A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R

This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.     

Multiplex mRNA assay using electrophoretic tags for high-throughput gene expression analysis

We describe a novel multiplexing technology using a library of small fluorescent molecules, termed eTag molecules, to code and quantify mRNA targets. eTag molecules, which have the same fluorometric property, but distinct charge-to-mass ratios possess pre-defined electrophoretic characteristics and can be resolved using capillary electrophoresis. Coupled with primary Invader® mRNA assay, eTag molecules were applied to simultaneously quantify up to 44 mRNA targets. This multiplexing approach was validated by examining a panel of inflammation responsive genes in human umbilical vein endothelial cells stimulated with inflammatory cytokine interleukin 1β. The laser-induced fluorescence detection and electrokinetic sample injection process in capillary electrophoresis allows sensitive quantification of thousands of copies of mRNA molecules in a reaction. The assay is precise, as evaluated by measuring qualified Z′ factor, a dimensionless and simple characteristic for applications in high-throughput screening using mRNA assays. Our data demonstrate the synergy between the multiplexing capability of eTag molecules by sensitive capillary electrophoresis detection and the isothermal linear amplification characteristics of the Invader® assay. eTag multiplex mRNA assay presents a unique platform for sensitive, high sample throughput and multiplex gene expression analysis.     

Gene expression profiling of human diseases by serial analysis of gene expression

Until recently, the approach to understanding the molecular basis of complex syndromes such as cancer, coronary artery disease, and diabetes was to study the behavior of individual genes. However, it is generally recognized that expression of a number of genes is coordinated both spatially and temporally and that this coordination changes during the development and progression of diseases. Newly developed functional genomic approaches, such as serial analysis of gene expression (SAGE) and DNA microarrays have enabled researchers to determine the expression pattern of thousands of genes simultaneously. One attractive feature of SAGE compared to microarrays is its ability to quantify gene expression without prior sequence information or information about genes that are thought to be expressed. SAGE has been successfully applied to the gene expression profiling of a number of human diseases. In this review, we will first discuss SAGE technique and contrast it to microarray. We will then highlight new biological insights that have emerged from its application to the study of human diseases.     

Differential allelic expression in the human genome: a robust approach to identify genetic and epigenetic cis-acting mechanisms regulating gene expression

The recent development of whole genome association studies has lead to the robust identification of several loci involved in different common human diseases. Interestingly, some of the strongest signals of association observed in these studies arise from non-coding regions located in very large introns or far away from any annotated genes, raising the possibility that these regions are involved in the etiology of the disease through some unidentified regulatory mechanisms. These findings highlight the importance of better understanding the mechanisms leading to inter-individual differences in gene expression in humans. Most of the existing approaches developed to identify common regulatory polymorphisms are based on linkage/association mapping of gene expression to genotypes. However, these methods have some limitations, notably their cost and the requirement of extensive genotyping information from all the individuals studied which limits their applications to a specific cohort or tissue. Here we describe a robust and high-throughput method to directly measure differences in allelic expression for a large number of genes using the Illumina Allele-Specific Expression BeadArray platform and quantitative sequencing of RT-PCR products. We show that this approach allows reliable identification of differences in the relative expression of the two alleles larger than 1.5-fold (i.e., deviations of the allelic ratio larger than 6040) and offers several advantages over the mapping of total gene expression, particularly for studying humans or outbred populations. Our analysis of more than 80 individuals for 2,968 SNPs located in 1,380 genes confirms that differential allelic expression is a widespread phenomenon affecting the expression of 20% of human genes and shows that our method successfully captures expression differences resulting from both genetic and epigenetic cis-acting mechanisms.     

SNP-based large-scale identification of allele-specific gene expression in human B cells

Polymorphism and variations in gene expression provide the genetic basis for human variation. Allelic variation of gene expression, in particular, may play a crucial role in phenotypic variation and disease susceptibility. To identify genes with allelic expression in human cells, we genotyped genomic DNA and cDNA isolated from 31 immortalized B cell lines from three Centre d'Etude du Polymorphisme Humain (CEPH) families using high-density single-nucleotide polymorphism (SNP) chips containing 13,900 exonic SNPs. We identified seven SNPs in five genes with monoallelic expression, 146 SNPs in 125 genes with allelic imbalance in expression with preferentially higher expression of one allele in a heterozygous individual. The monoallelically expressed genes (ERAP2, MDGA1, LOC644422, SDCCAG3P1 and CLTCL1) were regulated by cis-acting, non-imprinted differential allelic control. In addition, all monoallelic gene expression patterns and allelic imbalances in gene expression in B cells were transmitted from parents to offspring in the pedigree, indicating genetic transmission of allelic gene expression. Furthermore, frequent allele substitution, probably due to RNA editing, was also observed in 21 genes in 23 SNPs as well as in 48 SNPs located in regions containing no known genes. In this study, we demonstrated that allelic gene expression is frequently observed in human B cells, and SNP chips are very useful tools for detecting allelic gene expression. Overall, our data provide a valuable framework for better understanding allelic gene expression in human B cells.     

Widespread allele-specific topological domains in the human genome are not confined to imprinted gene clusters

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.

     

Discovery of cell-type specific regulatory elements in the human genome using differential chromatin modification analysis

Chromatin modifications have been comprehensively illustrated to play important roles in gene regulation and cell diversity in recent years. Given the rapid accumulation of genome-wide chromatin modification maps across multiple cell types, there is an urgent need for computational methods to analyze multiple maps to reveal combinatorial modification patterns and define functional DNA elements, especially those are specific to cell types or tissues. In this current study, we developed a computational method using differential chromatin modification analysis (dCMA) to identify cell-type-specific genomic regions with distinctive chromatin modifications. We then apply this method to a public data set with modification profiles of nine marks for nine cell types to evaluate its effectiveness. We found cell-type-specific elements unique to each cell type investigated. These unique features show significant cell-type-specific biological relevance and tend to be located within functional regulatory elements. These results demonstrate the power of a differential comparative epigenomic strategy in deciphering the human genome and characterizing cell specificity.