Transcriptomic analysis of succulent stem development of Chinese kale (Brassica oleracea var. alboglabra Bailey) and its synthetic allotetraploid via RNA sequencing

Zheng, Wen; Shi, Jianxin; Zhu, Zhengming; Jin, Ping; Chen, Jiahong; Zhang, Liang; Zhang, E.; Lin, Tao; Zhu, Zhujun; Zang, Yunxiang; Wu, Jing

doi:10.3389/fpls.2022.1004590

Cited by 3 publications

(1 citation statement)

References 55 publications

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“…The RNA-seq used here was from one of the inbred, non-hybrid parents, C-1 SRR21735970. The B. oleracea RNA-seq derived from a study of a Chinese kale allotetraploid, Project PRJNA885390 [ 68 ]. The RNA-seq used here was from the diploid parent, CC SRR21778809.…”

Section: Methodsmentioning

confidence: 99%

Machine learning on alignment features for parent-of-origin classification of simulated hybrid RNA-seq

Miller,

Adjeroh

2024

BMC Bioinformatics

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Background Parent-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. Results We 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. Conclusions For 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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Section: Methodsmentioning

confidence: 99%

Machine learning on alignment features for parent-of-origin classification of simulated hybrid RNA-seq

Miller,

Adjeroh

2024

BMC Bioinformatics

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Wind disturbance-based tomato seedlings growth control

Yang,

Hao,

et al. 2024

Biosystems Engineering

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RNA-seq Parent-of-Origin Classification with Machine Learning applied to Alignment Features

Miller

Adjeroh

2023

Preprint

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Background Parent-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. Results We used public data from four species pairs that are known to hybridize. For each pair, we obtained RNA-seq read pairs from both species and measured our ability to assign each read to its proper species by comparing reads to the transcriptome or genome references. We tested four 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 dataset tested, our machine-learning post-processor achieved higher accuracy than the aligner by itself at choosing the correct species per RNA-seq read pair. Conclusions For the parent-of-origin classification of RNA-seq, machine learning can improve the accuracy of alignment-based methods. This approach should be useful for enhancing ASE detection in interspecies hybrids. We believe this is the first application of machine learning to this problem domain.

show abstract

Transcriptomic analysis of succulent stem development of Chinese kale (Brassica oleracea var. alboglabra Bailey) and its synthetic allotetraploid via RNA sequencing

Cited by 3 publications

References 55 publications

Machine learning on alignment features for parent-of-origin classification of simulated hybrid RNA-seq

Machine learning on alignment features for parent-of-origin classification of simulated hybrid RNA-seq

Wind disturbance-based tomato seedlings growth control

RNA-seq Parent-of-Origin Classification with Machine Learning applied to Alignment Features

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