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1.
PLoS Comput Biol ; 16(9): e1008192, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32946433

RESUMO

Balanced excitation and inhibition is widely observed in cortex. How does this balance shape neural computations and stimulus representations? This question is often studied using computational models of neuronal networks in a dynamically balanced state. But balanced network models predict a linear relationship between stimuli and population responses. So how do cortical circuits implement nonlinear representations and computations? We show that every balanced network architecture admits stimuli that break the balanced state and these breaks in balance push the network into a "semi-balanced state" characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state produces nonlinear stimulus representations and nonlinear computations, is unavoidable in networks driven by multiple stimuli, is consistent with cortical recordings, and has a direct mathematical relationship to artificial neural networks.


Assuntos
Modelos Neurológicos , Rede Nervosa/fisiologia , Neurônios/fisiologia , Dinâmica não Linear , Animais , Córtex Cerebral/fisiologia , Biologia Computacional , Redes Neurais de Computação , Sinapses/fisiologia
2.
PLoS Comput Biol ; 16(9): e1008173, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32946435

RESUMO

Single-cell Hi-C (scHi-C) interrogates genome-wide chromatin interaction in individual cells, allowing us to gain insights into 3D genome organization. However, the extremely sparse nature of scHi-C data poses a significant barrier to analysis, limiting our ability to tease out hidden biological information. In this work, we approach this problem by applying topic modeling to scHi-C data. Topic modeling is well-suited for discovering latent topics in a collection of discrete data. For our analysis, we generate nine different single-cell combinatorial indexed Hi-C (sci-Hi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 19,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sci-Hi-C data in the form of "chromatin topics." We further show enrichment of particular compartment structures associated with locus pairs in these topics.


Assuntos
Cromatina , Biologia Computacional/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Célula Única/métodos , Linhagem Celular , Cromatina/química , Cromatina/genética , Análise por Conglomerados , Biblioteca Gênica , Humanos , Processamento de Linguagem Natural
3.
PLoS Comput Biol ; 16(9): e1007922, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32946455

RESUMO

Prions are self-replicative protein particles lacking nucleic acids. Originally discovered for causing infectious neurodegenerative disorders, they have also been found to play several physiological roles in a variety of species. Functional and pathogenic prions share a common mechanism of replication, characterized by the ability of an amyloid conformer to propagate by inducing the conversion of its physiological, soluble counterpart. Since time-resolved biophysical experiments are currently unable to provide full reconstruction of the physico-chemical mechanisms responsible for prion replication, one must rely on computer simulations. In this work, we show that a recently developed algorithm called Self-Consistent Path Sampling (SCPS) overcomes the computational limitations of plain MD and provides a viable tool to investigate prion replication processes using state-of-the-art all-atom force fields in explicit solvent. First, we validate the reliability of SCPS simulations by characterizing the folding of a class of small proteins and comparing against the results of plain MD simulations. Next, we use SCPS to investigate the replication of the prion forming domain of HET-s, a physiological fungal prion for which high-resolution structural data are available. Our atomistic reconstruction shows remarkable similarities with a previously reported mechanism of mammalian PrPSc propagation obtained using a simpler and more approximate path sampling algorithm. Together, these results suggest that the propagation of prions generated by evolutionary distant proteins may share common features. In particular, in both these cases, prions propagate their conformation through a very similar templating mechanism.


Assuntos
Proteínas Fúngicas , Simulação de Dinâmica Molecular , Príons , Algoritmos , Biologia Computacional , Proteínas Fúngicas/química , Proteínas Fúngicas/metabolismo , Príons/química , Príons/metabolismo , Conformação Proteica , Dobramento de Proteína
4.
PLoS Comput Biol ; 16(9): e1007836, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32960900

RESUMO

Early warning signals (EWS) identify systems approaching a critical transition, where the system undergoes a sudden change in state. For example, monitoring changes in variance or autocorrelation offers a computationally inexpensive method which can be used in real-time to assess when an infectious disease transitions to elimination. EWS have a promising potential to not only be used to monitor infectious diseases, but also to inform control policies to aid disease elimination. Previously, potential EWS have been identified for prevalence data, however the prevalence of a disease is often not known directly. In this work we identify EWS for incidence data, the standard data type collected by the Centers for Disease Control and Prevention (CDC) or World Health Organization (WHO). We show, through several examples, that EWS calculated on simulated incidence time series data exhibit vastly different behaviours to those previously studied on prevalence data. In particular, the variance displays a decreasing trend on the approach to disease elimination, contrary to that expected from critical slowing down theory; this could lead to unreliable indicators of elimination when calculated on real-world data. We derive analytical predictions which can be generalised for many epidemiological systems, and we support our theory with simulated studies of disease incidence. Additionally, we explore EWS calculated on the rate of incidence over time, a property which can be extracted directly from incidence data. We find that although incidence might not exhibit typical critical slowing down properties before a critical transition, the rate of incidence does, presenting a promising new data type for the application of statistical indicators.


Assuntos
Doenças Transmissíveis/epidemiologia , Biologia Computacional/métodos , Modelos Estatísticos , Vigilância em Saúde Pública/métodos , Controle de Doenças Transmissíveis , Humanos , Incidência , Prevalência
5.
IEEE J Biomed Health Inform ; 24(10): 2806-2813, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32915751

RESUMO

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution discrepancy. We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose to use a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets made up of CT images. Extensive experiments show that our approach consistently improves the performanceson both datasets, outperforming the original COVID-Net trained on each dataset by 12.16% and 14.23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.


Assuntos
Betacoronavirus , Técnicas de Laboratório Clínico/estatística & dados numéricos , Infecções por Coronavirus/diagnóstico por imagem , Infecções por Coronavirus/diagnóstico , Aprendizado Profundo , Pandemias , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/diagnóstico , Tomografia Computadorizada por Raios X/estatística & dados numéricos , Biologia Computacional , Sistemas Computacionais , Infecções por Coronavirus/classificação , Bases de Dados Factuais/estatística & dados numéricos , Humanos , Aprendizado de Máquina , Pandemias/classificação , Pneumonia Viral/classificação , Interpretação de Imagem Radiográfica Assistida por Computador/estatística & dados numéricos
6.
Nan Fang Yi Ke Da Xue Xue Bao ; 40(5): 683-692, 2020 May 30.
Artigo em Chinês | MEDLINE | ID: mdl-32897212

RESUMO

OBJECTIVE: To investigate the expression of BUB1 gene in gastric cancer. METHODS: Oncomine, GEPIA, BioGPS and Kaplan-Meier Plotter databases were used to analyze the difference of BUB1 gene expression between gastric cancer tissue and normal gastric tissue. The association of BUB1 expression level with the prognosis of gastric cancer patients was also analyzed. The Cancer Cell Line Encyclopedia (CCLE) was explored to analyze the expression of BUB1 in T cells and B cells in gastric cancer patients, and the String database was used to generate the network map of BUB1-related proteins and functional annotation of gene ontology (GO). The related pathways of KEGG were analyzed. Tumor immune assessment resource (TIMER) database was used to analyze the expression of BUB1 in immune infiltration and its effect on prognosis of gastric cancer patients. To further verify the results of gene chip analysis in Oncomine database, we collected 30 pairs of surgical specimens of gastric adenocarcinoma and adjacent tissues from patients admitted to the First Affiliated Hospital of Chengdu Medical College from March, 2018 to July, 2019. The results of BUB1 gene expression in Oncomine database were verified by PCR and immunohistochemistry. RESULTS: Oncomine, GEPIA and BioGPS analyses showed that BUB1 was highly expressed in gastric cancer compared with normal gastric tissue. Kaplan-Meier survival analysis showed that the progression-free survival time (HR=0.52, 95% CI:0.41-0.67, P < 0.05) and the overall survival time (HR=0.67, 95% CI:0.55-0.82, P < 0.05) were prolonged in gastric cancer patients with a high expression of BUB1. Through String data collection, BUB1-related proteins were mainly enriched in 13 cellular components, 4 molecular functions and 12 biological processes, involving 4 signal pathways. TIMER database analysis showed that CD4+ T cells and macrophages with high expressions of BUB1 mRNA in the immune microenvironment were associated with a favorable 5-year survival outcome of patients with gastric cancer. In the surgical specimens, real-time quantitative PCR showed that the expression level of BUB1 mRNA was significantly higher in gastric cancer tissues than in the adjacent gastric mucosa tissues, and immunohistochemical results demonstrated positive BUB1 staining in the gastric cancer tissues. CONCLUSIONS: BUB1 gene is highly expressed in gastric cancer. BUB1 may reduce tumor immunosuppression and helps to evaluate the prognosis of patients with gastric cancer.


Assuntos
Biologia Computacional , Proteínas Serina-Treonina Quinases/genética , Neoplasias Gástricas , Humanos , Estimativa de Kaplan-Meier , Prognóstico , Neoplasias Gástricas/genética , Microambiente Tumoral
7.
J Med Internet Res ; 22(10): e22299, 2020 10 02.
Artigo em Inglês | MEDLINE | ID: mdl-32931441

RESUMO

BACKGROUND: COVID-19 became a global pandemic not long after its identification in late 2019. The genomes of SARS-CoV-2 are being rapidly sequenced and shared on public repositories. To keep up with these updates, scientists need to frequently refresh and reclean data sets, which is an ad hoc and labor-intensive process. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes. OBJECTIVE: To address these challenges, we developed CoV-Seq, an integrated web server that enables simple and rapid analysis of SARS-CoV-2 genomes. METHODS: CoV-Seq is implemented in Python and JavaScript. The web server and source code URLs are provided in this article. RESULTS: Given a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are displayed in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is available for high-throughput processing. In addition, we aggregated all publicly available SARS-CoV-2 sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID), National Center for Biotechnology Information (NCBI), European Nucleotide Archive (ENA), and China National GeneBank (CNGB), and extracted genetic variants from these sequences for download and downstream analysis. The CoV-Seq database is updated weekly. CONCLUSIONS: We have developed CoV-Seq, an integrated web service for fast and easy analysis of custom SARS-CoV-2 sequences. The web server provides an interactive module for the analysis of custom sequences and a weekly updated database of genetic variants of all publicly accessible SARS-CoV-2 sequences. We believe CoV-Seq will help improve our understanding of the genetic underpinnings of COVID-19.


Assuntos
Betacoronavirus/genética , Infecções por Coronavirus/virologia , Visualização de Dados , Bases de Dados Genéticas , Genoma Viral/genética , Pneumonia Viral/virologia , Software , Biologia Computacional , Infecções por Coronavirus/epidemiologia , Humanos , Pandemias , Pneumonia Viral/epidemiologia
8.
PLoS One ; 15(9): e0239694, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32997699

RESUMO

With the novel COVID-19 pandemic disrupting and threatening the lives of millions, researchers and clinicians have been recently conducting clinical trials at an unprecedented rate to learn more about the virus and potential drugs/treatments/vaccines to treat its infection. As a result of the influx of clinical trials, researchers, clinicians, and the lay public, now more than ever, face a significant challenge in keeping up-to-date with the rapid rate of discoveries and advances. To remedy this problem, this research mined the ClinicalTrials.gov corpus to extract COVID-19 related clinical trials, produce unique reports to summarize findings and make the meta-data available via Application Programming Interfaces (APIs). Unique reports were created for each drug/intervention, Medical Subject Heading (MeSH) term, and Human Phenotype Ontology (HPO) term. These reports, which have been run over multiple time points, along with APIs to access meta-data, are freely available at http://covidresearchtrials.com. The pipeline, reports, association of COVID-19 clinical trials with MeSH and HPO terms, insights, public repository, APIs, and correlations produced are all novel in this work. The freely available, novel resources present up-to-date relevant biological information and insights in a robust, accessible manner, illustrating their invaluable potential to aid researchers overcome COVID-19 and save hundreds of thousands of lives.


Assuntos
Ontologias Biológicas , Ensaios Clínicos como Assunto , Infecções por Coronavirus/terapia , Processamento de Linguagem Natural , Pneumonia Viral/terapia , Betacoronavirus , Biologia Computacional , Humanos , Internet , Medical Subject Headings , Pandemias , Fenótipo , Software
9.
Anticancer Res ; 40(9): 5097-5106, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32878798

RESUMO

BACKGROUND/AIM: Accumulating evidence has shown therapeutic effects of herbals on breast cancer, a commonly diagnosed malignancy in women worldwide. However, their underlying mechanisms remain unclear. We aimed to explore the mode of action of a recently developed herbal combination at system-level. MATERIALS AND METHODS: We employed network pharmacological approaches to study the mechanism of a combination of three herbals, Astragalus membranaceus, Angelica gigas and Trichosanthes kirilowii by investigating active compounds and performing functional enrichment analysis for the interacting targets. RESULTS: For in silico pharmacokinetic evaluation, ten active ingredients interacted with fifty-six breast cancer-associated therapeutic targets. Functional enrichment analysis revealed that TNF, estrogen, PI3K-Akt and MAPK signaling pathways were involved in tumorigenesis and development of breast cancer. The pharmacological mechanisms might be associated with cellular effects on proliferation, cell cycle process and apoptosis. CONCLUSION: The present study provides novel insights into the system-level pharmacological mechanisms underlying a herbal combination used for breast cancer therapies.


Assuntos
Antineoplásicos Fitogênicos/farmacologia , Medicamentos de Ervas Chinesas/farmacologia , Redes Neurais de Computação , Biologia de Sistemas/métodos , Tecnologia Farmacêutica/métodos , Antineoplásicos Fitogênicos/química , Astragalus propinquus , Neoplasias da Mama , Linhagem Celular Tumoral , Biologia Computacional/métodos , Ensaios de Seleção de Medicamentos Antitumorais , Medicamentos de Ervas Chinesas/química , Feminino , Humanos , Medicina Tradicional Chinesa , Fluxo de Trabalho
10.
Medicine (Baltimore) ; 99(35): e21902, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871922

RESUMO

The function of miR-9 in osteosarcoma is not well-investigated and controversial. Therefore, we conducted meta-analysis to explore the role of miR-9 in osteosarcoma, and collected relevant TCGA data to further testify the result. In addition, bioinformatics analysis was conducted to investigate the mechanism and related pathways of miR-9-3p in osteosarcoma.Literature search was operated on databases up to February 19, 2020, including PubMed, Web of Science, Science Direct, Cochrane Central Register of Controlled Trials, and Wiley Online Library, China National Knowledge Infrastructure, China Biology Medicine disc, Chongqing VIP, and Wan Fang Data. The relation of miR-9 expression with survival outcome was estimated by hazard ratio (HRs) and 95% CIs. Meta-analysis was conducted on the Stata 12.0 (Stata Corporation, TX). To further assess the function of miR-9 in osteosarcoma, relevant data from the TCGA database was collected. Three databases, miRDB, miRPathDB 2.0, and Targetscan 7.2, were used for prediction of target genes. Genes present in these 3 databases were considered as predicted target genes of miR-9-3p. Venny 2.1 were used for intersection analysis. Subsequently, GO, KEGG, and PPI network analysis were conducted based on the overlapping target genes of miR-9-3p to explore the possible molecular mechanism in osteosarcoma.Meta-analysis shown that overexpression of miR-9 was associated with worse overall survival (OS) (HR = 4.180, 95% CI: 2.880-6.066, P < .001, I = 23.5%). Based on TCGA data, osteosarcoma patients with overexpression of miR-9-3p (HR = 1.603, 95% CI: 1.028-2.499, P = .037) and miR-9-5p (HR = 1.698, 95% CI: 1.133-2.545, P = .01) also suffered poor OS. In bioinformatics analysis, 2 significant and important pathways were enriched: Wnt signaling pathway from gene ontology analysis (gene ontology:0016055, P-adjust = .008); hippo signaling pathway from Kyoto Encyclopedia of Genes and Genomes analysis (P-adjust = .007). Moreover, network analysis relevant protein-protein interaction was visualized, revealing 117 nodes and 161 edges.High miR-9 expression was associated with poor prognosis. Based on bioinformatics analysis, this study enhanced the understanding of the mechanism and related pathways of miR-9 in osteosarcoma.


Assuntos
MicroRNAs/genética , Osteossarcoma/genética , Biologia Computacional , Bases de Dados Genéticas , Perfilação da Expressão Gênica , Humanos , Prognóstico , Transdução de Sinais/genética
11.
F1000Res ; 92020.
Artigo em Inglês | MEDLINE | ID: mdl-32983415

RESUMO

Launched in 2000 and held every year since, the Bioinformatics Open Source Conference (BOSC) is a volunteer-run meeting coordinated by the Open Bioinformatics Foundation (OBF) that covers open source software development and open science in bioinformatics. Most years, BOSC has been part of the Intelligent Systems for Molecular Biology (ISMB) conference, but in 2018, and again in 2020, BOSC partnered with the Galaxy Community Conference (GCC). This year's combined BOSC + GCC conference was called the Bioinformatics Community Conference (BCC2020, bcc2020.github.io). Originally slated to take place in Toronto, Canada, BCC2020 was moved online due to COVID-19. The meeting started with a wide array of training sessions; continued with a main program of keynote presentations, talks, posters, Birds of a Feather, and more; and ended with four days of collaboration (CoFest). Efforts to make the meeting accessible and inclusive included very low registration fees, talks presented twice a day, and closed captioning for all videos. More than 800 people from 61 countries registered for at least one part of the meeting, which was held mostly in the Remo.co video-conferencing platform.


Assuntos
Biologia Computacional , Congressos como Assunto , Canadá , Humanos
12.
BMC Med Res Methodol ; 20(1): 235, 2020 09 21.
Artigo em Inglês | MEDLINE | ID: mdl-32958001

RESUMO

BACKGROUND: Data analysis and visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance. RESULTS: Data on COVID-19 diagnosed cases and mortality, from January 1st, 2020, onwards is collected automatically from the European Centre for Disease Prevention and Control (ECDC). We have developed a Shiny application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using ECDC data. A country-specific tool for basic epidemiological surveillance, in an interactive and user-friendly manner. The available analyses cover time trends and projections, attack rate, population fatality rate, case fatality rate, and basic reproduction number. CONCLUSIONS: The COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.


Assuntos
Betacoronavirus/isolamento & purificação , Biologia Computacional/estatística & dados numéricos , Infecções por Coronavirus/epidemiologia , Visualização de Dados , Pandemias , Pneumonia Viral/epidemiologia , Algoritmos , Betacoronavirus/fisiologia , Biologia Computacional/métodos , Infecções por Coronavirus/transmissão , Infecções por Coronavirus/virologia , Europa (Continente)/epidemiologia , Saúde Global/estatística & dados numéricos , Humanos , Incidência , Internet , Pneumonia Viral/transmissão , Pneumonia Viral/virologia , Vigilância da População/métodos
13.
BMC Bioinformatics ; 21(1): 401, 2020 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-32912137

RESUMO

BACKGROUND: As an important non-coding RNA, microRNA (miRNA) plays a significant role in a series of life processes and is closely associated with a variety of Human diseases. Hence, identification of potential miRNA-disease associations can make great contributions to the research and treatment of Human diseases. However, to our knowledge, many existing computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules. RESULTS: In this paper, we propose a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information. Firstly, a heterogeneous network is constructed by integrating known associations among miRNA, protein and disease, and the network representation method Learning Graph Representations with Global Structural Information (GraRep) is implemented to learn the behavior information of miRNAs and diseases in the network. Then, the behavior information of miRNAs and diseases is combined with the attribute information of them to represent miRNA-disease association pairs. Finally, the prediction model is established based on the Random Forest algorithm. Under the five-fold cross validation, the proposed NEMPD model obtained average 85.41% prediction accuracy with 80.96% sensitivity at the AUC of 91.58%. Furthermore, the performance of NEMPD is also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases. CONCLUSIONS: The proposed NEMPD model has a good performance in predicting the potential associations between miRNAs and diseases, and has great potency in the field of miRNA-disease association prediction in the future.


Assuntos
Neoplasias da Mama/diagnóstico , Neoplasias do Colo/diagnóstico , Biologia Computacional/métodos , Neoplasias Pulmonares/diagnóstico , MicroRNAs/metabolismo , Algoritmos , Área Sob a Curva , Neoplasias da Mama/genética , Neoplasias do Colo/genética , Feminino , Humanos , Neoplasias Pulmonares/genética , MicroRNAs/genética , Curva ROC
14.
J Clin Virol ; 131: 104594, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32866812

RESUMO

INTRODUCTION: The SARS-CoV-2 pandemic of 2020 is a prime example of the omnipresent threat of emerging viruses that can infect humans. A protocol for the identification of novel coronaviruses by viral metagenomic sequencing in diagnostic laboratories may contribute to pandemic preparedness. AIM: The aim of this study is to validate a metagenomic virus discovery protocol as a tool for coronavirus pandemic preparedness. METHODS: The performance of a viral metagenomic protocol in a clinical setting for the identification of novel coronaviruses was tested using clinical samples containing SARS-CoV-2, SARS-CoV, and MERS-CoV, in combination with databases generated to contain only viruses of before the discovery dates of these coronaviruses, to mimic virus discovery. RESULTS: Classification of NGS reads using Centrifuge and Genome Detective resulted in assignment of the reads to the closest relatives of the emerging coronaviruses. Low nucleotide and amino acid identity (81% and 84%, respectively, for SARS-CoV-2) in combination with up to 98% genome coverage were indicative for a related, novel coronavirus. Capture probes targeting vertebrate viruses, designed in 2015, enhanced both sequencing depth and coverage of the SARS-CoV-2 genome, the latter increasing from 71% to 98%. CONCLUSION: The model used for simulation of virus discovery enabled validation of the metagenomic sequencing protocol. The metagenomic protocol with virus probes designed before the pandemic, can assist the detection and identification of novel coronaviruses directly in clinical samples.


Assuntos
Infecções por Coronavirus/virologia , Genoma Viral , Sequenciamento de Nucleotídeos em Larga Escala , Metagenômica , Pneumonia Viral/virologia , Betacoronavirus/isolamento & purificação , Técnicas de Laboratório Clínico/métodos , Biologia Computacional , Infecções por Coronavirus/diagnóstico , Humanos , Coronavírus da Síndrome Respiratória do Oriente Médio/isolamento & purificação , Nasofaringe/virologia , Pandemias , Vírus da SARS/isolamento & purificação
15.
J Clin Virol ; 131: 104581, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32889496

RESUMO

INTRODUCTION: During the first month of the SARS-CoV-2 outbreak, rapid development of PCR-based diagnostic tests became a global priority so that timely diagnosis, isolation, and contact tracing could minimize the advancing pandemic surge. Designing these tests for broad, long-term detection was complicated by limited information about the novel virus' genome sequence and how it might mutate during global spread and adaptation to humans. METHODS: We assessed eight widely adopted lab developed PCR tests for SARS-CoV-2 against 15,001 SARS-CoV-2 genome sequences. Using a custom bioinformatic pipeline called PCR_strainer, we identified all mismatches and sequence variants in genome locations targeted by 15 sets of primer/probe oligonucleotides from these assays. RESULTS: For 12 out of 15 primer/probe sets, over 98 % of SARS-CoV-2 genomes had no mismatches. Two primer/probe sets contained a single mismatch in the reverse primer that was present in over 99 % of genomes. One primer/probe set targeted a location with extensive polymorphisms with 23 sequence observed variants at the forward primer location. One of these variants, which contains three nucleotide mismatches, arose in February as part of the emergence of a viral clade and was present in 18.8 % of the genomes we analyzed. DISCUSSION: Most early PCR diagnostic tests for SARS-CoV-2 remain inclusive of circulating viral diversity, but three assays with extensive mismatches highlight assay design challenges for novel pathogens and provide valuable lessons for PCR assay design during future outbreaks. Our bioinformatics pipeline is also presented as a useful general-purpose tool for assessing PCR diagnostics assays against circulating strains.


Assuntos
Betacoronavirus/genética , Técnicas de Laboratório Clínico/métodos , Infecções por Coronavirus/diagnóstico , Oligonucleotídeos/genética , Pneumonia Viral/diagnóstico , Reação em Cadeia da Polimerase em Tempo Real/métodos , Biologia Computacional , Simulação por Computador , Infecções por Coronavirus/virologia , Genoma Viral , Humanos , Pandemias , Pneumonia Viral/virologia , RNA Viral , Sensibilidade e Especificidade
16.
Medicine (Baltimore) ; 99(33): e21706, 2020 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-32872046

RESUMO

MicroRNAs (miRNAs) have been suggested to act critical roles in the pathophysiology of traumatic osteonecrosis of the femoral head (TONFH). Unfortunately, their roles in the development of TONFH are still ambiguous. The purpose of this study is to identify promising miRNA biomarkers in traumatic osteonecrosis development.We conducted a comprehensive bioinformatics analysis using microarray datasets downloaded from the Gene Expression Omnibus database, and compared the expression of miRNAs in the serum of TONFH patients with controls. Next, we performed target prediction, function enrichment analysis, and protein-protein interaction network analysis based on differentially expressed (DE) miRNAs.We identified 26 DE miRNAs that may contribute to the pathophysiology of TONFH. The miRNAs were linked to ubiquitin proteasome system including conjugating protein ligase activity, ubiquitin-protein ligase activity and ubiquitin mediated proteolysis 5 pathway, and we exposed miR-181a-5p and miR-140-5p as promising biomarkers in TONFH.A predicting model consisting of 5 miRNAs may help discriminating high-risk patients who might develop TONFH after femur neck fracture. Among DE miRNAs, MiR-181a-5p and miR-140-5p may contribute to the development femoral head osteonecrosis after femur neck fracture via ubiquitin proteasome system.


Assuntos
Fraturas do Colo Femoral/genética , Necrose da Cabeça do Fêmur/genética , MicroRNAs/análise , Ubiquitina/genética , Biomarcadores/metabolismo , Biologia Computacional , Feminino , Fraturas do Colo Femoral/cirurgia , Perfilação da Expressão Gênica , Humanos , Masculino , MicroRNAs/genética
17.
Medicine (Baltimore) ; 99(35): e21997, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871953

RESUMO

BACKGROUND: Ulcerative colitis (UC) was a type of inflammatory bowel diseases, which was difficult to cure and even would malignant turn into colon cancer. The specific etiology and molecular mechanism of UC were unclear to date. The purpose of this study was to search for new targets for the diagnosis and treatment of UC. METHODS: Firstly, we downloaded the gene expression data of UC from the gene expression omnibus database database (GSE107499), and used multiple bioinformatics methods to find differently expressed genes (DEGs) in UC. Subsequently, we evaluated the lymphocyte infiltration in UC inflamed colon tissue by using the cell type identification by estimating relative subset of known RNA transcripts method. RESULTS: We obtained 1175 DEGs and 8 hub genes (IL6, TNF, PTPRC, CXCL8, FN1, CD44, IL1B, and MMP9) in this study. Among them, 903 DEGs were up-regulated and 272 DEGs were down-regulated. Compared with non-inflamed colon tissues, the inflamed colon tissues had higher levels of memory B cells, activated memory CD4 T cells, follicular helper T cells, M1 macrophages, resting dendritic cells, activated dendritic cells, activated mast cells, and neutrophils, whereas the proportions of plasma cells, resting memory CD4 T cells, gamma delta T cells, activated NK cells, M2 macrophages and resting mast cells were relatively lower. CONCLUSIONS: The DEGs, hub genes and different lymphatic infiltration conditions can provide new targets for diagnosis and treatment of UC. However, these were just predictions through some theoretical methods, and more basic experiments will be needed to prove in the future.


Assuntos
Colite Ulcerativa/metabolismo , Linfócitos/fisiologia , Colite Ulcerativa/genética , Colite Ulcerativa/imunologia , Biologia Computacional , Humanos , Mapas de Interação de Proteínas , Transcriptoma
18.
Medicine (Baltimore) ; 99(35): e22047, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871963

RESUMO

BACKGROUND: We identified the hub genes and pathways dysregulated in acute myeloid leukemia and the potential molecular mechanisms involved. METHODS: We downloaded the GSE15061 gene expression dataset from the Gene Expression Omnibus database and used weighted gene co-expression network analysis to identify hub genes. Differential expression of the genes was evaluated using the limma package in R software. Subsequently, we built a protein-protein interaction network followed by functional enrichment analysis. Then, the prognostic significance of gene expression was explored in terms of overall survival. Finally, transcription factor-mRNA (ribonucleic acid) and microRNA-mRNA interaction analysis was also explored. RESULTS: We identified 100 differentially expressed hub genes. Functional enrichment analysis indicated that the genes were principally involved in immune system regulation, host defense, and negative regulation of apoptosis and myeloid cell differentiation. We identified 4 hub genes, the expression of which was significantly correlated with overall survival. Finally, 26 key regulators for hub genes and 38 microRNA-mRNA interactions were identified. CONCLUSION: We performed a comprehensive bioinformatics analysis of hub genes potentially involved in acute myeloid leukemia development. Further molecular biological experiments are required to confirm the roles played by these genes.


Assuntos
Leucemia Mieloide Aguda/metabolismo , Biologia Computacional , Humanos , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/mortalidade , MicroRNAs/metabolismo , Mapas de Interação de Proteínas , Análise de Sobrevida , Fatores de Transcrição/metabolismo , Transcriptoma
20.
F1000Res ; 92020.
Artigo em Inglês | MEDLINE | ID: mdl-32489650

RESUMO

GTF (Gene Transfer Format) and GFF (General Feature Format) are popular file formats used by bioinformatics programs to represent and exchange information about various genomic features, such as gene and transcript locations and structure. GffRead and GffCompare are open source programs that provide extensive and efficient solutions to manipulate files in a GTF or GFF format. While GffRead can convert, sort, filter, transform, or cluster genomic features, GffCompare can be used to compare and merge different gene annotations. Availability and implementation: GFF utilities are implemented in C++ for Linux and OS X and released as open source under an MIT license  ( https://github.com/gpertea/gffread, https://github.com/gpertea/gffcompare).


Assuntos
Biologia Computacional , Genômica , Software , Genoma , Anotação de Sequência Molecular
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