BibTex format

author = {Ramlaul, K and Palmer, C and Nakane, T and Aylett, C},
doi = {10.1016/j.jsb.2020.107545},
journal = {Journal of Structural Biology},
pages = {1--9},
title = {Mitigating local over-fitting during single particle reconstruction with SIDESPLITTER},
url = {},
volume = {211},
year = {2020}

RIS format (EndNote, RefMan)

AB - Single particle analysis has become a key structural biology technique. Experimental images are extremely noisy, and during iterative refinement it is possible to stably incorporate noise into the reconstruction. Such “over-fitting” can lead to misinterpretation of the structure and flawed biological results. Several strategies are routinely used to prevent over-fitting, the most common being independent refinement of two sides of a split dataset. In this study, we show that over-fitting remains an issue within regions of low local signal-to-noise, despite independent refinement of half datasets. We propose a modification of the refinement process through the application of a local signal-to-noise filter: SIDESPLITTER. We show that our approach can reduce over-fitting for both idealised and experimental data while maintaining independence between the two sides of a split refinement. SIDESPLITTER refinement leads to improved density, and can also lead to improvement of the final resolution in extreme cases where datasets are prone to severe over-fitting, such as small membrane proteins.
AU - Ramlaul,K
AU - Palmer,C
AU - Nakane,T
AU - Aylett,C
DO - 10.1016/j.jsb.2020.107545
EP - 9
PY - 2020///
SN - 1047-8477
SP - 1
TI - Mitigating local over-fitting during single particle reconstruction with SIDESPLITTER
T2 - Journal of Structural Biology
UR -
UR -
UR -
VL - 211
ER -