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Cold Spring Harbor Protocols Mar 2017Because there is no widely used software for analyzing RNA-seq data that has a graphical user interface, this protocol provides an example of analyzing microarray data...
Because there is no widely used software for analyzing RNA-seq data that has a graphical user interface, this protocol provides an example of analyzing microarray data using Babelomics. This analysis entails performing quantile normalization and then detecting differentially expressed genes associated with the transgenesis of a human oncogene c- in mice. Finally, hierarchical clustering is performed on the differentially expressed genes using the Cluster program, and the results are visualized using TreeView.
Topics: Animals; Gene Expression Profiling; Humans; Mice; Microarray Analysis; Sequence Analysis, RNA
PubMed: 27574201
DOI: 10.1101/pdb.prot093112 -
MCN. the American Journal of Maternal... 2016Over the last half century, knowledge about genetics, genetic testing, and its complexity has flourished. Completion of the Human Genome Project provided a foundation...
Over the last half century, knowledge about genetics, genetic testing, and its complexity has flourished. Completion of the Human Genome Project provided a foundation upon which the accuracy of genetics, genomics, and integration of bioinformatics knowledge and testing has grown exponentially. What is lagging, however, are efforts to reach and engage nurses about this rapidly changing field. The purpose of this article is to familiarize nurses with several frequently ordered genetic tests including chromosomes and fluorescence in situ hybridization followed by a comprehensive review of chromosome microarray. It shares the complexity of microarray including how testing is performed and results analyzed. A case report demonstrates how this technology is applied in clinical practice and reveals benefits and limitations of this scientific and bioinformatics genetic technology. Clinical implications for maternal-child nurses across practice levels are discussed.
Topics: Congenital Abnormalities; Genetic Counseling; Genetic Testing; Human Genome Project; Humans; Microarray Analysis; Molecular Diagnostic Techniques
PubMed: 27276104
DOI: 10.1097/NMC.0000000000000260 -
Journal of Clinical Laboratory Analysis Dec 2020To compare karyotype and chromosomal microarray (CMA) analysis of aneuploid chromosome mosaicism in amniocentesis samples.
OBJECTIVE
To compare karyotype and chromosomal microarray (CMA) analysis of aneuploid chromosome mosaicism in amniocentesis samples.
MATERIALS AND METHODS
A total of 2091 amniocentesis samples from pregnant women were collected from March 1, 2019, to January 31, 2020. Karyotype analysis was performed using G-banding and CMA analysis used the Affymetrix CytoScan 750K SNP microarray.
RESULT
Thirteen cases with aneuploid chromosome mosaicism were detected and compared between the karyotype and CMA methods. Seven of these cases were trisomic mosaicism, and the levels of mosaicism calculated from CMA were higher than those detected from karyotype analysis; noting three cases of trisomy mosaicism were not detected by karyotype analysis. Four cases exhibited monomeric mosaicism, and the levels of mosaicism detected in three of these cases were higher in karyotype compared with CMA analysis; one case had equivalent levels of monomeric mosaicism from both karyotype and CMA analysis. Two other cases from karyotype analysis were a mix of monosomic and trisomic mosaicism, whereas the CMA result was restricted to monosomic mosaicism for these cases.
CONCLUSION
Both karyotype and CMA analysis can be used to detect aneuploid chromosome mosaicism. However, the two methods produced different results. CMA and karyotype analysis have their own advantages in detecting aneuploid mosaicism, and the combination of these methods provides a more rigorous diagnosis.
Topics: Aneuploidy; Chromosome Disorders; Cytogenetic Analysis; Female; Humans; Karyotyping; Microarray Analysis; Mosaicism; Pregnancy; Prenatal Diagnosis
PubMed: 32864771
DOI: 10.1002/jcla.23514 -
Genetics in Medicine : Official Journal... Sep 2007
Topics: Chromosome Aberrations; Clinical Competence; DNA; Gene Dosage; Genetic Variation; Humans; Microarray Analysis; Polymorphism, Single Nucleotide; Reproducibility of Results; Specimen Handling
PubMed: 17873655
DOI: 10.1097/gim.0b013e31814ce3d9 -
Analytical and Bioanalytical Chemistry Nov 2010Determination of the sequence of the human genome and knowledge of the genetic code have allowed rapid progress in the identification of mammalian proteins. However, far... (Review)
Review
Determination of the sequence of the human genome and knowledge of the genetic code have allowed rapid progress in the identification of mammalian proteins. However, far less is known about the molecular mechanisms that control expression of human genes and about the variations in gene expression that underlie many pathological states, including cancer. This is caused in part by lack of information about the binding specificities of DNA-binding proteins and particularly regulative important molecules such as transcription factors. It is consequently crucial to develop new technologies or improve existing ones for the analysis of DNA-protein interaction in order to identify and characterise DNA response elements and the related transcription factors or other DNA-binding proteins. The techniques that are currently available vary with respect to the type of result that can be expected from the assay: a mere qualitative demonstration of binding; the identification of response element sequences at high throughput; or a quantitative characterisation of affinities. This article gives an overview of early and recent methodologies applied to such ends.
Topics: Binding Sites; Collodion; DNA; DNA Methylation; DNA-Binding Proteins; Humans; Microarray Analysis; Protein Binding
PubMed: 20730525
DOI: 10.1007/s00216-010-4096-7 -
International Review of Neurobiology 2004
Review
Topics: Animals; Data Interpretation, Statistical; Gene Expression Profiling; Humans; Microarray Analysis; Oligonucleotide Array Sequence Analysis; Reproducibility of Results; Research Design; Sensitivity and Specificity
PubMed: 15474586
DOI: 10.1016/S0074-7742(04)60002-X -
British Journal of Biomedical Science 2011The analysis of the human genome has largely been undertaken in a research environment, but recent developments in technology and associated workflow have allowed... (Review)
Review
The analysis of the human genome has largely been undertaken in a research environment, but recent developments in technology and associated workflow have allowed diagnostic laboratories to interrogate DNA at significantly improved levels of resolution. Principally, whole genome-based analysis of copy number changes using microarrays has led to this method replacing conventional karyotyping as a routine diagnostic workhorse. The resolution offered by microarrays is an improvement of at least an order of magnitude compared to karyotyping, but it comes at a cost in terms of the time spent in data interpretation. Overall, however, the die has been cast and cytogeneticists need to become familiar with the tools use by molecular geneticists and bioinformaticists. The following review provides a brief background to array technology, but uses a series of case studies to illustrate the usefulness and challenges of interpreting array data.
Topics: Adult; Child, Preschool; Chromosome Aberrations; Chromosome Deletion; Chromosome Duplication; Chromosomes, Human, Pair 7; Female; Humans; Infant; Karyotyping; Male; Microarray Analysis; Pregnancy
PubMed: 21706924
DOI: 10.1080/09674845.2011.11730334 -
PloS One 2015When we were asked for help with high-level microarray data analysis (on Affymetrix HGU-133A microarray), we faced the problem of selecting an appropriate method. We...
MOTIVATION
When we were asked for help with high-level microarray data analysis (on Affymetrix HGU-133A microarray), we faced the problem of selecting an appropriate method. We wanted to select a method that would yield "the best result" (detected as many "really" differentially expressed genes (DEGs) as possible, without false positives and false negatives). However, life scientists could not help us--they use their "favorite" method without special argumentation. We also did not find any norm or recommendation. Therefore, we decided to examine it for our own purpose. We considered whether the results obtained using different methods of high-level microarray data analyses--Significant Analysis of Microarrays, Rank Products, Bland-Altman, Mann-Whitney test, T test and the Linear Models for Microarray Data--would be in agreement. Initially, we conducted a comparative analysis of the results on eight real data sets from microarray experiments (from the Array Express database). The results were surprising. On the same array set, the set of DEGs by different methods were significantly different. We also applied the methods to artificial data sets and determined some measures that allow the preparation of the overall scoring of tested methods for future recommendation.
RESULTS
We found a very low level concordance of results from tested methods on real array sets. The number of common DEGs (detected by all six methods on fixed array sets, checked on eight array sets) ranged from 6 to 433 (22,283 total array readings). Results on artificial data sets were better than those on the real data. However, they were not fully satisfying. We scored tested methods on accuracy, recall, precision, f-measure and Matthews correlation coefficient. Based on the overall scoring, the best methods were SAM and LIMMA. We also found TT to be acceptable. The worst scoring was MW. Based on our study, we recommend: 1. Carefully taking into account the need for study when choosing a method, 2. Making high-level analysis with more than one method and then only taking the genes that are common to all methods (which seems to be reasonable) and 3. Being very careful (while summarizing facts) about sets of differentially expressed genes: different methods discover different sets of DEGs.
Topics: Data Accuracy; Data Interpretation, Statistical; Microarray Analysis; Reproducibility of Results
PubMed: 26057385
DOI: 10.1371/journal.pone.0128845 -
Faraday Discussions Oct 2019Glycan microarrays have become a powerful technology to study biological processes, such as cell-cell interaction, inflammation, and infections. Yet, several challenges,... (Review)
Review
Glycan microarrays have become a powerful technology to study biological processes, such as cell-cell interaction, inflammation, and infections. Yet, several challenges, especially in multivalent display, remain. In this introductory lecture we discuss the state-of-the-art glycan microarray technology, with emphasis on novel approaches to access collections of pure glycans and their immobilization on surfaces. Future directions to mimic the natural glycan presentation on an array format, as well as in situ generation of combinatorial glycan collections, are discussed.
Topics: Animals; Bioprinting; Click Chemistry; Equipment Design; Glycomics; Humans; Microarray Analysis; Polysaccharides
PubMed: 31298252
DOI: 10.1039/c9fd00080a -
Journal of Molecular Neuroscience : MN 2005The application of microarray technology to basic and applied fields of science has been progressing rapidly and broadly since its initial description. The field of...
The application of microarray technology to basic and applied fields of science has been progressing rapidly and broadly since its initial description. The field of neuroscience stands to benefit particularly, as nervous tissue is the most transcriptionally active system within most biological organisms. Moreover, large numbers of cell and animal models have been created that mimic many biochemical and behavioral features of neurological states and diseases. In the present study, data on study designs, tissue sources, technology platforms, bioinformatic tools, and results obtained from 448 published microarray studies were collected. The data were then summarized to determine overall usage statistics of microarrays. Future directions and applications for microarrays in the neurosciences were then inferred from the data analyzed.
Topics: Animals; Brain Diseases; Computational Biology; Data Interpretation, Statistical; Female; Gene Expression Profiling; Humans; Male; Microarray Analysis; Neurosciences; Review Literature as Topic
PubMed: 16280595
DOI: 10.1385/JMN:27:3:261