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  • Oxford University Press (OUP)  (18)
  • Gao, Xin  (18)
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  • Oxford University Press (OUP)  (18)
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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
    EuPathDB: a portal to eukaryotic pathogen databases
    Oxford University Press (OUP) ; 2010
    In:  Nucleic Acids Research Vol. 38, No. suppl_1 ( 2010-01), p. D415-D419
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 38, No. suppl_1 ( 2010-01), p. D415-D419
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkp941
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2010
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Zippered G-quadruplex/hemin DNAzyme: exceptional catalyst for universal bioanalytical applications
    Oxford University Press (OUP) ; 2021
    In:  Nucleic Acids Research Vol. 49, No. 22 ( 2021-12-16), p. 13031-13044
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 49, No. 22 ( 2021-12-16), p. 13031-13044
    Abstract: G-quadruplex (G4)/hemin DNAzyme is promising horseradish peroxidase (HRP)-mimic candidate in the biological field. However, its relatively unsatisfactory catalytic capacity limits the potential applications. Inspired by nature protease, we conducted a proximity-enhanced cofactor assembly strategy (PECA) to form an exceptional HRP mimic, namely zippered G4/hemin DNAzyme (Z-G4/H). The hybridization of short oligonucleotides induced proximity assembly of the DNA-grafted hemin (DGH) with the complementary G4 sequences (cG4s), mimicking the tight configuration of protease cofactor and apoenzyme. The detailed investigations of catalytic efficiency and mechanism verified the higher activity, more rapid catalytic rate and high environmental tolerance of the Z-G4/H than the classical G4/hemin DNAzymes (C-G4/H). Furthermore, a proximity recognition transducer has been developed based on the PECA for sensitive detection of gene rearrangement and imaging human epidermal growth factor receptor 2 protein (HER2) dimerization on cell surfaces. Our studies demonstrate the high efficiency of Z-G4/H and its universal application potential in clinical diagnostics and biomolecule interaction research. It also may offer significant opportunities and inspiration for the engineering of the protease-free mimic enzyme.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkab1178
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    Accelerating bioactive peptide discovery via mutual information-based meta-learning
    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: Recently, machine learning methods have been developed to identify various peptide bio-activities. However, due to the lack of experimentally validated peptides, machine learning methods cannot provide a sufficiently trained model, easily resulting in poor generalizability. Furthermore, there is no generic computational framework to predict the bioactivities of different peptides. Thus, a natural question is whether we can use limited samples to build an effective predictive model for different kinds of peptides. To address this question, we propose Mutual Information Maximization Meta-Learning (MIMML), a novel meta-learning-based predictive model for bioactive peptide discovery. Using few samples from various functional peptides, MIMML can sufficiently learn the discriminative information amongst various functions and characterize functional differences. Experimental results show excellent performance of MIMML though using far fewer training samples as compared to the state-of-the-art methods. We also decipher the latent relationships among different kinds of functions to understand what meta-model learned to improve a specific task. In summary, this study is a pioneering work in the field of functional peptide mining and provides the first-of-its-kind solution for few-sample learning problems in biological sequence analysis, accelerating the new functional peptide discovery. The source codes and datasets are available on https://github.com/TearsWaiting/MIMML.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbab499
    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
    DeeReCT-PolyA: a robust and generic deep learning method for PAS identification
    Oxford University Press (OUP) ; 2019
    In:  Bioinformatics Vol. 35, No. 14 ( 2019-07-15), p. 2371-2379
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 35, No. 14 ( 2019-07-15), p. 2371-2379
    Abstract: Polyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PASs) identification is not only desired for the purpose of better transcripts’ end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif- and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals. Results In this work, we propose a robust, PAS motif agnostic, and highly interpretable and transferrable deep learning model for accurate PAS recognition, which requires no prior knowledge or human-designed features. We show that our single model trained over all human PAS motifs not only outperforms the state-of-the-art methods trained on specific motifs, but can also be generalized well to two mouse datasets. Moreover, we further increase the prediction accuracy by transferring the deep learning model trained on the data of one species to the data of a different species. Several novel underlying poly(A) patterns are revealed through the visualization of important oligomers and positions in our trained models. Finally, we interpret the deep learning models by converting the convolutional filters into sequence logos and quantitatively compare the sequence logos between human and mouse datasets. Availability and implementation https://github.com/likesum/DeeReCT-PolyA 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/bty991
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning
    Oxford University Press (OUP) ; 2022
    In:  Genomics, Proteomics & Bioinformatics Vol. 20, No. 3 ( 2022-06-01), p. 483-495
    Details
    In: Genomics, Proteomics & Bioinformatics, Oxford University Press (OUP), Vol. 20, No. 3 ( 2022-06-01), p. 483-495
    Abstract: Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic studies have mainly focused on the recognition of polyadenylation sites (PASs) in a given genomic sequence, which is a binary classification problem. Recently, computational methods for predicting the usage level of alternative PASs in the same gene have been proposed. However, all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into account. To address this, here we propose a deep learning architecture, Deep Regulatory Code and Tools for Alternative Polyadenylation (DeeReCT-APA), to quantitatively predict the usage of all alternative PASs of a given gene. To accommodate different genes with potentially different numbers of PASs, DeeReCT-APA treats the problem as a regression task with a variable-length target. Based on a convolutional neural network-long short-term memory (CNN-LSTM) architecture, DeeReCT-APA extracts sequence features with CNN layers, uses bidirectional LSTM to explicitly model the interactions among competing PASs, and outputs percentage scores representing the usage levels of all PASs of a gene. In addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene, we show that our method consistently outperforms other existing methods on three different tasks for which they are trained: pairwise comparison task, highest usage prediction task, and ranking task. Finally, we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA regulation. Our code and data are available at https://github.com/lzx325/DeeReCT-APA-repo.
    Type of Medium: Online Resource
    ISSN: 1672-0229 , 2210-3244
    URL: Article
    DOI: 10.1016/j.gpb.2020.05.004
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2233708-8
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  • 8
    Online Resource
    Online Resource
    Deep Learning Deepens the Analysis of Alternative Splicing
    Oxford University Press (OUP) ; 2019
    In:  Genomics, Proteomics & Bioinformatics Vol. 17, No. 2 ( 2019-04-01), p. 219-221
    Details
    In: Genomics, Proteomics & Bioinformatics, Oxford University Press (OUP), Vol. 17, No. 2 ( 2019-04-01), p. 219-221
    Type of Medium: Online Resource
    ISSN: 1672-0229 , 2210-3244
    URL: Article
    DOI: 10.1016/j.gpb.2019.05.001
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 2233708-8
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  • 9
    Online Resource
    Online Resource
    Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 16 ( 2017-08-15), p. 2523-2531
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 16 ( 2017-08-15), p. 2523-2531
    Abstract: Growth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment. Results We first demonstrate that detecting such ‘co-fit’ gene groups can be cast as a less well-studied problem in biclustering, i.e. constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype datasets for E. coli, proteobacteria and yeast. Availability and Implementation Our program is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx. 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/btx199
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 10
    Online Resource
    Online Resource
    QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs
    Oxford University Press (OUP) ; 2021
    In:  Genomics, Proteomics & Bioinformatics Vol. 19, No. 6 ( 2021-12-01), p. 998-1011
    Details
    In: Genomics, Proteomics & Bioinformatics, Oxford University Press (OUP), Vol. 19, No. 6 ( 2021-12-01), p. 998-1011
    Abstract: The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated.
    Type of Medium: Online Resource
    ISSN: 1672-0229 , 2210-3244
    URL: Article
    DOI: 10.1016/j.gpb.2021.02.001
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2233708-8
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