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Abstract BackgroundParent-of-origin allele-specific gene expression (ASE) can be detected in interspecies hybrids by virtue of RNA sequence variants between the parental haplotypes. ASE is detectable by differential expression analysis (DEA) applied to the counts of RNA-seq read pairs aligned to parental references, but aligners do not always choose the correct parental reference. ResultsWe used public data for species that are known to hybridize. We measured our ability to assign RNA-seq read pairs to their proper transcriptome or genome references. We tested software packages that assign each read pair to a reference position and found that they often favored the incorrect species reference. To address this problem, we introduce a post process that extracts alignment features and trains a random forest classifier to choose the better alignment. On each simulated hybrid dataset tested, our machine-learning post-processor achieved higher accuracy than the aligner by itself at choosing the correct parent-of-origin per RNA-seq read pair. ConclusionsFor the parent-of-origin classification of RNA-seq, machine learning can improve the accuracy of alignment-based methods. This approach could be useful for enhancing ASE detection in interspecies hybrids, though RNA-seq from real hybrids may present challenges not captured by our simulations. We believe this is the first application of machine learning to this problem domain.
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A pipeline for analysis of allele specific expression reveals salinity-dependent regulation in Nile tilapia
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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- Award ID(s):
- 2209383
- PAR ID:
- 10524890
- Publisher / Repository:
- Research Square
- Date Published:
- Format(s):
- Medium: X
- Institution:
- Research Square
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Posted Content:
- https://doi.org/10.21203/rs.3.rs-3735302/v1
