Glycan microarrays are widely used to elucidate carbohydrate binding specificity and affinity of various analytes including proteins, microorganisms, cells, and tissues. Glycan microarrays comprise a ...wide variety of platforms, differing in surface chemistry, presentation of carbohydrates, carbohydrate valency, and detection strategies, all of which impact on analyte performance. This chapter describes detailed methods for printing neoglycoprotein and glycoprotein microarrays on hydrogel-coated slides and incubation of these glycan microarrays with fluorescently labeled lectins.
Genomic microarrays used to assess DNA copy number are now recommended as first-tier tests for the postnatal evaluation of individuals with intellectual disability, autism spectrum disorders, and/or ...multiple congenital anomalies. Application of this technology has resulted in the discovery of widespread copy number variation in the human genome, both polymorphic variation in healthy individuals and novel pathogenic copy number imbalances. To assist clinical laboratories in the evaluation of copy number variants and to promote consistency in interpretation and reporting of genomic microarray results, the American College of Medical Genetics has developed the following professional guidelines for the interpretation and reporting of copy number variation. These guidelines apply primarily to evaluation of constitutional copy number variants detected in the postnatal setting.
Abstract
Glycan microarrays are essential tools in glycobiology and are being widely used for assignment of glycan ligands in diverse glycan recognition systems. We have developed a new software, ...called Carbohydrate microArray Analysis and Reporting Tool (CarbArrayART), to address the need for a distributable application for glycan microarray data management. The main features of CarbArrayART include: (i) Storage of quantified array data from different array layouts with scan data and array-specific metadata, such as lists of arrayed glycans, array geometry, information on glycan-binding samples, and experimental protocols. (ii) Presentation of microarray data as charts, tables, and heatmaps derived from the average fluorescence intensity values that are calculated based on the imaging scan data and array geometry, as well as filtering and sorting functions according to monosaccharide content and glycan sequences. (iii) Data export for reporting in Word, PDF, and Excel formats, together with metadata that are compliant with the guidelines of MIRAGE (Minimum Information Required for A Glycomics Experiment). CarbArrayART is designed for routine use in recording, storage, and management of any slide-based glycan microarray experiment. In conjunction with the MIRAGE guidelines, CarbArrayART addresses issues that are critical for glycobiology, namely, clarity of data for evaluation of reproducibility and validity.
Analytical microarrays feature great capabilities for simultaneous detection and quantification of multiple analytes in a single measurement. In this work, we present a rapid and simple method for ...bulk preparation of microarrays on polycarbonate sheets. Succinylated Jeffamine® ED-2003 was screen printed on polycarbonate sheets to create a polyfunctional shielding layer by baking at 100 °C. After microdispension of capture probes (antibodies, oligonucleotides, or small molecules) in a microarray format, chips were assembled with a flow cell from double-sided tape. It was shown that the shielding layer was firmly coated and suppressed unspecific binding of proteins. Universal applicability was demonstrated by transferring established flow-based chemiluminescence microarray measurement principles from glass slides to polycarbonate chips without loss of analytical performance. Higher chemiluminescence signals could be generated by performing heterogeneous asymmetric recombinase polymerase amplification on polycarbonate chips. Similar results could be shown for sandwich microarray immunoassays. Beyond that, lower inter- and intra-assay variances could be measured for the analysis of
Legionella pneumophila
Serogroup 1, strain Bellingham-1. Even surface regeneration of indirect competitive immunoassays was possible, achieving a limit of detection of 0.35 ng L
−1
for enrofloxacin with polycarbonate microarray chips. Succinylated Jeffamine ED-2003 coated polycarbonate chips have great potential to replace microtiter plates by flow-based chemiluminescence microarrays for rapid analysis. Therefore, it helps analytical microarrays to advance into routine analysis and diagnostics.
Graphical abstract
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Removal of, or adjustment for, batch effects or center differences is generally required when such effects are present in data. In particular, when preparing microarray gene expression data from ...multiple cohorts, array platforms, or batches for later analyses, batch effects can have confounding effects, inducing spurious differences between study groups. Many methods and tools exist for removing batch effects from data. However, when study groups are not evenly distributed across batches, actual group differences may induce apparent batch differences, in which case batch adjustments may bias, usually deflate, group differences. Some tools therefore have the option of preserving the difference between study groups, e.g. using a two-way ANOVA model to simultaneously estimate both group and batch effects. Unfortunately, this approach may systematically induce incorrect group differences in downstream analyses when groups are distributed between the batches in an unbalanced manner. The scientific community seems to be largely unaware of how this approach may lead to false discoveries.
MicroRNAs (miRNAs) are a series of promising molecules that could regulate gene expression, while its expression level and type are strictly related to the early diagnosis, targeted therapy, and ...prognosis of diseases. Improved detection accuracy of miRNAs would lead to unprecedented progress in early treatment. Numerous methods to improve sensitivity have been proposed and confirmed valuable in miRNAs detection recently. However, the research which especially targets at improving detection specificity remains rare. This Feature gives a retrospective summary of the most common detection methods including Northern blot, reverse transcription quantitative polymerase chain reaction, next-generation sequencing, and microarray. Common methods play an important role in many aspects because of maturity and stability. However, the problem of specificity still exists. Then, this Feature summarizes breakthroughs in traditional techniques which are closely related to progressive methods to improve specificity. In addition, considerable studies focusing on improving accuracy are described in detail. Most of them involve the reasonable combination of common methods, and the strategies that contribute to improving accuracy are classified. This Feature aims to illustrate the importance of detection accuracy and explore how to improve specificity on detecting miRNAs, especially homologous families.
Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant ...gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked using an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes’ group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 14 publicly available microarray expression datasets. The experimental results show that XGBoost–MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.
Graphical abstract
Circadian rhythms are oscillations of physiology, behavior, and metabolism that have period lengths near 24 hours. In several model organisms and humans, circadian clock genes have been characterized ...and found to be transcription factors. Because of this, researchers have used microarrays to characterize global regulation of gene expression and algorithmic approaches to detect cycling. This article presents a new algorithm, JTK_CYCLE, designed to efficiently identify and characterize cycling variables in large data sets. Compared with COSOPT and the Fisher's G test, two commonly used methods for detecting cycling transcripts, JTK_CYCLE distinguishes between rhythmic and nonrhythmic transcripts more reliably and efficiently. JTK_CYCLE's increased resistance to outliers results in considerably greater sensitivity and specificity. Moreover, JTK_CYCLE accurately measures the period, phase, and amplitude of cycling transcripts, facilitating downstream analyses. Finally, JTK_CYCLE is several orders of magnitude faster than COSOPT, making it ideal for large-scale data sets. JTK_CYCLE was used to analyze legacy data sets including NIH3T3 cells, which have comparatively low amplitude oscillations. JTK_CYCLE's improved power led to the identification of a novel cluster of RNA-interacting genes whose abundance is under clear circadian regulation. These data suggest that JTK_CYCLE is an ideal tool for identifying and characterizing oscillations in genome-scale data sets.
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