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  • Oxford University Press (OUP)  (89)
  • Biodiversity Research  (89)
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  • Oxford University Press (OUP)  (89)
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  • Biodiversity Research  (89)
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  • 1
    Online Resource
    Online Resource
    Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022
    Oxford University Press (OUP) ; 2022
    In:  Nucleic Acids Research Vol. 50, No. D1 ( 2022-01-07), p. D27-D38
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 50, No. D1 ( 2022-01-07), p. D27-D38
    Abstract: The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkab951
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2023
    Oxford University Press (OUP) ; 2023
    In:  Nucleic Acids Research Vol. 51, No. D1 ( 2023-01-06), p. D18-D28
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 51, No. D1 ( 2023-01-06), p. D18-D28
    Abstract: The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global academic and industrial communities. With the explosive accumulation of multi-omics data generated at an unprecedented rate, CNCB-NGDC constantly expands and updates core database resources by big data archive, integrative analysis and value-added curation. In the past year, efforts have been devoted to integrating multiple omics data, synthesizing the growing knowledge, developing new resources and upgrading a set of major resources. Particularly, several database resources are newly developed for infectious diseases and microbiology (MPoxVR, KGCoV, ProPan), cancer-trait association (ASCancer Atlas, TWAS Atlas, Brain Catalog, CCAS) as well as tropical plants (TCOD). Importantly, given the global health threat caused by monkeypox virus and SARS-CoV-2, CNCB-NGDC has newly constructed the monkeypox virus resource, along with frequent updates of SARS-CoV-2 genome sequences, variants as well as haplotypes. All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkac1073
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Association between GWAS-identified lung adenocarcinoma susceptibility loci and EGFR mutations in never-smoking Asian women, and comparison with findings from Western populations
    Oxford University Press (OUP) ; 2016
    In:  Human Molecular Genetics
    Details
    In: Human Molecular Genetics, Oxford University Press (OUP)
    Type of Medium: Online Resource
    ISSN: 0964-6906 , 1460-2083
    URL: Article
    DOI: 10.1093/hmg/ddw414
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2016
    detail.hit.zdb_id: 1474816-2
    SSG: 12
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information
    Oxford University Press (OUP) ; 2013
    In:  Nucleic Acids Research Vol. 41, No. 8 ( 2013-4), p. e97-e97
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 41, No. 8 ( 2013-4), p. e97-e97
    Type of Medium: Online Resource
    ISSN: 1362-4962 , 0305-1048
    URL: Article
    DOI: 10.1093/nar/gkt147
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2013
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer’s disease
    Oxford University Press (OUP) ; 2022
    In:  Briefings in Bioinformatics Vol. 23, No. 1 ( 2022-01-17)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 1 ( 2022-01-17)
    Abstract: Alzheimer’s disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer’s brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer’s Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer’s brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbab522
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Functional Evolution of Phosphatidylethanolamine Binding Proteins in Soybean and Arabidopsis
    Oxford University Press (OUP) ; 2015
    In:  The Plant Cell Vol. 27, No. 2 ( 2015-03-06), p. 323-336
    Details
    In: The Plant Cell, Oxford University Press (OUP), Vol. 27, No. 2 ( 2015-03-06), p. 323-336
    Abstract: Gene duplication provides resources for novel gene functions. Identification of the amino acids responsible for functional conservation and divergence of duplicated genes will strengthen our understanding of their evolutionary course. Here, we conducted a systemic functional investigation of phosphatidylethanolamine binding proteins (PEBPs) in soybean (Glycine max) and Arabidopsis thaliana. Our results demonstrated that after the ancestral duplication, the lineage of the common ancestor of the FLOWERING LOCUS T (FT) and TERMINAL FLOWER1 (TFL1) subfamilies functionally diverged from the MOTHER OF FT AND TFL1 (MFT) subfamily to activate flowering and repress flowering, respectively. They also underwent further specialization after subsequent duplications. Although the functional divergence increased with duplication age, we observed rapid functional divergence for a few pairs of young duplicates in soybean. Association analysis between amino acids and functional variations identified critical amino acid residues that led to functional differences in PEBP members. Using transgenic analysis, we validated a subset of these differences. We report clear experimental evidence for the functional evolution of the PEBPs in the MFT, FT, and TFL1 subfamilies, which predate the origin of angiosperms. Our results highlight the role of amino acid divergence in driving evolutionary novelty after duplication.
    Type of Medium: Online Resource
    ISSN: 1532-298X , 1040-4651
    URL: Article
    DOI: 10.1105/tpc.114.135103
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2015
    detail.hit.zdb_id: 623171-8
    detail.hit.zdb_id: 2004373-9
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    Bacterial communities are more sensitive to ocean acidification than fungal communities in estuarine sediments
    Oxford University Press (OUP) ; 2021
    In:  FEMS Microbiology Ecology Vol. 97, No. 5 ( 2021-04-13)
    Details
    In: FEMS Microbiology Ecology, Oxford University Press (OUP), Vol. 97, No. 5 ( 2021-04-13)
    Abstract: Ocean acidification (OA) in estuaries is becoming a global concern, and may affect microbial characteristics in estuarine sediments. Bacterial communities in response to acidification in this habitat have been well discussed; however, knowledge about how fungal communities respond to OA remains poorly understood. Here, we explored the effects of acidification on bacterial and fungal activities, structures and functions in estuarine sediments during a 50-day incubation experiment. Under acidified conditions, activities of three extracellular enzymes related to nutrient cycling were inhibited and basal respiration rates were decreased. Acidification significantly altered bacterial communities and their interactions, while weak alkalization had a minor impact on fungal communities. We distinguished pH-sensitive/tolerant bacteria and fungi in estuarine sediments, and found that only pH-sensitive/tolerant bacteria had strong correlations with sediment basal respiration activity. FUNGuild analysis indicated that animal pathogen abundances in sediment were greatly increased by acidification, while plant pathogens were unaffected. High-throughput quantitative PCR-based SmartChip analysis suggested that the nutrient cycling-related multifunctionality of sediments was reduced under acidified conditions. Most functional genes associated with nutrient cycling were identified in bacterial communities and their relative abundances were decreased by acidification. These new findings highlight that acidification in estuarine regions affects bacterial and fungal communities differently, increases potential pathogens and disrupts bacteria-mediated nutrient cycling.
    Type of Medium: Online Resource
    ISSN: 1574-6941
    URL: Article
    DOI: 10.1093/femsec/fiab058
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1501712-6
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  • 8
    Online Resource
    Online Resource
    Sprites: detection of deletions from sequencing data by re-aligning split reads
    Oxford University Press (OUP) ; 2016
    In:  Bioinformatics Vol. 32, No. 12 ( 2016-06-15), p. 1788-1796
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 32, No. 12 ( 2016-06-15), p. 1788-1796
    Abstract: Motivation: Advances of next generation sequencing technologies and availability of short read data enable the detection of structural variations (SVs). Deletions, an important type of SVs, have been suggested in association with genetic diseases. There are three types of deletions: blunt deletions, deletions with microhomologies and deletions with microsinsertions. The last two types are very common in the human genome, but they pose difficulty for the detection. Furthermore, finding deletions from sequencing data remains challenging. It is highly appealing to develop sensitive and accurate methods to detect deletions from sequencing data, especially deletions with microhomology and deletions with microinsertion. Results: We present a novel method called Sprites (SPlit Read re-alIgnment To dEtect Structural variants) which finds deletions from sequencing data. It aligns a whole soft-clipping read rather than its clipped part to the target sequence, a segment of the reference which is determined by spanning reads, in order to find the longest prefix or suffix of the read that has a match in the target sequence. This alignment aims to solve the problem of deletions with microhomologies and deletions with microinsertions. Using both simulated and real data we show that Sprites performs better on detecting deletions compared with other current methods in terms of F-score. Availability and implementation: Sprites is open source software and freely available at https://github.com/zhangzhen/sprites. Contact:  jxwang@mail.csu.edu.cn Supplementary data:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/btw053
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2016
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 9
    Online Resource
    Online Resource
    DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning
    Oxford University Press (OUP) ; 2019
    In:  Bioinformatics Vol. 35, No. 22 ( 2019-11-01), p. 4586-4595
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 35, No. 22 ( 2019-11-01), p. 4586-4595
    Abstract: The Oxford Nanopore sequencing enables to directly detect methylation states of bases in DNA from reads without extra laboratory techniques. Novel computational methods are required to improve the accuracy and robustness of DNA methylation state prediction using Nanopore reads. Results In this study, we develop DeepSignal, a deep learning method to detect DNA methylation states from Nanopore sequencing reads. Testing on Nanopore reads of Homo sapiens (H. sapiens), Escherichia coli (E. coli) and pUC19 shows that DeepSignal can achieve higher performance at both read level and genome level on detecting 6 mA and 5mC methylation states comparing to previous hidden Markov model (HMM) based methods. DeepSignal achieves similar performance cross different DNA methylation bases, different DNA methylation motifs and both singleton and mixed DNA CpG. Moreover, DeepSignal requires much lower coverage than those required by HMM and statistics based methods. DeepSignal can achieve 90% above accuracy for detecting 5mC and 6 mA using only 2× coverage of reads. Furthermore, for DNA CpG methylation state prediction, DeepSignal achieves 90% correlation with bisulfite sequencing using just 20× coverage of reads, which is much better than HMM based methods. Especially, DeepSignal can predict methylation states of 5% more DNA CpGs that previously cannot be predicted by bisulfite sequencing. DeepSignal can be a robust and accurate method for detecting methylation states of DNA bases. Availability and implementation DeepSignal is publicly available at https://github.com/bioinfomaticsCSU/deepsignal. Supplementary information Supplementary data are available at bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btz276
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 10
    Online Resource
    Online Resource
    Computational drug repositioning using low-rank matrix approximation and randomized algorithms
    Oxford University Press (OUP) ; 2018
    In:  Bioinformatics Vol. 34, No. 11 ( 2018-06-01), p. 1904-1912
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 34, No. 11 ( 2018-06-01), p. 1904-1912
    Abstract: Computational drug repositioning is an important and efficient approach towards identifying novel treatments for diseases in drug discovery. The emergence of large-scale, heterogeneous biological and biomedical datasets has provided an unprecedented opportunity for developing computational drug repositioning methods. The drug repositioning problem can be modeled as a recommendation system that recommends novel treatments based on known drug–disease associations. The formulation under this recommendation system is matrix completion, assuming that the hidden factors contributing to drug–disease associations are highly correlated and thus the corresponding data matrix is low-rank. Under this assumption, the matrix completion algorithm fills out the unknown entries in the drug–disease matrix by constructing a low-rank matrix approximation, where new drug–disease associations having not been validated can be screened. Results In this work, we propose a drug repositioning recommendation system (DRRS) to predict novel drug indications by integrating related data sources and validated information of drugs and diseases. Firstly, we construct a heterogeneous drug–disease interaction network by integrating drug–drug, disease–disease and drug–disease networks. The heterogeneous network is represented by a large drug–disease adjacency matrix, whose entries include drug pairs, disease pairs, known drug–disease interaction pairs and unknown drug–disease pairs. Then, we adopt a fast Singular Value Thresholding (SVT) algorithm to complete the drug–disease adjacency matrix with predicted scores for unknown drug–disease pairs. The comprehensive experimental results show that DRRS improves the prediction accuracy compared with the other state-of-the-art approaches. In addition, case studies for several selected drugs further demonstrate the practical usefulness of the proposed method. Availability and implementation http://bioinformatics.csu.edu.cn/resources/softs/DrugRepositioning/DRRS/index.html Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/bty013
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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