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Microarrays, Volume 5, Issue 4 (December 2016)

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Editorial

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Open AccessEditorial Computational Modeling and Analysis of Microarray Data: New Horizons
Microarrays 2016, 5(4), 26; doi:10.3390/microarrays5040026
Received: 11 October 2016 / Revised: 13 October 2016 / Accepted: 13 October 2016 / Published: 21 October 2016
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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
(This article belongs to the Special Issue Computational Modeling and Analysis of Microarray Data: New Horizons)
Open AccessEditorial SNP Arrays
Microarrays 2016, 5(4), 27; doi:10.3390/microarrays5040027
Received: 5 October 2016 / Accepted: 11 October 2016 / Published: 25 October 2016
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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
(This article belongs to the Special Issue SNP Array)

Research

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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
Received: 13 July 2016 / Revised: 27 August 2016 / Accepted: 19 September 2016 / Published: 23 September 2016
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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
(This article belongs to the Special Issue Next Generation Microarray Bioinformatics)
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Open AccessArticle Automated and Multiplexed Soft Lithography for the Production of Low-Density DNA Microarrays
Microarrays 2016, 5(4), 25; doi:10.3390/microarrays5040025
Received: 28 July 2016 / Revised: 15 September 2016 / Accepted: 20 September 2016 / Published: 26 September 2016
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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 Droplet Microarray Based on Superhydrophobic-Superhydrophilic Patterns for Single Cell Analysis
Microarrays 2016, 5(4), 28; doi:10.3390/microarrays5040028
Received: 12 October 2016 / Accepted: 18 November 2016 / Published: 9 December 2016
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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
(This article belongs to the Special Issue Cell-Based Microarrays)
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Open AccessArticle Using miRNA-Analyzer for the Analysis of miRNA Data
Microarrays 2016, 5(4), 29; doi:10.3390/microarrays5040029
Received: 28 June 2016 / Revised: 8 December 2016 / Accepted: 9 December 2016 / Published: 15 December 2016
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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
(This article belongs to the Special Issue Next Generation Microarray Bioinformatics)
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