Print Email Facebook Twitter Hypergeometric analysis of tiling-array and sequence data: Detection and interpretation of peaks Title Hypergeometric analysis of tiling-array and sequence data: Detection and interpretation of peaks Author Taskesen, E. Hoogeboezem, R. Delwel, R. Reinders, M.J.T. Faculty Electrical Engineering, Mathematics and Computer Science Department Intelligent Systems Date 2013-10-25 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. Subject bioinformaticsNGS analysisChIP-Seqpeak detectionOA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:693e8ecf-806a-49ae-97d2-520bbb245c9c DOI https://doi.org/10.2147/AABC.S51271 Publisher Dove Medical Press ISSN 1178-6949 Source Advances and Applications in Bioinformatics and Chemistry, 6, 2013 Part of collection Institutional Repository Document type journal article Rights (c) 2013 The Author(s)This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution Non Commercial (unported, v3.0) License. Files PDF Taskesen_2013.pdf 2.73 MB Close viewer /islandora/object/uuid:693e8ecf-806a-49ae-97d2-520bbb245c9c/datastream/OBJ/view