AIDE: Annotation-Assisted RNA Transcript Discovery with High Precision

• Activity Number: 181 - Statistical Methods in Gene Expression Data Analysis II
• Type: Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Wei Vivian Li* and Shan Li and Ling Deng and Xin Tong and Hubing Shi and Jingyi Jessica Li
• Companies: Rutgers, The State University of New Jersey and Sichuan University and Sichuan University and University of Southern California and Sichuan University and University of California, Los Angeles
• Keywords: RNA-seq; transcript assembly; transcript quantification; isoform abundance; likelihood estimation; stepwise selection

Abstract

Genome-wide identification and quantification of full-length mRNA transcripts is crucial for investigating transcriptional and post-transcriptional regulatory mechanisms. Here we introduce a novel statistical method, AIDE, the first approach that directly controls false isoform discoveries by implementing the statistical 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 datasets followed by a PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss due 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 will enable researchers to discover novel transcripts with high confidence.