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Microarrays
Special Issues
Next Generation Microarray Bioinformatics
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Next Generation Microarray Bioinformatics

A special issue of Microarrays (ISSN 2076-3905).

Deadline for manuscript submissions: closed (15 September 2016) | Viewed by 20577

Special Issue Editor

Prof. Dr. Mario Cannataro

E-Mail Website
Guest Editor
Department of Medical and Surgical Sciences, University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy
Interests: bioinformatics, parallel computing, artificial intelligence, data mining

Special Issue Information

Dear Colleagues,

DNA Microarrays are a key tool for molecular biology and biomedicine, and they are used to measure the expression levels of many genes simultaneously, or to genotype multiple regions of a genome, e.g., by detecting Single Nucleotide Polymorphisms (SNPs). Patterns found in microarray data can be used as biomarkers or prognosis markers for various diseases, and can be used to study the different responses to drugs in pharmacogenomics. The availability of high-throughput microarray platforms and their increasing use in clinical studies, is leading to an increasing production of experimental and clinical data that requires a complex analysis pipeline for data preprocessing, data transformation, statistical and data mining analysis, knowledge models building, and interpretation. Thus, the bottleneck in the microarray pipeline is moving from the wet lab (sample preparation and experiment execution) toward the "in silico" lab (storage, preprocessing, integration, and analysis of experimental data, as well as correlation and integration with publicly available databases). This Special Issue invites submissions on efficient algorithms, software tools, and comprehensive data analysis pipelines for the preprocessing and mining of microarray data; as well as submissions on applications of microarrays in biology, medicine, and clinical practice.

Prof. Dr. Mario Cannataro
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Microarrays is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • microarray data preprocessing
  • microarray statistical analysis
  • microarray data mining analysis
  • microarray databases
  • microarray standards
  • microarray comprehensive software pipelines
  • microarrays for disease diagnosis, prognosis, screening
  • microarrays for P4 (predictive, preventive, personalized, and participatory) medicine
  • microarrays for pharmacogenomics
  • ontology-based annotation of microarray data
  • integration of microarray and clinical data
  • biomarker discovery
  • gene expression
  • genotyping

Published Papers (4 papers)

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Research

928 KiB  
Article
A New Distribution Family for Microarray Data
by Diana Mabel Kelmansky and Lila Ricci
Microarrays 2017, 6(1), 5; https://doi.org/10.3390/microarrays6010005 - 10 Feb 2017
Cited by 2 | Viewed by 4636
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 [...] Read more.
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
(This article belongs to the Special Issue Next Generation Microarray Bioinformatics)
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1183 KiB  
Article
Using miRNA-Analyzer for the Analysis of miRNA Data
by Pietro Hiram Guzzi, Giuseppe Tradigo and Pierangelo Veltri
Microarrays 2016, 5(4), 29; https://doi.org/10.3390/microarrays5040029 - 15 Dec 2016
Viewed by 4197
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 [...] Read more.
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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3857 KiB  
Article
OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes
by Giuseppe Agapito, Cirino Botta, Pietro Hiram Guzzi, Mariamena Arbitrio, Maria Teresa Di Martino, Pierfrancesco Tassone, Pierosandro Tagliaferri and Mario Cannataro
Microarrays 2016, 5(4), 24; https://doi.org/10.3390/microarrays5040024 - 23 Sep 2016
Cited by 7 | Viewed by 5557
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) [...] Read more.
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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946 KiB  
Article
Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories
by Sriram Chockalingam, Maneesha Aluru and Srinivas Aluru
Microarrays 2016, 5(3), 23; https://doi.org/10.3390/microarrays5030023 - 19 Sep 2016
Cited by 10 | Viewed by 5540
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 [...] Read more.
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
(This article belongs to the Special Issue Next Generation Microarray Bioinformatics)
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