  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 (https://github.com/olivomao/boassembler), 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.
