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| 1 | The Genome Sequence Archive Family: Toward Explosive Data Growth and Diverse Data Types显示文摘The Genome Sequence Archive(GSA)is a data repository for archiving raw sequence data,which provides data storage and sharing services for worldwide scientific communities.Considering explosive data growth with diverse data types,here we present the GSA family by expanding into a set of resources for raw data archive with different purposes,namely,GSA(http://gffzz77e3413bc06540edso6wk5wvqqv6566xu.ffgz.tsg.suse.edu.cn/gsa/),GSA for Human(GSA-Human,http://gffzz77e3413bc06540edso6wk5wvqqv6566xu.ffgz.tsg.suse.edu.cn/gsa-human/),and Open Archive for Miscellaneous Data(OMIX,http://gffzz77e3413bc06540edso6wk5wvqqv6566xu.ffgz.tsg.suse.edu.cn/omix/).Compared with the 2017 version,GSA has been significantly updated in data model,online functionalities,and web interfaces.GSA-Human,as a new partner of GSA,is a data repository specialized in human genetics-related data with controlled access and security.OMIX,as a critical complement to the two resources mentioned above,is an open archive for miscellaneous data.Together,all these resources form a family of resources dedicated to archiving explosive data with diverse types,accepting data submissions from all over the world,and providing free open access to all publicly available data in support of worldwide research activities. | Tingting Chen Xu Chen Sisi Zhang Junwei Zhu Bixia Tang Anke Wang Lili Dong Zhewen Zhang Caixia Yu Yanling Sun Lianjiang Chi Huanxin Chen Shuang Zhai Yubin Sun Li Lan Xin Zhang Jingfa Xiao Yiming Bao Yanqing Wang Zhang Zhang Wenming Zhao | 2021 | Genomics, Proteomics & Bioinformatics2021，19，4： | 38 |
| 2 | GSA:Genome Sequence Archive显示文摘With the rapid development of sequencing technologies towards higher throughput and lower cost, sequence data are generated at an unprecedentedly explosive rate. To provide an efficient and easy-to-use platform for managing huge sequence data, here we present Genome Sequence Archive(GSA; http://gffzzdbc7b6aaae734bddho6wk5wvqqv6566xu.ffgz.tsg.suse.edu.cn/gsa or http://gffzz4eaf941a184140e3ho6wk5wvqqv6566xu.ffgz.tsg.suse.edu.cn), a data repository for archiving raw sequence data. In compliance with data standards and structures of the International Nucleotide Sequence Database Collaboration(INSDC), GSA adopts four data objects(Bio Project, Bio Sample,Experiment, and Run) for data organization, accepts raw sequence reads produced by a variety of sequencing platforms, stores both sequence reads and metadata submitted from all over the world,and makes all these data publicly available to worldwide scientific communities. In the era of big data, GSA is not only an important complement to existing INSDC members by alleviating the increasing burdens of handling sequence data deluge, but also takes the significant responsibility for global big data archive and provides free unrestricted access to all publicly available data in support of research activities throughout the world. | Yanqing Wang Fuhai Song Junwei Zhu Sisi Zhang Yadong Yang Tingting Chen Bixia Tang Lili Dong Nan Ding Qian Zhang Zhouxian Bai Xunong Dong Huanxin Chen Mingyuan Sun Shuang Zhai Yubin Sun Lei Yu Li Lan Jingfa Xiao Xiangdong Fang Hongxing Lei Zhang Zhang Wenming Zhao | 2017 | Genomics, Proteomics & Bioinformatics2017，15，1： | 13 |
| 3 | eGPS 1.0: comprehensive software for multi-omic and evolutionary analyses显示文摘It has become increasingly challenging for researchers to analyze the exponentially expanding amount of multi-omic data.Here,we describe multi-functional software named evolutionary GenotypePhenotype Systems(eGPS)that enables users to perform comprehensive multiomic and evolutionary analyses.The eGPS combines the power of cloud computing and the advantage of desktop application,has a user-friendly graphic interface and is highly interactive. | Dalang Yu Lili Dong Fangqi Yan Hailong Mu Bixia Tang Xiao Yang Tao Zeng Qing Zhou Feng Gao Zhonghuang Wang Ziqian Hao Hongen Kang Yi Zheng Hongwei Huang Yuzhang Wei Wei Pan Yaochen Xu Junwei Zhu Shilei Zhao Ciran Wang Pengyu Wang Long Dai Mushan Li Li Lan Yiwei Wang Hua Chen Yi-Xue Li Yun-Xin Fu Zhen Shao Yiming Bao Fangqing Zhao Luo-Nan Chen Guo-Qing Zhang Wenming Zhao Haipeng Li | 2019 | National Science Review2019，6，5： | 4 |
| 4 | Exp2 polymorphisms associated with variation for fiber quality properties in cotton(Gossypium spp.)显示文摘Plant expansins are a group of extracellular proteins thought to affect the quality of cotton fibers. Previous expression profile analysis revealed that six Expansin A genes are present in cotton, of which two(GhExp1 and GhExp2) produce transcripts that are specific to the developing cotton fiber. To identify the phenotypic function of Exp2, and to determine whether nucleotide variation among alleles of Exp2 affects fiber quality, candidate gene association mapping was conducted. Gene-specific primers were designed to amplify the Exp2 gene. By amplicon sequencing, the nucleotide diversity of Exp2 was investigated across92 accessions(including 7 Gossypium arboreum, 74 Gossypium hirsutum, and 11 Gossypium barbadense accessions) with different fiber qualities. Twenty-six SNPs and seven InDels including 14 from the coding region of Exp2 were detected, forming twelve distinct haplotypes in the cotton collection. Among the 14 SNPs in the coding region, five were missense mutations and nine were synonymous nucleotide changes. The average SNP/InDel per nucleotide ratio was 2.61%(one SNP per 39 bp), with 1.81 and 3.87% occurring in coding and non-coding regions, respectively. Nucleotide and haplotype diversity across the entire Exp2 region was 0.00603(π) and 0.844, respectively, and diversity in non-coding regions was higher than that in coding regions. For linkage disequilibrium(LD), the mean r2 value for all polymorphism loci pairs was 0.48, and LD did not decay over 748 bp. Based on132 simple sequence repeat(SSR) loci evenly covering 26 chromosomes, the population structure was estimated, and the accessions were divided into seven groups that agreed well with their genomic origin and evolutionary history. A general linear model was used to calculate the Exp2-wide diversity–trait associations of 5 fiber quality traits, considering population structure(Q). Four SNPs in Exp2 were associated with at least one of the fiber quality traits, but not with fiber elongation. The highest positive effect on UHML and STR was observed for haplotype Hap_6 of Exp2. There was a significant association of Exp2 with fiber quality traits. There were many haplotypes in the Exp2 region, of which the most favorable was Hap_6. The association between nucleotide diversity and these fiber traitssheds light on the gene's potential contribution to the improvement of fiber quality, and should be useful to facilitate MAS programs in cotton. | Daohua He Zhongping Lei Hongyi Xing Baoshan Tang Junxing Zhao Bixia Lu | 2014 | The Crop Journal2014，2，5： | 1 |
| 5 | Delta.AR:An augmented reality-based visualization platform for 3D genome显示文摘Many visualization tools have been developed for 3D genome data integration using two-dimensional(2D)devices such as PC monitors or smartphones.However,the 2D surface is only suitable for displaying linear data,and it has done little to inform our understanding of the complex interconnections between 3D genome architecture and its various associated-omics data.The breakthrough in immersive display technologies,e.g.,virtual reality(VR)and augmented reality(AR),has opened a completely new model for data visualization.Immersive visualization has proved a powerful way to enhance 3D structure-related research,e.g.,protein structure and drug design.However,visualization in immersive mode,coupled with the integration of 3D genome and its associated-omics data,is challenging.Only a few attempts have been made for single features,e.g.,Juicebox VR,which projects a Hi-C contact matrix into a virtual mountain field,and the WashU Epigenome Browser,which provides a 3D scene for epigenome tracks.A visualization tool for immersive integration of 3D genome architecture with high-dimensional-omics data has not yet been published. | Bixia Tang Xiaoxing Li Guan Li Dong Tian Feifei Li Zhihua Zhang | 2021 | The Innovation2021，2，3： | 0 |
| 6 | Global cold-chain related SARS-CoV-2 transmission identified by pandemic-scale phylogenomics显示文摘DEAR EDITOR,The COVID-19 pandemic caused by SARS-CoV-2 continues to pose a tremendous threat to human society. SARS-CoV-2is airborne and transmits primarily through social contact;however, whether cold chain-related transmission has occurred remains highly debated(Han & Liu, 2022;Lewis,2021;Ma et al., 2021;Mallapaty et al., 2021;Pang et al.,2020;Wu et al., 2021). Here, we present a novel method and identify two transmission routes based on lineage-specific reductions in the SARS-CoV-2 evolutionary rate. | Dalang Yu Junwei Zhu Jianing Yang Yi-Hsuan Pan Hailong Mu Ruifang Cao Bixia Tang Guangya Duan Zi-Qian Hao Long Dai Guo-Ping Zhao Ya-Ping Zhang Wenming Zhao Guoqing Zhang Haipeng Li | 2022 | Zoological Research2022，43，5： | 0 |
| 7 | Web Resources for Model Organism Studies显示文摘An ever-growing number of resources on model organisms have emerged with the continued development of sequencing technologies. In this paper, we review 13 databases of model organisms, most of which are reported by the National Institutes of Health of the United States(NIH; http://gffzz646a09fd14554561ho6wk5wvqqv6566xu.ffgz.tsg.suse.edu.cn/science/models/). We provide a brief description for each database, as well as detail its data source and types, functions, tools, and availability of access. In addition,we also provide a quality assessment about these databases. Significantly, the organism databases instituted in the early 1990s––such as the Mouse Genome Database(MGD), Saccharomyces Genome Database(SGD), and Fly Base––have developed into what are now comprehensive, core authority resources. Furthermore, all of the databases mentioned here update continually according to user feedback and with advancing technologies. | Bixia Tang Yanqing Wang Junwei Zhu Wenming Zhao | 2015 | Genomics, Proteomics & Bioinformatics2015，13，1： | 0 |
| 8 | Delta.EPI:a probabilistic voting-based enhancer-promoter interaction prediction platform显示文摘Enhancer promoter interaction(EPI)involves most of gene transcriptional regulation in the high eukaryotes.Predicting the EPIs from given genomic loci or DNA sequences is not a trivial task.The benchmarking work so far for EPI predictors is more or less empirical and lacks quantitative model-based comparisons,posing challenges for molecular biologists to obtain reliable EPI predictions.Here,we present an EPI prediction platform,namely Delta.EPI.Based on a statistic model of the data integration,Delta.EPI is capable of comprehensively assessing the predictions from four state-of-the-art EPI predictors.Equipped with a userfriendly interface and visualization platform,Delta.EPI presents the sorted results with the confidence of EPI relevance,which may guide the molecular biologists who lack the pre-knowledge of the algorithms of EPI prediction.Last,we showcase the utility of Delta.EPI with a case study.Delta.EPI provides a powerful tool to fuel the gene regulation and 3D genome studies by ease-to-access EPI predictions.Delta.EPI can be freely accessed at http://gffzz77e3413bc06540edso6wk5wvqqv6566xu.ffgz.tsg.suse.edu.cn/deltaEPI/. | Yuyang Zhang Haoyu Wang Jing Liu Junlin Li Qing Zhang Bixia Tang Zhihua Zhang | 2023 | Journal of Genetics and Genomics2023，50，7： | 0 |
| 9 | OBIA:An Open Biomedical Imaging Archive显示文摘With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Open Biomedical Imaging Archive(OBIA),a repository for archiving biomedical imaging and related clinical data.OBIA adopts five data objects(Collection,Individual,Study,Series,and Image)for data organization,and accepts the submission of biomedical images of multiple modalities,organs,and diseases.In order to protect personal privacy,OBIA has formulated a unified de-identification and quality control process.In addition,OBIA provides friendly and intuitive web interfaces for data submission,browsing,and retrieval,as well as image retrieval.As of September 2023,OBIA has housed data for a total of 937 individuals,4136 studies,24,701 series,and 1,938,309 images covering 9 modalities and 30 anatomical sites.Collectively,OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world.OBIA can be accessed at http://gffzz77e3413bc06540edso6wk5wvqqv6566xu.ffgz.tsg.suse.edu.cn/obia. | Enhui Jin Dongli Zhao Gangao Wu Junwei Zhu Zhonghuang Wang Zhiyao Wei Sisi Zhang Anke Wang Bixia Tang Xu Chen Yanling Sun Zhe Zhang Wenming Zhao Yuanguang Meng | 2023 | Genomics, Proteomics & Bioinformatics2023，21，5： | 0 |
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