TY - JOUR 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 - http://dx.doi.org/10.1016/j.jsb.2020.107545 UR - https://www.sciencedirect.com/science/article/pii/S1047847720301180?via%3Dihub UR - http://hdl.handle.net/10044/1/80825 VL - 211 ER -