Starr: Simple Tiling Array Analysis of Affymetrix ChIP-chip data

Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay for DNA-protein-binding or post-translational chromatin/histone modifications. As with all high-throughput technologies, it requires a thorough bioinformatic processing of the data for which there is no standard yet. The primary goal is the reliable identification and localization of genomic regions that bind a specific protein. The second step comprises comparison of binding profiles of functionally related proteins, or of binding profiles of the same protein in different genetic backgrounds or environmental conditions. Ultimately, one would like to gain a mechanistic understanding of the effects of DNA binding events on gene expression. We present a free, open-source R package Starr that, in combination with the package Ringo, facilitates the comparative analysis of ChIP-chip data across experiments and across different microarray platforms. Core features are data import, quality assessment, normalization and visualization of the data, and the detection of ChIP-enriched genomic regions. The use of common Bioconductor classes ensures the compatibility with other R packages. Most importantly, Starr provides methods for integration of complementary genomics data, e.g., it enables systematic investigation of the relation between gene expression and dna binding

Custom Design and Analysis of High-Density Oligonucleotide Bacterial Tiling Microarrays

Not until recently have custom made high-density oligonucleotide microarrays been available at an affordable price. The aim of this thesis was to design microarrays and analysis algorithms for DNA repair and DNA damage detection, and to apply the methods in real experiments. Thomassen et al. have used their custom designed whole genome-tiling microarrays for detection of transcriptional changes in Escherichia coli after exposure to DNA damageing reagents. The transcriptional changes in E. coli treated with UV light or the methylating reagent MNNG were shown to be larger and to include far more genes than previously reported. To optimize the data analysis for the custom made arrays, Thomassen and coworkers designed their own normalization and analysis algorithms, and showed these more suitable than established methods that are currently applied on custom tiling arrays. Among other findings several novel stress-induced transcripts were detected, of which one is predicted to be a UV-induced short transmembrane protein. Additionally, no upregulation of the previously described UV-inducible aidB is shown. In the MNNG study several genes are shown as downregulated in response to DNA damage although having upstream regulatory sequences similar to the established LexA box A and B. This indicates that the LexA regulon also might control gene repression and that the box A and B sequence can not alone answer for the LexA controlled gene regulation. Thomassen et al. have also custom designed a microarray for oncogenic fusion gene detection. Cancer specific fusion genes are often used to subgroup cancers and to define the optimal treatment, but currently the laboratory detection procedure is both laborious and tedious. In a blinded study on six cancer cell lines proof of principle was shown by detection of six out of six positive controls. The design and analysis methods for this microarray are now being refined to make a diagnostic fusion gene detection tool

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

Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome

Tiling arrays make possible a large scale exploration of the genome thanks to probes which cover the whole genome with very high density until 2 000 000 probes. Biological questions usually addressed are either the expression difference between two conditions or the detection of transcribed regions. In this work we propose to consider simultaneously both questions as an unsupervised classification problem by modeling the joint distribution of the two conditions. In contrast to previous methods, we account for all available information on the probes as well as biological knowledge like annotation and spatial dependence between probes. Since probes are not biologically relevant units we propose a classification rule for non-connected regions covered by several probes. Applications to transcriptomic and ChIP-chip data of Arabidopsis thaliana obtained with a NimbleGen tiling array highlight the importance of a precise modeling and the region classification

Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data

<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay used for investigating DNA-protein-binding or post-translational chromatin/histone modifications. As with all high-throughput technologies, it requires thorough bioinformatic processing of the data for which there is no standard yet. The primary goal is to reliably identify and localize genomic regions that bind a specific protein. Further investigation compares binding profiles of functionally related proteins, or binding profiles of the same proteins in different genetic backgrounds or experimental conditions. Ultimately, the goal is to gain a mechanistic understanding of the effects of DNA binding events on gene expression.</p> <p>Results</p> <p>We present a free, open-source <b>R</b>/Bioconductor package <it>Starr </it>that facilitates comparative analysis of ChIP-chip data across experiments and across different microarray platforms. The package provides functions for data import, quality assessment, data visualization and exploration. <it>Starr </it>includes high-level analysis tools such as the alignment of ChIP signals along annotated features, correlation analysis of ChIP signals with complementary genomic data, peak-finding and comparative display of multiple clusters of binding profiles. It uses standard Bioconductor classes for maximum compatibility with other software. Moreover, <it>Starr </it>automatically updates microarray probe annotation files by a highly efficient remapping of microarray probe sequences to an arbitrary genome.</p> <p>Conclusion</p> <p><it>Starr </it>is an <b>R </b>package that covers the complete ChIP-chip workflow from data processing to binding pattern detection. It focuses on the high-level data analysis, e.g., it provides methods for the integration and combined statistical analysis of binding profiles and complementary functional genomics data. <it>Starr </it>enables systematic assessment of binding behaviour for groups of genes that are alingned along arbitrary genomic features.</p

Comparison of sequence-dependent tiling array normalization approaches

<p>Abstract</p> <p>Background</p> <p>The detection of enriched DNA or RNA fragments by tiling microarrays has become more and more popular. These microarrays contain a high number of small probes covering genomic loci. However, to achieve high coverage the probe sequences cannot be selected for their hybridization properties. The affinity of the probes towards their targets varies in a sequence-dependent manner. In order to remove this bias a number of approaches have been developed and shown to increase the detection of enriched DNA or RNA fragments. However, these approaches also employ a peak detection algorithm that is different from the one used previously. Thus, it seems possible that the enhancement of detection is due to the peak detection algorithm rather than the sequence-dependent normalization.</p> <p>Results</p> <p>We compared three different sequence-dependent probe level normalization procedures to a naïve sequence-independent normalization technique. In order to achieve maximal comparability, we used the normalized intensity values as input to a single peak detection algorithm. A so-called "spike-in" data set served as benchmark for the performance. We will show that the sequence-dependent normalization procedures do not perform better than the naïve approach, suggesting that the benefit of using these normalization approaches is limited. Furthermore, we will show that the naïve approach does well, because it effectively removes the sequence-dependent component of the measured intensities with the help of the control hybridization experiment.</p> <p>Conclusion</p> <p>Sequence-dependent normalization of microarray data hardly improves the detection of enriched DNA or RNA fragments. The "success" of the sequence-independent naïve approach is only possible due to the control experiment and requires proper scaling of the measured intensities.</p

A tiling microarray for global analysis of chloroplast genome expression in cucumber and other plants

Plastids are small organelles equipped with their own genomes (plastomes). Although these organelles are involved in numerous plant metabolic pathways, current knowledge about the transcriptional activity of plastomes is limited. To solve this problem, we constructed a plastid tiling microarray (PlasTi-microarray) consisting of 1629 oligonucleotide probes. The oligonucleotides were designed based on the cucumber chloroplast genomic sequence and targeted both strands of the plastome in a non-contiguous arrangement. Up to 4 specific probes were designed for each gene/exon, and the intergenic regions were covered regularly, with 70-nt intervals. We also developed a protocol for direct chemical labeling and hybridization of as little as 2 micrograms of chloroplast RNA. We used this protocol for profiling the expression of the cucumber chloroplast plastome on the PlasTi-microarray. Owing to the high sequence similarity of plant plastomes, the newly constructed microarray can be used to study plants other than cucumber. Comparative hybridization of chloroplast transcriptomes from cucumber, Arabidopsis, tomato and spinach showed that the PlasTi-microarray is highly versatile

Analyzing ChIP-chip Data Using Bioconductor

Analyzing ChIP-chip Data Using Bioconducto

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

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies

<p>Abstract</p> <p>Background</p> <p>The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate.</p> <p>Results</p> <p>We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures.</p> <p>Conclusion</p> <p>T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially.</p