Dataset Information

Hypergeometric analysis of tiling-array and sequence data: detection and interpretation of peaks.

ABSTRACT: Probing protein-deoxyribonucleic acid (DNA) is gaining popularity as it sheds light on molecular mechanisms that regulate the expression of genes. Currently, tiling-arrays and next-generation sequencing technology can be used to measure these interactions. Both methods generate a signal over the genome in which contiguous regions of peaks on the genome represent the presence of an interacting molecule. Many methods do exist to identify functional regions of interest (ROIs) on the genome. However the detection of ROIs are often not an end-point in research questions and it therefore requires data dragging between tools to relate the ROIs to information present in databases, such as gene-ontology, pathway information, or enrichment of certain genomic content. We introduce hypergeometric analysis of tiling-array and sequence data (HATSEQ), a powerful tool that accurately identifies functional ROIs on the genome where a genomic signal significantly deviates from the general genome-wide behavior. HATSEQ also includes a number of built-in post-analyses with which biological meaning can be attached to the detected ROIs in terms of gene pathways and de-novo motif analysis, and provides different visualizations and statistical summaries for the detected ROIs. In addition, HATSEQ has an intuitive graphic user interface that lowers the barrier for researchers to analyze their data without the need of scripting languages. We compared the results of HATSEQ against two other popular chromatin immunoprecipitation sequencing (ChIP-Seq) methods and observed overlap in the detected ROIs but HATSEQ is more specific in delineating the peak boundaries. We also discuss the versatility of HATSEQ by using a Signal Transducer and Activator of Transcription 1 (STAT1) ChIP-Seq data-set, and show that the detected ROIs are highly specific for the expected STAT1 binding motif. HATSEQ is freely available at: http://hema13.erasmusmc.nl/index.php/HATSEQ.

SUBMITTER: Taskesen E

PROVIDER: S-EPMC3810201 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Hypergeometric analysis of tiling-array and sequence data: detection and interpretation of peaks.

Taskesen Erdogan E Hoogeboezem Remco R Delwel Ruud R Reinders Marcel Jt MJ

Advances and applications in bioinformatics and chemistry : AABC 20131025

Probing protein-deoxyribonucleic acid (DNA) is gaining popularity as it sheds light on molecular mechanisms that regulate the expression of genes. Currently, tiling-arrays and next-generation sequencing technology can be used to measure these interactions. Both methods generate a signal over the genome in which contiguous regions of peaks on the genome represent the presence of an interacting molecule. Many methods do exist to identify functional regions of interest (ROIs) on the genome. However ...[more]

PMID: 24187504

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Project description:BackgroundTiling-arrays are applicable to multiple types of biological research questions. Due to its advantages (high sensitivity, resolution, unbiased), the technology is often employed in genome-wide investigations. A major challenge in the analysis of tiling-array data is to define regions-of-interest, i.e., contiguous probes with increased signal intensity (as a result of hybridization of labeled DNA) in a region. Currently, no standard criteria are available to define these regions-of-interest as there is no single probe intensity cut-off level, different regions-of-interest can contain various numbers of probes, and can vary in genomic width. Furthermore, the chromosomal distance between neighboring probes can vary across the genome among different arrays.ResultsWe have developed Hypergeometric Analysis of Tiling-arrays (HAT), and first evaluated its performance for tiling-array datasets from a Chromatin Immunoprecipitation study on chip (ChIP-on-chip) for the identification of genome-wide DNA binding profiles of transcription factor Cebpa (used for method comparison). Using this assay, we can refine the detection of regions-of-interest by illustrating that regions detected by HAT are more highly enriched for expected motifs in comparison with an alternative detection method (MAT). Subsequently, data from a retroviral insertional mutagenesis screen were used to examine the performance of HAT among different applications of tiling-array datasets. In both studies, detected regions-of-interest have been validated with (q)PCR.ConclusionsWe demonstrate that HAT has increased specificity for analysis of tiling-array data in comparison with the alternative method, and that it accurately detects regions-of-interest in two different applications of tiling-arrays. HAT has several advantages over previous methods: i) as there is no single cut-off level for probe-intensity, HAT can detect regions-of-interest at various thresholds, ii) it can detect regions-of-interest of any size, iii) it is independent of probe-resolution across the genome, and across tiling-array platforms and iv) it employs a single user defined parameter: the significance level. Regions-of-interest are detected by computing the hypergeometric-probability, while controlling the Family Wise Error. Furthermore, the method does not require experimental replicates, common regions-of-interest are indicated, a sequence-of-interest can be examined for every detected region-of-interest, and flanking genes can be reported.

Dataset Information

Hypergeometric analysis of tiling-array and sequence data: detection and interpretation of peaks.

Publications

Hypergeometric analysis of tiling-array and sequence data: detection and interpretation of peaks.

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