In Silico Evaluation of Predicted Regulatory Interactions in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Prediction of transcriptional regulatory mechanisms in <it>Arabidopsis </it>has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>, that a combination of correlation measurements and <it>cis</it>-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF→target relationships on a genome-wide scale.</p> <p>Results</p> <p>We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and <it>cis-</it>regulatory element (CRE) detection (≥1 CRE or CRE over-representation), to determine which of these methods individually or in combination is the most effective by various measures for making regulatory predictions. To predict the regulatory targets of a transcription factor (TF) of interest, we applied these methods to microarray expression data for genes that were regulated over treatment and control conditions in wild type (WT) plants. Because the chosen data sets included identical experimental conditions used on TF over-expressor or T-DNA knockout plants, we were able to test the TF→target predictions made using microarray data from WT plants, with microarray data from mutant/transgenic plants. For each method, or combination of methods, we computed sensitivity, specificity, positive and negative predictive value and the F-measure of balance between sensitivity and positive predictive value (precision). This analysis revealed that the ≥1 CRE and Spearman correlation (used alone or in combination) were the most balanced CRE detection and correlation methods, respectively with regard to their power to accurately predict regulatory-target interactions.</p> <p>Conclusion</p> <p>These findings provide an approach and guidance for researchers interested in predicting transcriptional regulatory mechanisms using microarray data that they generate (or microarray data that is publically available) combined with CRE detection in promoter sequence data.</p

Improved prediction of protein interaction from microarray data using asymmetric correlation

Background:Detection of correlated gene expression is a fundamental process in the characterization of gene functions using microarray data. Commonly used methods such as the Pearson correlation can detect only a fraction of interactions between genes or their products. However, the performance of correlation analysis can be significantly improved either by providing additional biological information or by combining correlation with other techniques that can extract various mathematical or statistical properties of gene expression from microarray data. In this article, I will test the performance of three correlation methods-the Pearson correlation, the rank (Spearman) correlation, and the Mutual Information approach-in detection of protein-protein interactions, and I will further examine the properties of these techniques when they are used together. I will also develop a new correlation measure which can be used with other measures to improve predictive power.&#xd;&#xa; &#xd;&#xa;Results:Using data from 5,896 microarray hybridizations, the three measures were obtained for 30,499 known protein-interacting pairs in the Human Protein Reference Database (HPRD). Pearson correlation showed the best sensitivity (0.305) but the three measures showed similar specificity (0.240 - 0.257). When the three measures were compared, it was found that better specificity could be obtained at a high Pearson coefficient combined with a low Spearman coefficient or Mutual Information. Using a toy model of two gene interactions, I found that such measure combinations were most likely to exist at stronger curvature. I therefore introduced a new measure, termed asymmetric correlation (AC), which directly quantifies the degree of curvature in the expression levels of two genes as a degree of asymmetry. I found that AC performed better than the other measures, particularly when high specificity was required. Moreover, a combination of AC with other measures significantly improved specificity and sensitivity, by up to 50%. &#xd;&#xa; &#xd;&#xa;Conclusions: A combination of correlation measures, particularly AC and Pearson correlation, can improve prediction of protein-protein interactions. Further studies are required to assess the biological significance of asymmetry in expression patterns of gene pairs. &#xd;&#xa

Genome Network Project: An Integrated Genomic Platform

With the objective of elucidating the structure of gene interactions in the human genome, the Genome Network Project has generated a vast quantity of experimental data, mainly focusing on transcriptional control and transcription-factor related protein-protein interactions (PPI). This data has been collected and organized into the Genome Network Platform (&#x22;http://genomenetwork.nig.ac.jp/&#x22;:http://genomenetwork.nig.ac.jp/) at the National Institute of Genetics. Expression data was obtained through CAGE (Cap Analysis Gene Expression), qRT-PCR, tiling array, microarray and short RNA analysis, while PPI information was gathered through yeast two hybrid (Y2H), mammalian two hybrid (M2H) and _in vitro_ virus (IVV) methods. The Genome Network Platform Viewer provides an integrated user interface to the complete database, including services of gene search, whole genome browsing, PPI network viewer, and expression profile analysis. Our platform represents an extremely useful resource for researchers in the field of genomics, and provides access to high quality data through the combination of intuitive browsing and visualization capabilities

Single-cell whole-genome amplification technique impacts the accuracy of SNP microarray-based genotyping and copy number analyses

Methods of comprehensive microarray-based aneuploidy screening in single cells are rapidly emerging. Whole-genome amplification (WGA) remains a critical component for these methods to be successful. A number of commercially available WGA kits have been independently utilized in previous single-cell microarray studies. However, direct comparison of their performance on single cells has not been conducted. The present study demonstrates that among previously published methods, a single-cell GenomePlex WGA protocol provides the best combination of speed and accuracy for single nucleotide polymorphism microarray-based copy number (CN) analysis when compared with a REPLI-g- or GenomiPhi-based protocol. Alternatively, for applications that do not have constraints on turnaround time and that are directed at accurate genotyping rather than CN assignments, a REPLI-g-based protocol may provide the best solution

An introduction to low-level analysis methods of DNA microarray data

This article gives an overview over the methods used in the low--level analysis of gene expression data generated using DNA microarrays. This type of experiment allows to determine relative levels of nucleic acid abundance in a set of tissues or cell populations for thousands of transcripts or loci simultaneously. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. This includes the design of probes, the experimental design, the image analysis of microarray scanned images, the normalization of fluorescence intensities, the assessment of the quality of microarray data and incorporation of quality information in subsequent analyses, the combination of information across arrays and across sets of experiments, the discovery and recognition of patterns in expression at the single gene and multiple gene levels, and the assessment of significance of these findings, considering the fact that there is a lot of noise and thus random features in the data. For all of these components, access to a flexible and efficient statistical computing environment is an essential aspect

Identification and validation of oncologic miRNA biomarkers for Luminal A-like breast cancer

Introduction: Breast cancer is a common disease with distinct tumor subtypes phenotypically characterized by ER and HER2/neu receptor status. MiRNAs play regulatory roles in tumor initiation and progression, and altered miRNA expression has been demonstrated in a variety of cancer states presenting the potential for exploitation as cancer biomarkers. Blood provides an excellent medium for biomarker discovery. This study investigated systemic miRNAs differentially expressed in Luminal A-like (ER+PR+HER2/neu-) breast cancer and their effectiveness as oncologic biomarkers in the clinical setting. Methods: Blood samples were prospectively collected from patients with Luminal A-like breast cancer (n=54) and controls (n=56). RNA was extracted, reverse transcribed and subjected to microarray analysis (n=10 Luminal A-like; n=10 Control). Differentially expressed miRNAs were identified by artificial neural network (ANN) data-mining algorithms. Expression of specific miRNAs was validated by RQ-PCR (n=44 Luminal A; n=46 Control) and potential relationships between circulating miRNA levels and clinicopathological features of breast cancer were investigated. Results: Microarray analysis identified 76 differentially expressed miRNAs. ANN revealed 10 miRNAs for further analysis ( miR-19b, miR-29a, miR-93, miR-181a, miR-182, miR-223, miR-301a, miR-423-5p, miR-486-5 and miR-652 ). The biomarker potential of 4 miRNAs ( miR-29a, miR-181a , miR-223 and miR-652 ) was confirmed by RQ-PCR, with significantly reduced expression in blood of women with Luminal A-like breast tumors compared to healthy controls (p=0.001, 0.004, 0.009 and 0.004 respectively). Binary logistic regression confirmed that combination of 3 of these miRNAs ( miR-29a, miR-181a and miR-652 ) could reliably differentiate between cancers and controls with an AUC of 0.80. Conclusion: This study provides insight into the underlying molecular portrait of Luminal A-like breast cancer subtype. From an initial 76 miRNAs, 4 were validated with altered expression in the blood of women with Luminal A-like breast cancer. The expression profiles of these 3 miRNAs, in combination with mammography, has potential to facilitate accurate subtype- specific breast tumor detection

Serendipitous discoveries in microarray analysis

Background Scientists are capable of performing very large scale gene expression experiments with current microarray technologies. In order to find significance in the expression data, it is common to use clustering algorithms to group genes with similar expression patterns. Clusters will often contain related genes, such as co-regulated genes or genes in the same biological pathway. It is too expensive and time consuming to test all of the relationships found in large scale microarray experiments. There are many bioinformatics tools that can be used to infer the significance of microarray experiments and cluster analysis. Materials and methods In this project we review several existing tools and used a combination of them to narrow down the number of significant clusters from a microarray experiment. Microarray data was obtained through the Cerebellar Gene Regulation in Time and Space (Cb GRiTS) database [2]. The data was clustered using paraclique, a graph-based clustering algorithm. Each cluster was evaluated using Gene-Set Cohesion Analysis Tool (GCAT) [3], ONTO-Pathway Analysis [4], and Allen Brain Atlas data [1]. The clusters with the lowest p-values in each of the three analysis methods were researched to determine good candidate clusters for further experimental confirmation of gene relationships. Results and conclusion While looking for genes important to cerebellar development, we serendipitously came across interesting clusters related to neural diseases. For example, we found two clusters that contain genes known to be associated with Parkinson’s disease, Huntington’s disease, and Alzheimer’s disease pathways. Both clusters scored low in all three analyses and have very similar expression patterns but at different expression levels. Such unexpected discoveries help unlock the real power of high throughput data analysis

Development of Oligonucleotide Microarray for High Throughput Dna Methylation Analysis

DNA methylation is a key event regulating gene expression. DNA methylation analysis plays a pivotal role in unlocking association of epigenetic events with cancer. However, simultaneous evaluation of the methylation status of multiple genes is still a technical challenge. Microarray is a promising approach for high-throughput analysis of the methylation status at numerous CpG sites within multiple genes of interest. In this dissertation study, we conducted a systematic study to examine the use of microarray methods for methylation analysis. First, a robust universal microarray was established with more flexible in design and content, and potential cost saving over commercial arrays. In order to produce high quality microarray data, we optimized the attachment chemistry for the modified oligonucleotides, searched for the good combination of fluorescent dyes, and hybridization conditions. To improve the specificity of the microarray, we conducted a study to experimentally search for a set of highly discriminative tag Sequences. Second, SBE-TAGs microarray was successfully adapted from the SNP detection for methylation analysis of multiple genes. SBE-TAGs microarray performed quite well in multiplex methylation analysis of cell lines if a standard calibration curve method was used. 10 CpG sites of 9 tumor suppressor genes (MGMT, GATA4, HLTF, SOCS1, p16, RASSF2, CHFR, TPEF, and Reprimo) were selected for this study. Third, a novel method called CHZMA (Competing-Hybridization- Zipcode-MicroArray) was developed for methylation analysis of tumor tissue samples, which is based on two steps of hybridization to achieve the specific detection of methylation on microarray. On the basis of analysis of seven genes (MGMT, GATA4, HLTF, SOCS1, RASSF2, ER, 3-OST-2), we found that the CHZMA assay can robustly detect methylation of multiple genes in the samples containing as low as 10 of methylated DNA. With the strict control group test and statistical analysis, CHZMA can be a good high-throughput method in place of MSP for meth

Development of Oligonucleotide Microarray for High Throughput Dna Methylation Analysis

DNA methylation is a key event regulating gene expression. DNA methylation analysis plays a pivotal role in unlocking association of epigenetic events with cancer. However, simultaneous evaluation of the methylation status of multiple genes is still a technical challenge. Microarray is a promising approach for high-throughput analysis of the methylation status at numerous CpG sites within multiple genes of interest. In this dissertation study, we conducted a systematic study to examine the use of microarray methods for methylation analysis. First, a robust universal microarray was established with more flexible in design and content, and potential cost saving over commercial arrays. In order to produce high quality microarray data, we optimized the attachment chemistry for the modified oligonucleotides, searched for the good combination of fluorescent dyes, and hybridization conditions. To improve the specificity of the microarray, we conducted a study to experimentally search for a set of highly discriminative tag Sequences. Second, SBE-TAGs microarray was successfully adapted from the SNP detection for methylation analysis of multiple genes. SBE-TAGs microarray performed quite well in multiplex methylation analysis of cell lines if a standard calibration curve method was used. 10 CpG sites of 9 tumor suppressor genes (MGMT, GATA4, HLTF, SOCS1, p16, RASSF2, CHFR, TPEF, and Reprimo) were selected for this study. Third, a novel method called CHZMA (Competing-Hybridization- Zipcode-MicroArray) was developed for methylation analysis of tumor tissue samples, which is based on two steps of hybridization to achieve the specific detection of methylation on microarray. On the basis of analysis of seven genes (MGMT, GATA4, HLTF, SOCS1, RASSF2, ER, 3-OST-2), we found that the CHZMA assay can robustly detect methylation of multiple genes in the samples containing as low as 10 of methylated DNA. With the strict control group test and statistical analysis, CHZMA can be a good high-throughput method in place of MSP for meth

Development of Oligonucleotide Microarray for High Throughput Dna Methylation Analysis

DNA methylation is a key event regulating gene expression. DNA methylation analysis plays a pivotal role in unlocking association of epigenetic events with cancer. However, simultaneous evaluation of the methylation status of multiple genes is still a technical challenge. Microarray is a promising approach for high-throughput analysis of the methylation status at numerous CpG sites within multiple genes of interest. In this dissertation study, we conducted a systematic study to examine the use of microarray methods for methylation analysis. First, a robust universal microarray was established with more flexible in design and content, and potential cost saving over commercial arrays. In order to produce high quality microarray data, we optimized the attachment chemistry for the modified oligonucleotides, searched for the good combination of fluorescent dyes, and hybridization conditions. To improve the specificity of the microarray, we conducted a study to experimentally search for a set of highly discriminative tag Sequences. Second, SBE-TAGs microarray was successfully adapted from the SNP detection for methylation analysis of multiple genes. SBE-TAGs microarray performed quite well in multiplex methylation analysis of cell lines if a standard calibration curve method was used. 10 CpG sites of 9 tumor suppressor genes (MGMT, GATA4, HLTF, SOCS1, p16, RASSF2, CHFR, TPEF, and Reprimo) were selected for this study. Third, a novel method called CHZMA (Competing-Hybridization- Zipcode-MicroArray) was developed for methylation analysis of tumor tissue samples, which is based on two steps of hybridization to achieve the specific detection of methylation on microarray. On the basis of analysis of seven genes (MGMT, GATA4, HLTF, SOCS1, RASSF2, ER, 3-OST-2), we found that the CHZMA assay can robustly detect methylation of multiple genes in the samples containing as low as 10 of methylated DNA. With the strict control group test and statistical analysis, CHZMA can be a good high-throughput method in place of MSP for meth