羊高海拔适应性的基因控制

Genomics, Proteomics & Bioinformatics Pub Date : 2024-04-02 DOI: 10.1093/gpbjnl/qzae030

Chao Li, Bingchun Chen, Langda Suo, Peng Pu, Xiaojia Zhu, Shiwei Zhou, P. Kalds, Ke Zhang, Meenu Bhati, A. Leonard, Shuhong Huang, Ran Li, Awang Cuoji, Xiran Wang, Haolin Zhu, Yujiang Wu, Renqing Cuomu, Ba Gui, Ming Li, Yutao Wang, Yan Li, Wenwen Fang, Ting Jia, T. Pu, Xiangyu Pan, Yudong Cai, Chong He, Liming Wang, Yu Jiang, Jianxin Han, Yulin Chen, P. Zhou, H. Pausch, Xiaolong Wang

{"title":"Multi-omic Analyses Shed Light on The Genetic Control of High-altitude Adaptation in Sheep","authors":"Chao Li, Bingchun Chen, Langda Suo, Peng Pu, Xiaojia Zhu, Shiwei Zhou, P. Kalds, Ke Zhang, Meenu Bhati, A. Leonard, Shuhong Huang, Ran Li, Awang Cuoji, Xiran Wang, Haolin Zhu, Yujiang Wu, Renqing Cuomu, Ba Gui, Ming Li, Yutao Wang, Yan Li, Wenwen Fang, Ting Jia, T. Pu, Xiangyu Pan, Yudong Cai, Chong He, Liming Wang, Yu Jiang, Jianxin Han, Yulin Chen, P. Zhou, H. Pausch, Xiaolong Wang","doi":"10.1093/gpbjnl/qzae030","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae030","url":null,"abstract":"\u0000 Sheep were domesticated in the Fertile Crescent and then spread globally, where they have been encountering various environmental conditions. The Tibetan sheep has adapted to high altitudes on the Qinghai-Tibetan Plateau over the past 3000 years. To explore genomic variants associated with high-altitude adaptation in Tibetan sheep, we analyzed Illumina short-reads of 994 whole genomes representing ∼ 60 sheep breeds/populations at varied altitudes, PacBio High fidelity (HiFi) reads of 13 breeds, and 96 transcriptomes from 12 sheep organs. Association testing between the inhabited altitudes and 34,298,967 variants was conducted to investigate the genetic architecture of altitude adaptation. Highly accurate HiFi reads were used to complement the current ovine reference assembly at the most significantly associated β-globin locus and to validate the presence of two haplotypes A and B among 13 sheep breeds. Of which, the haplotype A carried two homologous gene clusters: (1) HBE1, HBE2, HBB-like, and HBBC, and (2) HBE1-like, HBE2-like, HBB-like, and HBB; while the haplotype B lacked the first cluster. The high-altitude sheep showed highly frequent or nearly fixed haplotype A, while the low-altitude sheep dominated by haplotype B. We further demonstrated that sheep with the haplotype A had an increased hemoglobin (Hb)–O2 affinity compared to those carrying the haplotype B. Another highly associated genomic region contained the EGLN1 gene which showed a varied expression between high-altitude and low-altitude sheep. Our results provide evidence that the rapid adaptive evolution of advantageous alleles play an important role in facilitating the environmental adaptation of Tibetan sheep.","PeriodicalId":170516,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"23 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140753953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chasing Sequencing Perfection: Marching Toward Higher Accuracy and Lower Costs 追求完美测序：向更高精度和更低成本迈进

Genomics, Proteomics & Bioinformatics Pub Date : 2024-03-11 DOI: 10.1093/gpbjnl/qzae024

Hangxing Jia, Shengjun Tan, Yong E. Zhang

引用次数: 0

Genomics, Proteomics &amp; Bioinformatics最新文献

Genomics, Proteomics & Bioinformatics最新文献