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1.
Bioinformatics ; 2022 Jan 18.
Artigo em Inglês | MEDLINE | ID: mdl-35040932

RESUMO

MOTIVATION: Alzheimer's disease (AD) is a complex brain disorder with risk genes incompletely identified. The candidate genes are dominantly obtained by computational approaches. In order to obtain biological insights of candidate genes or screen genes for experimental testing, it is essential to assess their relevance to AD. A platform that integrates different types of omics data and approaches would facilitate the analysis of candidate genes and is in great need. RESULTS: We report AlzCode, a platform for multiview analysis of genes related to AD. First, this platform integrates a rich collection of functional genomic data, including expression data of AD samples (gene expression, single-cell RNA-seq data, and protein expression), AD-specific biological networks (co-expression networks and functional gene networks), neuropathological and clinical traits (CERAD score, Braak staging score, Clinical Dementia Rating, cognitive function, and clinical severity), as well as general data such as protein-protein interaction, regulatory networks, sequence similarity and miRNA-target interactions. These data provide basis for analyzing genes from different views. Second, the platform integrates multiple approaches designed for the various types of data. We implement functions to analyze both individual genes and gene sets. We also compare AlzCode with two existing platforms for AD analysis, which are Agora and AD Altas. We pinpoint the features of each platform and highlight their differences. This platform would be valuable to the understanding of AD genetics and pathological mechanisms. AVAILABILITY AND IMPLEMENTATION: AlzCode is freely available at: http://www.alzcode.xyz. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

2.
Nucleic Acids Res ; 50(D1): D710-D718, 2022 01 07.
Artigo em Inglês | MEDLINE | ID: mdl-34850130

RESUMO

Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.


Assuntos
Bases de Dados Genéticas , Redes Reguladoras de Genes/genética , Especificidade de Órgãos/genética , RNA-Seq , Curadoria de Dados , Gerenciamento de Dados , Genoma Humano/genética , Humanos , Software
3.
Brief Bioinform ; 23(1)2022 01 17.
Artigo em Inglês | MEDLINE | ID: mdl-34953465

RESUMO

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.


Assuntos
Doença de Alzheimer/genética , Doença de Alzheimer/metabolismo , Encéfalo/metabolismo , Redes Reguladoras de Genes , Predisposição Genética para Doença , Envelhecimento/genética , Perfilação da Expressão Gênica , Humanos , Estudos Longitudinais , Memória , Proteômica , RNA-Seq , Transcriptoma
4.
Front Genet ; 12: 665843, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34386033

RESUMO

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

5.
Brief Bioinform ; 22(6)2021 11 05.
Artigo em Inglês | MEDLINE | ID: mdl-34131702

RESUMO

In single-cell RNA-seq (scRNA-seq) data analysis, a fundamental problem is to determine the number of cell clusters based on the gene expression profiles. However, the performance of current methods is still far from satisfactory, presumably due to their limitations in capturing the expression variability among cell clusters. Batch effects represent the undesired variability between data measured in different batches. When data are obtained from different labs or protocols batch effects occur. Motivated by the practice of batch effect removal, we considered cell clusters as batches. We hypothesized that the number of cell clusters (i.e. batches) could be correctly determined if the variances among clusters (i.e. batch effects) were removed. We developed a new method, namely, removal of batch effect and testing (REBET), for determining the number of cell clusters. In this method, cells are first partitioned into k clusters. Second, the batch effects among these k clusters are then removed. Third, the quality of batch effect removal is evaluated with the average range of normalized mutual information (ARNMI), which measures how uniformly the cells with batch-effects-removal are mixed. By testing a range of k values, the k value that corresponds to the lowest ARNMI is determined to be the optimal number of clusters. We compared REBET with state-of-the-art methods on 32 simulated datasets and 14 published scRNA-seq datasets. The results show that REBET can accurately and robustly estimate the number of cell clusters and outperform existing methods. Contact: H.D.L. (hongdong@csu.edu.cn) or Q.S.X. (qsxu@csu.edu.cn).


Assuntos
Análise por Conglomerados , RNA-Seq/métodos , Análise de Célula Única/métodos , Algoritmos , Bases de Dados Genéticas , Reprodutibilidade dos Testes
6.
Mol Neurodegener ; 16(1): 32, 2021 05 06.
Artigo em Inglês | MEDLINE | ID: mdl-33957936

RESUMO

INTRODUCTION: Passive immunotherapies targeting Aß continue to be evaluated as Alzheimer's disease (AD) therapeutics, but there remains debate over the mechanisms by which these immunotherapies work. Besides the amount of preexisting Aß deposition and the type of deposit (compact or diffuse), there is little data concerning what factors, independent of those intrinsic to the antibody, might influence efficacy. Here we (i) explored how constitutive priming of the underlying innate activation states by Il10 and Il6 might influence passive Aß immunotherapy and (ii) evaluated transcriptomic data generated in the AMP-AD initiative to inform how these two cytokines and their receptors' mRNA levels are altered in human AD and an APP mouse model. METHODS: rAAV2/1 encoding EGFP, Il6 or Il10 were delivered by somatic brain transgenesis to neonatal (P0) TgCRND8 APP mice. Then, at 2 months of age, the mice were treated bi-weekly with a high-affinity anti-Aß1-16 mAb5 monoclonal antibody or control mouse IgG until 6 months of age. rAAV mediated transgene expression, amyloid accumulation, Aß levels and gliosis were assessed. Extensive transcriptomic data was used to evaluate the mRNA expression levels of IL10 and IL6 and their receptors in the postmortem human AD temporal cortex and in the brains of TgCRND8 mice, the later at multiple ages. RESULTS: Priming TgCRND8 mice with Il10 increases Aß loads and blocks efficacy of subsequent mAb5 passive immunotherapy, whereas priming with Il6 priming reduces Aß loads by itself and subsequent Aß immunotherapy shows only a slightly additive effect. Transcriptomic data shows that (i) there are significant increases in the mRNA levels of Il6 and Il10 receptors in the TgCRND8 mouse model and temporal cortex of humans with AD and (ii) there is a great deal of variance in individual mouse brain and the human temporal cortex of these interleukins and their receptors. CONCLUSIONS: The underlying immune activation state can markedly affect the efficacy of passive Aß immunotherapy. These results have important implications for ongoing human AD immunotherapy trials, as they indicate that underlying immune activation states within the brain, which may be highly variable, may influence the ability for passive immunotherapy to alter Aß deposition.


Assuntos
Doença de Alzheimer/imunologia , Peptídeos beta-Amiloides/antagonistas & inibidores , Anticorpos Monoclonais/farmacologia , Imunidade Inata/efeitos dos fármacos , Imunização Passiva/métodos , Animais , Humanos , Interleucina-10/imunologia , Interleucina-6/imunologia , Camundongos , Camundongos Transgênicos
7.
Alzheimers Dement ; 17(6): 984-1004, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-33480174

RESUMO

Intron retention (IR) has been implicated in the pathogenesis of complex diseases such as cancers; its association with Alzheimer's disease (AD) remains unexplored. We performed genome-wide analysis of IR through integrating genetic, transcriptomic, and proteomic data of AD subjects and mouse models from the Accelerating Medicines Partnership-Alzheimer's Disease project. We identified 4535 and 4086 IR events in 2173 human and 1736 mouse genes, respectively. Quantitation of IR enabled the identification of differentially expressed genes that conventional exon-level approaches did not reveal. There were significant correlations of intron expression within innate immune genes, like HMBOX1, with AD in humans. Peptides with a high probability of translation from intron-retained mRNAs were identified using mass spectrometry. Further, we established AD-specific intron expression Quantitative Trait Loci, and identified splicing-related genes that may regulate IR. Our analysis provides a novel resource for the search for new AD biomarkers and pathological mechanisms.


Assuntos
Doença de Alzheimer , Autopsia , Encéfalo/patologia , Modelos Animais de Doenças , Genômica , Íntrons/genética , Doença de Alzheimer/genética , Doença de Alzheimer/patologia , Animais , Proteínas de Homeodomínio/genética , Humanos , Camundongos , Proteômica , Locos de Características Quantitativas , Transcriptoma
8.
Bioinformatics ; 37(4): 522-530, 2021 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-32966552

RESUMO

MOTIVATION: 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.


Assuntos
Adaptação Fisiológica , Processamento Alternativo , Biologia Computacional , Mutação , Isoformas de Proteínas/genética , Isoformas de Proteínas/metabolismo
9.
Artigo em Inglês | MEDLINE | ID: mdl-31369384

RESUMO

Single-cell RNA sequencing (scRNA-seq) technology provides quantitative gene expression profiles at single-cell resolution. As a result, researchers have established new ways to explore cell population heterogeneity and genetic variability of cells. One of the current research directions for scRNA-seq data is to identify different cell types accurately through unsupervised clustering methods. However, scRNA-seq data analysis is challenging because of their high noise level, high dimensionality and sparsity. Moreover, the impact of multiple latent factors on gene expression heterogeneity and on the ability to accurately identify cell types remains unclear. How to overcome these challenges to reveal the biological difference between cell types has become the key to analyze scRNA-seq data. For these reasons, the unsupervised learning for cell population discovery based on scRNA-seq data analysis has become an important research area. A cell similarity assessment method plays a significant role in cell clustering. Here, we present BioRank, a new cell similarity assessment method based on annotated gene sets and gene ranks. To evaluate the performances, we cluster cells by two classical clustering algorithms based on the similarity between cells obtained by BioRank. In addition, BioRank can be used by any clustering algorithm that requires a similarity matrix. Applying BioRank to 12 public scRNA-seq datasets, we show that it is better than or at least as well as several popular similarity assessment methods for single cell clustering.


Assuntos
Biologia Computacional/métodos , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Algoritmos , Animais , Análise por Conglomerados , Bases de Dados Genéticas , Ontologia Genética , Humanos , Camundongos , Transcriptoma/genética
10.
Brief Bioinform ; 22(3)2021 05 20.
Artigo em Inglês | MEDLINE | ID: mdl-32427285

RESUMO

Advances in sequencing technologies facilitate personalized disease-risk profiling and clinical diagnosis. In recent years, some great progress has been made in noninvasive diagnoses based on cell-free DNAs (cfDNAs). It exploits the fact that dead cells release DNA fragments into the circulation, and some DNA fragments carry information that indicates their tissues-of-origin (TOOs). Based on the signals used for identifying the TOOs of cfDNAs, the existing methods can be classified into three categories: cfDNA mutation-based methods, methylation pattern-based methods and cfDNA fragmentation pattern-based methods. In cfDNA mutation-based methods, the SNP information or the detected mutations in driven genes of certain diseases are employed to identify the TOOs of cfDNAs. Methylation pattern-based methods are developed to identify the TOOs of cfDNAs based on the tissue-specific methylation patterns. In cfDNA fragmentation pattern-based methods, cfDNA fragmentation patterns, such as nucleosome positioning or preferred end coordinates of cfDNAs, are used to predict the TOOs of cfDNAs. In this paper, the strategies and challenges in each category are reviewed. Furthermore, the representative applications based on the TOOs of cfDNAs, including noninvasive prenatal testing, noninvasive cancer screening, transplantation rejection monitoring and parasitic infection detection, are also reviewed. Moreover, the challenges and future work in identifying the TOOs of cfDNAs are discussed. Our research provides a comprehensive picture of the development and challenges in identifying the TOOs of cfDNAs, which may benefit bioinformatics researchers to develop new methods to improve the identification of the TOOs of cfDNAs.


Assuntos
Ácidos Nucleicos Livres/genética , Neoplasias/diagnóstico , Biomarcadores Tumorais/genética , Ácidos Nucleicos Livres/sangue , Metilação de DNA , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Humanos , Mutação , Neoplasias/genética
11.
EMBO Rep ; 22(1): e50535, 2021 01 07.
Artigo em Inglês | MEDLINE | ID: mdl-33319461

RESUMO

Alternative splicing (AS) leads to transcriptome diversity in eukaryotic cells and is one of the key regulators driving cellular differentiation. Although AS is of crucial importance for normal hematopoiesis and hematopoietic malignancies, its role in early hematopoietic development is still largely unknown. Here, by using high-throughput transcriptomic analyses, we show that pervasive and dynamic AS takes place during hematopoietic development of human pluripotent stem cells (hPSCs). We identify a splicing factor switch that occurs during the differentiation of mesodermal cells to endothelial progenitor cells (EPCs). Perturbation of this switch selectively impairs the emergence of EPCs and hemogenic endothelial progenitor cells (HEPs). Mechanistically, an EPC-induced alternative spliced isoform of NUMB dictates EPC specification by controlling NOTCH signaling. Furthermore, we demonstrate that the splicing factor SRSF2 regulates splicing of the EPC-induced NUMB isoform, and the SRSF2-NUMB-NOTCH splicing axis regulates EPC generation. The identification of this splicing factor switch provides a new molecular mechanism to control cell fate and lineage specification.


Assuntos
Linhagem da Célula , Células-Tronco Pluripotentes , Fatores de Processamento de Serina-Arginina/genética , Diferenciação Celular , Linhagem da Célula/genética , Hematopoese/genética , Células-Tronco Hematopoéticas , Humanos , Proteínas de Membrana , Proteínas do Tecido Nervoso
12.
Front Genet ; 11: 586, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32733531

RESUMO

Intron retention (IR) is an alternative splicing mode whereby introns, rather than being spliced out as usual, are retained in mature mRNAs. It was previously considered a consequence of mis-splicing and received very limited attention. Only recently has IR become of interest for transcriptomic data analysis owing to its recognized roles in gene expression regulation and associations with complex diseases. In this article, we first review the function of IR in regulating gene expression in a number of biological processes, such as neuron differentiation and activation of CD4+ T cells. Next, we briefly review its association with diseases, such as Alzheimer's disease and cancers. Then, we describe state-of-the-art methods for IR detection, including RNA-seq analysis tools IRFinder and iREAD, highlighting their underlying principles and discussing their advantages and limitations. Finally, we discuss the challenges for IR detection and potential ways in which IR detection methods could be improved.

13.
J Bioinform Comput Biol ; 18(3): 2040009, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32698720

RESUMO

Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity between samples for clustering. While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets that most likely contribute to the clustering, thus facilitating biological interpretation. We compare ClusterMine with conventional approaches on nine real-world experimental datasets that represent different application scenarios in biology. We find that ClusterMine achieves better performances and that the gene sets prioritized by our method are biologically meaningful. ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php.


Assuntos
Análise por Conglomerados , Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Algoritmos , Ciclo Celular/genética , Bases de Dados Genéticas , Humanos , Anotação de Sequência Molecular , Neoplasias/genética , Neoplasias/patologia , Células-Tronco Pluripotentes/fisiologia , Células Receptoras Sensoriais/fisiologia
14.
Interdiscip Sci ; 12(2): 117-130, 2020 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-32086753

RESUMO

Clustering of single-cell RNA sequencing (scRNA-seq) data enables discovering cell subtypes, which is helpful for understanding and analyzing the processes of diseases. Determining the weight of edges is an essential component in graph-based clustering methods. While several graph-based clustering algorithms for scRNA-seq data have been proposed, they are generally based on k-nearest neighbor (KNN) and shared nearest neighbor (SNN) without considering the structure information of graph. Here, to improve the clustering accuracy, we present a novel method for single-cell clustering, called structural shared nearest neighbor-Louvain (SSNN-Louvain), which integrates the structure information of graph and module detection. In SSNN-Louvain, based on the distance between a node and its shared nearest neighbors, the weight of edge is defined by introducing the ratio of the number of the shared nearest neighbors to that of nearest neighbors, thus integrating structure information of the graph. Then, a modified Louvain community detection algorithm is proposed and applied to identify modules in the graph. Essentially, each community represents a subtype of cells. It is worth mentioning that our proposed method integrates the advantages of both SNN graph and community detection without the need for tuning any additional parameter other than the number of neighbors. To test the performance of SSNN-Louvain, we compare it to five existing methods on 16 real datasets, including nonnegative matrix factorization, single-cell interpretation via multi-kernel learning, SNN-Cliq, Seurat and PhenoGraph. The experimental results show that our approach achieves the best average performance in these datasets.


Assuntos
Algoritmos , Células , RNA , Análise de Sequência de RNA , Células/classificação , Análise por Conglomerados , Humanos
15.
BMC Genomics ; 21(1): 128, 2020 Feb 06.
Artigo em Inglês | MEDLINE | ID: mdl-32028886

RESUMO

BACKGROUND: Intron retention (IR) has been traditionally overlooked as 'noise' and received negligible attention in the field of gene expression analysis. In recent years, IR has become an emerging field for interrogating transcriptomes because it has been recognized to carry out important biological functions such as gene expression regulation and it has been found to be associated with complex diseases such as cancers. However, methods for detecting IR today are limited. Thus, there is a need to develop novel methods to improve IR detection. RESULTS: Here we present iREAD (intron REtention Analysis and Detector), a tool to detect IR events genome-wide from high-throughput RNA-seq data. The command line interface for iREAD is implemented in Python. iREAD takes as input a BAM file, representing the transcriptome, and a text file containing the intron coordinates of a genome. It then 1) counts all reads that overlap intron regions, 2) detects IR events by analyzing the features of reads such as depth and distribution patterns, and 3) outputs a list of retained introns into a tab-delimited text file. iREAD provides significant added value in detecting IR compared with output from IRFinder with a higher AUC on all datasets tested. Both methods showed low false positive rates and high false negative rates in different regimes, indicating that use together is generally beneficial. The output from iREAD can be directly used for further exploratory analysis such as differential intron expression and functional enrichment. The software is freely available at https://github.com/genemine/iread. CONCLUSION: Being complementary to existing tools, iREAD provides a new and generic tool to interrogate poly-A enriched transcriptomic data of intron regions. Intron retention analysis provides a complementary approach for understanding transcriptome.


Assuntos
Íntrons , RNA-Seq , Software , Algoritmos , Animais , Humanos , Camundongos
16.
Front Genet ; 11: 604790, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33384718

RESUMO

Clustering is an efficient way to analyze single-cell RNA sequencing data. It is commonly used to identify cell types, which can help in understanding cell differentiation processes. However, different clustering results can be obtained from different single-cell clustering methods, sometimes including conflicting conclusions, and biologists will often fail to get the right clustering results and interpret the biological significance. The cluster ensemble strategy can be an effective solution for the problem. As the graph partitioning-based clustering methods are good at clustering single-cell, we developed Sc-GPE, a novel cluster ensemble method combining five single-cell graph partitioning-based clustering methods. The five methods are SNN-cliq, PhenoGraph, SC3, SSNN-Louvain, and MPGS-Louvain. In Sc-GPE, a consensus matrix is constructed based on the five clustering solutions by calculating the probability that the cell pairs are divided into the same cluster. It solved the problem in the hypergraph-based ensemble approach, including the different cluster labels that were assigned in the individual clustering method, and it was difficult to find the corresponding cluster labels across all methods. Then, to distinguish the different importance of each method in a clustering ensemble, a weighted consensus matrix was constructed by designing an importance score strategy. Finally, hierarchical clustering was performed on the weighted consensus matrix to cluster cells. To evaluate the performance, we compared Sc-GPE with the individual clustering methods and the state-of-the-art SAME-clustering on 12 single-cell RNA-seq datasets. The results show that Sc-GPE obtained the best average performance, and achieved the highest NMI and ARI value in five datasets.

17.
J Craniofac Surg ; 30(7): 2174-2177, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31425405

RESUMO

BACKGROUND: Underdevelopment of nose and chin in East Asians is quite common. Rhinoplasty and mentoplasty are effective procedures to solve the above-depicted defects and can achieve remarkable cosmetic effects. An autologous costal cartilage graft has become an ideal material for rhinoplasty, especially for revision surgery. However, many problems in the clinical application of costal cartilage remain unresolved. This study is to investigate application strategies of autologous costal cartilage grafts in rhino- and mentoplasty. METHODS: The methods involved are as follows: application of an integrated cartilage scaffold; comprehensive application of diced cartilage; and chin augmentation of an autologous costal cartilage graft. RESULTS: In this study, satisfactory facial contour appearance was immediately achieved in 28 patients after surgery; 21 patients had satisfactory appearance of the nose and chin during the 6- to 18-month follow-up. Cartilage resorption was not observed. Two patients had nasal tip skin redness and were cured after treatment. CONCLUSION: This procedure can be used to effectively solve: curvature of the costal cartilage segment itself; warping of the carved costal cartilage; and effective use of the costal cartilage segment. The procedure has achieved satisfactory outcomes, and its application is worth extending to clinical practice.


Assuntos
Cartilagem Costal/transplante , Mentoplastia , Rinoplastia , Adolescente , Adulto , Autoenxertos/cirurgia , Queixo/cirurgia , Feminino , Humanos , Nariz/cirurgia , Adulto Jovem
18.
Dev Biol ; 450(2): 101-114, 2019 06 15.
Artigo em Inglês | MEDLINE | ID: mdl-30940539

RESUMO

Congenital cardiac malformations are among the most common birth defects in humans. Here we show that Trim33, a member of the Tif1 subfamily of tripartite domain containing transcriptional cofactors, is required for appropriate differentiation of the pre-cardiogenic mesoderm during a narrow time window in late gastrulation. While mesoderm-specific Trim33 mutants did not display noticeable phenotypes, epiblast-specific Trim33 mutant embryos developed ventricular septal defects, showed sparse trabeculation and abnormally thin compact myocardium, and died as a result of cardiac failure during late gestation. Differentiating embryoid bodies deficient in Trim33 showed an enrichment of gene sets associated with cardiac differentiation and contractility, while the total number of cardiac precursor cells was reduced. Concordantly, cardiac progenitor cell proliferation was reduced in Trim33-deficient embryos. ChIP-Seq performed using antibodies against Trim33 in differentiating embryoid bodies revealed more than 4000 peaks, which were significantly enriched close to genes implicated in stem cell maintenance and mesoderm development. Nearly half of the Trim33 peaks overlapped with binding sites of the Ctcf insulator protein. Our results suggest that Trim33 is required for appropriate differentiation of precardiogenic mesoderm during late gastrulation and that it will likely mediate some of its functions via multi-protein complexes, many of which include the chromatin architectural and insulator protein Ctcf.


Assuntos
Embrião de Mamíferos/embriologia , Gastrulação , Mesoderma/embriologia , Miocárdio/metabolismo , Células-Tronco/metabolismo , Fatores de Transcrição/metabolismo , Animais , Embrião de Mamíferos/citologia , Corpos Embrioides/citologia , Corpos Embrioides/metabolismo , Mesoderma/citologia , Camundongos , Camundongos Transgênicos , Células-Tronco/citologia , Fatores de Transcrição/genética
19.
Genes (Basel) ; 10(2)2019 01 29.
Artigo em Inglês | MEDLINE | ID: mdl-30700040

RESUMO

Single-cell RNA sequencing (scRNA-seq) has recently brought new insight into cell differentiation processes and functional variation in cell subtypes from homogeneous cell populations. A lack of prior knowledge makes unsupervised machine learning methods, such as clustering, suitable for analyzing scRNA-seq . However, there are several limitations to overcome, including high dimensionality, clustering result instability, and parameter adjustment complexity. In this study, we propose a method by combining structure entropy and k nearest neighbor to identify cell subpopulations in scRNA-seq data. In contrast to existing clustering methods for identifying cell subtypes, minimized structure entropy results in natural communities without specifying the number of clusters. To investigate the performance of our model, we applied it to eight scRNA-seq datasets and compared our method with three existing methods (nonnegative matrix factorization, single-cell interpretation via multikernel learning, and structural entropy minimization principle). The experimental results showed that our approach achieves, on average, better performance in these datasets compared to the benchmark methods.


Assuntos
Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Animais , Análise por Conglomerados , Humanos , Aprendizado de Máquina não Supervisionado
20.
Genes (Basel) ; 10(1)2019 01 14.
Artigo em Inglês | MEDLINE | ID: mdl-30646604

RESUMO

The advent of third-generation sequencing (TGS) technologies, such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines, provides new possibilities for contig assembly, scaffolding, and high-performance computing in bioinformatics due to its long reads. However, the high error rate and poor quality of TGS reads provide new challenges for accurate genome assembly and long-read alignment. Efficient processing methods are in need to prioritize high-quality reads for improving the results of error correction and assembly. In this study, we proposed a novel Read Quality Evaluation and Selection Tool (REQUEST) for evaluating the quality of third-generation long reads. REQUEST generates training data of high-quality and low-quality reads which are characterized by their nucleotide combinations. A linear regression model was built to score the quality of reads. The method was tested on three datasets of different species. The results showed that the top-scored reads prioritized by REQUEST achieved higher alignment accuracies. The contig assembly results based on the top-scored reads also outperformed conventional approaches that use all reads. REQUEST is able to distinguish high-quality reads from low-quality ones without using reference genomes, making it a promising alternative sequence-quality evaluation method to alignment-based algorithms.


Assuntos
Sequenciamento de Nucleotídeos em Larga Escala/normas , Controle de Qualidade , Análise de Sequência de DNA/normas , Software , Animais , Drosophila melanogaster , Escherichia coli , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Sequência de DNA/métodos , Yersinia pestis
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