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  • Oxford University Press (OUP)  (28)
  • Wu, Fang-Xiang  (28)
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  • Oxford University Press (OUP)  (28)
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  • 1
    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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  • 2
    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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  • 3
    Online Resource
    Online Resource
    MultiGuideScan: a multi-processing tool for designing CRISPR guide RNA libraries
    Oxford University Press (OUP) ; 2020
    In:  Bioinformatics Vol. 36, No. 3 ( 2020-02-01), p. 920-921
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 36, No. 3 ( 2020-02-01), p. 920-921
    Abstract: The recent advance in genome engineering technologies based on CRISPR/Cas9 system is enabling people to systematically understand genomic functions. A short RNA string (the CRISPR guide RNA) can guide the Cas9 endonuclease to specific locations in complex genomes to cut DNA double-strands. The CRISPR guide RNA is essential for gene editing systems. Recently, the GuideScan software is developed to design CRISPR guide RNA libraries, which can be used for genome editing of coding and non-coding genomic regions effectively. However, GuideScan is a serial program and computationally expensive for designing CRISPR guide RNA libraries from large genomes. Here, we present an efficient guide RNA library designing tool (MultiGuideScan) by implementing multiple processes of GuideScan. MultiGuideScan speeds up the guide RNA library designing about 9–12 times on a 32-process mode comparing to GuideScan. MultiGuideScan makes it possible to design guide RNA libraries from large genomes. Availability and implementation: MultiGuideScan is available at GitHub https://github.com/bioinfomaticsCSU/MultiGuideScan. 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/btz616
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    A sensitive repeat identification framework based on short and long reads
    Oxford University Press (OUP) ; 2021
    In:  Nucleic Acids Research Vol. 49, No. 17 ( 2021-09-27), p. e100-e100
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 49, No. 17 ( 2021-09-27), p. e100-e100
    Abstract: Numerous studies have shown that repetitive regions in genomes play indispensable roles in the evolution, inheritance and variation of living organisms. However, most existing methods cannot achieve satisfactory performance on identifying repeats in terms of both accuracy and size, since NGS reads are too short to identify long repeats whereas SMS (Single Molecule Sequencing) long reads are with high error rates. In this study, we present a novel identification framework, LongRepMarker, based on the global de novo assembly and k-mer based multiple sequence alignment for precisely marking long repeats in genomes. The major characteristics of LongRepMarker are as follows: (i) by introducing barcode linked reads and SMS long reads to assist the assembly of all short paired-end reads, it can identify the repeats to a greater extent; (ii) by finding the overlap sequences between assemblies or chomosomes, it locates the repeats faster and more accurately; (iii) by using the multi-alignment unique k-mers rather than the high frequency k-mers to identify repeats in overlap sequences, it can obtain the repeats more comprehensively and stably; (iv) by applying the parallel alignment model based on the multi-alignment unique k-mers, the efficiency of data processing can be greatly optimized and (v) by taking the corresponding identification strategies, structural variations that occur between repeats can be identified. Comprehensive experimental results show that LongRepMarker can achieve more satisfactory results than the existing de novo detection methods (https://github.com/BioinformaticsCSU/LongRepMarker).
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkab563
    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
    EPGA: de novo assembly using the distributions of reads and insert size
    Oxford University Press (OUP) ; 2015
    In:  Bioinformatics Vol. 31, No. 6 ( 2015-03-15), p. 825-833
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 31, No. 6 ( 2015-03-15), p. 825-833
    Abstract: Motivation: In genome assembly, the primary issue is how to determine upstream and downstream sequence regions of sequence seeds for constructing long contigs or scaffolds. When extending one sequence seed, repetitive regions in the genome always cause multiple feasible extension candidates which increase the difficulty of genome assembly. The universally accepted solution is choosing one based on read overlaps and paired-end (mate-pair) reads. However, this solution faces difficulties with regard to some complex repetitive regions. In addition, sequencing errors may produce false repetitive regions and uneven sequencing depth leads some sequence regions to have too few or too many reads. All the aforementioned problems prohibit existing assemblers from getting satisfactory assembly results. Results: In this article, we develop an algorithm, called extract paths for genome assembly (EPGA), which extracts paths from De Bruijn graph for genome assembly. EPGA uses a new score function to evaluate extension candidates based on the distributions of reads and insert size. The distribution of reads can solve problems caused by sequencing errors and short repetitive regions. Through assessing the variation of the distribution of insert size, EPGA can solve problems introduced by some complex repetitive regions. For solving uneven sequencing depth, EPGA uses relative mapping to evaluate extension candidates. On real datasets, we compare the performance of EPGA and other popular assemblers. The experimental results demonstrate that EPGA can effectively obtain longer and more accurate contigs and scaffolds. Availability and implementation: EPGA is publicly available for download at https://github.com/bioinfomaticsCSU/EPGA. Contact:  jxwang@csu.edu.cn Supplementary information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/btu762
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2015
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    A survey on predicting microbe-disease associations: biological data and computational methods
    Oxford University Press (OUP) ; 2021
    In:  Briefings in Bioinformatics Vol. 22, No. 3 ( 2021-05-20)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 22, No. 3 ( 2021-05-20)
    Abstract: Various microbes have proved to be closely related to the pathogenesis of human diseases. While many computational methods for predicting human microbe-disease associations (MDAs) have been developed, few systematic reviews on these methods have been reported. In this study, we provide a comprehensive overview of the existing methods. Firstly, we introduce the data used in existing MDA prediction methods. Secondly, we classify those methods into different categories by their nature and describe their algorithms and strategies in detail. Next, experimental evaluations are conducted on representative methods using different similarity data and calculation methods to compare their prediction performances. Based on the principles of computational methods and experimental results, we discuss the advantages and disadvantages of those methods and propose suggestions for the improvement of prediction performances. Considering the problems of the MDA prediction at present stage, we discuss future work from three perspectives including data, methods and formulations at the end.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbaa157
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    SCOP: a novel scaffolding algorithm based on contig classification and optimization
    Oxford University Press (OUP) ; 2019
    In:  Bioinformatics Vol. 35, No. 7 ( 2019-04-01), p. 1142-1150
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 35, No. 7 ( 2019-04-01), p. 1142-1150
    Abstract: Scaffolding is an essential step during the de novo sequence assembly process to infer the direction and order relationships between the contigs and make the sequence assembly results more continuous and complete. However, scaffolding still faces the challenges of repetitive regions in genome, sequencing errors and uneven sequencing depth. Moreover, the accuracy of scaffolding greatly depends on the quality of contigs. Generally, the existing scaffolding methods construct a scaffold graph, and then optimize the graph by deleting spurious edges. Nevertheless, due to the wrong joints between contigs, some correct edges connecting contigs may be deleted. Results In this study, we present a novel scaffolding method SCOP, which is the first method to classify the contigs and utilize the vertices and edges to optimize the scaffold graph. Specially, SCOP employs alignment features and GC-content of paired reads to evaluate the quality of contigs (vertices), and divide the contigs into three types (True, Uncertain and Misassembled), and then optimizes the scaffold graph based on the classification of contigs together with the alignment of edges. The experiment results on the datasets of GAGE-A and GAGE-B demonstrate that SCOP performs better than 12 other competing scaffolders. Availability and implementation SCOP is publicly available for download at https://github.com/bioinfomaticsCSU/SCOP. 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/bty773
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 8
    Online Resource
    Online Resource
    IsoResolve: predicting splice isoform functions by integrating gene and isoform-level features with domain adaptation
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 37, No. 4 ( 2021-05-01), p. 522-530
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 4 ( 2021-05-01), p. 522-530
    Abstract: High resolution annotation of gene functions is a central goal in functional genomics. A single gene may produce multiple isoforms with different functions through alternative splicing. Conventional approaches, however, consider a gene as a single entity without differentiating these functionally different isoforms. Towards understanding gene functions at higher resolution, recent efforts have focused on predicting the functions of isoforms. However, the performance of existing methods is far from satisfactory mainly because of the lack of isoform-level functional annotation. Results We present IsoResolve, a novel approach for isoform function prediction, which leverages the information from gene function prediction models with domain adaptation (DA). IsoResolve treats gene-level and isoform-level features as source and target domains, respectively. It uses DA to project the two domains into a latent variable space in such a way that the latent variables from the two domains have similar distribution, which enables the gene domain information to be leveraged for isoform function prediction. We systematically evaluated the performance of IsoResolve in predicting functions. Compared with five state-of-the-art methods, IsoResolve achieved significantly better performance. IsoResolve was further validated by case studies of genes with isoform-level functional annotation. Availability and implementation IsoResolve is freely available at https://github.com/genemine/IsoResolve. 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/btaa829
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 9
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    Online Resource
    SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement
    Oxford University Press (OUP) ; 2021
    In:  Genomics, Proteomics & Bioinformatics Vol. 19, No. 2 ( 2021-04-01), p. 282-291
    Details
    In: Genomics, Proteomics & Bioinformatics, Oxford University Press (OUP), Vol. 19, No. 2 ( 2021-04-01), p. 282-291
    Abstract: Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells affects the result significantly. Although many approaches for cell type identification have been proposed, the accuracy still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. SSRE models the relationships between cells based on subspace assumption, and generates a sparse representation of the cell-to-cell similarity. The sparse representation retains the most similar neighbors for each cell. Besides, three classical pairwise similarities are incorporated with a gene selection and enhancement strategy to further improve the effectiveness of SSRE. Tested on ten real scRNA-seq datasets and five simulated datasets, SSRE achieved the superior performance in most cases compared to several state-of-the-art single-cell clustering methods. In addition, SSRE can be extended to visualization of scRNA-seq data and identification of differentially expressed genes. The matlab and python implementations of SSRE are available at https://github.com/CSUBioGroup/SSRE.
    Type of Medium: Online Resource
    ISSN: 1672-0229 , 2210-3244
    URL: Article
    DOI: 10.1016/j.gpb.2020.09.004
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2233708-8
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  • 10
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    LDAP: a web server for lncRNA-disease association prediction
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 3 ( 2017-02-01), p. 458-460
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 3 ( 2017-02-01), p. 458-460
    Abstract: Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods have been developed to infer lncRNA-disease associations. However, most of these methods infer lncRNA-disease associations only based on single data resource. Results In this paper, we propose a new computational method to predict lncRNA-disease associations by integrating multiple biological data resources. Then, we implement this method as a web server for lncRNA-disease association prediction (LDAP). The input of the LDAP server is the lncRNA sequence. The LDAP predicts potential lncRNA-disease associations by using a bagging SVM classifier based on lncRNA similarity and disease similarity. Availability and Implementation The web server is available at http://bioinformatics.csu.edu.cn/ldap 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/btw639
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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