Affymetrix Tiling Research Articles

Sequence characteristics define trade-offs between on-target and genome-wide off-target hybridization of oligoprobes.

Off-target oligoprobe’s interaction with partially complementary nucleotide sequences represents a problem for many bio-techniques. The goal of the study was to identify oligoprobe sequence characteristics that control the ratio between on-target and off-target hybridization. To understand the complex interplay between specific and genome-wide off-target (cross-hybridization) signals, we analyzed a database derived from genomic comparison hybridization experiments performed with an Affymetrix tiling array. The database included two types of probes with signals derived from (i) a combination of specific signal and cross-hybridization and (ii) genomic cross-hybridization only. All probes from the database were grouped into bins according to their sequence characteristics, where both hybridization signals were averaged separately. For selection of specific probes, we analyzed the following sequence characteristics: vulnerability to self-folding, nucleotide composition bias, numbers of G nucleotides and GGG-blocks, and occurrence of probe’s k-mers in the human genome. Increases in bin ranges for these characteristics are simultaneously accompanied by a decrease in hybridization specificity—the ratio between specific and cross-hybridization signals. However, both averaged hybridization signals exhibit growing trends along with an increase of probes’ binding energy, where the hybridization specific signal increases significantly faster in comparison to the cross-hybridization. The same trend is evident for the S function, which serves as a combined evaluation of probe binding energy and occurrence of probe’s k-mers in the genome. Application of S allows extracting a larger number of specific probes, as compared to using only binding energy. Thus, we showed that high values of specific and cross-hybridization signals are not mutually exclusive for probes with high values of binding energy and S. In this study, the application of a new set of sequence characteristics allows detection of probes that are highly specific to their targets for array design and other bio-techniques that require selection of specific probes.

Correction.

The Plant JournalVolume 91, Issue 6 p. 1129-1130 CorrectionFree Access Correction This article corrects the following: Quantitative RNA expression analysis with Affymetrix Tiling 1.0R arrays identifies new E2F target genes Naïra Naouar, Klaas Vandepoele, Tim Lammens, Tineke Casneuf, Georg Zeller, Paul Van Hummelen, Detlef Weigel, Gunnar Rätsch, Dirk Inzé, Martin Kuiper, Lieven De Veylder, Marnik Vuylsteke, Volume 57Issue 1The Plant Journal pages: 184-194 First Published online: September 19, 2008 First published: 01 September 2017 https://doi.org/10.1111/tpj.13653AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat In the article “Quantitative RNA expression analysis with Affymetrix Tiling 1.0R arrays identifies new E2F target genes” by Naïra Naouar et al. (2009), Figure 7 (original), 7 (corrected) holds duplicated and incorrect joined gel lane fragments. Because only part of the original data could be retrieved, the experiments were reproduced according to today's experimental standards, using quantitative RT-PCR instead of RT-PCR. As the original E2Fa serum lost its original quality, a new serum was used (kindly provided by Dr. Magyar Zoltan) of which the details will be described elsewhere. E2Fa and E2Fb ChIP experiments were performed on an E2Fa overexpressing and, wild type Col-0 line respectively. The data confirm that the majority of the predicted E2F target genes are bound by E2Fa or E2Fb, supporting the previous statement that the Affymetrix Tilling 1.0R array allowed prediction of novel E2F target genes. The third author Tim Lammens takes the entire responsibility for making the faulty collage and for not informing the other authors. Co-corresponding authors Lieven De Veylder and Marnik Vuysteke take responsibility for not recognizing the faulty collage before submitting the paper for publication. All raw data and materials described are freely available upon request. Figure 7 (original)Open in figure viewerPowerPoint Chromatin immunoprecipitation (ChIP) analysis confirming direct E2Fa/E2Fb binding to the promoter of 10 randomly selected potential direct targets (gene identifiers on the left) of E2Fa-DPa, identified uniquely on the Tiling 1.0R platform. ChIP was performed on 8-day-old E2Fa-DPaOE seedlings using E2Fa- or E2Fb- specific antibodies. An immunoprecipitation without any added antibody served as a control. Figure 7 (corrected)Open in figure viewerPowerPoint Chromatin immunoprecipitation (ChIP) analysis confirming direct E2Fa/E2Fb binding to the promoter of 10 randomly selected potential direct targets (gene identifiers on the left) of E2Fa-DPa, identified uniquely on the Tiling 1.0R platform. ChIP was performed using E2Fa- (E2Fa IP) or E2Fb-specific (E2Fb IP) antibodies on 8-day-old E2FaOE and wild type Col-0 seedlings, respectively. An immunoprecipitation without any added antibody served as a control (No Ab). The ACT2 (At3g46520) and CDKA;1 (At3g48750) genes serve as negative controls, whereas UVI4 (At2g42260) represents a positive control. The dashed lines indicate the background level. Reference Naouar, N., Vandepoele, K., Lammens, T. et al. (2009) Quantitative RNA expression analysis with Affymetrix Tiling 1.0R arrays identifies new E2F target genes. Plant J. 57, 184– 194. Volume91, Issue6September 2017Pages 1129-1130 FiguresReferencesRelatedInformation

Repression of meiotic genes by antisense transcription and by Fkh2 transcription factor in Schizosaccharomyces pombe.

In S. pombe, about 5% of genes are meiosis-specific and accumulate little or no mRNA during vegetative growth. Here we use Affymetrix tiling arrays to characterize transcripts in vegetative and meiotic cells. In vegetative cells, many meiotic genes, especially those induced in mid-meiosis, have abundant antisense transcripts. Disruption of the antisense transcription of three of these mid-meiotic genes allowed vegetative sense transcription. These results suggest that antisense transcription represses sense transcription of meiotic genes in vegetative cells. Although the mechanism(s) of antisense mediated transcription repression need to be further explored, our data indicates that RNAi machinery is not required for repression. Previously, we and others used non-strand specific methods to study splicing regulation of meiotic genes and concluded that 28 mid-meiotic genes are spliced only in meiosis. We now demonstrate that the “unspliced” signal in vegetative cells comes from the antisense RNA, not from unspliced sense RNA, and we argue against the idea that splicing regulates these mid-meiotic genes. Most of these mid-meiotic genes are induced in mid-meiosis by the forkhead transcription factor Mei4. Interestingly, deletion of a different forkhead transcription factor, Fkh2, allows low levels of sense expression of some mid-meiotic genes in vegetative cells. We propose that vegetative expression of mid-meiotic genes is repressed at least two independent ways: antisense transcription and Fkh2 repression.

Bayesian Modeling of ChIP‐chip Data Through a High‐Order Ising Model

ChIP-chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein-DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP-chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP-chip data through an Ising model with high-order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios.

TiArA: A Virtual Appliance for the Analysis of Tiling Array Data

BackgroundGenomic tiling arrays have been described in the scientific literature since 2003, yet there is a shortage of user-friendly applications available for their analysis.Methodology/Principal FindingsTiling Array Analyzer (TiArA) is a software program that provides a user-friendly graphical interface for the background subtraction, normalization, and summarization of data acquired through the Affymetrix tiling array platform. The background signal is empirically measured using a group of nonspecific probes with varying levels of GC content and normalization is performed to enforce a common dynamic range.Conclusions/SignificanceTiArA is implemented as a standalone program for Linux systems and is available as a cross-platform virtual machine that will run under most modern operating systems using virtualization software such as Sun VirtualBox or VMware. The software is available as a Debian package or a virtual appliance at http://purl.org/NET/tiara.

Genome-wide analysis of endogenous abscisic acid-mediated transcription in dry and imbibed seeds of Arabidopsis using tiling arrays

The phytohormone abscisic acid (ABA) plays important roles in the induction and maintenance of seed dormancy. Although application of exogenous ABA inhibits germination, the effects of exogenous ABA on ABA-mediated gene transcription differ from those of endogenous ABA. To understand how endogenous ABA regulates the transcriptomes in seeds, we performed comprehensive expression analyses using whole-genome Affymetrix tiling arrays in two ABA metabolism mutants - an ABA-deficient mutant (aba2) and an ABA over-accumulation mutant (cyp707a1a2a3 triple mutant). Hierarchical clustering and principal components analyses showed that differences in endogenous ABA levels do not influence global expression of stored mRNA in dry seeds. However, the transcriptome after seed imbibition was related to endogenous ABA levels in both types of mutant. Endogenous ABA-regulated genes expressed in imbibed seeds included those encoding key ABA signaling factors and gibberellin-related components. In addition, cohorts of ABA-upregulated genes partially resembled those of dormant genes, whereas ABA-downregulated genes were partially overlapped with after-ripening-regulated genes. Bioinformatic analyses revealed that 6105 novel genes [non-Arabidopsis Genome Initiative (AGI) transcriptional units (TUs)] were expressed from unannotated regions. Interestingly, approximately 97% of non-AGI TUs possibly encoded hypothetical non-protein-coding RNAs, including a large number of antisense RNAs. In dry and imbibed seeds, global expression profiles of non-AGI TUs were similar to those of AGI genes. For both non-AGI TUs and AGI code genes, we identified those that were regulated differently in embryo and endosperm tissues. Our results suggest that transcription in Arabidopsis seeds is more complex and dynamic than previously thought.

RMAT - an R/Bioconductor package for analyzing ChIP-chip experiments

Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) has evolved as a popular technique to study DNA-protein binding or post-translational chromatin/histone modifications at the genomic level. However, the raw microarray intensities generate a massive amount of data, creating a need for efficient analysis algorithms and statistical methods to identify enriched regions. We present a fast, free and powerful, open source R package, rMAT, that allows the identification of regions enriched for transcription factor binding sites in ChIP-chip experiments on Affymetrix tiling arrays. The R-package rMAT is available from the Bioconductor web site at http://bioconductor.org and runs on Linux, MAC OS and MS-Windows. rMAT is distributed under the terms of the Artistic Licence 2.0.

Discovery of novel human transcript variants by analysis of intronic single-block EST with polyadenylation site

BackgroundAlternative polyadenylation sites within a gene can lead to alternative transcript variants. Although bioinformatic analysis has been conducted to detect polyadenylation sites using nucleic acid sequences (EST/mRNA) in the public databases, one special type, single-block EST is much less emphasized. This bias leaves a large space to discover novel transcript variants.ResultsIn the present study, we identified novel transcript variants in the human genome by detecting intronic polyadenylation sites. Poly(A/T)-tailed ESTs were obtained from single-block ESTs and clustered into 10,844 groups standing for 5,670 genes. Most sites were not found in other alternative splicing databases. To verify that these sites are from expressed transcripts, we analyzed the supporting EST number of each site, blasted representative ESTs against known mRNA sequences, traced terminal sequences from cDNA clones, and compared with the data of Affymetrix tiling array. These analyses confirmed about 84% (9,118/10,844) of the novel alternative transcripts, especially, 33% (3,575/10,844) of the transcripts from 2,704 genes were taken as high-reliability. Additionally, RT-PCR confirmed 38% (10/26) of predicted novel transcript variants.ConclusionOur results provide evidence for novel transcript variants with intronic poly(A) sites. The expression of these novel variants was confirmed with computational and experimental tools. Our data provide a genome-wide resource for identification of novel human transcript variants with intronic polyadenylation sites, and offer a new view into the mystery of the human transcriptome.

Global Identification of Targets of theArabidopsisMADS Domain Protein AGAMOUS-Like15

AGAMOUS-Like15 (AGL15) is a MADS domain transcriptional regulator that promotes somatic embryogenesis by binding DNA and regulating gene expression. Chromatin immunoprecipitation (ChIP) analysis previously identified DNA fragments with which AGL15 associates in vivo, and a low-throughput approach revealed a role for AGL15 in gibberellic acid catabolism that is relevant to embryogenesis. However, higher throughput methods are needed to identify targets of AGL15. Here, we mapped AGL15 in vivo binding sites using a ChIP-chip approach and the Affymetrix tiling arrays for Arabidopsis thaliana and found that approximately 2000 sites represented in three biological replicates of the experiment are annotated to nearby genes. These results were combined with high-throughput measurement of gene expression in response to AGL15 accumulation to discriminate responsive direct targets from those further downstream in the network. LEAFY COTYLEDON2, FUSCA3, and ABA INSENSITIVE3, which encode B3 domain transcription factors that are key regulators of embryogenesis, were identified and verified as direct target genes of AGL15. Genes identified as targets of the B3 genes are also targets of AGL15, and we found that INDOLEACETIC ACID-INDUCED PROTEIN30 is involved in promotion of somatic embryo development. The data presented here and elsewhere suggest that much cross-regulation occurs in gene regulatory networks underpinning embryogenesis.

TileProbe: modeling tiling array probe effects using publicly available data

Motivation: Individual probes on an Affymetrix tiling array usually behave differently. Modeling and removing these probe effects are critical for detecting signals from the array data. Current data processing techniques either require control samples or use probe sequences to model probe-specific variability, such as with MAT. Although the MAT approach can be applied without control samples, residual probe effects continue to distort the true biological signals.Results: We propose TileProbe, a new technique that builds upon the MAT algorithm by incorporating publicly available data sets to remove tiling array probe effects. By using a large number of these readily available arrays, TileProbe robustly models the residual probe effects that MAT model cannot explain. When applied to analyzing ChIP-chip data, TileProbe performs consistently better than MAT across a variety of analytical conditions. This shows that TileProbe resolves the issue of probe-specific effects more completely.Availability: http://www.biostat.jhsph.edu/∼hji/cisgenome/index_files/tileprobe.htmContact: hji@jhsph.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Correction: Pre-Columbian Origins for North American Anthrax

There were several errors in the author byline. Drs. Lingxia Jiang and Pawel Gajer were mistakenly omitted from the author byline. Lingxia Jiang should be listed as the tenth author and is affiliated with the Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America. Her contributions were as follows: Optimized the Affymetrix tiling chip protocol and processed all the Affymetrix tiling GeneChips (>125). Contributed to the analysis of the GeneChip data. Pawel Gajer should be listed as the 11th author and is affilated with the Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America. His contributions were as follows: Developed the algorithm to extract SNP data from the microbial Affymetrix whole genome tiling GeneChip. Processed the GeneChip and extracted SNP data. The eighth author's name appears incorrectly. It should be: Carla B. Trim. The sixth author, Jacques Ravel, should appear as the fourteenth author.

STATISTICAL METHODS FOR AFFYMETRIX TILING ARRAY DATA

Tiling arrays are a microarray technology currently being used for a variety of genomic and epigenomic applications, such as the mapping of transcription, DNA methylation, and histone modifications. Tiling arrays provide high-density coverage of a genome, or a genomic region, through the systematic and sequential placement of probes without regard to genome annotation. In this paper we compare the Affymetrix tiling array to the Affymetrix GeneChip® 3’ expression array and propose methods that address statistical and bioinformatic issues that accompany gene expression data that are generated from Affymetrix tiling arrays. Real data from the model organism Arabidopsis thaliana motivate this work and application.

Arabidopsis Transcriptome Analysis under Drought, Cold, High-Salinity and ABA Treatment Conditions using a Tiling Array

Plants respond and adapt to drought, cold and high-salinity stresses in order to survive. In this study, we applied Arabidopsis Affymetrix tiling arrays to study the whole genome transcriptome under drought, cold, high-salinity and ABA treatment conditions. The bioinformatic analysis using the tiling array data showed that 7,719 non-AGI transcriptional units (TUs) exist in the unannotated "intergenic" regions of Arabidopsis genome. These include 1,275 and 181 TUs that are induced and downregulated, respectively, by the stress or ABA treatments. Most of the non-AGI TUs are hypothetical non-protein-coding RNAs. About 80% of the non-AGI TUs belong to pairs of the fully overlapping sense-antisense transcripts (fSATs). Significant linear correlation between the expression ratios (treated/untreated) of the sense TUs and the ratios of the antisense TUs was observed in the SATs of AGI code/non-AGI TU. We studied the biogenesis mechanisms of the stress- or ABA-inducible antisense RNAs and found that the expression of sense TUs is necessary for the stress- or ABA-inducible expression of the antisense TUs in the fSATs (AGI code/non-AGI TU).

High-Resolution Analyses of Epigenetic Aberrations in Myelodysplastic Syndrome.

Myelodysplastic syndromes are heterogeneous groups of clonal hematopoietic disorders characterized by ineffective blood cells production and predisposition to acute myeloid leukemia, and as such, it is well established that these syndromes actually represent neoplastic processes in which a series of gene mutations accumulate in blood cell precursors, leading to neoplastic expansion of dominant clones. During the past two decades, a number of genetic abnormalities have been described in MDS cases, including copy number alterations of particular chromosomal segments, mutations of Ras, p53, runx1, and Flt3 genes, and translocation involving Evi-1 family genes, TEL, MLL and Nup98 genes. On the other hand, epigenetic abnormalities are also thought to play an important role in the pathogenesis of MDS, because demethylating agents such as 5-azacydine and decitabine are often effective for high risk MDS. Unfortunately, however, only a few genes, such as INK4B gene, have been implicated in MDS pathogenesis. Especially, no genome-wide analysis of epigenetic changes in MDS has been reported. So, in the current study, we comprehensively investigated abnormalities of DNA methylation in 30 MDS specimens, using Affymetrix tiling array combined with methylated DNA immunoprecipitation (MeDIP). In this method, genomic DNA from MDS specimens was first fragmentized with ultrasonication and immuno-precipitated with anti-methylcytosine antibody (MeDIP). The immunoprecipitated DNA was then amplified by PCR and subjected to hybridization to the promorter tiling array. In this array, regulatory regions of more than 25,000 genes are tiled by 6.5 millions of oligonucleotide probes to enable sensitive detection of target sequences and approximately 59% of CpG islands in the human genomes are covered in a single array. The extent and distribution of methylation were highly variable between specimens, although some CpG islands, such as p15INK4B and HOX gene clusters, seemed to be commonly involved in different cases (Figure). In conclusion, MeDIP on chip analysis could be a powerful method for genome-wide detection of DNA methylation and facilitate our understanding of the pathogenesis of MDS. [Display omitted]

A physical model for tiling array analysis

Chromatin immunoprecipitation (ChIP) is a powerful experimental approach to identify in vivo binding sites of sequence-specific transcription factors (TFs). These experiments are designed to specifically enrich DNA fragments that are bound to the TF. Tiling arrays have become more and more popular for the identification of these DNA fragments. However, many studies showed that only a fraction of the identified DNA fragments contains bona fide binding sites for the TF, suggesting that indirect binding mechanisms play a very important role. We explored the possibility that the lack of binding sites can also be explained by problems in identifying ChIP-enriched DNA fragments from the measured intensities. We derived a physical model that explains some (but not all) variation of the measured probe intensities of Affymetrix tiling arrays. We used the physical model to estimate the probe-specific behavior and corrected for it. Subsequently, we developed a method to identify ChIP-enriched DNA fragments. We termed it physical model for tiling array analysis (PMT). We applied PMT to the data of ChIP-chip experiments interrogating chromosome 21 and 22 of the human genome for binding of the TFs MYC, SP1 and P53. Almost all regions recovered by PMT showed evidence for sequence-specific binding of the TFs.

Microarray blob-defect removal improves array analysis

New generation Affymetrix oligonucleotide microarrays often have blob-like image defects that will require investigators to either repeat their hybridization assays or analyze their data with the defects left in place. We investigated the effect of analyzing a spike-in experiment on Affymetrix ENCODE tiling arrays in the presence of simulated blobs covering between 1 and 9% of the array area. Using two different ChIP-chip tiling array analysis programs (Affymetrix tiling array software, TAS, and model-based analysis of tiling arrays, MAT), we found that even the smallest blob defects significantly decreased the sensitivity and increased the false discovery rate (FDR) of the spike-in target prediction. We introduced a new software tool, the microarray blob remover (MBR), which allows rapid visualization, detection and removal of various blob defects from the .CEL files of different types of Affymetrix microarrays. It is shown that using MBR significantly improves the sensitivity and FDR of a tiling array analysis compared to leaving the affected probes in the analysis. The MBR software and the sample array .CEL files used in this article are available at: http://liulab.dfci.harvard.edu/Software/MBR/MBR.htm

Model-based analysis of tiling-arrays for ChIP-chip

We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/ approximately wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms.

A RecA-mediated exon profiling method

We have developed a RecA-mediated simple, rapid and scalable method for identifying novel alternatively spliced full-length cDNA candidates. This method is based on the principle that RecA proteins allow to carry radioisotope-labeled probe DNAs to their homologous sequences, resulting in forming triplexes. The resulting complex is easily detected by mobility difference on electrophoresis. We applied this exon profiling method to four selected mouse genes as a feasibility study. To design probes for detection, the information on known exonic regions was extracted from public database, RefSeq. Concerning the potentially transcribed novel exonic regions, RNA mapping experiment using Affymetrix tiling array was performed. As a result, we were able to identify alternative splice variants of Thioredoxin domain containing 5, Interleukin1β, Interleukin 1 family 6 and glutamine-rich hypothetical protein. In addition, full-length sequencing demonstrated that our method could profile exon structures with >90% accuracy. This reliable method can allow us to screen novel splice variants from a huge number of cDNA clone set effectively.