AIDE: Annotation-assisted isoform discovery with high precision

TY - JOUR

T1 - AIDE

T2 - Annotation-assisted isoform discovery with high precision

AU - Li, Wei Vivian

AU - Li, Shan

AU - Tong, Xin

AU - Deng, Ling

AU - Shi, Hubing

AU - Li, Jingyi Jessica

N1 - Funding Information: We thank Dr. Kin Fai Au and Dr. Yunhao Wang for providing their second-and third-generation human ESC RNA-seq data and processed mRNA isoforms from the third-generation data. We also thank Dr. Alexander Hoffmann at UCLA for his insightful feedbacks. This work was supported by the following grants: UCLA Dissertation Year Fellowship (to W.V.L.); PhRMA Foundation Research Starter Grant in Informatics, Johnson & Johnson WiSTEM2D Award, and Sloan Research Fellowship (to J.J.L.); and National Key Research and Development Program of China (no. 2016YFC0906000 [2016YFC0906003]), National Natural Science Foundation of China (no. 81773752), Key Program of the Science and Technology Bureau of Sichuan (no. 2017SZ0005), and the Recruitment Program of Global Young Experts (“the Thousand Young Talents Plan”; to H.S.). Publisher Copyright: © 2019 Li et al.

PY - 2019

Y1 - 2019

N2 - Genome-wide accurate identification and quantification of full-length mRNA isoforms is crucial for investigating transcriptional and posttranscriptional regulatory mechanisms of biological phenomena. Despite continuing efforts in developing effective computational tools to identify or assemble full-length mRNA isoforms from second-generation RNA-seq data, it remains a challenge to accurately identify mRNA isoforms from short sequence reads owing to the substantial information loss in RNA-seq experiments. Here, we introduce a novel statistical method, annotation-assisted isoform discovery (AIDE), the first approach that directly controls false isoform discoveries by implementing the testing-based model selection principle. Solving the isoform discovery problem in a stepwise and conservative manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. We evaluate the performance of AIDE based on multiple simulated and real RNA-seq data sets followed by PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss owing to short read lengths. AIDE achieves the highest precision in isoform discovery and the lowest error rates in isoform abundance estimation, compared with three state-of-the-art methods Cufflinks, SLIDE, and StringTie. As a robust bioinformatics tool for transcriptome analysis, AIDE enables researchers to discover novel transcripts with high confidence.

AB - Genome-wide accurate identification and quantification of full-length mRNA isoforms is crucial for investigating transcriptional and posttranscriptional regulatory mechanisms of biological phenomena. Despite continuing efforts in developing effective computational tools to identify or assemble full-length mRNA isoforms from second-generation RNA-seq data, it remains a challenge to accurately identify mRNA isoforms from short sequence reads owing to the substantial information loss in RNA-seq experiments. Here, we introduce a novel statistical method, annotation-assisted isoform discovery (AIDE), the first approach that directly controls false isoform discoveries by implementing the testing-based model selection principle. Solving the isoform discovery problem in a stepwise and conservative manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. We evaluate the performance of AIDE based on multiple simulated and real RNA-seq data sets followed by PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss owing to short read lengths. AIDE achieves the highest precision in isoform discovery and the lowest error rates in isoform abundance estimation, compared with three state-of-the-art methods Cufflinks, SLIDE, and StringTie. As a robust bioinformatics tool for transcriptome analysis, AIDE enables researchers to discover novel transcripts with high confidence.

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U2 - https://doi.org/10.1101/gr.251108.119

DO - https://doi.org/10.1101/gr.251108.119

M3 - Article

C2 - 31694868

SN - 1088-9051

VL - 29

SP - 2056

EP - 2072

JO - Genome research

JF - Genome research

IS - 12

ER -