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Biomedical subjects

Rafael A Irizarry

Publications and source records attributed to Rafael A Irizarry.

At least 19 recordsLinked to original sources
PubMed2006

Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data.

In most microarray technologies, a number of critical steps are required to convert raw intensity measurements into the data relied upon by data analysts, biologists, and clinicians. These data manipulations, referred to as preprocessing, can influence the quality of the ultimate measurements. In the last few years, the high-throughput measurement of gene expression is the most popular application of microarray technology. For this application, various groups have demonstrated that the use of modern statistical methodology can substantially improve accuracy and precision of the gene expression measurements, relative to ad hoc procedures introduced by designers and manufacturers of the technology. Currently, other applications of microarrays are becoming more and more popular. In this paper, we describe a preprocessing methodology for a technology designed for the identification of DNA sequence variants in specific genes or regions of the human genome that are associated with phenotypes of interest such as disease. In particular, we describe a methodology useful for preprocessing Affymetrix single-nucleotide polymorphism chips and obtaining genotype calls with the preprocessed data. We demonstrate how our procedure improves existing approaches using data from 3 relatively large studies including the one in which large numbers of independent calls are available. The proposed methods are implemented in the package oligo available from Bioconductor.

Algorithms↗
PubMed2006

Transposon insertion site profiling chip (TIP-chip).

Mobile elements are important components of our genomes, with diverse and significant effects on phenotype. Not only can transposons inactivate genes by direct disruption and shuffle the genome through recombination, they can also alter gene expression subtly or powerfully. Currently active transposons are highly polymorphic in host populations, including, among hundreds of others, L1 and Alu elements in humans and Ty1 elements in yeast. For this reason, we wished to develop a simple genome-wide method for identifying all transposons in any given sample. We have designed a transposon insertion site profiling chip (TIP-chip), a microarray intended for use as a high-throughput technique for mapping transposon insertions. By selectively amplifying transposon flanking regions and hybridizing them to the array, we can locate all transposons present in a sample. We have tested the TIP-chip extensively to map Ty1 retrotransposon insertions in yeast and have achieved excellent results in two laboratory strains as well as in evolved Ty1 high-copy strains. We are able to identify all of the theoretically detectable transposons in the FY2 lab strain, with essentially no false positives. In addition, we mapped many new transposon copies in the high-copy Ty1 strain and determined its Ty1 insertion pattern.

Animals↗
PubMed2006

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB.

BACKGROUND: Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support. RESULTS: We propose a simple tab-delimited, spreadsheet-based format, MAGE-TAB, which will become a part of the MAGE microarray data standard and can be used for annotating and communicating microarray data in a MIAME compliant fashion. CONCLUSION: MAGE-TAB will enable laboratories without bioinformatics experience or support to manage, exchange and submit well-annotated microarray data in a standard format using a spreadsheet. The MAGE-TAB format is self-contained, and does not require an understanding of MAGE-ML or XML.

Computational Biology↗
PubMed2006

High-resolution spatial normalization for microarrays containing embedded technical replicates.

MOTIVATION: Microarray data are susceptible to a wide-range of artifacts, many of which occur on physical scales comparable to the spatial dimensions of the array. These artifacts introduce biases that are spatially correlated. The ability of current methodologies to detect and correct such biases is limited. RESULTS: We introduce a new approach for analyzing spatial artifacts, termed 'conditional residual analysis for microarrays' (CRAM). CRAM requires a microarray design that contains technical replicates of representative features and a limited number of negative controls, but is free of the assumptions that constrain existing analytical procedures. The key idea is to extract residuals from sets of matched replicates to generate residual images. The residual images reveal spatial artifacts with single-feature resolution. Surprisingly, spatial artifacts were found to coexist independently as additive and multiplicative errors. Efficient procedures for bias estimation were devised to correct the spatial artifacts on both intensity scales. In a survey of 484 published single-channel datasets, variance fell 4- to 12-fold in 5% of the datasets after bias correction. Thus, inclusion of technical replicates in a microarray design affords benefits far beyond what one might expect with a conventional 'n = 5' averaging, and should be considered when designing any microarray for which randomization is feasible. AVAILABILITY: CRAM is implemented as version 2 of the hoptag software package for R, which is included in the Supplementary information.

Algorithms↗
PubMed2006

A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database.

BACKGROUND: Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA. RESULTS: We assess performance based on multiple sets of samples processed over HG U133A Affymetrix GeneChip arrays. We show that the refRMA workflow, when used in conjunction with a large, biologically diverse training set, results in the same general characteristics as that of RMA in its classic form when comparing overall data structure, sample-to-sample correlation, and variation. Further, we demonstrate that the refRMA workflow and reference set can be robustly applied to naïve organ types and to benchmark data where its performance indicates respectable results. CONCLUSION: Our results indicate that a biologically diverse reference database can be used to train a model for estimating probe set intensities of exclusive test sets, while retaining the overall characteristics of the base algorithm. Although the results we present are specific for RMA, similar versions of other multi-array normalization and summarization schemes can be developed.

Algorithms↗
PubMed2006

Nucleocytosolic acetyl-coenzyme a synthetase is required for histone acetylation and global transcription.

Metabolic enzymes rarely regulate informational processes like gene expression. Yeast acetyl-CoA synthetases (Acs1p and 2p) are exceptional, as they are important not only for carbon metabolism but also are shown here to supply the acetyl-CoA for histone acetylation by histone acetyltransferases (HATs). acs2-Ts mutants exhibit global histone deacetylation, transcriptional defects, and synthetic growth defects with HAT mutants at high temperatures. In glycerol with ethanol, Acs1p is an alternate acetyl-CoA source for HATs. Rapid deacetylation after Acs2p inactivation suggests nuclear acetyl-CoA synthesis is rate limiting for histone acetylation. Different histone lysines exhibit distinct deacetylation rates, with N-terminal tail lysines deacetylated rapidly and H3 lysine 56 slowly. Yeast mitochondrial and nucleocytosolic acetyl-CoA pools are biochemically isolated. Thus, acetyl-CoA metabolism is directly linked to chromatin regulation and may affect diverse cellular processes in which acetylation and metabolism intersect, such as disease states and aging.

Acetate-CoA Ligase↗
PubMed2006

Comparison of Affymetrix GeneChip expression measures.

MOTIVATION: In the Affymetrix GeneChip system, preprocessing occurs before one obtains expression level measurements. Because the number of competing preprocessing methods was large and growing we developed a benchmark to help users identify the best method for their application. A webtool was made available for developers to benchmark their procedures. At the time of writing over 50 methods had been submitted. RESULTS: We benchmarked 31 probe set algorithms using a U95A dataset of spike in controls. Using this dataset, we found that background correction, one of the main steps in preprocessing, has the largest effect on performance. In particular, background correction appears to improve accuracy but, in general, worsen precision. The benchmark results put this balance in perspective. Furthermore, we have improved some of the original benchmark metrics to provide more detailed information regarding precision and accuracy. A handful of methods stand out as providing the best balance using spike-in data with the older U95A array, although different experiments on more current arrays may benchmark differently. AVAILABILITY: The affycomp package, now version 1.5.2, continues to be available as part of the Bioconductor project (http://www.bioconductor.org). The webtool continues to be available at http://affycomp.biostat.jhsph.edu CONTACT: rafa@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Algorithms↗
PubMed2006

Prenatal development of intrafetal and maternal-fetal synchrony.

Fetal and maternal data were monitored serially at 6 gestational ages from 20 to 38 weeks in 195 Peruvian fetuses. Digitized data included fetal heart rate and motor activity, as well as maternal heart rate and electrodermal conductance. Time series analysis evaluated the development of synchrony in 2 streams of fetal functioning and between mothers and fetuses. Intrafetal synchrony between heart rate and motor activity developed in an orderly fashion, with peak cross-correlation approaching an asymptote at 5 s at 28 weeks. Synchrony was not observed between fetal heart rate and maternal measures. Fetal motor activity exhibited synchrony with both maternal electrodermal and heart rate activity. Implications for revealing fundamental properties of neural development prior to birth are discussed.

Cardiotocography↗
PubMed2006

Stacking the deck: double-tiled DNA microarrays.

Microarrays-high-throughput platforms for analyzing the gene expression and features of total genomic DNA, among other applications-are gaining in popularity as researchers discover ever more uses for their unbiased and broad feature sets. At present, microarray analyses are limited by the number of individual features that can be placed on each array. Here we describe a double-tiling method that significantly increases the number of sequences present on an array, and we show that successful transcriptional profiling is possible and straightforward with such arrays. With this method, we and others can save money and precious samples by using fewer arrays to cover a region, or can carry out investigations at significantly higher resolution without incurring prohibitive costs or increasing the amount of sample required for the experiment.

Carbocyanines↗
PubMed2006

Analysis of Affymetrix GeneChip data using amplified RNA.

When small biological samples are collected by microdissection or other methods, amplification techniques are required to provide sufficient target for hybridization to expression arrays. One such technique is to perform two successive rounds of T7-based in vitro transcription. However the use of random primers, required to regenerate cDNA from the first round of transcription, results in shortened copies of cDNA from which the 5' end is missing. In this paper we describe an experiment designed to compare the quality of data obtained from labeling small RNA samples using the Affymetrix Two-Cycle Eukaryotic. Target Labeling procedure to that of data obtained using the One-Cycle Eukaryotic Target Labeling protocol. We utilized different preprocessing algorithms to compare the data generated using both labeling methods and present a new algorithm that improves upon existing ones in this setting.

Algorithms↗
PubMed2005

Global synthetic-lethality analysis and yeast functional profiling.

The Saccharomyces genome-deletion project created >5900 'molecularly barcoded' yeast knockout mutants (YKO mutants). The YKO mutant collections have facilitated large-scale analyses of a multitude of mutant phenotypes. For example, both synthetic genetic array (SGA) and synthetic-lethality analysis by microarray (SLAM) methods have been used for synthetic-lethality screens. Global analysis of synthetic lethality promises to identify cellular pathways that 'buffer' each other biologically. The combination of global synthetic-lethality analysis, together with global protein-protein interaction analyses, mRNA expression profiling and functional profiling will, in principle, enable construction of a cellular 'wiring diagram' that will help frame a deeper understanding of human biology and disease.

Chromosome Mapping↗
PubMed2005

Improved statistical analysis of budding yeast TAG microarrays revealed by defined spike-in pools.

Saccharomyces cerevisiae knockout collection TAG microarrays are an emergent platform for rapid, genome-wide functional characterization of yeast genes. TAG arrays report abundance of unique oligonucleotide 'TAG' sequences incorporated into each deletion mutation of the yeast knockout collection, allowing measurement of relative strain representation across experimental conditions for all knockout mutants simultaneously. One application of TAG arrays is to perform genome-wide synthetic lethality screens, known as synthetic lethality analyzed by microarray (SLAM). We designed a fully defined spike-in pool to resemble typical SLAM experiments and performed TAG microarray hybridizations. We describe a method for analyzing two-color array data to efficiently measure the differential knockout strain representation across two experimental conditions, and use the spike-in pool to show that the sensitivity and specificity of this method exceed typical current approaches.

Data Interpretation, Statistical↗
PubMed2005

Gene expression in giant cell myocarditis: Altered expression of immune response genes.

BACKGROUND: Giant cell myocarditis is a rapidly progressive and often fatal condition without a clear etiology or treatment. A better understanding of giant cell myocarditis pathogenesis is critical to developing treatments to prevent progression and reverse damage. We compared the gene expression of giant cell myocarditis with that of nonfailing hearts. METHODS: Left ventricular samples from two giant cell myocarditis patients harvested during ventricular assist device placement and six unused donor hearts were examined using Affymetrix U133A microarrays. Differential gene expression was defined with a Bonferroni-adjusted p value < or = 0.05 from a Student's t-test and an absolute fold change > or = 2.0. Select gene expression was confirmed with quantitative PCR. RESULTS: Of 115 differentially expressed genes, most were upregulated in giant cell myocarditis and involved in immune response, transcriptional regulation, and metabolism. T-cell activation genes included chemokine receptor 4; chemokine ligands 5, 9, 13, and 18; interleukin-10 receptor alpha; and beta-2 integrin. CONCLUSIONS: Gene expression analysis of giant cell myocarditis offers novel insights into its pathogenesis, namely the role of T-cell activators of the Th1 subset and immune response genes previously implicated in heart failure. This forms the basis for future work aimed at defining novel therapeutic targets for giant cell myocarditis.

Adult↗
PubMed2005

Improved microarray methods for profiling the Yeast Knockout strain collection.

A remarkable feature of the Yeast Knockout strain collection is the presence of two unique 20mer TAG sequences in almost every strain. In principle, the relative abundances of strains in a complex mixture can be profiled swiftly and quantitatively by amplifying these sequences and hybridizing them to microarrays, but TAG microarrays have not been widely used. Here, we introduce a TAG microarray design with sophisticated controls and describe a robust method for hybridizing high concentrations of dye-labeled TAGs in single-stranded form. We also highlight the importance of avoiding PCR contamination and provide procedures for detection and eradication. Validation experiments using these methods yielded false positive (FP) and false negative (FN) rates for individual TAG detection of 3-6% and 15-18%, respectively. Analysis demonstrated that cross-hybridization was the chief source of FPs, while TAG amplification defects were the main cause of FNs. The materials, protocols, data and associated software described here comprise a suite of experimental resources that should facilitate the use of TAG microarrays for a wide variety of genetic screens.

Coloring Agents↗
PubMed2005

In vitro identification and in silico utilization of interspecies sequence similarities using GeneChip technology.

BACKGROUND: Genomic approaches in large animal models (canine, ovine etc) are challenging due to insufficient genomic information for these species and the lack of availability of corresponding microarray platforms. To address this problem, we speculated that conserved interspecies genetic sequences can be experimentally detected by cross-species hybridization. The Affymetrix platform probe redundancy offers flexibility in selecting individual probes with high sequence similarities between related species for gene expression analysis. RESULTS: Gene expression profiles of 40 canine samples were generated using the human HG-U133A GeneChip (U133A). Due to interspecies genetic differences, only 14 +/- 2% of canine transcripts were detected by U133A probe sets whereas profiling of 40 human samples detected 49 +/- 6% of human transcripts. However, when these probe sets were deconstructed into individual probes and examined performance of each probe, we found that 47% of human probes were able to find their targets in canine tissues and generate a detectable hybridization signal. Therefore, we restricted gene expression analysis to these probes and observed the 60% increase in the number of identified canine transcripts. These results were validated by comparison of transcripts identified by our restricted analysis of cross-species hybridization with transcripts identified by hybridization of total lung canine mRNA to new Affymetrix Canine GeneChip. CONCLUSION: The experimental identification and restriction of gene expression analysis to probes with detectable hybridization signal drastically increases transcript detection of canine-human hybridization suggesting the possibility of broad utilization of cross-hybridizations of related species using GeneChip technology.

Animals↗
PubMed2005

Multiple-laboratory comparison of microarray platforms.

Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC-Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.

Baltimore↗