{"oa":1,"oa_version":"Published Version","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2018-12-11T11:52:22Z","publisher":"Oxford University Press","scopus_import":1,"quality_controlled":"1","title":"Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data","status":"public","file":[{"date_updated":"2020-07-14T12:44:58Z","file_id":"5768","file_size":6863297,"date_created":"2018-12-20T14:18:57Z","checksum":"385b83854fd0eb2e4f386867da2823e2","relation":"main_file","content_type":"application/pdf","creator":"dernst","access_level":"open_access","file_name":"2015_NucleicAcidsRes_Andergassen.pdf"}],"type":"journal_article","day":"21","month":"07","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2021-01-12T06:51:09Z","publication_status":"published","date_published":"2015-07-21T00:00:00Z","issue":"21","department":[{"_id":"GaNo"}],"abstract":[{"lang":"eng","text":"Detecting allelic biases from high-throughput sequencing data requires an approach that maximises sensitivity while minimizing false positives. Here, we present Allelome.PRO, an automated user-friendly bioinformatics pipeline, which uses high-throughput sequencing data from reciprocal crosses of two genetically distinct mouse strains to detect allele-specific expression and chromatin modifications. Allelome.PRO extends approaches used in previous studies that exclusively analyzed imprinted expression to give a complete picture of the ‘allelome’ by automatically categorising the allelic expression of all genes in a given cell type into imprinted, strain-biased, biallelic or non-informative. Allelome.PRO offers increased sensitivity to analyze lowly expressed transcripts, together with a robust false discovery rate empirically calculated from variation in the sequencing data. We used RNA-seq data from mouse embryonic fibroblasts from F1 reciprocal crosses to determine a biologically relevant allelic ratio cutoff, and define for the first time an entire allelome. Furthermore, we show that Allelome.PRO detects differential enrichment of H3K4me3 over promoters from ChIP-seq data validating the RNA-seq results. This approach can be easily extended to analyze histone marks of active enhancers, or transcription factor binding sites and therefore provides a powerful tool to identify candidate cis regulatory elements genome wide."}],"intvolume":"        43","publist_id":"5682","volume":43,"year":"2015","file_date_updated":"2020-07-14T12:44:58Z","acknowledgement":"Austrian Science Fund [FWF P25185-B22, FWF F4302- B09, FWFW1207-B09]. Funding for open access charge: Austrian Science Fund.\r\nWe thank Florian Breitwieser for advice during the early stages of this project. High-throughput sequencing was conducted by the Biomedical Sequencing Facility (BSF) at CeMM in Vienna.","ddc":["570"],"doi":"10.1093/nar/gkv727","has_accepted_license":"1","author":[{"full_name":"Andergassen, Daniel","last_name":"Andergassen","first_name":"Daniel"},{"last_name":"Dotter","first_name":"Christoph","id":"4C66542E-F248-11E8-B48F-1D18A9856A87","full_name":"Dotter, Christoph"},{"last_name":"Kulinski","first_name":"Tomasz","full_name":"Kulinski, Tomasz"},{"last_name":"Guenzl","first_name":"Philipp","full_name":"Guenzl, Philipp"},{"full_name":"Bammer, Philipp","last_name":"Bammer","first_name":"Philipp"},{"last_name":"Barlow","first_name":"Denise","full_name":"Barlow, Denise"},{"full_name":"Pauler, Florian","last_name":"Pauler","first_name":"Florian"},{"full_name":"Hudson, Quanah","last_name":"Hudson","first_name":"Quanah"}],"publication":"Nucleic Acids Research","citation":{"short":"D. Andergassen, C. Dotter, T. Kulinski, P. Guenzl, P. Bammer, D. Barlow, F. Pauler, Q. Hudson, Nucleic Acids Research 43 (2015).","ista":"Andergassen D, Dotter C, Kulinski T, Guenzl P, Bammer P, Barlow D, Pauler F, Hudson Q. 2015. Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data. Nucleic Acids Research. 43(21), e146.","chicago":"Andergassen, Daniel, Christoph Dotter, Tomasz Kulinski, Philipp Guenzl, Philipp Bammer, Denise Barlow, Florian Pauler, and Quanah Hudson. “Allelome.PRO, a Pipeline to Define Allele-Specific Genomic Features from High-Throughput Sequencing Data.” <i>Nucleic Acids Research</i>. Oxford University Press, 2015. <a href=\"https://doi.org/10.1093/nar/gkv727\">https://doi.org/10.1093/nar/gkv727</a>.","apa":"Andergassen, D., Dotter, C., Kulinski, T., Guenzl, P., Bammer, P., Barlow, D., … Hudson, Q. (2015). Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data. <i>Nucleic Acids Research</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/nar/gkv727\">https://doi.org/10.1093/nar/gkv727</a>","ama":"Andergassen D, Dotter C, Kulinski T, et al. Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data. <i>Nucleic Acids Research</i>. 2015;43(21). doi:<a href=\"https://doi.org/10.1093/nar/gkv727\">10.1093/nar/gkv727</a>","ieee":"D. Andergassen <i>et al.</i>, “Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data,” <i>Nucleic Acids Research</i>, vol. 43, no. 21. Oxford University Press, 2015.","mla":"Andergassen, Daniel, et al. “Allelome.PRO, a Pipeline to Define Allele-Specific Genomic Features from High-Throughput Sequencing Data.” <i>Nucleic Acids Research</i>, vol. 43, no. 21, e146, Oxford University Press, 2015, doi:<a href=\"https://doi.org/10.1093/nar/gkv727\">10.1093/nar/gkv727</a>."},"language":[{"iso":"eng"}],"_id":"1497","article_number":"e146"}