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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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Accuracy in social judgment does not exclude the potential for bias
Abstract Cesario claims that all bias research tells us is that people “end up using the information they have come to learn as being probabilistically accurate in their daily lives” (sect. 5, para. 4). We expose Cesario's flawed assumptions about the relationship between accuracy and bias. Through statistical simulations and empirical work, we show that even probabilistically accurate responses are regularly accompanied by bias.
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- PAR ID:
- 10398135
- Date Published:
- Journal Name:
- Behavioral and Brain Sciences
- Volume:
- 45
- ISSN:
- 0140-525X
- 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/10.1017/S0140525X2100073X
