Quinoa: Efficient and Robust CTF Estimation for CryoET Tilt Series

Authors

Zhang, P., Frosio, T.

Abstract

Accurate contrast transfer function (CTF) estimation is a critical first step in cryo-electron tomography (cryoET), enabling recovery of information from thick, heterogeneous specimens. This challenge is especially acute in in situ cryoET, where macromolecular complexes and disease-relevant structures are imaged at high tilt and low signal-to-noise ratio. Initial estimates CTF parameters remain difficult to automate and are essential for downstream processing. Here, we present Quinoa, a software package for robust tilt-series CTF estimation. Quinoa validates tilt geometry, assesses data quality, and generates initial estimates of defocus and phase shift, which are then refined in a global model to fit per-image defoci, astigmatism, phase shifts, specimen orientation (rotation, tilt, and pitch), and specimen thickness. A key feature is equiphase-binned polar power spectra, which reduce computational cost without compromising accuracy. In benchmarks against Warp, Ctfplotter, CTFMeasure, and AreTomo, Quinoa was most robust across challenging conditions, including severe astigmatism, high inclination, and variable phase shift. GPU acceleration enables real-time monitoring and high-throughput processing, supporting accurate in situ structural analysis of cells and tissues.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 18 Jul 2026.

Stats

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.