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Abstract Species living in changing environments require the acclimatization of individual organisms, which may be significantly influenced by allele specific expression (ASE). Data from RNA-seq experiments can be used to identify and quantify the expressed alleles. However, conventional allele matching to the reference genome creates a mapping bias towards the reference allele that prevents a reliable estimation of the allele counts. We developed a pipeline that allows identification and unbiased quantification of the alleles corresponding to an RNA-seq dataset, without any previous knowledge of the haplotype. To achieve the unbiased mapping, we generate two pseudogenomes by substituting the alternative alleles on the reference genome. The SNPs are further called against each pseudogenome, providing two SNP data-sets that are averaged for calculation of the allele depth to be merged in a final SNP calling file. The pipeline presented here can calculate ASE in non-model organisms and can be applied to previous RNA-seq data-sets for expanding studies in gene expression regulation.
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Genes of the pig, Sus scrofa , reconstructed with EvidentialGene
The pig is a well-studied model animal of biomedical and agricultural importance. Genes of this species, Sus scrofa , are known from experiments and predictions, and collected at the NCBI reference sequence database section. Gene reconstruction from transcribed gene evidence of RNA-seq now can accurately and completely reproduce the biological gene sets of animals and plants. Such a gene set for the pig is reported here, including human orthologs missing from current NCBI and Ensembl reference pig gene sets, additional alternate transcripts, and other improvements. Methodology for accurate and complete gene set reconstruction from RNA is used: the automated SRA2Genes pipeline of EvidentialGene project.
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- Award ID(s):
- 1759906
- PAR ID:
- 10290726
- Date Published:
- Journal Name:
- PeerJ
- Volume:
- 7
- ISSN:
- 2167-8359
- Page Range / eLocation ID:
- e6374
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7717/peerj.6374
