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Microarrays, Volume 3, Issue 1 (March 2014), Pages 1-90

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Editorial

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Open AccessEditorial Acknowledgement to Reviewers of Microarrays in 2013
Microarrays 2014, 3(1), 89-90; doi:10.3390/microarrays3010089
Received: 28 February 2014 / Accepted: 28 February 2014 / Published: 28 February 2014
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Abstract The editors of Microarrays would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2013 [...] Full article

Research

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Open AccessArticle Pigeons: A Novel GUI Software for Analysing and Parsing High Density Heterologous Oligonucleotide Microarray Probe Level Data
Microarrays 2014, 3(1), 1-23; doi:10.3390/microarrays3010001
Received: 11 November 2013 / Revised: 17 December 2013 / Accepted: 19 December 2013 / Published: 3 January 2014
Cited by 3 | PDF Full-text (867 KB) | HTML Full-text | XML Full-text
Abstract
Genomic DNA-based probe selection by using high density oligonucleotide arrays has recently been applied to heterologous species (Xspecies). With the advent of this new approach, researchers are able to study the genome and transcriptome of a non-model or an underutilised crop species through
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Genomic DNA-based probe selection by using high density oligonucleotide arrays has recently been applied to heterologous species (Xspecies). With the advent of this new approach, researchers are able to study the genome and transcriptome of a non-model or an underutilised crop species through current state-of-the-art microarray platforms. However, a software package with a graphical user interface (GUI) to analyse and parse the oligonucleotide probe pair level data is still lacking when an experiment is designed on the basis of this cross species approach. A novel computer program called Pigeons has been developed for customised array data analysis to allow the user to import and analyse Affymetrix GeneChip® probe level data through XSpecies. One can determine empirical boundaries for removing poor probes based on genomic hybridisation of the test species to the Xspecies array, followed by making a species-specific Chip Description File (CDF) file for transcriptomics in the heterologous species, or Pigeons can be used to examine an experimental design to identify potential Single-Feature Polymorphisms (SFPs) at the DNA or RNA level. Pigeons is also focused around visualization and interactive analysis of the datasets. The software with its manual (the current release number version 1.2.1) is freely available at the website of the Nottingham Arabidopsis Stock Centre (NASC). Full article
Open AccessArticle Comparative Analysis of Biologically Relevant Response Curves in Gene Expression Experiments: Heteromorphy, Heterochrony, and Heterometry
Microarrays 2014, 3(1), 39-51; doi:10.3390/microarrays3010039
Received: 20 December 2013 / Revised: 7 February 2014 / Accepted: 11 February 2014 / Published: 14 February 2014
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Abstract
To gain biological insights, investigators sometimes compare sequences of gene expression measurements under two scenarios (such as two drugs or species). For this situation, we developed an algorithm to fit, identify, and compare biologically relevant response curves in terms of heteromorphy (different curves),
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To gain biological insights, investigators sometimes compare sequences of gene expression measurements under two scenarios (such as two drugs or species). For this situation, we developed an algorithm to fit, identify, and compare biologically relevant response curves in terms of heteromorphy (different curves), heterochrony (different transition times), and heterometry (different magnitudes). The curves are flat, linear, sigmoid, hockey-stick (sigmoid missing a steady state), transient (sigmoid missing two steady states), impulse (with peak or trough), step (with intermediate-level plateau), impulse+ (impulse with an extra parameter), step+ (step with an extra parameter), further characterized by upward or downward trend. To reduce overfitting, we fit the curves to every other response, evaluated the fit in the remaining responses, and identified the most parsimonious curves that yielded a good fit. We measured goodness of fit using a statistic comparable over different genes, namely the square root of the mean squared prediction error as a percentage of the range of responses, which we call the relative prediction error (RPE). We illustrated the algorithm using data on gene expression at 14 times in the embryonic development in two species of frogs. Software written in Mathematica is freely available. Full article
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Open AccessArticle Identifying Potential Regions of Copy Number Variation for Bipolar Disorder
Microarrays 2014, 3(1), 52-71; doi:10.3390/microarrays3010052
Received: 1 December 2013 / Revised: 10 February 2014 / Accepted: 12 February 2014 / Published: 28 February 2014
Cited by 1 | PDF Full-text (495 KB) | HTML Full-text | XML Full-text
Abstract
Bipolar disorder is a complex psychiatric disorder with high heritability, but its genetic determinants are still largely unknown. Copy number variation (CNV) is one of the sources to explain part of the heritability. However, it is a challenge to estimate discrete values of
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Bipolar disorder is a complex psychiatric disorder with high heritability, but its genetic determinants are still largely unknown. Copy number variation (CNV) is one of the sources to explain part of the heritability. However, it is a challenge to estimate discrete values of the copy numbers using continuous signals calling from a set of markers, and to simultaneously perform association testing between CNVs and phenotypic outcomes. The goal of the present study is to perform a series of data filtering and analysis procedures using a DNA pooling strategy to identify potential CNV regions that are related to bipolar disorder. A total of 200 normal controls and 200 clinically diagnosed bipolar patients were recruited in this study, and were randomly divided into eight control and eight case pools. Genome-wide genotyping was employed using Illumina Human Omni1-Quad array with approximately one million markers for CNV calling. We aimed at setting a series of criteria to filter out the signal noise of marker data and to reduce the chance of false-positive findings for CNV regions. We first defined CNV regions for each pool. Potential CNV regions were reported based on the different patterns of CNV status between cases and controls. Genes that were mapped into the potential CNV regions were examined with association testing, Gene Ontology enrichment analysis, and checked with existing literature for their associations with bipolar disorder. We reported several CNV regions that are related to bipolar disorder. Two CNV regions on chromosome 11 and 22 showed significant signal differences between cases and controls (p < 0.05). Another five CNV regions on chromosome 6, 9, and 19 were overlapped with results in previous CNV studies. Experimental validation of two CNV regions lent some support to our reported findings. Further experimental and replication studies could be designed for these selected regions. Full article
Open AccessArticle Can Archival Tissue Reveal Answers to Modern Research Questions?: Computer-Aided Histological Assessment of Neuroblastoma Tumours Collected over 60 Years
Microarrays 2014, 3(1), 72-88; doi:10.3390/microarrays3010072
Received: 20 January 2014 / Revised: 13 February 2014 / Accepted: 24 February 2014 / Published: 28 February 2014
Cited by 1 | PDF Full-text (3289 KB) | HTML Full-text | XML Full-text
Abstract
Despite neuroblastoma being the most common extracranial solid cancer in childhood, it is still a rare disease. Consequently, the unavailability of tissue for research limits the statistical power of studies. Pathology archives are possible sources of rare tissue, which, if proven to remain
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Despite neuroblastoma being the most common extracranial solid cancer in childhood, it is still a rare disease. Consequently, the unavailability of tissue for research limits the statistical power of studies. Pathology archives are possible sources of rare tissue, which, if proven to remain consistent over time, could prove useful to research of rare disease types. We applied immunohistochemistry to investigate whether long term storage caused any changes to antigens used diagnostically for neuroblastoma. We constructed and quantitatively assessed a tissue microarray containing neuroblastoma archival material dating between 1950 and 2007. A total of 119 neuroblastoma tissue cores were included spanning 6 decades. Fourteen antibodies were screened across the tissue microarray (TMA). These included seven positive neuroblastoma diagnosis markers (NB84, Chromogranin A, NSE, Ki-67, INI1, Neurofilament Protein, Synaptophysin), two anticipated to be negative (S100A, CD99), and five research antibodies (IL-7, IL-7R, JAK1, JAK3, STAT5). The staining of these antibodies was evaluated using Aperio ImageScope software along with novel pattern recognition and quantification algorithms. This analysis demonstrated that marker signal intensity did not decrease over time and that storage for 60 years had little effect on antigenicity. The construction and assessment of this neuroblastoma TMA has demonstrated the feasibility of using archival samples for research. Full article
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Review

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Open AccessReview Copy Number Variation in Chickens: A Review and Future Prospects
Microarrays 2014, 3(1), 24-38; doi:10.3390/microarrays3010024
Received: 15 December 2013 / Revised: 22 January 2014 / Accepted: 23 January 2014 / Published: 5 February 2014
Cited by 3 | PDF Full-text (158 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
DNA sequence variations include nucleotide substitution, deletion, insertion, translocation and inversion. Deletion or insertion of a large DNA segment in the genome, referred to as copy number variation (CNV), has caught the attention of many researchers recently. It is believed that CNVs contribute
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DNA sequence variations include nucleotide substitution, deletion, insertion, translocation and inversion. Deletion or insertion of a large DNA segment in the genome, referred to as copy number variation (CNV), has caught the attention of many researchers recently. It is believed that CNVs contribute significantly to genome variability, and thus contribute to phenotypic variability. In chickens, genome-wide surveys with array comparative genome hybridization (aCGH), SNP chip detection or whole genome sequencing have revealed a large number of CNVs. A large portion of chicken CNVs involves protein coding or regulatory sequences. A few CNVs have been demonstrated to be the determinant factors for single gene traits, such as late-feathering, pea-comb and dermal hyperpigmentation. The phenotypic effects of the majority of chicken CNVs are to be delineated. Full article

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