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	<title>2019Ramlaul Filtering - Revision history</title>
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	<updated>2026-05-24T20:15:43Z</updated>
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		<title>WikiSysop: Created page with &quot;== Citation ==  Ramlaul, K.; Palmer, C. M.,  Aylett, C. H. S. A Local Agreement Filtering Algorithm for Transmission EM Reconstructions. Journal of structural biology, 2019, 2...&quot;</title>
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		<updated>2019-02-27T05:48:40Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;== Citation ==  Ramlaul, K.; Palmer, C. M.,  Aylett, C. H. S. A Local Agreement Filtering Algorithm for Transmission EM Reconstructions. Journal of structural biology, 2019, 2...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;== Citation ==&lt;br /&gt;
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Ramlaul, K.; Palmer, C. M.,  Aylett, C. H. S. A Local Agreement Filtering Algorithm for Transmission EM Reconstructions. Journal of structural biology, 2019, 205, 30-40 &lt;br /&gt;
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== Abstract ==&lt;br /&gt;
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We present LAFTER, an algorithm for de-noising single particle reconstructions from cryo-EM. Single particle analysis entails the reconstruction of high-resolution volumes from tens of thousands of particle images with low individual signal-to-noise. Imperfections in this process result in substantial variations in the local signal-to-noise ratio within the resulting reconstruction, complicating the interpretation of molecular structure. An effective local de-noising filter could therefore improve interpretability and maximise the amount of useful information obtained from cryo-EM maps. LAFTER is a local de-noising algorithm based on a pair of serial real-space filters. It compares independent half-set reconstructions to identify and retain shared features that have power greater than the noise. It is capable of recovering features across a wide range of signal-to-noise ratios, and we demonstrate recovery of the strongest features at Fourier shell correlation (FSC) values as low as 0.144 over a 256 -voxel cube. A fast and computationally efficient implementation of LAFTER is freely available. We also propose a new way to evaluate the effectiveness of real-space filters for noise suppression, based on the correspondence between two FSC curves: 1) the FSC between the filtered and unfiltered volumes, and 2) C , the FSC between the unfiltered volume and a hypothetical noiseless volume, which can readily be estimated from the FSC between two half-set reconstructions. &lt;br /&gt;
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== Keywords ==&lt;br /&gt;
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== Links ==&lt;br /&gt;
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https://www.sciencedirect.com/science/article/pii/S1047847718303113&lt;br /&gt;
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== Related software ==&lt;br /&gt;
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== Related methods ==&lt;br /&gt;
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== Comments ==&lt;/div&gt;</summary>
		<author><name>WikiSysop</name></author>
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