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1.
Biosci Biotechnol Biochem ; 84(1): 111-117, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31512553

RESUMO

Slow skeletal muscle troponin T (TNNT1) has been reported to be correlated with several cancers, but there are no evidences proving that TNNT1 is required in colon adenocarcinoma (COAD). TNNT1 expression in COAD tissues and its prognostic significance were acquired from TCGA database. The proliferative, migratory, and invasive abilities of COAD cells were detected by CCK-8 and transwell assays, respectively. Correlations between TNNT1 and epithelial-mesenchymal transition (EMT)-related markers were determined using western blotting and Pearson's analysis. Our results stated that TNNT1 expression was high-regulated in COAD tissues, which was related with unfavorable prognosis of COAD patients. Functional analyses suggested that TNNT1 promoted the cellular behaviors. Moreover, aberrant expression of TNNT1 affected the expression level of EMT-related proteins. And TNNT1 was negatively linked with E-cadherin. In conclusion, our findings indicated that TNNT1 may promote the progression of COAD, mediating EMT process, and thus shed a novel light on COAD therapeutic treatments.


Assuntos
Adenocarcinoma/patologia , Movimento Celular , Proliferação de Células , Neoplasias do Colo/patologia , Transição Epitelial-Mesenquimal , Troponina T/genética , Troponina T/metabolismo , Antígenos CD/metabolismo , Caderinas/metabolismo , Bases de Dados Genéticas , Expressão Gênica , Técnicas de Silenciamento de Genes , Células HCT116 , Humanos , Invasividade Neoplásica , Prognóstico , Transfecção
2.
Adv Exp Med Biol ; 1168: 103-115, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31713167

RESUMO

The past two decades have seen unprecedented advances in the field of oncogenomics. The ongoing characterization of neoplastic tissues through genomic techniques has transformed many aspects of cancer research, diagnosis, and treatment. However, identifying sequence variants with biological and clinical significance is a challenging endeavor. In order to accomplish this task, variants must be annotated and interpreted using various online resources. Data on protein structure, functional prediction, variant frequency in relevant populations, and multiple other factors have been compiled in useful databases for this purpose. Thus, understanding the available online resources for the annotation and interpretation of sequence variants is critical to aid molecular pathologists and researchers working in this space.


Assuntos
Bases de Dados Genéticas , Privacidade Genética , Neoplasias , Farmacogenética , Privacidade Genética/tendências , Variação Genética , Recursos em Saúde , Humanos , Internet , Neoplasias/fisiopatologia , Neoplasias/terapia , Análise de Sequência de DNA/normas , Análise de Sequência de DNA/tendências
3.
Tumour Biol ; 41(11): 1010428319883721, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31718480

RESUMO

The involvement of microRNA in cancers plays a significant role in their pathogenesis. Specific expressions of these non-coding RNAs also serve as biomarkers for early colorectal cancer diagnosis, but their laboratory/molecular identification is challenging and expensive. The aim of this study was to identify potential microRNAs for colorectal cancer diagnosis using in silico approach. Sequence similarity search was employed to obtain the candidate microRNA from the datasets, and three target prediction software were employed to determine their target genes. To determine the involvement of these microRNAs in colorectal cancer, the microRNA gene list obtained was used alongside with colorectal cancer expressed genes from gbCRC and CoReCG databases for gene intersection analysis. The involvement of these genes in the cancer subtype was further strengthened with the DAVID database. KEGG and Gene Ontology were used for the pathway and functional analysis, while STRING was employed for the interactions of protein network and further visualized by Cytoscape. The cBioPortal database was used to prioritize the target genes; prognostic and expression analysis were finally performed on the candidate microRNAs and the prioritized targets. This study, therefore, identified five candidate microRNAs, two hub genes (CTNNB1 and epidermal growth factor receptor), and seven significant target genes associated with colorectal cancer. The molecular validation studies are ongoing to ascertain the biological fitness of these findings.


Assuntos
Biomarcadores Tumorais/genética , Neoplasias Colorretais/genética , MicroRNAs/genética , Transcriptoma/genética , Neoplasias Colorretais/patologia , Biologia Computacional , Simulação por Computador , Bases de Dados Genéticas , Detecção Precoce de Câncer , Receptores ErbB/genética , Regulação Neoplásica da Expressão Gênica/genética , Ontologia Genética , Redes Reguladoras de Genes , Humanos , Mapeamento de Interação de Proteínas , beta Catenina/genética
4.
Genome Biol ; 20(1): 202, 2019 10 08.
Artigo em Inglês | MEDLINE | ID: mdl-31594544

RESUMO

BACKGROUND: A series of miRNA-disease association prediction methods have been proposed to prioritize potential disease-associated miRNAs. Independent benchmarking of these methods is warranted to assess their effectiveness and robustness. RESULTS: Based on more than 8000 novel miRNA-disease associations from the latest HMDD v3.1 database, we perform systematic comparison among 36 readily available prediction methods. Their overall performances are evaluated with rigorous precision-recall curve analysis, where 13 methods show acceptable accuracy (AUPRC > 0.200) while the top two methods achieve a promising AUPRC over 0.300, and most of these methods are also highly ranked when considering only the causal miRNA-disease associations as the positive samples. The potential of performance improvement is demonstrated by combining different predictors or adopting a more updated miRNA similarity matrix, which would result in up to 16% and 46% of AUPRC augmentations compared to the best single predictor and the predictors using the previous similarity matrix, respectively. Our analysis suggests a common issue of the available methods, which is that the prediction results are severely biased toward well-annotated diseases with many associated miRNAs known and cannot further stratify the positive samples by discriminating the causal miRNA-disease associations from the general miRNA-disease associations. CONCLUSION: Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate miRNA-disease association predictors for their purpose, but also suggest the future directions for the development of more robust miRNA-disease association predictors.


Assuntos
Biologia Computacional/métodos , Doença/genética , MicroRNAs , Benchmarking , Bases de Dados Genéticas
5.
Gan To Kagaku Ryoho ; 46(10): 1599-1601, 2019 Oct.
Artigo em Japonês | MEDLINE | ID: mdl-31631148

RESUMO

A man in his 40s presented with fever and nasal congestion. NK-T cell lymphoma was pathologically diagnosed. A central venous(CV)port was implanted via a right subclavian approach. Four months later, redness and swelling were observed around the implanted CV port. Initially, an infection of the CV port was suspected, and the CV port was removed. Regardless of the removal of the port, the wound healing was refractory, and an ulcer formed. Surgical biopsy from the ulcer showed skin infiltration of NK-T cell lymphoma. In cases ofref ractory wound healing around an implanted CV port in patients with lymphoma, lymphoma recurrence and port infection should be considered.


Assuntos
Cateterismo Venoso Central , Linfoma , Biópsia , Bases de Dados Genéticas , Humanos , Masculino , Próteses e Implantes , Recidiva
6.
BMC Bioinformatics ; 20(1): 451, 2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31481014

RESUMO

BACKGROUND: High-throughput gene expression technologies provide complex datasets reflecting mechanisms perturbed in an experiment, typically in a treatment versus control design. Analysis of these information-rich data can be guided based on a priori knowledge, such as networks of related proteins or genes. Assessing the response of a specific mechanism and investigating its biological basis is extremely important in systems toxicology; as compounds or treatment need to be assessed with respect to a predefined set of key mechanisms that could lead to toxicity. Two-layer networks are suitable for this task, and a robust computational methodology specifically addressing those needs was previously published. The NPA package ( https://github.com/philipmorrisintl/NPA ) implements the algorithm, and a data package of eight two-layer networks representing key mechanisms, such as xenobiotic metabolism, apoptosis, or epithelial immune innate activation, is provided. RESULTS: Gene expression data from an animal study are analyzed using the package and its network models. The functionalities are implemented using R6 classes, making the use of the package seamless and intuitive. The various network responses are analyzed using the leading node analysis, and an overall perturbation, called the Biological Impact Factor, is computed. CONCLUSIONS: The NPA package implements the published network perturbation amplitude methodology and provides a set of two-layer networks encoded in the Biological Expression Language.


Assuntos
Metodologias Computacionais , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Software , Algoritmos , Animais , Apoptose/genética , Ciclo Celular/genética , Bases de Dados Genéticas , Matriz Extracelular/metabolismo , Camundongos Endogâmicos C57BL , Estresse Oxidativo , Transcriptoma/genética
7.
BMC Bioinformatics ; 20(1): 458, 2019 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-31492109

RESUMO

BACKGROUND: Despite the availability of many ready-made testing software, reliable detection of differentially expressed genes in RNA-seq data is not a trivial task. Even though the data collection is considered high-throughput, data analysis has intricacies that require careful human attention. Researchers should use modern data analysis techniques that incorporate visual feedback to verify the appropriateness of their models. While some RNA-seq packages provide static visualization tools, their capabilities should be expanded and their meaningfulness should be explicitly demonstrated to users. RESULTS: In this paper, we 1) introduce new interactive RNA-seq visualization tools, 2) compile a collection of examples that demonstrate to biologists why visualization should be an integral component of differential expression analysis. We use public RNA-seq datasets to show that our new visualization tools can detect normalization issues, differential expression designation problems, and common analysis errors. We also show that our new visualization tools can identify genes of interest in ways undetectable with models. Our R package "bigPint" includes the plotting tools introduced in this paper, many of which are unique additions to what is currently available. The "bigPint" website is located at https://lindsayrutter.github.io/bigPint and contains short vignette articles that introduce new users to our package, all written in reproducible code. CONCLUSIONS: We emphasize that interactive graphics should be an indispensable component of modern RNA-seq analysis, which is currently not the case. This paper and its corresponding software aim to persuade 1) users to slightly modify their differential expression analyses by incorporating statistical graphics into their usual analysis pipelines, 2) developers to create additional complex and interactive plotting methods for RNA-seq data, possibly using lessons learned from our open-source codes. We hope our work will serve a small part in upgrading the RNA-seq analysis world into one that more wholistically extracts biological information using both models and visuals.


Assuntos
Gráficos por Computador , Perfilação da Expressão Gênica , Análise de Sequência de RNA , Bases de Dados Genéticas , Humanos , RNA/genética , Software
8.
BMC Bioinformatics ; 20(1): 453, 2019 Sep 05.
Artigo em Inglês | MEDLINE | ID: mdl-31488068

RESUMO

BACKGROUND: Metagenomics caused a quantum leap in microbial ecology. However, the inherent size and complexity of metagenomic data limit its interpretation. The quantification of metagenomic traits in metagenomic analysis workflows has the potential to improve the exploitation of metagenomic data. Metagenomic traits are organisms' characteristics linked to their performance. They are measured at the genomic level taking a random sample of individuals in a community. As such, these traits provide valuable information to uncover microorganisms' ecological patterns. The Average Genome Size (AGS) and the 16S rRNA gene Average Copy Number (ACN) are two highly informative metagenomic traits that reflect microorganisms' ecological strategies as well as the environmental conditions they inhabit. RESULTS: Here, we present the ags.sh and acn.sh tools, which analytically derive the AGS and ACN metagenomic traits. These tools represent an advance on previous approaches to compute the AGS and ACN traits. Benchmarking shows that ags.sh is up to 11 times faster than state-of-the-art tools dedicated to the estimation AGS. Both ags.sh and acn.sh show comparable or higher accuracy than existing tools used to estimate these traits. To exemplify the applicability of both tools, we analyzed the 139 prokaryotic metagenomes of TARA Oceans and revealed the ecological strategies associated with different water layers. CONCLUSION: We took advantage of recent advances in gene annotation to develop the ags.sh and acn.sh tools to combine easy tool usage with fast and accurate performance. Our tools compute the AGS and ACN metagenomic traits on unassembled metagenomes and allow researchers to improve their metagenomic data analysis to gain deeper insights into microorganisms' ecology. The ags.sh and acn.sh tools are publicly available using Docker container technology at https://github.com/pereiramemo/AGS-and-ACN-tools .


Assuntos
Dosagem de Genes , Tamanho do Genoma , Metagenoma/genética , Metagenômica/métodos , RNA Ribossômico 16S/genética , Benchmarking , Variações do Número de Cópias de DNA , Bases de Dados Genéticas , Oceanos e Mares , Fatores de Tempo
9.
Toxicol Lett ; 316: 49-59, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31520698

RESUMO

Epidemiological studies have established the correlations between PM2.5 and a wide variety of pulmonary diseases. However, their underlying pathogeneses have not been clearly elucidated yet. In the present study, the epithelial-mesenchymal transition (EMT) phenotype with enhanced proliferation and migration activity of human pulmonary epithelial cell line BEAS-2B was observed after exposure to low dose PM2.5 exposure (50 µg/ml) for 30 passages. Then, epithelial cells derived-exosomal micro-RNA (miRNA) and intracellular total RNA were extracted, and the differentially expressed exosomal miRNAs (DE-Exo-MiRs) as well as differentially expressed protein coding genes (DEGs) were identified by RNA sequencing (RNA-seq) and transcriptome analysis. We found that chronic PM2.5 exposure stimulated the release of pulmonary epithelium derived exosomes. 45 DE-Exo-MiRs including 32 novelly predicted miRNAs and 843 DEGs between PM2.5 exposed group and the normal control were detected. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DEGs were significantly enriched in extracellular matrix organization, focal adhesion and cancer related terms. Besides, the enrichment analyses on 7774 mRNA targets of 27 DE-Exo-MiRs predicted by MiRanda software also revealed the potential regulatory role of exosomal miRNAs in pathways in cancer, Wingless/Integrated (Wnt) signaling pathway, focal adhesion related genes and other multiple pathogenic pathways. Moreover, the interactive exosomal miRNA-mRNA pair networks were constructed using Cytoscape software. Our results provided a novel basis for a better understanding of the mechanisms of chronic PM2.5 exposure induced pulmonary disorders including pulmonary fibrosis and cancer, in which exosomal miRNAs (Exo-MiRs) potentially functions by dynamically regulating gene expressions.


Assuntos
Células Epiteliais/efeitos dos fármacos , Exossomos/efeitos dos fármacos , Perfilação da Expressão Gênica/métodos , Pulmão/efeitos dos fármacos , MicroRNAs/genética , Material Particulado/toxicidade , RNA Mensageiro/genética , Transcriptoma/efeitos dos fármacos , Linhagem Celular , Movimento Celular/efeitos dos fármacos , Movimento Celular/genética , Proliferação de Células/efeitos dos fármacos , Proliferação de Células/genética , Biologia Computacional , Bases de Dados Genéticas , Células Epiteliais/metabolismo , Células Epiteliais/ultraestrutura , Transição Epitelial-Mesenquimal/efeitos dos fármacos , Transição Epitelial-Mesenquimal/genética , Exossomos/genética , Exossomos/metabolismo , Exossomos/patologia , Redes Reguladoras de Genes , Humanos , Pulmão/metabolismo , Pulmão/ultraestrutura , MicroRNAs/metabolismo , Tamanho da Partícula , RNA Mensageiro/metabolismo , Medição de Risco , Fatores de Tempo , Testes de Toxicidade Crônica
10.
Stud Health Technol Inform ; 267: 126-133, 2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31483264

RESUMO

Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues. Although the acquisition is highly accelerated, the reconstruction time remains a problem, as the state-of-the-art template matching compares every signal with a set of possible signals. To overcome this limitation, deep learning based approaches, e.g. Convolutional Neural Networks (CNNs) have been proposed. In this work, we investigate the applicability of Recurrent Neural Networks (RNNs) for this reconstruction problem, as the signals are correlated in time. Compared to previous methods based on CNNs, RNN models yield significantly improved results using in-vivo data.


Assuntos
Algoritmos , Bases de Dados Genéticas , Espectroscopia de Ressonância Magnética
11.
Stud Health Technol Inform ; 266: 76-82, 2019 Aug 08.
Artigo em Inglês | MEDLINE | ID: mdl-31397305

RESUMO

SRA, NCBI's Sequence Read Archive, is a valuable resource holding a near definitive collection of the world's collective sequenced reads for academic purposes. Increasingly, these reads are being used for both basic research and clinical investigations. When time is a critical factor in analysis, such as during bacterial outbreaks, the geographical separation between Australia and the offshore NCBI SRA servers can result in significant delays that may have adverse clinical outcomes. To address this, Queensland Genomics commissioned a pilot program for the establishment of a local Australian SRA Cache. Utilizing the hosting capabilities of the NeCTAR Research Cloud, QRIScloud's HTC infrastructure and the MeDiCI data fabric as a storage solution, and the software stack of Cromwell for workflow management, PostgreSQL database for sample and job metadata, and a coordinator Python Flask application, a local cache of seventeen bacterial species was established. Furthermore, the workflow capabilities of Cromwell were leveraged to provide analysis solutions for cached sample data, including quality control and taxonomic profiling, and individual and multiple sample analysis. Moving forward to a broader rollout of increased bacterial species, it was found that the initial storage estimation did not keep up with the exponential increase sequencing reads uploaded to NCBI SRA, which while highlighting the increasing availability and importance in modern research, will need to be addressed.


Assuntos
Bases de Dados Genéticas , Software , Austrália , Genômica , Queensland
12.
Zool Res ; 40(6): 506-521, 2019 Nov 18.
Artigo em Inglês | MEDLINE | ID: mdl-31418539

RESUMO

Chinese tree shrews (Tupaia belangeri chinensis) have become an increasingly important experimental animal in biomedical research due to their close relationship to primates. An accurately sequenced and assembled genome is essential for understanding the genetic features and biology of this animal. In this study, we used long-read single-molecule sequencing and high-throughput chromosome conformation capture (Hi-C) technology to obtain a high-qualitychromosome-scale scaffolding of the Chinese tree shrew genome. The new reference genome (KIZ version 2: TS_2.0) resolved problems in presently available tree shrew genomes and enabled accurate identification of large and complex repeat regions, gene structures, and species-specific genomic structural variants. In addition, by sequencing the genomes of six Chinese tree shrew individuals, we produced a comprehensive map of 12.8 M single nucleotide polymorphisms and confirmed that the major histocompatibility complex (MHC) loci and immunoglobulin gene family exhibited high nucleotide diversity in the tree shrew genome. We updated the tree shrew genome database (TreeshrewDB v2.0: http://www.treeshrewdb.org) to include the genome annotation information and genetic variations. The new high-quality reference genome of the Chinese tree shrew and the updated TreeshrewDB will facilitate the use of this animal in many different fields of research.


Assuntos
Cromossomos/genética , Cromossomos/fisiologia , Genoma , Polimorfismo Genético , Tupaia/genética , Animais , Bases de Dados Genéticas , Especificidade da Espécie
13.
BMC Bioinformatics ; 20(1): 433, 2019 Aug 22.
Artigo em Inglês | MEDLINE | ID: mdl-31438843

RESUMO

BACKGROUND: Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously. RESULTS: Elastic-net logistic regression, a type of machine learning, was used to construct separate classifiers for ten different types of immune cell and for five T helper cell subsets. The resulting classifiers were then used to develop gene signatures that best discriminate among immune cell types and T helper cell subsets using RNA-seq datasets. We validated the approach using single-cell RNA-seq (scRNA-seq) datasets, which gave consistent results. In addition, we classified cell types that were previously unannotated. Finally, we benchmarked the proposed gene signatures against other existing gene signatures. CONCLUSIONS: Developed classifiers can be used as priors in predicting the extent and functional orientation of the host immune response in diseases, such as cancer, where transcriptomic profiling of bulk tissue samples and single cells are routinely employed. Information that can provide insight into the mechanistic basis of disease and therapeutic response. The source code and documentation are available through GitHub: https://github.com/KlinkeLab/ImmClass2019 .


Assuntos
Algoritmos , Perfilação da Expressão Gênica , Subpopulações de Linfócitos/metabolismo , Linfócitos T Auxiliares-Indutores/metabolismo , Bases de Dados Genéticas , Regulação da Expressão Gênica , Humanos , Modelos Logísticos , Anotação de Sequência Molecular , Curva ROC , Reprodutibilidade dos Testes , Análise de Sequência de RNA , Software
14.
BMC Bioinformatics ; 20(1): 436, 2019 Aug 22.
Artigo em Inglês | MEDLINE | ID: mdl-31438850

RESUMO

BACKGROUND: Creating a scalable computational infrastructure to analyze the wealth of information contained in data repositories is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared data science infrastructures like Boag is needed to efficiently process and parse data contained in large data repositories. The main features of Boag are inspired from existing languages for data intensive computing and can easily integrate data from biological data repositories. RESULTS: As a proof of concept, Boa for genomics, Boag, has been implemented to analyze RefSeq's 153,848 annotation (GFF) and assembly (FASTA) file metadata. Boag provides a massive improvement from existing solutions like Python and MongoDB, by utilizing a domain-specific language that uses Hadoop infrastructure for a smaller storage footprint that scales well and requires fewer lines of code. We execute scripts through Boag to answer questions about the genomes in RefSeq. We identify the largest and smallest genomes deposited, explore exon frequencies for assemblies after 2016, identify the most commonly used bacterial genome assembly program, and address how animal genome assemblies have improved since 2016. Boag databases provide a significant reduction in required storage of the raw data and a significant speed up in its ability to query large datasets due to automated parallelization and distribution of Hadoop infrastructure during computations. CONCLUSIONS: In order to keep pace with our ability to produce biological data, innovative methods are required. The Shared Data Science Infrastructure, Boag, provides researchers a greater access to researchers to efficiently explore data in new ways. We demonstrate the potential of a the domain specific language Boag using the RefSeq database to explore how deposited genome assemblies and annotations are changing over time. This is a small example of how Boag could be used with large biological datasets.


Assuntos
Ciência de Dados , Genômica , Disseminação de Informação , Animais , Bases de Dados Factuais , Bases de Dados Genéticas , Éxons/genética , Genoma , Análise de Sequência de DNA , Software
15.
Medicine (Baltimore) ; 98(33): e16807, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31415393

RESUMO

BACKGROUND: Sepsis is a serious clinical condition with a poor prognosis, despite improvements in diagnosis and treatment.Therefore, novel biomarkers are necessary that can help with estimating prognosis and improving clinical outcomes of patients with sepsis. METHODS: The gene expression profiles GSE54514 and GSE63042 were downloaded from the GEO database. DEGs were screened by t test after logarithmization of raw data; then, the common DEGs between the 2 gene expression profiles were identified by up-regulation and down-regulation intersection. The DEGs were analyzed using bioinformatics, and a protein-protein interaction (PPI) survival network was constructed using STRING. Survival curves were constructed to explore the relationship between core genes and the prognosis of sepsis patients based on GSE54514 data. RESULTS: A total of 688 common DEGs were identified between survivors and non-survivors of sepsis, and 96 genes were involved in survival networks. The crucial genes Signal transducer and activator of transcription 5A (STAT5A), CCAAT/enhancer-binding protein beta (CEBPB), Myc proto-oncogene protein (MYC), and REL-associated protein (RELA) were identified and showed increased expression in sepsis survivors. These crucial genes had a positive correlation with patients' survival time according to the survival analysis. CONCLUSIONS: Our findings indicate that the genes STAT5A, CEBPB, MYC, and RELA may be important in predicting the prognosis of sepsis patients.


Assuntos
Proteína beta Intensificadora de Ligação a CCAAT/metabolismo , Proteínas Proto-Oncogênicas c-myc/metabolismo , Fator de Transcrição STAT5/metabolismo , Sepse/genética , Sepse/mortalidade , Fator de Transcrição RelA/metabolismo , Proteínas Supressoras de Tumor/metabolismo , Idoso , Idoso de 80 Anos ou mais , Biologia Computacional , Bases de Dados Genéticas , Regulação para Baixo , Feminino , Marcadores Genéticos , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Mapas de Interação de Proteínas , Fatores de Tempo , Transcriptoma , Regulação para Cima
16.
BMC Bioinformatics ; 20(1): 426, 2019 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-31416413

RESUMO

BACKGROUND: Advances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the integration of multiple high throughput data sources. MKL remains to be under-utilized by genomic researchers partly due to the lack of unified guidelines for its use, and benchmark genomic datasets. RESULTS: We provide three implementations of MKL in R. These methods are applied to simulated data to illustrate that MKL can select appropriate models. We also apply MKL to combine clinical information with miRNA gene expression data of ovarian cancer study into a single analysis. Lastly, we show that MKL can identify gene sets that are known to play a role in the prognostic prediction of 15 cancer types using gene expression data from The Cancer Genome Atlas, as well as, identify new gene sets for the future research. CONCLUSION: Multiple kernel learning coupled with modern optimization techniques provides a promising learning tool for building predictive models based on multi-source genomic data. MKL also provides an automated scheme for kernel prioritization and parameter tuning. The methods used in the paper are implemented as an R package called RMKL package, which is freely available for download through CRAN at https://CRAN.R-project.org/package=RMKL .


Assuntos
Algoritmos , Mineração de Dados , Genômica/métodos , Bases de Dados Genéticas , Humanos , MicroRNAs/genética , MicroRNAs/metabolismo , Neoplasias/genética
17.
Yi Chuan ; 41(8): 746-753, 2019 Aug 20.
Artigo em Chinês | MEDLINE | ID: mdl-31447425

RESUMO

Personal genomic information benefits from accumulated big data and its application is no longer limited to scientific research. Presently, it is undergoing the transformation to daily medical practice. Systematic arrangement, archiving and rational utilization of disease-related genomic information is an important foundation of future precision medicine. Hemoglobinopathy is prevalent in southern China, but its molecular pathological basis has racial specificity. To facilitate clinical diagnosis and genetic screening of hemoglobinopathy in southern China, we established the LOVD gene data management system for the variation and phenotype spectrum of hemoglobinopathy. Then we designed an integrated and efficient on-line auxiliary accurate diagnosis and risk assessment system in order to assist clinicians to make comprehensive diagnosis and genetic counseling in a short time based on cloud standardized annotated library of specific hemoglobinopathy variants and diagnostic repository. The methodology and experience of improving the clinical decision-making efficiency of diseases with big data and artificial intelligence technology can be used as an example in the clinical and preventive application of other diseases.


Assuntos
Bases de Dados Genéticas , Sistemas de Apoio a Decisões Clínicas , Hemoglobinopatias/genética , Mutação , China , Aconselhamento Genético , Testes Genéticos , Humanos
18.
Yi Chuan ; 41(8): 761-772, 2019 Aug 20.
Artigo em Chinês | MEDLINE | ID: mdl-31447427

RESUMO

Genetic resources are important national strategic resources. Their preservation, protection and rational utilization form a solid foundation to guarantee national security and to build national competitiveness for the future. Due to a relatively late starting point, China is actively catching up with global peers in storing genetic samples and data. In view of this, in 2011 China approved a plan to build its first nation-level comprehensive gene bank, the China National GeneBank (CNGB), and entrusted BGI-Research to implement its construction and operation. It is China's first gene bank for "reading, writing and storing" bioresources. In this paper, we summarize the development of influential platforms at home and abroad, and focus on CNGB's position, mission, and its structure of "Three Banks and Two Platforms". CNGB launched its official operation in September 2016 and aims to develop a world-class, non-profit and strategic platform that supports science and technology development. It has built capacities to store tens of millions of traceable samples and to analyze handreds of thousanda of WGS each year. It has also set up China's first Pb-level digitalization platform and a high-efficient synthesis platform with a production rate of ten million bases per year. Based on such capacities, CNGB has established its open sharing mechanism for biological samples and data, provided public platform services for life science research, and achieved initial results in supporting innovation and development of the bio-industry.


Assuntos
Bases de Dados Genéticas , Pesquisa , China , Disseminação de Informação
19.
World J Microbiol Biotechnol ; 35(9): 139, 2019 Aug 26.
Artigo em Inglês | MEDLINE | ID: mdl-31451943

RESUMO

Exploitation of microbes, especially fungi, has the potential to help humankind meet the UN's sustainable development goals, help feed the worlds growing population and improve bioeconomies of poorer nations. The majority of the world's fungal genetic resources are held in collections in developed countries, primarily within the USA, Europe and Japan. Very little capacity exists in low to middle income countries, which are often rich in biodiversity but lack resources to be able to conserve and exploit their own microbial resources. In this paper we review the current challenges facing culture collections and the challenges of integrating new approaches, the worth of collaborative networks, and the importance of technology, taxonomy and data handling. We address the need to underpin research and development in developing countries through the need to build 'in country' infrastructure to address these challenges, whilst tackling the global challenges to meet the requirements of the research community through the impacts of legislation and the Nagoya protocol on access to biological resources.


Assuntos
Fungos , Desenvolvimento Sustentável/tendências , Biodiversidade , Bases de Dados Genéticas , Fungos/classificação , Fungos/genética , Fungos/isolamento & purificação , Fungos/fisiologia , Disseminação de Informação , Internacionalidade
20.
DNA Cell Biol ; 38(10): 1088-1099, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31424267

RESUMO

The biological functions of lipocalin-1 (LCN1) are involved in innate immune responses and act as a physiological scavenger of potentially harmful lipophilic molecules. However, the relevance of LCN1 with cancer is rarely concerned currently. The aim of this study is to address the relevance of LCN1 with BRCA by bioinformatics. In this study, we found that the expressions of LCN1 increased significantly in various cancerous tissues, including BRCA, compared with their adjacent normal tissues through the TIMER database. Furthermore, UALCAN database analysis showed that the expression of LCN1 increased gradually from stage 1 to stage 4 and was upregulated in BRCA patients with different races and subtypes compared with that in the normal. In addition, those patients with perimenopause and postmenopause status displayed higher LCN1 expression. Importantly, LCN1 genetic alterations, including copy number amplification, deep deletion, and missense mutation, could be found, and the alteration frequency showed difference in various invasive BRCA through cBioPortal database. Moreover, a positive correlation between LCN1 somatic copy number alterations and immune cell enrichments was revealed in basal like BRCA by GISTIC 2.0. Finally, analysis on prognostic value of LCN1 by Kaplan-Meier plotter showed that low LCN1 expression correlated with poor prognosis for relapse-free survival in all types of BRCA, overall survival in luminal B BRCA, distant metastasis free survival in human epithelial growth factor receptor-2 (HER2) positive BRCA, and postprogression survival (PPS) in luminal A BRCA. But high LCN1 expression also displayed poor prognosis for PPS in HER2 positive BRCA. The results together verified the significance of LCN1 in BRCA, suggesting that it may be a potential biomarker for BRCA diagnosis.


Assuntos
Biomarcadores Tumorais/genética , Neoplasias da Mama/genética , Regulação Neoplásica da Expressão Gênica , Lipocalina 1/genética , Recidiva Local de Neoplasia/genética , Receptor ErbB-2/genética , Biomarcadores Tumorais/imunologia , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/imunologia , Neoplasias da Mama/mortalidade , Variações do Número de Cópias de DNA , Bases de Dados Genéticas , Feminino , Humanos , Lipocalina 1/imunologia , Pessoa de Meia-Idade , Mutação , Recidiva Local de Neoplasia/diagnóstico , Recidiva Local de Neoplasia/imunologia , Recidiva Local de Neoplasia/mortalidade , Estadiamento de Neoplasias , Perimenopausa/genética , Pós-Menopausa/genética , Pós-Menopausa/imunologia , Receptor ErbB-2/imunologia , Análise de Sobrevida
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