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		<title>WikiSysop: Created page with &quot;== Citation ==  Chung, S.-C.; Lin, H.-H.; Niu, P.-Y.; Huang, S.-H.; Tu, I.-P. &amp;amp; Chang, W.-H. Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D cl...&quot;</title>
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		<updated>2021-01-05T10:37:41Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;== Citation ==  Chung, S.-C.; Lin, H.-H.; Niu, P.-Y.; Huang, S.-H.; Tu, I.-P. &amp;amp; Chang, W.-H. Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D cl...&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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Chung, S.-C.; Lin, H.-H.; Niu, P.-Y.; Huang, S.-H.; Tu, I.-P. &amp;amp;amp; Chang, W.-H. Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification. Communications biology, 2020, 3, 1-12 &lt;br /&gt;
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== Abstract ==&lt;br /&gt;
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2D classification plays a pivotal role in analyzing single particle cryo-electron microscopy images. Here, we introduce a simple and loss-less pre-processor that incorporates a fast dimension-reduction (2SDR) de-noiser to enhance 2D classification. By implementing this 2SDR pre-processor prior to a representative classification algorithm like RELION and ISAC, we compare the performances with and without the pre-processor. Tests on multiple cryo-EM experimental datasets show the pre-processor can make classification faster, improve yield of good particles and increase the number of class-average images to generate better initial models. Testing on the nanodisc-embedded TRPV1 dataset with high heterogeneity using a 3D reconstruction workflow with an initial model from class-average images highlights the pre-processor improves the final resolution to 2.82 Å, close to 0.9 Nyquist. Those findings and analyses suggest the 2SDR pre-processor, of minimal cost, is widely applicable for boosting 2D classification, while its generalization to accommodate neural network de-noisers is envisioned.&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.nature.com/articles/s42003-020-01229-0&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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