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1.
Int. microbiol ; 22(4): 437-449, dic. 2019. graf, tab
Artigo em Inglês | IBECS | ID: ibc-185062

RESUMO

Azurin, a bacteriocin produced by a human gut bacterium Pseudomonas aeruginosa, can reveal selectively cytotoxic and induce apoptosis in cancer cells. After overcoming two phase I trials, a functional region of Azurin called p28 has been approved as a drug for the treatment of brain tumor glioma by FDA. The present study aims to improve a screening procedure and assess genetic diversity of Azurin genes in P. aeruginosa and Azurin-like genes in the gut microbiome of a specific population in Vietnam and global populations. Firstly, both cultivation-dependent and cultivation-independent techniques based on genomic and metagenomic DNAs extracted from fecal samples of the healthy specific population were performed and optimized to detect Azurin genes. Secondly, the Azurin gene sequences were analyzed and compared with global populations by using bioinformatics tools. Finally, the screening procedure improved from the first step was applied for screening Azurin-like genes, followed by the protein synthesis and NCI in vitro screening for anticancer activity. As a result, this study has successfully optimized the annealing temperatures to amplify DNAs for screening Azurin genes and applying to Azurin-like genes from human gut microbiota. The novelty of this study is the first of its kind to classify Azurin genes into five different genotypes at a global scale and confirm the potential anticancer activity of three Azurin-like synthetic proteins (Cnazu1, Dlazu11, and Ruazu12). The results contribute to the procedure development applied for screening anticancer proteins from human microbiome and a comprehensive understanding of their therapeutic response at a genetic level


No disponible


Assuntos
Azurina/genética , Técnicas In Vitro/métodos , Variação Genética/efeitos dos fármacos , Microbioma Gastrointestinal/genética , Azurina/uso terapêutico , Bacteriocinas/genética , Microbioma Gastrointestinal/efeitos dos fármacos , Metagenômica , Biologia Computacional/métodos , Antineoplásicos/farmacologia
2.
Medicine (Baltimore) ; 98(52): e18493, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31876736

RESUMO

Bronchopulmonary dysplasia (BPD) is a common disease of premature infants with very low birth weight. The mechanism is inconclusive. The aim of this study is to systematically explore BPD-related genes and characterize their functions.Natural language processing analysis was used to identify BPD-related genes. Gene data were extracted from PubMed database. Gene ontology, pathway, and network analysis were carried out, and the result was integrated with corresponding database.In this study, 216 genes were identified as BPD-related genes with P < .05, and 30 pathways were identified as significant. A network of BPD-related genes was also constructed with 17 hub genes identified. In particular, phosphatidyl inositol-3-enzyme-serine/threonine kinase signaling pathway involved the largest number of genes. Insulin was found to be a promising candidate gene related with BPD, suggesting that it may serve as an effective therapeutic target.Our data may help to better understand the molecular mechanisms underlying BPD. However, the mechanisms of BPD are elusive, and further studies are needed.


Assuntos
Displasia Broncopulmonar/genética , Mineração de Dados , Algoritmos , Displasia Broncopulmonar/etiologia , Displasia Broncopulmonar/metabolismo , Biologia Computacional/métodos , Mineração de Dados/métodos , Ontologia Genética , Genes/genética , Genes/fisiologia , Predisposição Genética para Doença/genética , Humanos , Recém-Nascido , Redes e Vias Metabólicas/genética , Processamento de Linguagem Natural , Transdução de Sinais/genética
3.
Life Sci ; 237: 116914, 2019 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-31622606

RESUMO

AIMS: The aim of the presente study was to examine the effects of oral gallic acid (GA) administration on the brown adipose tissue of obese mice fed with high-fat diet. New mechanisms and interactions pathways in thermogenesis were accessed through bioinformatics analyses. MAIN METHODS: Swiss male mice were divided into four groups and fed during 60 days with: standard diet, standard diet combined with gallic acid, high-fat diet and high-fat diet combined with gallic acid. Body weight, food intake, and blood parameters (glucose tolerance test, total-cholesterol, high-density low-c, triglyceride and glucose levels) were evaluated. Brown and subcutaneous white adipose tissue histological analysis were performed. SIRT1 and PGC1-α mRNA expression in the brown adipose tissue were assessed. KEY FINDINGS: Our main findings showed that the gallic acid improved glucose tolerance and metabolic parameters. These results were accompanied by bioinformatics analyses that evidenced SIRT1 as main target in the thermogenesis process, confirmed as increased SIRT1 mRNA expression was evidenced in the brown adipose tissue. SIGNIFICANCE: Together, the data suggest that the gallic acid effect in brown adipose tissue may improve body metabolism, glucose homeostasis and increase thermogenesis.


Assuntos
Tecido Adiposo Marrom/metabolismo , Biologia Computacional/métodos , Dieta Hiperlipídica/efeitos adversos , Ácido Gálico/farmacologia , Metaboloma/efeitos dos fármacos , Obesidade/metabolismo , Sirtuína 1/metabolismo , Tecido Adiposo Marrom/efeitos dos fármacos , Tecido Adiposo Marrom/patologia , Animais , Regulação da Expressão Gênica/efeitos dos fármacos , Masculino , Camundongos , Camundongos Obesos , Obesidade/tratamento farmacológico , Obesidade/etiologia , Sirtuína 1/genética , Termogênese/efeitos dos fármacos
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.
Chemosphere ; 235: 1030-1040, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31561292

RESUMO

Organic pesticides are one of the main environmental pollutants, and how to reduce their environmental risks is an important issue. In this contribution, we disclose the molecular basis for the resistance of American sloughgrass to aryloxyphenoxypropionic acid pesticides using site-directed mutagenesis and molecular modeling and then construct an effective screening model. The results indicated that the target-site mutation (Trp-1999-Leu) in acetyl-coenzyme A carboxylase (ACCase) can affect the effectiveness of the pesticides (clodinafop, fenoxaprop, cyhalofop, and metamifop), and the plant resistance to fenoxaprop, clodinafop, cyhalofop, and metamifop was found to be 564, 19.5, 10, and 0.19 times, respectively. The established computational models (i.e. wild-type/mutant ACCase models) could be used for rational screening and evaluation of the resistance to pesticides. The resistance induced by target gene mutation can markedly reduce the bioreactivity of the ACCase-clodinafop/fenoxaprop adducts, and the magnitudes are 10 and 102, respectively. Such event will seriously aggravate environmental pollution. However, the biological issue has no distinct effect on cyhalofop (RI=10), and meanwhile it may markedly increase the bioefficacy of metamifop (RI=0.19). We could selectively adopt the two chemicals so as to decrease the residual pesticides in the environment. Significantly, research findings from the computational screening models were found to be negatively correlated with the resistance level derived from the bioassay testing, suggesting that the screening models can be used to guide the usage of pesticides. Obviously, this story may shed novel insight on the reduction of environmental risks of pesticides and other organic pollutants.


Assuntos
Acetil-CoA Carboxilase/antagonistas & inibidores , Biologia Computacional/métodos , Resistência a Herbicidas/genética , Praguicidas/toxicidade , Proteínas de Plantas/antagonistas & inibidores , Poaceae/crescimento & desenvolvimento , Acetil-CoA Carboxilase/genética , Acetil-CoA Carboxilase/metabolismo , Anilidas/toxicidade , Benzoxazóis/toxicidade , Regulação Enzimológica da Expressão Gênica/efeitos dos fármacos , Regulação da Expressão Gênica de Plantas/efeitos dos fármacos , Modelos Moleculares , Simulação de Acoplamento Molecular , Mutagênese Sítio-Dirigida , Mutação , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Poaceae/efeitos dos fármacos , Poaceae/enzimologia , Propionatos/toxicidade , Conformação Proteica , Piridinas/toxicidade , Estados Unidos
6.
Gene ; 720: 144103, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31491435

RESUMO

Clear cell renal cell carcinoma (ccRCC) is a highly invasive urological malignant tumor that results in shorter patient survival. At present, the mechanism of ccRCC metastasis is not clear. We explored the possible mechanisms of ccRCC metastasis by analyzing the transcriptome of ccRCC patients from the Cancer Genome Atlas (TCGA) database. Comparing the differences in transcriptome in patients with and without metastasis, we found 323 differential genes (|log2FoldChange| > 1 and P < 0.001). KEGG and GO enrichment analyses of differentially expressed genes (DEGs) suggest that the transfer mechanism of ccRCC may be related to complement and coagulation cascades and cholesterol metabolism. To explore the key genes affecting tumor metastasis, we analyzed the association of these genes with patient survival time and found that 16 genes were significantly associated (P < 0.05). We compared the differences in expression of these 16 genes between ccRCC patients and the normal population, and the results showed that TF and B4GALNT1 were overexpressed in patients. Co-expression gene analysis indicated that TF may participate in the metastasis of cancer through the complement system and mucopolysaccharide biosynthesis. B4GALNT1 may affect metastasis through focal adhesion, calcium signaling pathways, and Hippo signaling pathways. Our studies suggest that the complement system and the coagulation cascade, cholesterol metabolism, calcium pathway and iron transport may be associated in the mechanism of metastasis. TF and B4GALNT1 may be the key genes for metastasis, and they may be potential diagnostic markers and therapeutic targets for ccRCC.


Assuntos
Biomarcadores Tumorais/genética , Carcinoma de Células Renais/genética , Biologia Computacional/métodos , Regulação Neoplásica da Expressão Gênica , Neoplasias Renais/genética , Transcriptoma , Biomarcadores Tumorais/metabolismo , Carcinoma de Células Renais/metabolismo , Carcinoma de Células Renais/secundário , Estudos de Casos e Controles , Feminino , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Neoplasias Renais/metabolismo , Neoplasias Renais/patologia , Masculino , Prognóstico , Mapas de Interação de Proteínas , Transdução de Sinais , Taxa de Sobrevida
7.
BMC Bioinformatics ; 20(1): 456, 2019 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-31492094

RESUMO

*: Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD). *: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively. *: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.


Assuntos
Biologia Computacional/métodos , Aprendizado Profundo , Peptídeos/uso terapêutico , Bases de Dados de Ácidos Nucleicos , Descoberta de Drogas
8.
BMC Bioinformatics ; 20(1): 455, 2019 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-31492097

RESUMO

BACKGROUND: Evolutionary information contained in the amino acid sequences of proteins specifies the biological function and fold, but exactly what information contained in the protein sequence drives both of these processes? Considerable progress has been made to answer this fundamental question, but it remains challenging to explore the potential space of cooperative interactions between amino acids. Statistical analysis plays a significant role in studying such interactions and its use has expanded in recent years to studies ranging from coevolution-guided rational protein design to protein folding in silico. RESULTS: Here we describe a computational tool named Sibe for use in studies of protein sequence, folding, and design using evolutionary coupling between amino acids as a driving factor. In this study, Sibe is used to identify positionally conserved couplings between pairwise amino acids and aid rational protein design. In this process, pairwise couplings are filtered according to the relative entropy computed from the positional conservations and grouped into several 'blocks', which could contribute to driving protein folding and design. A human ß2-adrenergic receptor (ß2AR) was used to demonstrate that those 'blocks' contribute the rational design for specifying functional residues. Sibe also provides folding modules based on both the positionally conserved couplings and well-established statistical potentials for simulating protein folding in silico and predicting tertiary structure. Our results show that statistically inferences of basic evolutionary principles, such as conservations and coupled-mutations, can be used to rapidly design a diverse set of proteins and study protein folding. CONCLUSIONS: The developed software Sibe provides a computational tool for systematical analysis from protein primary to its tertiary structure using the evolutionary couplings as a driving factor. Sibe, written in C++, accounts for compatibility with the 'big data' era in biological science, and it primarily focuses on protein sequence analysis, but it is also applicable to extend to other modeling and predictions of experimental measurements.


Assuntos
Biologia Computacional/métodos , Simulação por Computador , Engenharia de Proteínas , Dobramento de Proteína , Proteínas/química , Proteínas/genética , Sequência de Aminoácidos , Entropia , Humanos , Mutação , Receptores Adrenérgicos beta 2/química , Receptores Adrenérgicos beta 2/genética , Análise de Sequência , Software
10.
BMC Bioinformatics ; 20(1): 466, 2019 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-31500560

RESUMO

BACKGROUND: Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how the organism regulates its transcriptomic profile, enabling cells to survive in hostile environments. Here, to computationally infer the gene regulatory network for Mycobacterium abscessus we propose a novel statistical computational modelling approach: BayesIan gene regulatory Networks inferreD via gene coExpression and compaRative genomics (BINDER). In tandem with derived experimental coexpression data, the property of genomic conservation is exploited to probabilistically infer a gene regulatory network in Mycobacterium abscessus.Inference on regulatory interactions is conducted by combining 'primary' and 'auxiliary' data strata. The data forming the primary and auxiliary strata are derived from RNA-seq experiments and sequence information in the primary organism Mycobacterium abscessus as well as ChIP-seq data extracted from a related proxy organism Mycobacterium tuberculosis. The primary and auxiliary data are combined in a hierarchical Bayesian framework, informing the apposite bivariate likelihood function and prior distributions respectively. The inferred relationships provide insight to regulon groupings in Mycobacterium abscessus. RESULTS: We implement BINDER on data relating to a collection of 167,280 regulator-target pairs resulting in the identification of 54 regulator-target pairs, across 5 transcription factors, for which there is strong probability of regulatory interaction. CONCLUSIONS: The inferred regulatory interactions provide insight to, and a valuable resource for further studies of, transcriptional control in Mycobacterium abscessus, and in the family of Mycobacteriaceae more generally. Further, the developed BINDER framework has broad applicability, useable in settings where computational inference of a gene regulatory network requires integration of data sources derived from both the primary organism of interest and from related proxy organisms.


Assuntos
Biologia Computacional/métodos , Redes Reguladoras de Genes , Mycobacterium abscessus/genética , Software , Área Sob a Curva , Bactérias/genética , Simulação por Computador , Regulação Bacteriana da Expressão Gênica , Curva ROC , Regulon/genética
11.
Nucleic Acids Res ; 47(18): 9480-9494, 2019 10 10.
Artigo em Inglês | MEDLINE | ID: mdl-31504786

RESUMO

Small endonucleolytic ribozymes promote the self-cleavage of their own phosphodiester backbone at a specific linkage. The structures of and the reactions catalysed by members of individual families have been studied in great detail in the past decades. In recent years, bioinformatics studies have uncovered a considerable number of new examples of known catalytic RNA motifs. Importantly, entirely novel ribozyme classes were also discovered, for most of which both structural and biochemical information became rapidly available. However, for the majority of the new ribozymes, which are found in the genomes of a variety of species, a biological function remains elusive. Here, we concentrate on the different approaches to find catalytic RNA motifs in sequence databases. We summarize the emerging principles of RNA catalysis as observed for small endonucleolytic ribozymes. Finally, we address the biological functions of those ribozymes, where relevant information is available and common themes on their cellular activities are emerging. We conclude by speculating on the possibility that the identification and characterization of proteins that we hypothesize to be endogenously associated with catalytic RNA might help in answering the ever-present question of the biological function of the growing number of genomically encoded, small endonucleolytic ribozymes.


Assuntos
Biologia Computacional/métodos , Motivos de Nucleotídeos/genética , RNA Catalítico/genética , Análise de Sequência de RNA/métodos , Catálise , Modelos Moleculares , Conformação de Ácido Nucleico , RNA Catalítico/química , RNA Catalítico/isolamento & purificação
12.
Nat Protoc ; 14(10): 3013-3031, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-31520072

RESUMO

Functionally linked genes in bacterial and archaeal genomes are often organized into operons. However, the composition and architecture of operons are highly variable and frequently differ even among closely related genomes. Therefore, to efficiently extract reliable functional predictions for uncharacterized genes from comparative analyses of the rapidly growing genomic databases, dedicated computational approaches are required. We developed a protocol to systematically and automatically identify genes that are likely to be functionally associated with a 'bait' gene or locus by using relevance metrics. Given a set of bait loci and a genomic database defined by the user, this protocol compares the genomic neighborhoods of the baits to identify genes that are likely to be functionally linked to the baits by calculating the abundance of a given gene within and outside the bait neighborhoods and the distance to the bait. We exemplify the performance of the protocol with three test cases, namely, genes linked to CRISPR-Cas systems using the 'CRISPRicity' metric, genes associated with archaeal proviruses and genes linked to Argonaute genes in halobacteria. The protocol can be run by users with basic computational skills. The computational cost depends on the sizes of the genomic dataset and the list of reference loci and can vary from one CPU-hour to hundreds of hours on a supercomputer.


Assuntos
Biologia Computacional/métodos , Genes Arqueais , Genes Bacterianos , Genômica/métodos , Sistemas CRISPR-Cas , Genoma Arqueal , Genoma Bacteriano , Anotação de Sequência Molecular/métodos , Fases de Leitura Aberta , Óperon
13.
BMC Bioinformatics ; 20(1): 471, 2019 Sep 14.
Artigo em Inglês | MEDLINE | ID: mdl-31521132

RESUMO

BACKGROUND: Protein complex identification from protein-protein interaction (PPI) networks is crucial for understanding cellular organization principles and functional mechanisms. In recent decades, numerous computational methods have been proposed to identify protein complexes. However, most of the current state-of-the-art studies still have some challenges to resolve, including their high false-positives rates, incapability of identifying overlapping complexes, lack of consideration for the inherent organization within protein complexes, and absence of some biological attachment proteins. RESULTS: In this paper, to overcome these limitations, we present a protein complex identification method based on an edge weight method and core-attachment structure (EWCA) which consists of a complex core and some sparse attachment proteins. First, we propose a new weighting method to assess the reliability of interactions. Second, we identify protein complex cores by using the structural similarity between a seed and its direct neighbors. Third, we introduce a new method to detect attachment proteins that is able to distinguish and identify peripheral proteins and overlapping proteins. Finally, we bind attachment proteins to their corresponding complex cores to form protein complexes and discard redundant protein complexes. The experimental results indicate that EWCA outperforms existing state-of-the-art methods in terms of both accuracy and p-value. Furthermore, EWCA could identify many more protein complexes with statistical significance. Additionally, EWCA could have better balance accuracy and efficiency than some state-of-the-art methods with high accuracy. CONCLUSIONS: In summary, EWCA has better performance for protein complex identification by a comprehensive comparison with twelve algorithms in terms of different evaluation metrics. The datasets and software are freely available for academic research at https://github.com/RongquanWang/EWCA .


Assuntos
Biologia Computacional/métodos , Mapas de Interação de Proteínas , Software , Algoritmos , Humanos , Conformação Proteica , Saccharomyces cerevisiae/metabolismo
15.
Nat Protoc ; 14(10): 2749-2780, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-31471598

RESUMO

Circulating cell-free DNA (cfDNA) comprises small DNA fragments derived from normal and tumor tissue that are released into the bloodstream. Recently, methylation profiling of cfDNA as a liquid biopsy tool has been gaining prominence due to the presence of tissue-specific markers in cfDNA. We have previously reported cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) as a sensitive, low-input, cost-efficient and bisulfite-free approach to profiling DNA methylomes of plasma cfDNA. cfMeDIP-seq is an extension of a previously published MeDIP-seq protocol and is adapted to allow for methylome profiling of samples with low input (ranging from 1 to 10 ng) of DNA, which is enabled by the addition of 'filler DNA' before immunoprecipitation. This protocol is not limited to plasma cfDNA; it can also be applied to other samples that are naturally sheared and at low availability (e.g., urinary cfDNA and cerebrospinal fluid cfDNA), and is potentially applicable to other applications beyond cancer detection, including prenatal diagnostics, cardiology and monitoring of immune response. The protocol presented here should enable any standard molecular laboratory to generate cfMeDIP-seq libraries from plasma cfDNA in ~3-4 d.


Assuntos
Ácidos Nucleicos Livres/análise , Ácidos Nucleicos Livres/metabolismo , Metilação de DNA , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Ácidos Nucleicos Livres/isolamento & purificação , Biologia Computacional/métodos , Biblioteca Gênica , Humanos , Imunoprecipitação , Fluxo de Trabalho
16.
Nat Methods ; 16(9): 843-852, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31471613

RESUMO

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.


Assuntos
Biologia Computacional/métodos , Doença/genética , Redes Reguladoras de Genes , Estudo de Associação Genômica Ampla , Modelos Biológicos , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Algoritmos , Perfilação da Expressão Gênica , Humanos , Fenótipo , Mapas de Interação de Proteínas
17.
Nat Methods ; 16(9): 875-878, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31471617

RESUMO

Single-cell RNA sequencing (scRNA-seq) data are noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions and divergent species, to denoise new target datasets.


Assuntos
Neoplasias da Mama/metabolismo , Biologia Computacional/métodos , Leucócitos Mononucleares/metabolismo , Análise de Sequência de RNA/normas , Análise de Célula Única/métodos , Linfócitos T/metabolismo , Transcriptoma , Animais , Teorema de Bayes , Feminino , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Humanos , Camundongos , Análise de Sequência de RNA/métodos
18.
Comput Biol Chem ; 81: 9-15, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31472418

RESUMO

Position-Specific Scoring Matrix (PSSM) is an excellent feature extraction method that was proposed early in protein classifying prediction, but within the restriction of feature shape in PSSM, researchers make a lot attempts to process it so that PSSM can be input to the traditional machine learning algorithms. These processes drop information provided by PSSM in a way thus the feature representation is limited. Moreover, the high-dimensional feature representation of PSSM makes it incompatible with other feature extraction methods. We use the PSSM as the input of Recurrent Neural Network without any post-processing, the amino acids in protein sequences are regarded as time step in RNN. This way takes full advantage of the information that PSSM provides. In this study, the PSSM is input to the model directly and the internal information of PSSM is fully utilized, we propose an end-to-end solution and achieve state-of-the-art performance. Ultimately, the exploration of how to combine PSSM with traditional feature extraction methods is carried out and achieve slightly improved performance. Our network architecture is implemented in Python and is available at https://github.com/YellowcardD/RNN-for-membrane-protein-types-prediction.


Assuntos
Proteínas de Membrana/classificação , Matrizes de Pontuação de Posição Específica , Biologia Computacional/métodos , Bases de Dados de Proteínas/estatística & dados numéricos , Proteínas de Membrana/química
19.
Gene ; 720: 144088, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31476404

RESUMO

BACKGROUND: Secretory leukocyte protease inhibitor (SPLI) was a secreted protein which belongs to a member of whey acidic protein four-disulfide core family. In breast cancer (BC) it may inhibit cell proliferation and promote cancer metastasis. In this study, a comprehensive bioinformatics analysis was performed to identify the expression and prognostic value of SLPI in breast cancer. METHODS: SLPI expression in breast cancer was analyzed in Oncomine online database, which was subsequently confirmed by quantitative PCR (qPCR) in 18 BC samples and western blotting in 26 BC samples. Breast cancer gene-expression miner v4.1 was used to access the expression level with clinicopathological parameters in breast cancer patients. The prognostic values of SLPI in breast cancer were evaluated using the PrognoScan database. RESULTS: Our results indicated that SLPI was downregulated in breast cancer than in normal tissues. SLPI expression was found to be negatively correlated with estrogen receptor (ER) and progesterone receptor (PR) status. SLPI expression level was decreased in negative basal-like status patients compared with positive basal-like status. Meanwhile, triple-negative breast cancer status positive correlated with SLPI. We confirmed a positive correlation between SLPI and interleukin 17 receptor B (IL17RB) express in breast cancer tissues via oncomine co-expression analysis. Ten proteins: Elastase, Granulin, Lipocalin, Defensin beta 103B, Defensin beta 103A, Tubulin, Heparin-binding EGF-like growth factor, Interleukin 6, Epidermal growth factor, Phospholipid scramblase 1 were determinate interactions with SLPI by STRING. CONCLUSION: SLPI could as a biomarker to predict the prognosis values of breast cancer. However, further comprehensive study and mining more evidence are needed to clarify our results.


Assuntos
Biologia Computacional/métodos , Regulação Neoplásica da Expressão Gênica , Inibidor Secretado de Peptidases Leucocitárias/genética , Neoplasias de Mama Triplo Negativas/genética , Adulto , Idoso , Feminino , Humanos , Pessoa de Meia-Idade , Prognóstico , Mapas de Interação de Proteínas , Inibidor Secretado de Peptidases Leucocitárias/metabolismo , Neoplasias de Mama Triplo Negativas/metabolismo , Neoplasias de Mama Triplo Negativas/patologia
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