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波利:使用近似似然法的RNA测序分析

Polee: RNA-Seq analysis using approximate likelihood.

作者信息

Jones Daniel C, Ruzzo Walter L

机构信息

Paul G. Allen School of Computer Science & Engineering, University of Washington, Box 352350, Seattle, WA 98195-2350, USA.

出版信息

NAR Genom Bioinform. 2021 May 25;3(2):lqab046. doi: 10.1093/nargab/lqab046. eCollection 2021 Jun.

DOI:10.1093/nargab/lqab046
PMID:34056596
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8152449/
Abstract

The analysis of mRNA transcript abundance with RNA-Seq is a central tool in molecular biology research, but often analyses fail to account for the uncertainty in these estimates, which can be significant, especially when trying to disentangle isoforms or duplicated genes. Preserving uncertainty necessitates a full probabilistic model of the all the sequencing reads, which quickly becomes intractable, as experiments can consist of billions of reads. To overcome these limitations, we propose a new method of approximating the likelihood function of a sparse mixture model, using a technique we call the Pólya tree transformation. We demonstrate that substituting this approximation for the real thing achieves most of the benefits with a fraction of the computational costs, leading to more accurate detection of differential transcript expression and transcript coexpression.

摘要

利用RNA测序分析mRNA转录本丰度是分子生物学研究的核心工具,但这类分析往往未能考虑到这些估计值中的不确定性,而这种不确定性可能很大,尤其是在试图区分异构体或重复基因时。要保留不确定性就需要对所有测序读数建立完整的概率模型,但由于实验可能包含数十亿条读数,这很快就会变得难以处理。为克服这些限制,我们提出了一种新方法,即使用一种我们称为波利亚树变换的技术来近似稀疏混合模型的似然函数。我们证明,用这种近似方法替代实际模型,能以一小部分计算成本实现大部分益处,从而更准确地检测差异转录本表达和转录本共表达。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/9744760acbbb/lqab046fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/3ead83b61ea8/lqab046fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/3ef52bdcc029/lqab046fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/c95dbfd79af2/lqab046fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/9a7b93004d4a/lqab046fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/e9cfc80274b6/lqab046fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/c95a98abfd84/lqab046fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/aad60d9f97fe/lqab046fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/9833596de9fa/lqab046fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/176331bfe669/lqab046fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/9744760acbbb/lqab046fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/3ead83b61ea8/lqab046fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/3ef52bdcc029/lqab046fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/c95dbfd79af2/lqab046fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/9a7b93004d4a/lqab046fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/e9cfc80274b6/lqab046fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/c95a98abfd84/lqab046fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/aad60d9f97fe/lqab046fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/9833596de9fa/lqab046fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/176331bfe669/lqab046fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d38a/8152449/9744760acbbb/lqab046fig10.jpg

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本文引用的文献

1
Stan: A Probabilistic Programming Language.斯坦:一种概率编程语言。
J Stat Softw. 2017;76. doi: 10.18637/jss.v076.i01. Epub 2017 Jan 11.
2
Ensembl 2021.Ensembl 2021.
Nucleic Acids Res. 2021 Jan 8;49(D1):D884-D891. doi: 10.1093/nar/gkaa942.
3
BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty.BANDITS:贝叶斯差异剪接考虑了样本间的可变性和映射不确定性。
Genome Biol. 2020 Mar 16;21(1):69. doi: 10.1186/s13059-020-01967-8.
4
Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis.柳:快速且可解释的基于片段的剪接变体和基因表达分析。
BMC Bioinformatics. 2019 Aug 13;20(1):421. doi: 10.1186/s12859-019-2947-6.
5
Nonparametric expression analysis using inferential replicate counts.使用推断重复计数的非参数表达分析。
Nucleic Acids Res. 2019 Oct 10;47(18):e105. doi: 10.1093/nar/gkz622.
6
Using equivalence class counts for fast and accurate testing of differential transcript usage.使用等价类计数进行差异转录本使用情况的快速准确测试。
F1000Res. 2019 Mar 7;8:265. doi: 10.12688/f1000research.18276.2. eCollection 2019.
7
A discriminative learning approach to differential expression analysis for single-cell RNA-seq.一种用于单细胞 RNA-seq 差异表达分析的判别式学习方法。
Nat Methods. 2019 Feb;16(2):163-166. doi: 10.1038/s41592-018-0303-9. Epub 2019 Jan 21.
8
Annotation-free quantification of RNA splicing using LeafCutter.无注释 RNA 剪接定量分析使用 LeafCutter。
Nat Genet. 2018 Jan;50(1):151-158. doi: 10.1038/s41588-017-0004-9. Epub 2017 Dec 11.
9
Co-expression networks reveal the tissue-specific regulation of transcription and splicing.共表达网络揭示了转录和剪接的组织特异性调控。
Genome Res. 2017 Nov;27(11):1843-1858. doi: 10.1101/gr.216721.116. Epub 2017 Oct 11.
10
Improved data-driven likelihood factorizations for transcript abundance estimation.改进的基于数据的似然因子分解方法用于转录本丰度估计。
Bioinformatics. 2017 Jul 15;33(14):i142-i151. doi: 10.1093/bioinformatics/btx262.