Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data

D. Andergassen, C. Dotter, T. Kulinski, P. Guenzl, P. Bammer, D. Barlow, F. Pauler, Q. Hudson, Nucleic Acids Research 43 (2015).

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Journal Article | Published | English
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Abstract
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.
Publishing Year
Date Published
2015-07-21
Journal Title
Nucleic Acids Research
Acknowledgement
Austrian Science Fund [FWF P25185-B22, FWF F4302- B09, FWFW1207-B09]. Funding for open access charge: Austrian Science Fund. We 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.
Volume
43
Issue
21
Article Number
e146
IST-REx-ID

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Andergassen D, Dotter C, Kulinski T, et al. Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data. Nucleic Acids Research. 2015;43(21). doi:10.1093/nar/gkv727
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. Nucleic Acids Research, 43(21). https://doi.org/10.1093/nar/gkv727
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.” Nucleic Acids Research 43, no. 21 (2015). https://doi.org/10.1093/nar/gkv727.
D. Andergassen et al., “Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data,” Nucleic Acids Research, vol. 43, no. 21, 2015.
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).
Andergassen, Daniel, et al. “Allelome.PRO, a Pipeline to Define Allele-Specific Genomic Features from High-Throughput Sequencing Data.” Nucleic Acids Research, vol. 43, no. 21, e146, Oxford University Press, 2015, doi:10.1093/nar/gkv727.
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