Name
Abstract | ||
|---|---|---|
Currently, most computational methods estimate the expression of circular RNAs (circRNAs) using the number of sequencing reads that support back-splicing junctions (BSJ) in RNA-seq data, which may introduce biased estimation of circRNA expression due to the uneven distribution of sequencing reads. To overcome this, we previously developed a model-based strategy for circRNA quantification, enabling consideration of sequencing reads from the entire transcript. Yet, the lack of exact transcript structure of circRNAs may limit its accuracy. Here, we proposed a substantially improved circRNA quantification tool, AQUARIUM, by introducing the full-length RNA structure of circular isoforms. We assessed its performance in circRNA quantification using both biological and simulated rRNA-depleted RNA-seq datasets, and demonstrated its superior performance at both BSJ and isoform level. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1093/bioinformatics/btab435 | BIOINFORMATICS |
DocType | Volume | Issue |
Conference | 37 | 24 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoxia Wen | 1 | 0 | 0.34 |
| Musheng Li | 2 | 0 | 0.34 |
| Fuyu Li | 3 | 0 | 0.34 |
| Zengyan Yang | 4 | 0 | 0.34 |
| Tong Zhou | 5 | 0 | 1.35 |
| Wanjun Gu | 6 | 12 | 3.34 |