Tiling Array Data Research Articles

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).

A TWO-STAGE APPROACH FOR ESTIMATING THE EFFECT OF DNA METHYLATION ON DIFFERENTIAL EXPRESSION USING TILING ARRAY TECHNOLOGY

Epigenetics is the study of heritable alterations in gene function without changing the DNA sequence itself. It is known that epigenetic modifications such as DNA methylation and histone modifications are highly correlated with the regulation of gene expression. A twostage analysis is proposed that employs a hidden Markov model and a linear model to evaluate differential expression as related to DNA methylation for the purpose of examining the effects of DNA methylation on gene regulation using tiling array technology. In the first stage, a hidden Markov model (HMM) is employed to estimate the methylation status per tile by utilizing information of neighboring tiles. In the second stage, a linear model is applied to the expression data to identify significantly differentially expressed tiles given the changes in methylation status. The two-stage analysis is applied to Arabidopsis chromosome 4 tiling array data and the results are compared with a traditional ANOVA model which has been employed to identify significantly differentially expressed genes using microarray data.

Transcriptional analysis of highly syntenic regions between Medicago truncatula and Glycine max using tiling microarrays

The comparative transcriptional analysis of highly syntenic regions in six different organ types between Medicago truncatula (barrel medic) and Glycine max (soybean), using nucleotide tiling microarrays, provides insights into genome organization and transcriptional regulation in these legume plants.

Model-based deconvolution of genome-wide DNA binding

Chromatin immunoprecipitation followed by hybridization to a genomic tiling microarray (ChIP-chip) is a routinely used protocol for localizing the genomic targets of DNA-binding proteins. The resolution to which binding sites in this assay can be identified is commonly considered to be limited by two factors: (1) the resolution at which the genomic targets are tiled in the microarray and (2) the large and variable lengths of the immunoprecipitated DNA fragments. We have developed a generative model of binding sites in ChIP-chip data and an approach, MeDiChI, for efficiently and robustly learning that model from diverse data sets. We have evaluated MeDiChI's performance using simulated data, as well as on several diverse ChIP-chip data sets collected on widely different tiling array platforms for two different organisms (Saccharomyces cerevisiae and Halobacterium salinarium NRC-1). We find that MeDiChI accurately predicts binding locations to a resolution greater than that of the probe spacing, even for overlapping peaks, and can increase the effective resolution of tiling array data by a factor of 5x or better. Moreover, the method's performance on simulated data provides insights into effectively optimizing the experimental design for increased binding site localization accuracy and efficacy. MeDiChI is available as an open-source R package, including all data, from http://baliga.systemsbiology.net/medichi.

Use of tiling array data and RNA secondary structure predictions to identify noncoding RNA genes

BackgroundWithin the last decade a large number of noncoding RNA genes have been identified, but this may only be the tip of the iceberg. Using comparative genomics a large number of sequences that have signals concordant with conserved RNA secondary structures have been discovered in the human genome. Moreover, genome wide transcription profiling with tiling arrays indicate that the majority of the genome is transcribed.ResultsWe have combined tiling array data with genome wide structural RNA predictions to search for novel noncoding and structural RNA genes that are expressed in the human neuroblastoma cell line SK-N-AS. Using this strategy, we identify thousands of human candidate RNA genes. To further verify the expression of these genes, we focused on candidate genes that had a stable hairpin structures or a high level of covariance. Using northern blotting, we verify the expression of 2 out of 3 of the hairpin structures and 3 out of 9 high covariance structures in SK-N-AS cells.ConclusionOur results demonstrate that many human noncoding, structured and conserved RNA genes remain to be discovered and that tissue specific tiling array data can be used in combination with computational predictions of sequences encoding structural RNAs to improve the search for such genes.

Optimized design and assessment of whole genome tiling arrays

MotivationRecent advances in microarray technologies have made it feasible to interrogate whole genomes with tiling arrays and this technique is rapidly becoming one of the most important high-throughput functional genomics assays. For large mammalian genomes, analyzing oligonucleotide tiling array data is complicated by the presence of non-unique sequences on the array, which increases the overall noise in the data and may lead to false positive results due to cross-hybridization. The ability to create custom microarrays using maskless array synthesis has led us to consider ways to optimize array design characteristics for improving data quality and analysis. We have identified a number of design parameters to be optimized including uniqueness of the probe sequences within the whole genome, melting temperature and self-hybridization potential.ResultsWe introduce the uniqueness score, U, a novel quality measure for oligonucleotide probes and present a method to quickly compute it. We show that U is equivalent to the number of shortest unique substrings in the probe and describe an efficient greedy algorithm to design mammalian whole genome tiling arrays using probes that maximize U. Using the mouse genome, we demonstrate how several optimizations influence the tiling array design characteristics. With a sensible set of parameters, our designs cover 78% of the mouse genome including many regions previously considered ‘untilable’ due to the presence of repetitive sequence. Finally, we compare our whole genome tiling array designs with commercially available designs.AvailabilitySource code is available under an open source license from http://www.ebi.ac.uk/~graef/arraydesign/

Identification of an in vitro transcription-based artifact affecting oligonucleotide microarrays

This study identified the widely used T7 in vitro transcription system as a major source of artifact in the tiling array data from nine eukaryotic genomes. The most affected probes contained a sequence motif complementary to the +1 to +9 initial transcribed sequence (ITS) of the T7-(dT)24 primer. The abundance of 5′ ITS cRNA fragments produced during target preparation was sufficient to drive undesirable hybridization. A new T7-(dT)24 primer with a modified ITS was designed that shifts the artifactual motifs as predicted and reduces the effect of the artifact. A computational algorithm was generated to filter out the likely artifactual probes from existing whole-genome tiling array data and improve probe selection. Further studies of Arabidopsis thaliana were conducted using both T7-(dT)24 primers. While the artifact affected transcript discovery with tiling arrays, it showed only a minor impact on measurements of gene expression using commercially available ‘gene-only’ expression arrays.

Error-pooling-based statistical methods for identifying novel temporal replication profiles of human chromosomes observed by DNA tiling arrays

Statistical analysis on tiling array data is extremely challenging due to the astronomically large number of sequence probes, high noise levels of individual probes and limited number of replicates in these data. To overcome these difficulties, we first developed statistical error estimation and weighted ANOVA modeling approaches to high-density tiling array data, especially the former based on an advanced error-pooling method to accurately obtain heterogeneous technical error of small-sample tiling array data. Based on these approaches, we analyzed the high-density tiling array data of the temporal replication patterns during cell-cycle S phase of synchronized HeLa cells on human chromosomes 21 and 22. We found many novel temporal replication patterns, identifying about 26% of over 1 million tiling array sequence probes with significant differential replication during the four 2-h time periods of S phase. Among these differentially replicated probes, 126 941 sequence probes were matched to 417 known genes. The majority of these genes were found to be replicated within one or two consecutive time periods, while the others were replicated at two non-consecutive time periods. Also, coding regions found to be more differentially replicated in particular time periods than noncoding regions in the gene-poor chromosome 21 (25% differentially replicated among genic probes versus 18.6% among intergenic probes), while such a phenomenon was less prominent in gene-rich chromosome 22. A rigorous statistical testing for local proximity of differentially replicated genic and intergenic probes was performed to identify significant stretches of differentially replicated sequence regions. From this analysis, we found that adjacent genes were frequently replicated at different time periods, potentially implying the existence of quite dense replication origins. Evaluating the conditional probability significance of identified gene ontology terms on chromosomes 21 and 22, we detected some over-represented molecular functions and biological processes among these differentially replicated genes, such as the ones relevant to hydrolase, transferase and receptor-binding activities. Some of these results were confirmed showing >70% consistency with cDNA microarray data that were independently generated in parallel with the tiling arrays. Thus, our improved analysis approaches specifically designed for high-density tiling array data enabled us to reliably and sensitively identify many novel temporal replication patterns on human chromosomes.

A large number of novel coding small open reading frames in the intergenic regions of the Arabidopsis thaliana genome are transcribed and/or under purifying selection

Large-scale cDNA sequencing projects and tiling array studies have revealed the presence of many unannotated genes. For protein coding genes, small coding sequences may not be identified by gene finders because of the conservative nature of prediction algorithms. In this study, we identified small open reading frames (sORFs) with high coding potential by a simple gene finding method (Coding Index, CI) based on the nucleotide composition bias found in most coding sequences. Applying this method to 18 Arabidopsis thaliana and 84 yeast sORF genes with evidence of expression at the protein level gives 100% accurate prediction. In the A. thaliana genome, we identified 7159 sORFs that are likely coding sequences (coding sORFs) with the CI measure at the 1% false-positive rate. To determine if these coding sORFs are parts of functional genes, we evaluated each coding sORF for evidence of transcription or evolutionary conservation. At the 5% false-positive rate, we found that 2996 coding sORFs are likely expressed in at least one experimental condition of the A. thaliana tiling array data. In addition, the evolutionary conservation of each A. thaliana sORF was examined within A. thaliana or between A. thaliana and five plants with complete or partial genome sequences. In 3997 coding sORFs with readily identifiable homologous sequences, 2376 are subject to purifying selection at the 1% false-positive rate. After eliminating coding sORFs with similarity to known transposable elements and those that are likely missing exons of known genes, the remaining 3241 coding sORFs with either evidence of transcription or purifying selection likely belong to novel coding genes in the A. thaliana genome.

Generalized Hierarchical Markov Models for the Discovery of Length‐Constrained Sequence Features from Genome Tiling Arrays

A generalized hierarchical Markov model for sequences that contain length-restricted features is introduced. This model is motivated by the recent development of high-density tiling array data for determining genomic elements of functional importance. Due to length constraints on certain features of interest, as well as variability in probe behavior, usual hidden Markov-type models are not always applicable. A robust Bayesian framework that can incorporate length constraints, probe variability, and bias is developed. Moreover, a novel recursion-based Monte Carlo algorithm is proposed to estimate the parameters and impute hidden states under length constraints. Application of this methodology to yeast chromosomal arrays demonstrate substantial improvement over currently existing methods in terms of sensitivity as well as biological interpretability.

Comparative analysis of genome tiling array data reveals many novel primate-specific functional RNAs in human

BackgroundWidespread transcription activities in the human genome were recently observed in high-resolution tiling array experiments, which revealed many novel transcripts that are outside of the boundaries of known protein or RNA genes. Termed as "TARs" (Transcriptionally Active Regions), these novel transcribed regions represent "dark matter" in the genome, and their origin and functionality need to be explained. Many of these transcripts are thought to code for novel proteins or non-protein-coding RNAs. We have applied an integrated bioinformatics approach to investigate the properties of these TARs, including cross-species conservation, and the ability to form stable secondary structures. The goal of this study is to identify a list of potential candidate sequences that are likely to code for functional non-protein-coding RNAs. We are particularly interested in the discovery of those functional RNA candidates that are primate-specific, i.e. those that do not have homologs in the mouse or dog genomes but in rhesus.ResultsUsing sequence conservation and the probability of forming stable secondary structures, we have identified ~300 possible candidates for primate-specific noncoding RNAs. We are currently in the process of sequencing the orthologous regions of these candidate sequences in several other primate species. We will then be able to apply a "phylogenetic shadowing" approach to analyze the functionality of these ncRNA candidates.ConclusionThe existence of potential primate-specific functional transcripts has demonstrated the limitation of previous genome comparison studies, which put too much emphasis on conservation between human and rodents. It also argues for the necessity of sequencing additional primate species to gain a better and more comprehensive understanding of the human genome.

Tilescope: online analysis pipeline for high-density tiling microarray data

Tilescope is a fully integrated and automated new data-processing pipeline for analyzing high-density tiling-array data.

The Transcriptome of the Sea Urchin Embryo

The sea urchin Strongylocentrotus purpuratus is a model organism for study of the genomic control circuitry underlying embryonic development. We examined the complete repertoire of genes expressed in the S. purpuratus embryo, up to late gastrula stage, by means of high-resolution custom tiling arrays covering the whole genome. We detected complete spliced structures even for genes known to be expressed at low levels in only a few cells. At least 11,000 to 12,000 genes are used in embryogenesis. These include most of the genes encoding transcription factors and signaling proteins, as well as some classes of general cytoskeletal and metabolic proteins, but only a minor fraction of genes encoding immune functions and sensory receptors. Thousands of small asymmetric transcripts of unknown function were also detected in intergenic regions throughout the genome. The tiling array data were used to correct and authenticate several thousand gene models during the genome annotation process.

OmicBrowse: a browser of multidimensional omics annotations

OmicBrowse is a browser to explore multiple datasets coordinated in the multidimensional omic space integrating omics knowledge ranging from genomes to phenomes and connecting evolutional correspondences among multiple species. OmicBrowse integrates multiple data servers into a single omic space through secure peer-to-peer server communications, so that a user can easily obtain an integrated view of distributed data servers, e.g. an integrated view of numerous whole-genome tiling-array data retrieved from a user's in-house private-data server, along with various genomic annotations from public internet servers. OmicBrowse is especially appropriate for positional-cloning purposes. It displays both genetic maps and genomic annotations within wide chromosomal intervals and assists a user to select candidate genes by filtering their annotations or associated documents against user-specified keywords or ontology terms. We also show that an omic-space chart effectively represents schemes for integrating multiple datasets of multiple species. OmicBrowse is developed by the Genome-Phenome Superbrain Project and is released as free open-source software under the GNU General Public License at http://omicspace.riken.jp.

A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments: systematically incorporating validated biological knowledge

Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into 'active regions' (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that the HMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments.

The Arabidopsis Unannotated Secreted Peptide Database, a Resource for Plant Peptidomics

In the era of genomics, if a gene is not annotated, it is not investigated. Due to their small size, genes encoding peptides are often missed in genome annotations. Secreted peptides are important regulators of plant growth, development, and physiology. Identification of additional peptide signals by sequence homology searches has had limited success due to sequence heterogeneity. A bioinformatics approach was taken to find unannotated Arabidopsis (Arabidopsis thaliana) peptides. Arabidopsis chromosome sequences were searched for all open reading frames (ORFs) encoding peptides and small proteins between 25 and 250 amino acids in length. The translated ORFs were then sequentially queried for the presence of an amino-terminal cleavable signal peptide, the absence of transmembrane domains, and the absence of endoplasmic reticulum lumenal retention sequences. Next, the ORFs were filtered against the The Arabidopsis Information Resource 6.0 annotated Arabidopsis genes to remove those ORFs overlapping known genes. The remaining 33,809 ORFs were placed in a relational database to which additional annotation data were deposited. Genome-wide tiling array data were compared with the coordinates of the ORFs, supporting the possibility that many of the ORFs may be expressed. In addition, clustering and sequence similarity analyses revealed that many of the putative peptides are in gene families and/or appear to be present in the rice (Oryza sativa) genome. A subset of the ORFs was evaluated by reverse transcription-PCR and, for one-fifth of those, expression was detected. These results support the idea that the number and diversity of plant peptides is broader than currently assumed. The peptides identified and their annotation data may be viewed or downloaded through a searchable Web interface at peptidome.missouri.edu.

Detecting transcriptionally active regions using genomic tiling arrays

A new method for designing and integrating genomic tiling array data is described and applied to Anopheles and human arrays.

Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome

In contrast to the fairly reliable and complete annotation of the protein coding genes in the human genome, comparable information is lacking for noncoding RNAs (ncRNAs). We present a comparative screen of vertebrate genomes for structural noncoding RNAs, which evaluates conserved genomic DNA sequences for signatures of structural conservation of base-pairing patterns and exceptional thermodynamic stability. We predict more than 30,000 structured RNA elements in the human genome, almost 1,000 of which are conserved across all vertebrates. Roughly a third are found in introns of known genes, a sixth are potential regulatory elements in untranslated regions of protein-coding mRNAs and about half are located far away from any known gene. Only a small fraction of these sequences has been described previously. A comparison with recent tiling array data shows that more than 40% of the predicted structured RNAs overlap with experimentally detected sites of transcription. The widespread conservation of secondary structure points to a large number of functional ncRNAs and cis-acting mRNA structures in the human genome.

A hidden Markov model for analyzing ChIP-chip experiments on genome tiling arrays and its application to p53 binding sequences

Transcription factors (TFs) regulate gene expression by recognizing and binding to specific regulatory regions on the genome, which in higher eukaryotes can occur far away from the regulated genes. Recently, Affymetrix developed the high-density oligonucleotide arrays that tile all the non-repetitive sequences of the human genome at 35 bp resolution. This new array platform allows for the unbiased mapping of in vivo TF binding sequences (TFBSs) using Chromatin ImmunoPrecipitation followed by microarray experiments (ChIP-chip). The massive dataset generated from these experiments pose great challenges for data analysis. We developed a fast, scalable and sensitive method to extract TFBSs from ChIP-chip experiments on genome tiling arrays. Our method takes advantage of tiling array data from many experiments to normalize and model the behavior of each individual probe, and identifies TFBSs using a hidden Markov model (HMM). When applied to the data of p53 ChIP-chip experiments from an earlier study, our method discovered many new high confidence p53 targets including all the regions verified by quantitative PCR. Using a de novo motif finding algorithm MDscan, we also recovered the p53 motif from our HMM identified p53 target regions. Furthermore, we found substantial p53 motif enrichment in these regions comparing with both genomic background and the TFBSs identified earlier. Several of the newly identified p53 TFBSs are in the promoter region of known genes or associated with previously characterized p53-responsive genes. Available at the following URL http://genome.dfci.harvard.edu/~xsliu/HMMTiling/index.html.

AMPDB: the Arabidopsis Mitochondrial Protein Database.

The Arabidopsis Mitochondrial Protein Database is an Internet-accessible relational database containing information on the predicted and experimentally confirmed protein complement of mitochondria from the model plant Arabidopsis thaliana (http://www.ampdb.bcs.uwa.edu.au/). The database was formed using the total non-redundant nuclear and organelle encoded sets of protein sequences and allows relational searching of published proteomic analyses of Arabidopsis mitochondrial samples, a set of predictions from six independent subcellular-targeting prediction programs, and orthology predictions based on pairwise comparison of the Arabidopsis protein set with known yeast and human mitochondrial proteins and with the proteome of Rickettsia. A variety of precomputed physical–biochemical parameters are also searchable as well as a more detailed breakdown of mass spectral data produced from our proteomic analysis of Arabidopsis mitochondria. It contains hyperlinks to other Arabidopsis genomic resources (MIPS, TIGR and TAIR), which provide rapid access to changing gene models as well as hyperlinks to T-DNA insertion resources, Massively Parallel Signature Sequencing (MPSS) and Genome Tiling Array data and a variety of other Arabidopsis online resources. It also incorporates basic analysis tools built into the query structure such as a BLAST facility and tools for protein sequence alignments for convenient analysis of queried results.