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

Authors Taskesen E, Hoogeboezem R, Delwel R, Reinders MJT

Received 9 July 2013

Accepted for publication 11 August 2013

Published 25 October 2013 Volume 2013:6 Pages 55—62

DOI https://doi.org/10.2147/AABC.S51271

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 4

Erdogan Taskesen,1,2 Remco Hoogeboezem,1 Ruud Delwel,1 Marcel JT Reinders2,3

1Department of Hematology, Erasmus University Medical Center, Rotterdam, 2Delft Bioinformatics Lab (DBL), Delft University of Technology, Delft, 3Netherlands Bioinformatics Centre (NBIC), Nijmegen, The Netherlands

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.

Keywords: bioinformatics, NGS analysis, ChIP-Seq, peak detection

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