Name
Title | ||
|---|---|---|
BOAssembler - A Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance. |
Abstract | ||
|---|---|---|
High throughput sequencing of RNA (RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample. How to better recover the original RNA transcripts from those fragments (RNA-Seq assembly) is still a difficult task. For example, RNA-Seq assembly tools typically require hyper-parameter tuning to achieve good performance for particular datasets. This kind of tuning is usually unintuitive and time-consuming. Consequently, users often resort to default parameters, which do not guarantee consistent good performance for various datasets. Here we propose BOAssembler (this https URL), a framework that enables end-to-end automatic tuning of RNA-Seq assemblers, based on Bayesian Optimization principles. Experiments show this data-driven approach is effective to improve the overall assembly performance. The approach would be helpful for downstream (e.g. gene, protein, cell) analysis, and more broadly, for future bioinformatics benchmark studies. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-42266-0_15 | AlCoB |
Field | DocType | Citations |
RNA,Gene,Biology,RNA-Seq,Bayesian optimization,Automatic tuning,DNA sequencing,Computational biology,Genetics | Conference | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shunfu Mao | 1 | 0 | 1.35 |
| Yihan Jiang | 2 | 0 | 0.68 |
| Edwin Basil Mathew | 3 | 0 | 0.34 |
| Sreeram Kannan | 4 | 120 | 21.76 |