Open AccessArticle
A New Distribution Family for Microarray Data
Microarrays 2017, 6(1), 5; doi:10.3390/microarrays6010005 -
Abstract
The traditional approach with microarray data has been to apply transformations that approximately normalize them, with the drawback of losing the original scale. The alternative stand point taken here is to search for models that fit the data, characterized by the presence of
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The traditional approach with microarray data has been to apply transformations that approximately normalize them, with the drawback of losing the original scale. The alternative stand point taken here is to search for models that fit the data, characterized by the presence of negative values, preserving their scale; one advantage of this strategy is that it facilitates a direct interpretation of the results. A new family of distributions named gpower-normal indexed by p∈R is introduced and it is proven that these variables become normal or truncated normal when a suitable gpower transformation is applied. Expressions are given for moments and quantiles, in terms of the truncated normal density. This new family can be used to model asymmetric data that include non-positive values, as required for microarray analysis. Moreover, it has been proven that the gpower-normal family is a special case of pseudo-dispersion models, inheriting all the good properties of these models, such as asymptotic normality for small variances. A combined maximum likelihood method is proposed to estimate the model parameters, and it is applied to microarray and contamination data. Rcodes are available from the authors upon request. Full article
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Open AccessReview
DNA Microarray‐Based Screening and  Characterization of Traditional Chinese Medicine
Microarrays 2017, 6(1), 4; doi:10.3390/microarrays6010004 -
Abstract
The application of DNA microarray assay (DMA) has entered a new era owing to recent innovations in omics technologies. This review summarizes recent applications of DMA‐based gene expression profiling by focusing on the screening and characterizationof traditional Chinese medicine. First, herbs, mushrooms, and
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The application of DNA microarray assay (DMA) has entered a new era owing to recent innovations in omics technologies. This review summarizes recent applications of DMA‐based gene expression profiling by focusing on the screening and characterizationof traditional Chinese medicine. First, herbs, mushrooms, and dietary plants analyzed by DMA along with their effective components and their biological/physiological effects are summarized and discussed by examining their comprehensive list and a list of representative effective chemicals. Second, the mechanisms of action of traditional Chinese medicine are summarized by examining the genes and pathways responsible for the action, the cell functions involved in the action, and the activities found by DMA (silent estrogens). Third, applications of DMA for traditional Chinese medicine are discussed by examining reported examples and new protocols for its use in quality control. Further innovations in the signaling pathway based evaluation of beneficial effects and the assessment of potential risks of traditional Chinese medicine are expected, just as are observed in other closely related fields, such as the therapeutic, environmental, nutritional, and pharmacological fields. Full article
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Open AccessReview
Microarray Technology Applied to Human Allergic Disease
Microarrays 2017, 6(1), 3; doi:10.3390/microarrays6010003 -
Abstract
IgE antibodies serve as the gatekeeper for the release of mediators from sensitized (IgE positive) mast cells and basophils following a relevant allergen exposure which can lead to an immediate-type hypersensitivity (allergic) reaction. Purified recombinant and native allergens were combined in the 1990s
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IgE antibodies serve as the gatekeeper for the release of mediators from sensitized (IgE positive) mast cells and basophils following a relevant allergen exposure which can lead to an immediate-type hypersensitivity (allergic) reaction. Purified recombinant and native allergens were combined in the 1990s with state of the art chip technology to establish the first microarray-based IgE antibody assay. Triplicate spots to over 100 allergenic molecules are immobilized on an amine-activated glass slide to form a single panel multi-allergosorbent assay. Human antibodies, typically of the IgE and IgG isotypes, specific for one or many allergens bind to their respective allergen(s) on the chip. Following removal of unbound serum proteins, bound IgE antibody is detected with a fluorophore-labeled anti-human IgE reagent. The fluorescent profile from the completed slide provides a sensitization profile of an allergic patient which can identify IgE antibodies that bind to structurally similar (cross-reactive) allergen families versus molecules that are unique to a single allergen specificity. Despite its ability to rapidly analyze many IgE antibody specificities in a single simple assay format, the chip-based microarray remains less analytically sensitive and quantitative than its singleplex assay counterpart (ImmunoCAP, Immulite). Microgram per mL quantities of allergen-specific IgG antibody can also complete with nanogram per mL quantities of specific IgE for limited allergen binding sites on the chip. Microarray assays, while not used in clinical immunology laboratories for routine patient IgE antibody testing, will remain an excellent research tool for defining sensitization profiles of populations in epidemiological studies. Full article
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Open AccessEditorial
Acknowledgement to Reviewers of Microarrays in 2016
Microarrays 2017, 6(1), 2; doi:10.3390/microarrays6010002 -
Abstract The editors of Microarrays would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016.[...] Full article
Open AccessTechnical Note
Application of Lectin Array Technology for Biobetter Characterization: Its Correlation with FcγRIII Binding and ADCC
Microarrays 2017, 6(1), 1; doi:10.3390/microarrays6010001 -
Abstract
Lectin microarray technology was applied to compare the glycosylation pattern of the monoclonal antibody MB311 expressed in SP2.0 cells to an antibody-dependent cellular cytotoxic effector function (ADCC)-optimized variant (MB314). MB314 was generated by a plant expression system that uses genetically modified moss protoplasts
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Lectin microarray technology was applied to compare the glycosylation pattern of the monoclonal antibody MB311 expressed in SP2.0 cells to an antibody-dependent cellular cytotoxic effector function (ADCC)-optimized variant (MB314). MB314 was generated by a plant expression system that uses genetically modified moss protoplasts (Physcomitrella patens) to generate a de-fucosylated version of MB311. In contrast to MB311, no or very low interactions of MB314 with lectins Aspergillus oryzae l-fucose (AOL), Pisum sativum agglutinin (PSA), Lens culinaris agglutinin (LCA), and Aleuria aurantia lectin (AAL) were observed. These lectins are specific for mono-/biantennary N-glycans containing a core fucose residue. Importantly, this fucose indicative lectin-binding pattern correlated with increased MB314 binding to CD16 (FcγRIII; receptor for the constant region of an antibody)—whose affinity is mediated through core fucosylation—and stronger ADCC. In summary, these results demonstrate that lectin microarrays are useful orthogonal methods during antibody development and for characterization. Full article
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Open AccessArticle
Using miRNA-Analyzer for the Analysis of miRNA Data
Microarrays 2016, 5(4), 29; doi:10.3390/microarrays5040029 -
Abstract
MicroRNAs (miRNAs) are small biological molecules that play an important role during the mechanisms of protein formation. Recent findings have demonstrated that they act as both positive and negative regulators of protein formation. Thus, the investigation of miRNAs, i.e., the determination of their
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MicroRNAs (miRNAs) are small biological molecules that play an important role during the mechanisms of protein formation. Recent findings have demonstrated that they act as both positive and negative regulators of protein formation. Thus, the investigation of miRNAs, i.e., the determination of their level of expression, has developed a huge interest in the scientific community. One of the leading technologies for extracting miRNA data from biological samples is the miRNA Affymetrix platform. It provides the quantification of the level of expression of the miRNA in a sample, thus enabling the accumulation of data and allowing the determination of relationships among miRNA, genes, and diseases. Unfortunately, there is a lack of a comprehensive platform able to provide all the functions needed for the extraction of information from miRNA data. We here present miRNA-Analyzer, a complete software tool providing primary functionalities for miRNA data analysis. The current version of miRNA-Analyzer wraps the Affymetrix QCTool for the preprocessing of binary data files, and then provides feature selection (the filtering by species and by the associated p-value of preprocessed files). Finally, preprocessed and filtered data are analyzed by the Multiple Experiment Viewer (T-MEV) and Short Time Series Expression Miner (STEM) tools, which are also wrapped into miRNA-Analyzer, thus providing a unique environment for miRNA data analysis. The tool offers a plug-in interface so it is easily extensible by adding other algorithms as plug-ins. Users may download the tool freely for academic use at https://sites.google.com/site/mirnaanalyserproject/d. Full article
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Open AccessArticle
Droplet Microarray Based on Superhydrophobic-Superhydrophilic Patterns for Single Cell Analysis
Microarrays 2016, 5(4), 28; doi:10.3390/microarrays5040028 -
Abstract
Single-cell analysis provides fundamental information on individual cell response to different environmental cues and is a growing interest in cancer and stem cell research. However, current existing methods are still facing challenges in performing such analysis in a high-throughput manner whilst being cost-effective.
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Single-cell analysis provides fundamental information on individual cell response to different environmental cues and is a growing interest in cancer and stem cell research. However, current existing methods are still facing challenges in performing such analysis in a high-throughput manner whilst being cost-effective. Here we established the Droplet Microarray (DMA) as a miniaturized screening platform for high-throughput single-cell analysis. Using the method of limited dilution and varying cell density and seeding time, we optimized the distribution of single cells on the DMA. We established culturing conditions for single cells in individual droplets on DMA obtaining the survival of nearly 100% of single cells and doubling time of single cells comparable with that of cells cultured in bulk cell population using conventional methods. Our results demonstrate that the DMA is a suitable platform for single-cell analysis, which carries a number of advantages compared with existing technologies allowing for treatment, staining and spot-to-spot analysis of single cells over time using conventional analysis methods such as microscopy. Full article
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Open AccessEditorial
SNP Arrays
Microarrays 2016, 5(4), 27; doi:10.3390/microarrays5040027 -
Abstract
The papers published in this Special Issue “SNP arrays” (Single Nucleotide Polymorphism Arrays) focus on several perspectives associated with arrays of this type. The range of papers vary from a case report to reviews, thereby targeting wider audiences working in this field. The
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The papers published in this Special Issue “SNP arrays” (Single Nucleotide Polymorphism Arrays) focus on several perspectives associated with arrays of this type. The range of papers vary from a case report to reviews, thereby targeting wider audiences working in this field. The research focus of SNP arrays is often human cancers but this Issue expands that focus to include areas such as rare conditions, animal breeding and bioinformatics tools. Given the limited scope, the spectrum of papers is nothing short of remarkable and even from a technical point of view these papers will contribute to the field at a general level. Three of the papers published in this Special Issue focus on the use of various SNP array approaches in the analysis of three different cancer types. Two of the papers concentrate on two very different rare conditions, applying the SNP arrays slightly differently. Finally, two other papers evaluate the use of the SNP arrays in the context of genetic analysis of livestock. The findings reported in these papers help to close gaps in the current literature and also to give guidelines for future applications of SNP arrays. Full article
Open AccessEditorial
Computational Modeling and Analysis of Microarray Data: New Horizons
Microarrays 2016, 5(4), 26; doi:10.3390/microarrays5040026 -
Abstract
High-throughput microarray technologies have long been a source of data for a wide range of biomedical investigations. Over the decades, variants have been developed and sophistication of measurements has improved, with generated data providing both valuable insight and considerable analytical challenge. The cost-effectiveness
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High-throughput microarray technologies have long been a source of data for a wide range of biomedical investigations. Over the decades, variants have been developed and sophistication of measurements has improved, with generated data providing both valuable insight and considerable analytical challenge. The cost-effectiveness of microarrays, as well as their fundamental applicability, made them a first choice for much early genomic research and efforts to improve accessibility, quality and interpretation have continued unabated. In recent years, however, the emergence of new generations of sequencing methods and, importantly, reduction of costs, has seen a preferred shift in much genomic research to the use of sequence data, both less ‘noisy’ and, arguably, with species information more directly targeted and easily interpreted. Nevertheless, new microarray data are still being generated and, together with their considerable legacy, can offer a complementary perspective on biological systems and disease pathogenesis. The challenge now is to exploit novel methods for enhancing and combining these data with those generated by alternative high-throughput techniques, such as sequencing, to provide added value. Augmentation and integration of microarray data and the new horizons this opens up, provide the theme for the papers in this Special Issue. Full article
Open AccessArticle
Automated and Multiplexed Soft Lithography for the Production of Low-Density DNA Microarrays
Microarrays 2016, 5(4), 25; doi:10.3390/microarrays5040025 -
Abstract
Microarrays are established research tools for genotyping, expression profiling, or molecular diagnostics in which DNA molecules are precisely addressed to the surface of a solid support. This study assesses the fabrication of low-density oligonucleotide arrays using an automated microcontact printing device, the InnoStamp
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Microarrays are established research tools for genotyping, expression profiling, or molecular diagnostics in which DNA molecules are precisely addressed to the surface of a solid support. This study assesses the fabrication of low-density oligonucleotide arrays using an automated microcontact printing device, the InnoStamp 40®. This automate allows a multiplexed deposition of oligoprobes on a functionalized surface by the use of a MacroStampTM bearing 64 individual pillars each mounted with 50 circular micropatterns (spots) of 160 µm diameter at 320 µm pitch. Reliability and reuse of the MacroStampTM were shown to be fast and robust by a simple washing step in 96% ethanol. The low-density microarrays printed on either epoxysilane or dendrimer-functionalized slides (DendriSlides) showed excellent hybridization response with complementary sequences at unusual low probe and target concentrations, since the actual probe density immobilized by this technology was at least 10-fold lower than with the conventional mechanical spotting. In addition, we found a comparable hybridization response in terms of fluorescence intensity between spotted and printed oligoarrays with a 1 nM complementary target by using a 50-fold lower probe concentration to produce the oligoarrays by the microcontact printing method. Taken together, our results lend support to the potential development of this multiplexed microcontact printing technology employing soft lithography as an alternative, cost-competitive tool for fabrication of low-density DNA microarrays. Full article
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Open AccessArticle
OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes
Microarrays 2016, 5(4), 24; doi:10.3390/microarrays5040024 -
Abstract
Background: The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion)
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Background: The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. Methods: In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. Results: The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. Conclusions: Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients. Full article
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Open AccessFeature PaperArticle
Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories
Microarrays 2016, 5(3), 23; doi:10.3390/microarrays5030023 -
Abstract
Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable
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Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp. Full article
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Open AccessArticle
Serology in the Digital Age: Using Long Synthetic Peptides Created from Nucleic Acid Sequences as Antigens in Microarrays
Microarrays 2016, 5(3), 22; doi:10.3390/microarrays5030022 -
Abstract
Background: Antibodies to microbes, or to autoantigens, are important markers of disease. Antibody detection (serology) can reveal both past and recent infections. There is a great need for development of rational ways of detecting and quantifying antibodies, both for humans and animals. Traditionally,
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Background: Antibodies to microbes, or to autoantigens, are important markers of disease. Antibody detection (serology) can reveal both past and recent infections. There is a great need for development of rational ways of detecting and quantifying antibodies, both for humans and animals. Traditionally, serology using synthetic antigens covers linear epitopes using up to 30 amino acid peptides. Methods: We here report that peptides of 100 amino acids or longer (“megapeptides”), designed and synthesized for optimal serological performance, can successfully be used as detection antigens in a suspension multiplex immunoassay (SMIA). Megapeptides can quickly be created just from pathogen sequences. A combination of rational sequencing and bioinformatic routines for definition of diagnostically-relevant antigens can, thus, rapidly yield efficient serological diagnostic tools for an emerging infectious pathogen. Results: We designed megapeptides using bioinformatics and viral genome sequences. These long peptides were tested as antigens for the presence of antibodies in human serum to the filo-, herpes-, and polyoma virus families in a multiplex microarray system. All of these virus families contain recently discovered or emerging infectious viruses. Conclusion: Long synthetic peptides can be useful as serological diagnostic antigens, serving as biomarkers, in suspension microarrays. Full article
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Open AccessArticle
A Combinatorial Protein Microarray for Probing Materials Interaction with Pancreatic Islet Cell Populations
Microarrays 2016, 5(3), 21; doi:10.3390/microarrays5030021 -
Abstract
Pancreatic islet transplantation has become a recognized therapy for insulin-dependent diabetes mellitus. During isolation from pancreatic tissue, the islet microenvironment is disrupted. The extracellular matrix (ECM) within this space not only provides structural support, but also actively signals to regulate islet survival and
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Pancreatic islet transplantation has become a recognized therapy for insulin-dependent diabetes mellitus. During isolation from pancreatic tissue, the islet microenvironment is disrupted. The extracellular matrix (ECM) within this space not only provides structural support, but also actively signals to regulate islet survival and function. In addition, the ECM is responsible for growth factor presentation and sequestration. By designing biomaterials that recapture elements of the native islet environment, losses in islet function and number can potentially be reduced. Cell microarrays are a high throughput screening tool able to recreate a multitude of cellular niches on a single chip. Here, we present a screening methodology for identifying components that might promote islet survival. Automated fluorescence microscopy is used to rapidly identify islet derived cell interaction with ECM proteins and immobilized growth factors printed on arrays. MIN6 mouse insulinoma cells, mouse islets and, finally, human islets are progressively screened. We demonstrate the capability of the platform to identify ECM and growth factor protein candidates that support islet viability and function and reveal synergies in cell response. Full article
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Open AccessReview
The Potentials and Pitfalls of Microarrays in Neglected Tropical Diseases: A Focus on Human Filarial Infections
Microarrays 2016, 5(3), 20; doi:10.3390/microarrays5030020 -
Abstract
Data obtained from expression microarrays enables deeper understanding of the molecular signatures of infectious diseases. It provides rapid and accurate information on how infections affect the clustering of gene expression profiles, pathways and networks that are transcriptionally active during various infection states compared
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Data obtained from expression microarrays enables deeper understanding of the molecular signatures of infectious diseases. It provides rapid and accurate information on how infections affect the clustering of gene expression profiles, pathways and networks that are transcriptionally active during various infection states compared to conventional diagnostic methods, which primarily focus on single genes or proteins. Thus, microarray technologies offer advantages in understanding host-parasite interactions associated with filarial infections. More importantly, the use of these technologies can aid diagnostics and helps translate current genomic research into effective treatment and interventions for filarial infections. Studying immune responses via microarray following infection can yield insight into genetic pathways and networks that can have a profound influence on the development of anti-parasitic vaccines. Full article
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Open AccessArticle
Protein Profiling Gastric Cancer and Neighboring Control Tissues Using High-Content Antibody Microarrays
Microarrays 2016, 5(3), 19; doi:10.3390/microarrays5030019 -
Abstract
In this study, protein profiling was performed on gastric cancer tissue samples in order to identify proteins that could be utilized for an effective diagnosis of this highly heterogeneous disease and as targets for therapeutic approaches. To this end, 16 pairs of postoperative
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In this study, protein profiling was performed on gastric cancer tissue samples in order to identify proteins that could be utilized for an effective diagnosis of this highly heterogeneous disease and as targets for therapeutic approaches. To this end, 16 pairs of postoperative gastric adenocarcinomas and adjacent non-cancerous control tissues were analyzed on microarrays that contain 813 antibodies targeting 724 proteins. Only 17 proteins were found to be differentially regulated, with much fewer molecules than the numbers usually identified in studies comparing tumor to healthy control tissues. Insulin-like growth factor-binding protein 7 (IGFBP7), S100 calcium binding protein A9 (S100A9), interleukin-10 (IL‐10) and mucin 6 (MUC6) exhibited the most profound variations. For an evaluation of the proteins’ capacity for discriminating gastric cancer, a Receiver Operating Characteristic curve analysis was performed, yielding an accuracy (area under the curve) value of 89.2% for distinguishing tumor from non-tumorous tissue. For confirmation, immunohistological analyses were done on tissue slices prepared from another cohort of patients with gastric cancer. The utility of the 17 marker proteins, and particularly the four molecules with the highest specificity for gastric adenocarcinoma, is discussed for them to act as candidates for diagnosis, even in serum, and targets for therapeutic approaches. Full article
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Open AccessArticle
MicroRNA Profile of Lung Tumor Tissues Is Associated with a High Risk Plasma miRNA Signature
Microarrays 2016, 5(3), 18; doi:10.3390/microarrays5030018 -
Abstract
Lung cancer is the most common cause of cancer deaths worldwide. MicroRNAs (miRNAs) are short, non-coding RNAs that regulate gene expression. Many studies have reported that alterations in miRNA expression are involved in several human tumors. We have previously identified a circulating miRNA
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Lung cancer is the most common cause of cancer deaths worldwide. MicroRNAs (miRNAs) are short, non-coding RNAs that regulate gene expression. Many studies have reported that alterations in miRNA expression are involved in several human tumors. We have previously identified a circulating miRNA signature classifier (MSC) able to discriminate lung cancer with more aggressive features. In the present work, microarray miRNA profiling of tumor tissues collected from 19 lung cancer patients with an available MSC result were perform in order to find a possible association between miRNA expression and the MSC risk level. Eleven tissue mature miRNAs and six miRNA precursors were observed to be associated with the plasma MSC risk level of patients. Not one of these miRNAs was included in the MSC algorithm. A pathway enrichment analysis revealed a role of these miRNA in the main pathways determining lung cancer aggressiveness. Overall, these findings add to the knowledge that tissue and plasma miRNAs behave as excellent diagnostic and prognostic biomarkers, which may find rapid application in clinical settings. Full article
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Open AccessArticle
SNPConvert: SNP Array Standardization and Integration in Livestock Species
Microarrays 2016, 5(2), 17; doi:10.3390/microarrays5020017 -
Abstract
One of the main advantages of single nucleotide polymorphism (SNP) array technology is providing genotype calls for a specific number of SNP markers at a relatively low cost. Since its first application in animal genetics, the number of available SNP arrays for each
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One of the main advantages of single nucleotide polymorphism (SNP) array technology is providing genotype calls for a specific number of SNP markers at a relatively low cost. Since its first application in animal genetics, the number of available SNP arrays for each species has been constantly increasing. However, conversely to that observed in whole genome sequence data analysis, SNP array data does not have a common set of file formats or coding conventions for allele calling. Therefore, the standardization and integration of SNP array data from multiple sources have become an obstacle, especially for users with basic or no programming skills. Here, we describe the difficulties related to handling SNP array data, focusing on file formats, SNP allele coding, and mapping. We also present SNPConvert suite, a multi-platform, open-source, and user-friendly set of tools to overcome these issues. This tool, which can be integrated with open-source and open-access tools already available, is a first step towards an integrated system to standardize and integrate any type of raw SNP array data. The tool is available at: https://github. com/nicolazzie/SNPConvert.git. Full article
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Open AccessFeature PaperArticle
Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge
Microarrays 2016, 5(2), 15; doi:10.3390/microarrays5020015 -
Abstract
Biological interpretability is a key requirement for the output of microarray data analysis pipelines. The most used pipeline first identifies a gene signature from the acquired measurements and then uses gene enrichment analysis as a tool for functionally characterizing the obtained results. Recently
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Biological interpretability is a key requirement for the output of microarray data analysis pipelines. The most used pipeline first identifies a gene signature from the acquired measurements and then uses gene enrichment analysis as a tool for functionally characterizing the obtained results. Recently Knowledge Driven Variable Selection (KDVS), an alternative approach which performs both steps at the same time, has been proposed. In this paper, we assess the effectiveness of KDVS against standard approaches on a Parkinson’s Disease (PD) dataset. The presented quantitative analysis is made possible by the construction of a reference list of genes and gene groups associated to PD. Our work shows that KDVS is much more effective than the standard approach in enhancing the interpretability of the obtained results. Full article
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Open AccessFeature PaperArticle
Evaluation of Solid Supports for Slide- and Well-Based Recombinant Antibody Microarrays
Microarrays 2016, 5(2), 16; doi:10.3390/microarrays5020016 -
Abstract
Antibody microarrays have emerged as an important tool within proteomics, enabling multiplexed protein expression profiling in both health and disease. The design and performance of antibody microarrays and how they are processed are dependent on several factors, of which the interplay between the
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Antibody microarrays have emerged as an important tool within proteomics, enabling multiplexed protein expression profiling in both health and disease. The design and performance of antibody microarrays and how they are processed are dependent on several factors, of which the interplay between the antibodies and the solid surfaces plays a central role. In this study, we have taken on the first comprehensive view and evaluated the overall impact of solid surfaces on the recombinant antibody microarray design. The results clearly demonstrated the importance of the surface-antibody interaction and showed the effect of the solid supports on the printing process, the array format of planar arrays (slide- and well-based), the assay performance (spot features, reproducibility, specificity and sensitivity) and assay processing (degree of automation). In the end, two high-end recombinant antibody microarray technology platforms were designed, based on slide-based (black polymer) and well-based (clear polymer) arrays, paving the way for future large-scale protein expression profiling efforts. Full article
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