Reproducibility of microarray studies: concordance of current analysis methods

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for large-scale analysis of gene expression. Microarrays can be used to measure the relative quantities of specific mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent "noise" in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data.

Application of the S-score algorithm for analysis of oligonucleotide microarrays

BackgroundNext-Generation-Sequencing (NGS) enables detection of microorganisms present in biological and other matrices of various origin and nature, allowing not only the identification of known phyla and strains but also the discovery of novel ones. The large amount of metagenomic shotgun data produced by NGS require comprehensive and user-friendly pipelines for data analysis, that speed up the bioinformatics steps, relieving the users from the need to manually perform complex and time-consuming tasks.ResultsWe describe here HOME-BIO (sHOtgun MEtagenomic analysis of BIOlogical entities), an exhaustive pipeline for metagenomics data analysis, comprising three independent analytical modules designed for an inclusive analysis of large NGS datasets.ConclusionsHOME-BIO is a powerful and easy-to-use tool that can be run also by users with limited computational expertise. It allows in-depth analyses by removing low-complexity/ problematic reads, integrating the analytical steps that lead to a comprehensive taxonomy profile of each sample by querying different source databases, and it is customizable according to specific users’ needs.

Gene Expression Profiles During Hepatic Stellate Cell Activation in Culture and In Vivo

In the past decade, DNA microarrays have fundamentally changed the way we study complex biological systems. By measuring the expression levels of thousands of transcripts, the paradigm of studying organisms has shifted from focusing on the local phenomena of a few genes to surveying the whole genome. DNA microarrays are used in a variety of ways, from simple comparisons between two samples to more intricate time-series studies. With the large number of genes being studied, the dimensionality of the problem is inevitably high. The analysis of microarray data thus requires specific approaches. In the case of time-series microarray studies, data analysis is further complicated by the correlation between successive time points in a series.In this review, we survey the methodologies used in the analysis of static and time-series microarray data, covering data pre-processing, identification of differentially expressed genes, profile pattern recognition, pathway analysis, and network reconstruction. When available, examples of their use in mammalian cell cultures are presented.

The biological processes underlying the development of pharmacoresistance in epilepsy are poorly understood, but multiple interconnected genes and pathways are likely to be involved. At least 12 microarray studies on...

In the quest of identifying newer molecular targets for the management of glucocorticoid-induced ocular hypertension (GC-OHT) and glaucoma (GCG), several microarray studies have attempted to investigate the genome-wide transcriptome profiling of primary human trabecular meshwork (TM) cells in response to dexamethasone (DEX). However, no studies are reported so far to demonstrate the temporal changes in the expression of genes in the cultured human TM cells in response to DEX treatment. Therefore, in the present study, the time-dependent changes in the genome-wide expression of genes in primary human TM cells after short (16 hours: 16 h) and long exposure (7 days: 7 d) of DEX was investigated using RNA sequencing. There were 199 (118 up-regulated; 81 down-regulated) and 525 (119 up-regulated; 406 down-regulated) DEGs in 16 h and 7 d treatment groups respectively. The unique genes identified in 16 h and 7 d treatment groups were 152 and 478 respectively. This study found a distinct gene signature and pathways between two treatment regimes. Longer exposure of DEX treatment showed a dys-regulation of Wnt and Rap1 signaling and so highlighted potential therapeutic targets for pharmacological management of GC-OHT/glaucoma.

The use of a constant fold-change to determine significant changes in gene expression has been widely accepted for its intuition and ease of use in microarray data analysis, but this concept has been increasingly criticized because it does not reflect signal intensity and can result in a substantial number of false positives and false negatives. To resolve this dilemma, we have analyzed 65 replicate Affymetrix chip-chip comparisons and determined a series of user adjustable signal-dependent thresholds which do not require replicates and offer a 95% confidence interval. Quantitative RT-PCR shows that such thresholds significantly improve the power to discriminate biological changes in mRNA from noise and reduce false calls compared to the traditional two-fold threshold. The user-friendly nature of this approach means that it can be easily applied by any user of microarray analysis, even those without any specialized knowledge of computational techniques or statistics. Noise is a function of signal intensity not only for Affymetrix data but also for cDNA array data, analysis of which may also be benefited by our methodology.

Transplantomics and Biomarkers in Organ Transplantation: A Report From the First International Conference

Characterizing changes in gene expression through transcriptomics and transcription regulator activity has become a fundamental approach for understanding the diverse responses involved in fungal pathogenesis. This paper introduces two computational tools designed to address key challenges in the study of transcriptional regulation in fungal pathogens, particularly non-modelwith limited genomic annotation. First, we present WonderPeaks, a novel peak-calling algorithm that leverages the first derivative of mapped genomic data from next-generation sequencing (NGS) experiments to identify enriched peaks in Chromatin ImmunoPrecipitation followed by sequencing (ChIP-seq). Second, we introduce PeakStream, an extension of WonderPeaks for annotating 3' untranslated regions (UTRs) in transcriptomic data generated using poly(A)-primed library preparation. Together, these tools provide an end-to-end data analysis pipeline, offering a user-friendly solution for researchers studying transcription regulation in fungi. We demonstrate their effectiveness with data from the fungal pathogen Candida albicans, successfully identifying verified peaks in ChIP-seq data and annotating validated UTRs through comparison with total RNA sequencing data under the same conditions. We also discuss the limitations of WonderPeaks for ChIP-seq data compared to current state-of-the-art methods and propose directions for future improvements. Ultimately, this work provides practical guidance and powerful resources for studying transcriptional regulation, with immediate relevance to pathogenic fungi and potential applications in broader genomic studies.

A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification

We created a web-based microarray data analysis pipeline for managing the volumes of data created by production microarray experiments. Experiments are formalized by grouping array data into hierarchies based on types such as 'dye swap' or 'replicate'. Grouping determines the analysis to be performed and enables the tool to automatically generate reports and charts appropriate to the experiment results. Subsets of data across arrays may also be hierarchically grouped into types such as 'gene' or 'list'. The group hierarchy is similar to a document object model (DOM), which enables queries to be posed in an XPath or XQuery language. Analyzer modules provide the complicated statistical processing and may be custom written or implemented as wrappers around existing tools. For speculative data analysis or publication, the results may be exported to a standard format.

BackgroundEvidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, the function of circRNAs in gastric cancer (GC) are still unknown. Here, we aimed to determine the regulatory mechanism of circRNAs in GC.MethodsExpression profiles of circRNAs were downloaded from four Gene Expression Omnibus (GEO) microarray datasets. Expression profiles of miRNAs and mRNAs were collected from The Cancer Genome Atlas (TCGA) database. We used the robust rank aggregation method to identify differentially expressed circRNAs (DEcircRNAs) and a ceRNA network was constructed based on circRNA–miRNA pairs and miRNA–mRNA pairs. Functional and pathway enrichment analyses were performed and interactions between proteins were predicted using Cytoscape. Aa subnetwork regulatory module was built using the MCODE plugin.ResultsA total of eight DEcircRNAs, 240 DEmiRNAs, and 4578 DEmRNAs were identified. The circRNA–miRNA–mRNA network was constructed based on seven circRNAs, 33 miRNAs, 69 mRNAs in GC. GO and KEGG pathway analysis indicated DEmRNAs might be associated with GC onset and progression. A PPI network was established and four hub genes (MCM4, KIF23, MCM8, and NCAPD2) were determined from the network. Then a circRNA–miRNA-hub gene subnetwork was constructed based on the four DEcircRNAs, three DEmiRNAs, and four DEmRNAs.ConclusionsOur findings provide a deeper understanding the circRNA-related competing endogenous RNA regulatory mechanism in GC pathogenesis.

Resveratrol Delays Age-Related Deterioration and Mimics Transcriptional Aspects of Dietary Restriction without Extending Life Span