Bayesian nonparametric discovery of isoforms and individual specific quantification.
Bayesian nonparametric discovery of isoforms and individual specific quantification.
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DOI：
10.1038/s41467-018-03402-w
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发表时间：
2018-04-27
影响因子：
16.6
通讯作者：
Engelhardt BE
中科院分区：
文献类型：
--
作者：
Aguiar D;Cheng LF;Dumitrascu B;Mordelet F;Pai AA;Engelhardt BE
大多数人类蛋白质编码基因可以转录成多种不同的mRNA亚型。这些选择性剪接模式促进分子多样性，并且同种型表达的失调在疾病病因学中起重要作用。然而，亚型很难从短读段RNA-seq数据中表征，因为它们具有相同的毒力，并且在组织和样品中以不同的频率出现。在这里，我们开发了biisq，这是一种贝叶斯非参数模型，用于从短读RNA-seq数据中发现亚型和个体特异性定量。BIISQ不需要同种型参考序列，而是估计样品间共有的同种型目录。我们使用随机变分推理有效的后验估计和表现出上级精度和召回的模拟相比，国家的最先进的异构体重建方法。BIISQ显示出低丰度同种型的最大增益，与多样品方法相比，在低覆盖度下正确推断的同种型多36%，与单样品方法相比多170%。我们估计GEUVADIS RNA-seq数据中的异构体，并通过将遗传变异与异构体比率相关联来验证推断的异构体。选择性剪接导致转录异构体多样性。在这里，Aguiar等人开发了biisq，这是一种贝叶斯非参数方法，用于从RNA-seq数据中发现和量化异构体。
Most human protein-coding genes can be transcribed into multiple distinct mRNA isoforms. These alternative splicing patterns encourage molecular diversity, and dysregulation of isoform expression plays an important role in disease etiology. However, isoforms are difficult to characterize from short-read RNA-seq data because they share identical subsequences and occur in different frequencies across tissues and samples. Here, we develop biisq, a Bayesian nonparametric model for isoform discovery and individual specific quantification from short-read RNA-seq data. biisq does not require isoform reference sequences but instead estimates an isoform catalog shared across samples. We use stochastic variational inference for efficient posterior estimates and demonstrate superior precision and recall for simulations compared to state-of-the-art isoform reconstruction methods. biisq shows the most gains for low abundance isoforms, with 36% more isoforms correctly inferred at low coverage versus a multi-sample method and 170% more versus single-sample methods. We estimate isoforms in the GEUVADIS RNA-seq data and validate inferred isoforms by associating genetic variants with isoform ratios. Alternative splicing leads to transcript isoform diversity. Here, Aguiar et al. develop biisq, a Bayesian nonparametric approach to discover and quantify isoforms from RNA-seq data.
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遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
7
作者：
Harrow J;Frankish A;Gonzalez JM;Tapanari E;Diekhans M;Kokocinski F;Aken BL;Barrell D;Zadissa A;Searle S;Barnes I;Bignell A;Boychenko V;Hunt T;Kay M;Mukherjee G;Rajan J;Despacio-Reyes G;Saunders G;Steward C;Harte R;Lin M;Howald C;Tanzer A;Derrien T;Chrast J;Walters N;Balasubramanian S;Pei B;Tress M;Rodriguez JM;Ezkurdia I;van Baren J;Brent M;Haussler D;Kellis M;Valencia A;Reymond A;Gerstein M;Guigó R;Hubbard TJ
通讯作者：
Hubbard TJ
影响因子：
7
作者：
Battle A;Mostafavi S;Zhu X;Potash JB;Weissman MM;McCormick C;Haudenschild CD;Beckman KB;Shi J;Mei R;Urban AE;Montgomery SB;Levinson DF;Koller D
通讯作者：
Koller D
影响因子：
3.7
作者：
Gordon SP;Tseng E;Salamov A;Zhang J;Meng X;Zhao Z;Kang D;Underwood J;Grigoriev IV;Figueroa M;Schilling JS;Chen F;Wang Z
通讯作者：
Wang Z
DOI：
10.1126/science.1262110
发表时间：
2015-05-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
GTEx Consortium
通讯作者：
GTEx Consortium