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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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This content will become publicly available on August 1, 2025
Ornaments for efficient allele-specific expression estimation with bias correction
Allele-specific expression has been used to elucidate various biological mechanisms, such as genomic imprinting and gene expression variation caused by genetic changes in cis-regulatory elements. However, existing methods for obtaining allele-specific expression from RNA-seq reads do not adequately and efficiently remove various biases, such as reference bias, where reads containing the alternative allele do not map to the reference transcriptome, or ambiguous mapping bias, where reads containing the reference allele map differently from reads containing the alternative allele. We present Ornaments, a computational tool for rapid and accurate estimation of allele-specific expression at unphased heterozygous loci from RNA-seq reads while correcting for allele-specific read mapping bias. Ornaments removes reference bias by mapping reads to a personalized transcriptome, and ambiguous mapping bias by probabilistically assigning reads to multiple transcripts and variant loci they map to. Ornaments is a lightweight extension of kallisto, a popular tool for fast RNA-seq quantification, that improves the efficiency and accuracy of WASP, a popular tool for bias correction in allele-specific read mapping. Our experiments on simulated and human lymphoblastoid cell-line RNA-seq reads with the genomes of the 1000 Genomes Project show that Ornaments is more accurate than WASP and kallisto and nearly as efficient as kallisto per sample, and despite the additional cost of constructing a personalized index for multiple samples, an order of magnitude faster than WASP. In addition, Ornaments detected imprinted transcripts with higher sensitivity, compared to WASP which detected the imprinted signals only at the gene level.
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- Award ID(s):
- 2154089
- PAR ID:
- 10546205
- Publisher / Repository:
- Cell Press
- Date Published:
- Journal Name:
- The American Journal of Human Genetics
- Volume:
- 111
- Issue:
- 8
- ISSN:
- 0002-9297
- Page Range / eLocation ID:
- 1770 to 1781
- Subject(s) / Keyword(s):
- allele-specific expression RNA-seq genetic variants SNPs indels transcriptome quantification bias correction de Bruijn graph mixture model
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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