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    <title>Nanopore Foundations &amp; Signal Analysis</title>
    <link>https://reading.michaelmvh.com/feeds/nanopore-foundations/</link>
    <description>Nanopore Foundations &amp; Signal Analysis. Recent publications parsed from OpenAlex.</description>
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    <ttl>1440</ttl>
    <lastBuildDate>Thu, 29 Oct 2026 23:02:51 GMT</lastBuildDate>
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    <item>
      <title>Improving All-Atom Molecular Dynamics Models for Quantitative Prediction of Nanopore Blockade Current</title>
      <link>https://reading.michaelmvh.com/article/8b85569145b8b3987a927dad17b524b50ed4026bfc6e00f7b982d84918db8b19/</link>
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      <description>&lt;p&gt;All-atom molecular dynamics has become an indispensable tool in development of nanopore sensors of biological information. In a typical nanopore experiment, measurements of ionic current flowing through a nanopore report on the chemical structure of biomolecules that pass through the nanopore. Such experiments alone are often insufficient to relate the structure of the biomolecules to the ionic current modulations. The molecular dynamics method can establish such a relationship directly through a brute force simulation under applied electric field. Here, we examine the ability of molecular dynamics force fields to reproduce experimentally measured nanopore blockade currents produced by single-stranded DNA. Our simulations show that none of the &amp;quot;off the shelf&amp;quot; force fields (CHARMM36, AMBER Parmbsc1 and DES-AMBER) is capable of reproducing experimental data with the desired level of accuracy. To improve the accuracy, we examined and refined interactions between ions, protein nanopores and DNA, guided by experiments designed specifically for this purpose. Ultimately, the introduction of surgical corrections to non-bonded interactions within the CHARMM36 force field produced a favorable agreement between simulation and experiment. This refined parameterization, initially developed for nanopore sensing simulations, may have broader applications in computational studies of DNA-protein systems.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2026-09-25&quot; title=&quot;Source date; precision not specified&quot;&gt;September 25, 2026&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Journal of Chemical Theory and Computation&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Discovered via &lt;a href=&quot;https://openalex.org/&quot; rel=&quot;external&quot;&gt;OpenAlex&lt;/a&gt;&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Preprint source: &lt;a href=&quot;https://www.biorxiv.org/collection/biophysics&quot; rel=&quot;external&quot;&gt;bioRxiv: Biophysics&lt;/a&gt;&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Preprint · Signal analysis · Signal modeling&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1021/acs.jctc.6c01180&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Fri, 25 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Jingqian Liu</dc:creator>
      <dc:creator>Crystal Rodriguez</dc:creator>
      <dc:creator>Min Chen</dc:creator>
      <dc:creator>Aleksei Aksimentiev</dc:creator>
      <category>OpenAlex</category>
      <category>bioRxiv: Biophysics</category>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Sequential reading of a stepwise-shortened peptide immobilized on nanopore</title>
      <link>https://reading.michaelmvh.com/article/01f28340f3fca0e132bebbab9dedee1eb2fdd5efbdd19babd1c8c8934b88731a/</link>
      <guid isPermaLink="false">urn:sha256:01f28340f3fca0e132bebbab9dedee1eb2fdd5efbdd19babd1c8c8934b88731a</guid>
      <description>&lt;p&gt;No abstract is available.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2026-07-29&quot; title=&quot;Source date; precision not specified&quot;&gt;July 29, 2026&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Nature&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Discovered via &lt;a href=&quot;https://openalex.org/&quot; rel=&quot;external&quot;&gt;OpenAlex&lt;/a&gt;&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Protein analysis · Rereading · Sequential cleavage · Transient pore analyte looping&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1038/s41586-026-10881-1&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Wed, 29 Jul 2026 00:00:00 GMT</pubDate>
      <dc:creator>Jialu Chen</dc:creator>
      <dc:creator>Hanhan Zhang</dc:creator>
      <dc:creator>Kefan Wang</dc:creator>
      <dc:creator>Tian Li</dc:creator>
      <dc:creator>Wen SUN</dc:creator>
      <dc:creator>Zixuan Wang</dc:creator>
      <dc:creator>Xinmeng Gao</dc:creator>
      <dc:creator>Zhenyuan Cao</dc:creator>
      <dc:creator>Yusheng Ouyang</dc:creator>
      <dc:creator>Lang Yao</dc:creator>
      <dc:creator>Yifan Wang</dc:creator>
      <dc:creator>Yunqi Xiao</dc:creator>
      <dc:creator>Ruidong Li</dc:creator>
      <dc:creator>Xingwang An</dc:creator>
      <dc:creator>Xinyi Dai</dc:creator>
      <dc:creator>Lulu Zhao</dc:creator>
      <dc:creator>Qian Lu</dc:creator>
      <dc:creator>Panke Zhang</dc:creator>
      <dc:creator>Shuo Huang</dc:creator>
      <category>OpenAlex</category>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>High-Rate Fingerprinting of Protein Isoforms by Quasi-regulated Enzyme-free Transport Through CytK Nanopores</title>
      <link>https://reading.michaelmvh.com/article/210cbdc4abe29b9c1bb6f5c0686093596ca05e08bc91ea19b61eaa430f7be95b/</link>
      <guid isPermaLink="false">urn:sha256:210cbdc4abe29b9c1bb6f5c0686093596ca05e08bc91ea19b61eaa430f7be95b</guid>
      <description>&lt;p&gt;No abstract is available.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2026-06-16&quot; title=&quot;Source date; precision not specified&quot;&gt;June 16, 2026&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Research Square&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Discovered via &lt;a href=&quot;https://openalex.org/&quot; rel=&quot;external&quot;&gt;OpenAlex&lt;/a&gt;&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Fingerprinting · Motor-free transport · Preprint · Protein analysis&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.21203/rs.3.rs-8098579/v1&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.researchsquare.com/article/rs-8098579/latest.pdf&quot;&gt;Open PDF&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate>
      <dc:creator>Meni Wanunu</dc:creator>
      <dc:creator>Amr K. Makhamreh</dc:creator>
      <dc:creator>Ali Fallahi</dc:creator>
      <dc:creator>Rik Dhar</dc:creator>
      <dc:creator>Monika Kumari</dc:creator>
      <dc:creator>Siddarth Krishnan</dc:creator>
      <dc:creator>Michele Meseonznik</dc:creator>
      <dc:creator>Luning Yu</dc:creator>
      <dc:creator>Dinara Boyko</dc:creator>
      <dc:creator>Keira Reich-Veillette</dc:creator>
      <dc:creator>Yuhaohua Zheng</dc:creator>
      <dc:creator>Elizabeth A. Libby</dc:creator>
      <dc:creator>Aleksei Aksimentiev</dc:creator>
      <category>OpenAlex</category>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>A framework for peptide identification on commercial nanopore sequencing platforms</title>
      <link>https://reading.michaelmvh.com/article/2ae29d8d77ff737f2899aa0dff4f05e13ee10261c3ce7152072519f441a6f56e/</link>
      <guid isPermaLink="false">urn:sha256:2ae29d8d77ff737f2899aa0dff4f05e13ee10261c3ce7152072519f441a6f56e</guid>
      <description>&lt;p&gt;Abstract Direct single-molecule peptide analysis could in principle enable rapid and sensitive identification of pathogen-derived or disease-associated biomarkers without reliance on mass spectrometry. However, existing nanopore peptide sensing methods are typically constrained by limited throughput and lack of accessibility beyond specialized setups. Here, we present an integrated experimental-computational framework for DNA-linked peptide translocation on a commercially available, high-throughput nanopore sequencing platform, the MinION. Synthetic peptides were covalently bound to oligonucleotides at both termini. The resulting peptide-DNA constructs were then translocated through the CsgG-CsgF pores using a DNA motor protein. Current traces were segmented using the known DNA sequences to extract peptide-associated signal regions. From these segments, we extracted signal features and trained feature-based and deep-learning classifiers to distinguish peptides, balancing interpretability and classification performance. We establish a framework for peptide identification using standard nanopore sequencing hardware. Across a diverse panel of synthetic peptides, our approach resolves single-amino-acid substitutions, maintains performance across independent sequencing runs, and correctly identifies peptides in blind mixtures. Interpretable model analyses connect classifier decisions and common errors to specific signal motifs. By combining commercially available instrumentation with a reproducible experimental and computational workflow, this framework lowers the barrier to nanopore-based proteomics and enables broader adoption across laboratories. It provides a foundation for future developments in amino acid modification detection and sequence analysis.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2026-05-21&quot; title=&quot;Source date; precision not specified&quot;&gt;May 21, 2026&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; bioRxiv (Cold Spring Harbor Laboratory)&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Discovered via &lt;a href=&quot;https://openalex.org/&quot; rel=&quot;external&quot;&gt;OpenAlex&lt;/a&gt;&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Preprint source: &lt;a href=&quot;https://www.biorxiv.org/collection/bioinformatics&quot; rel=&quot;external&quot;&gt;bioRxiv: Bioinformatics&lt;/a&gt;&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · DNA-motor-driven transport · Identification · Preprint · Protein analysis&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.64898/2026.05.19.726067&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Thu, 21 May 2026 00:00:00 GMT</pubDate>
      <dc:creator>Denis Beslic</dc:creator>
      <dc:creator>Martin Kucklick</dc:creator>
      <dc:creator>Eric Graap</dc:creator>
      <dc:creator>Somayyeh Sedaghatjoo</dc:creator>
      <dc:creator>Bernhard Y. Renard</dc:creator>
      <dc:creator>Stephan Fuchs</dc:creator>
      <dc:creator>Susanne Engelmann</dc:creator>
      <dc:creator>Nils Körber</dc:creator>
      <category>OpenAlex</category>
      <category>bioRxiv: Bioinformatics</category>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>The Next Generation of Protein Sequencing and Analysis Methods</title>
      <link>https://reading.michaelmvh.com/article/e82197578eb03bf7e6723f2f4d4229ab62b0788ae8b0eb00ee54b643286c4c33/</link>
      <guid isPermaLink="false">urn:sha256:e82197578eb03bf7e6723f2f4d4229ab62b0788ae8b0eb00ee54b643286c4c33</guid>
      <description>&lt;p&gt;Advances in protein sequencing and analysis are poised to transform proteomics through an ability to link sequence, structure, and function at scale, thereby accelerating biological discovery and biomedical innovation. However, interrogating proteins is uniquely challenging because they cannot be amplified, are composed of complex chemical structures, and exist across a vast landscape of proteoforms. Techniques such as mass spectrometry typically drive large-scale proteomics studies; however, a new generation of technologies is pushing the boundaries, promising new features such as de novo, single-molecule, and/or higher-throughput sequencing and analysis. While many strategies are still in an early stage, a few modalities, such as fluorosequencing, single-molecule sequencing, digital proteomics mapping, and nanopore-based protein sequencing, have now reached or are thought to be nearing commercial implementation. In this review, we evaluate the mechanisms, current progress, and remaining challenges of these technologies while also highlighting how recent innovations are converging toward a new generation of proteomic technologies.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2026-02-23&quot; title=&quot;Source date; precision not specified&quot;&gt;February 23, 2026&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Annual Review of Analytical Chemistry&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Discovered via &lt;a href=&quot;https://openalex.org/&quot; rel=&quot;external&quot;&gt;OpenAlex&lt;/a&gt;&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Protein analysis · Review&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1146/annurev-anchem-071724-035726&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Mon, 23 Feb 2026 00:00:00 GMT</pubDate>
      <dc:creator>K. C. Wong</dc:creator>
      <dc:creator>Mattias Tolhurst</dc:creator>
      <dc:creator>Oren A. Fox</dc:creator>
      <dc:creator>Safwan Diwan</dc:creator>
      <dc:creator>Nicholas Bogard</dc:creator>
      <dc:creator>Jeff Nivala</dc:creator>
      <category>OpenAlex</category>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Single-molecule identification of full-length proteins with single-amino-acid resolution using nanopores</title>
      <link>https://reading.michaelmvh.com/article/56454623769e9b6ec5e134efa48cc4d6d9e8fc8fa0fe87488f162fe924d81448/</link>
      <guid isPermaLink="false">urn:sha256:56454623769e9b6ec5e134efa48cc4d6d9e8fc8fa0fe87488f162fe924d81448</guid>
      <description>&lt;p&gt;Nanopore-based technologies show promise in single-molecule protein sequencing. By using an unfoldase and a nanopore with enhanced electroosmotic flow, here we show the continuous identification of generic proteins during single nanopore passes. This approach enables the recording of differences in charge and size from single amino acid substitutions compared to reference signals, paving the way for single-molecule protein sequencing and high-throughput proteomics.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2026-01-08&quot; title=&quot;Source date; precision not specified&quot;&gt;January 8, 2026&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; bioRxiv (Cold Spring Harbor Laboratory)&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Discovered via &lt;a href=&quot;https://openalex.org/&quot; rel=&quot;external&quot;&gt;OpenAlex&lt;/a&gt;&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Preprint source: &lt;a href=&quot;https://www.biorxiv.org/collection/biophysics&quot; rel=&quot;external&quot;&gt;bioRxiv: Biophysics&lt;/a&gt;&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Electroosmotic flow · Identification · Preprint · Protein analysis · Unfoldase-driven transport&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.64898/2026.01.08.698148&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate>
      <dc:creator>Andrea Bonini</dc:creator>
      <dc:creator>Chunzhe Lu</dc:creator>
      <dc:creator>Luca Mantovanelli</dc:creator>
      <dc:creator>Roderick Corstiaan Abraham Versloot</dc:creator>
      <dc:creator>Alwin Jansen</dc:creator>
      <dc:creator>Peter O Connell Stack</dc:creator>
      <dc:creator>Vasiliki Tsousi</dc:creator>
      <dc:creator>Pascal Knecht</dc:creator>
      <dc:creator>Kathrin Lang</dc:creator>
      <dc:creator>Andrew Heron</dc:creator>
      <dc:creator>Giovanni Maglia</dc:creator>
      <category>OpenAlex</category>
      <category>bioRxiv: Biophysics</category>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Toward single-molecule protein sequencing using nanopores</title>
      <link>https://reading.michaelmvh.com/article/8ccdfd8c7c3e0a7ca6218ca8c27d2ed847b2b6972923816854ae598411176356/</link>
      <guid isPermaLink="false">urn:sha256:8ccdfd8c7c3e0a7ca6218ca8c27d2ed847b2b6972923816854ae598411176356</guid>
      <description>&lt;p&gt;No abstract is available.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2025-03-01&quot; title=&quot;Source date; precision not specified&quot;&gt;March 1, 2025&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Nature Biotechnology&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Protein analysis · Review&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1038/s41587-025-02587-y&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Sat, 01 Mar 2025 00:00:00 GMT</pubDate>
      <dc:creator>Chunzhe Lu</dc:creator>
      <dc:creator>Andrea Bonini</dc:creator>
      <dc:creator>Jakob H. Viel</dc:creator>
      <dc:creator>Giovanni Maglia</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Multi-pass, single-molecule nanopore reading of long protein strands</title>
      <link>https://reading.michaelmvh.com/article/00e955bbe2a4325ea2f34a8eb70db00a7c3e4f59dbf3f0420455b19f9b8ab975/</link>
      <guid isPermaLink="false">urn:sha256:00e955bbe2a4325ea2f34a8eb70db00a7c3e4f59dbf3f0420455b19f9b8ab975</guid>
      <description>&lt;p&gt;The ability to sequence single protein molecules in their native, full-length form would enable a more comprehensive understanding of proteomic diversity. Current technologies, however, are limited in achieving this goal1,2. Here, we establish a method for the long-range, single-molecule reading of intact protein strands on a commercial nanopore sensor array. By using the ClpX unfoldase to ratchet proteins through a CsgG nanopore3,4, we provide single-molecule evidence that ClpX translocates substrates in two-residue steps. This mechanism achieves sensitivity to single amino acids on synthetic protein strands hundreds of amino acids in length, enabling the sequencing of combinations of single-amino-acid substitutions and the mapping of post-translational modifications, such as phosphorylation. To enhance classification accuracy further, we demonstrate the ability to reread individual protein molecules multiple times, and we explore the potential for highly accurate protein barcode sequencing. Furthermore, we develop a biophysical model that can simulate raw nanopore signals a priori on the basis of residue volume and charge, enhancing the interpretation of raw signal data. Finally, we apply these methods to examine full-length, folded protein domains for complete end-to-end analysis. These results provide proof of concept for a platform that has the potential to identify and characterize full-length proteoforms at single-molecule resolution. A technique for threading long protein strands through a nanopore by electrophoresis and back using a protein unfoldase motor, ClpX, enables single protein molecules to be analyzed multiple times with single-amino-acid sensitivity.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2024-09-11&quot; title=&quot;Source date; precision not specified&quot;&gt;September 11, 2024&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Nature&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Landmark · Protein analysis · Rereading · Unfoldase-driven transport&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1038/s41586-024-07935-7&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.nature.com/articles/s41586-024-07935-7.pdf&quot;&gt;Open PDF&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Wed, 11 Sep 2024 00:00:00 GMT</pubDate>
      <dc:creator>Keisuke Motone</dc:creator>
      <dc:creator>Daphne Kontogiorgos-Heintz</dc:creator>
      <dc:creator>Jasmine Wee</dc:creator>
      <dc:creator>Kyoko Kurihara</dc:creator>
      <dc:creator>Sangbeom Yang</dc:creator>
      <dc:creator>Gwendolin Roote</dc:creator>
      <dc:creator>Oren E Fox</dc:creator>
      <dc:creator>Yishu Fang</dc:creator>
      <dc:creator>Melissa Queen</dc:creator>
      <dc:creator>Mattias Tolhurst</dc:creator>
      <dc:creator>Nicolás Cardozo</dc:creator>
      <dc:creator>Miten Jain</dc:creator>
      <dc:creator>Jeff Nivala</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Detection of phosphorylation post-translational modifications along single peptides with nanopores</title>
      <link>https://reading.michaelmvh.com/article/d3a27f156f2c5d74c5405b424aa5f7bbed2177a9a5834711841254da318422d7/</link>
      <guid isPermaLink="false">urn:sha256:d3a27f156f2c5d74c5405b424aa5f7bbed2177a9a5834711841254da318422d7</guid>
      <description>&lt;p&gt;No abstract is available.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2023-06-29&quot; title=&quot;Source date; precision not specified&quot;&gt;June 29, 2023&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Nature Biotechnology&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · DNA-motor-driven transport · Post-translational modifications · Protein analysis&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1038/s41587-023-01839-z&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.nature.com/articles/s41587-023-01839-z.pdf&quot;&gt;Open PDF&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Thu, 29 Jun 2023 00:00:00 GMT</pubDate>
      <dc:creator>Ian C. Nova</dc:creator>
      <dc:creator>Justas Ritmejeris</dc:creator>
      <dc:creator>Henry D. Brinkerhoff</dc:creator>
      <dc:creator>Theo J. R. Koenig</dc:creator>
      <dc:creator>Jens H. Gundlach</dc:creator>
      <dc:creator>Cees Dekker</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Unidirectional single-file transport of full-length proteins through a nanopore</title>
      <link>https://reading.michaelmvh.com/article/fc686519ca9f7b93e073937de04a285041bff30abb403c7d4d7002863f0cedad/</link>
      <guid isPermaLink="false">urn:sha256:fc686519ca9f7b93e073937de04a285041bff30abb403c7d4d7002863f0cedad</guid>
      <description>&lt;p&gt;No abstract is available.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2023-01-09&quot; title=&quot;Source date; precision not specified&quot;&gt;January 9, 2023&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Nature Biotechnology&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Electroosmotic flow · Motor-free transport · Protein analysis&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1038/s41587-022-01598-3&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.biorxiv.org/content/biorxiv/early/2021/09/28/2021.09.28.462155.full.pdf&quot;&gt;Open PDF&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Mon, 09 Jan 2023 00:00:00 GMT</pubDate>
      <dc:creator>Luning Yu</dc:creator>
      <dc:creator>Xinqi Kang</dc:creator>
      <dc:creator>Fanjun Li</dc:creator>
      <dc:creator>Behzad Mehrafrooz</dc:creator>
      <dc:creator>Amr K. Makhamreh</dc:creator>
      <dc:creator>Ali Fallahi</dc:creator>
      <dc:creator>Joshua C. Foster</dc:creator>
      <dc:creator>Aleksei Aksimentiev</dc:creator>
      <dc:creator>Min Chen</dc:creator>
      <dc:creator>Meni Wanunu</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Multiple rereads of single proteins at single–amino acid resolution using nanopores</title>
      <link>https://reading.michaelmvh.com/article/03e45e35ff7c8cb6627cabe099b91bdc78101cfb5c97945b9b5f7b6a9b547fd4/</link>
      <guid isPermaLink="false">urn:sha256:03e45e35ff7c8cb6627cabe099b91bdc78101cfb5c97945b9b5f7b6a9b547fd4</guid>
      <description>&lt;p&gt;Abstract As identifying proteins is of paramount importance for cell biology and applications, it is of interest to develop a protein sequencer with the ultimate sensitivity of decoding individual proteins. Here, we demonstrate a nanopore-based single-molecule sequencing approach capable of reliably detecting single amino-acid substitutions within individual peptides. A peptide is linked to a DNA molecule that is pulled through the biological nanopore MspA by a DNA helicase in single amino-acid steps. The peptide sequence yields clear stepping ion current signals which allows to discriminate single-amino-acid substitutions in single reads. Molecular dynamics simulations show these signals to result from size exclusion and pore binding. Notably, we demonstrate the capability to ‘rewind’ peptide reads, obtaining indefinitely many independent reads of the same individual molecule, yielding virtually 100% read accuracy in variant identification, with an error rate less than 10 −6 . These proof-of-concept experiments constitute a promising basis for developing a single-molecule protein sequencer. One-sentence summary This paper presents proof-of-concept experiments and simulations of a nanopore-based approach to sequencing individual proteins.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2021-11-04&quot; title=&quot;Source date; precision not specified&quot;&gt;November 4, 2021&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Science&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · DNA-motor-driven transport · Protein analysis · Rereading · Variant discrimination&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1126/science.abl4381&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.biorxiv.org/content/biorxiv/early/2021/07/14/2021.07.13.452225.full.pdf&quot;&gt;Open PDF&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Thu, 04 Nov 2021 00:00:00 GMT</pubDate>
      <dc:creator>Henry D. Brinkerhoff</dc:creator>
      <dc:creator>Albert S. W. Kang</dc:creator>
      <dc:creator>Jingqian Liu</dc:creator>
      <dc:creator>Aleksei Aksimentiev</dc:creator>
      <dc:creator>Cees Dekker</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal data</title>
      <link>https://reading.michaelmvh.com/article/ad1165cb030cca26cd25547572432ec8661187f3d2273a2b12d8c27f6471258d/</link>
      <guid isPermaLink="false">urn:sha256:ad1165cb030cca26cd25547572432ec8661187f3d2273a2b12d8c27f6471258d</guid>
      <description>&lt;p&gt;Nanopore sequencing provides signal data corresponding to the nucleotide motifs sequenced. Through machine learning-based methods, these signals are translated into long-read sequences that overcome the read size limit of short-read sequencing. However, analyzing the raw nanopore signal data provides many more opportunities beyond just sequencing genomes and transcriptomes: algorithms that use machine learning approaches to extract biological information from these signals allow the detection of DNA and RNA modifications, the estimation of poly(A) tail length, and the prediction of RNA secondary structures. In this review, we discuss how developments in machine learning methodologies contributed to more accurate basecalling and lower error rates, and how these methods enable new biological discoveries. We argue that direct nanopore sequencing of DNA and RNA provides a new dimensionality for genomics experiments and highlight challenges and future directions for computational approaches to extract the additional information provided by nanopore signal data.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2021-10-25&quot; title=&quot;Source date; precision not specified&quot;&gt;October 25, 2021&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Trends in Genetics&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Review · Signal analysis&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1016/j.tig.2021.09.001&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;http://www.cell.com/article/S0168952521002572/pdf&quot;&gt;Open PDF&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Mon, 25 Oct 2021 00:00:00 GMT</pubDate>
      <dc:creator>Yuk Kei Wan</dc:creator>
      <dc:creator>Christopher Hendra</dc:creator>
      <dc:creator>Ploy N. Pratanwanich</dc:creator>
      <dc:creator>Jonathan Göke</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning</title>
      <link>https://reading.michaelmvh.com/article/6fdbf17f984a31e5885f7fca125973f4482a078ba49ceee0c97dc47e658b6f68/</link>
      <guid isPermaLink="false">urn:sha256:6fdbf17f984a31e5885f7fca125973f4482a078ba49ceee0c97dc47e658b6f68</guid>
      <description>&lt;p&gt;Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2018-04-10&quot; title=&quot;Source date; precision not specified&quot;&gt;April 10, 2018&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; GigaScience&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Basecalling · Connectionist temporal classification · Signal analysis&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1093/gigascience/giy037&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://academic.oup.com/gigascience/article-pdf/7/5/giy037/28599334/giy037.pdf&quot;&gt;Open PDF&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Tue, 10 Apr 2018 00:00:00 GMT</pubDate>
      <dc:creator>Haotian Teng</dc:creator>
      <dc:creator>Minh Duc Cao</dc:creator>
      <dc:creator>Michael B. Hall</dc:creator>
      <dc:creator>Tania P. S. Duarte</dc:creator>
      <dc:creator>Sheng Wang</dc:creator>
      <dc:creator>Lachlan Coin</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads</title>
      <link>https://reading.michaelmvh.com/article/78f58cbd9f2e2000fea1bd0eda8cfc0f09327d0f1c3d018ed1ab916e6bcdbb8a/</link>
      <guid isPermaLink="false">urn:sha256:78f58cbd9f2e2000fea1bd0eda8cfc0f09327d0f1c3d018ed1ab916e6bcdbb8a</guid>
      <description>&lt;p&gt;The MinION device by Oxford Nanopore produces very long reads (reads over 100 kBp were reported); however it suffers from high sequencing error rate. We present an open-source DNA base caller based on deep recurrent neural networks and show that the accuracy of base calling is much dependent on the underlying software and can be improved by considering modern machine learning methods. By employing carefully crafted recurrent neural networks, our tool significantly improves base calling accuracy on data from R7.3 version of the platform compared to the default base caller supplied by the manufacturer. On R9 version, we achieve results comparable to Nanonet base caller provided by Oxford Nanopore. Availability of an open source tool with high base calling accuracy will be useful for development of new applications of the MinION device, including infectious disease detection and custom target enrichment during sequencing.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2017-06-05&quot; title=&quot;Source date; precision not specified&quot;&gt;June 5, 2017&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; PLoS ONE&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Basecalling · Recurrent neural networks · Signal analysis&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1371/journal.pone.0178751&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0178751&amp;amp;type=printable&quot;&gt;Open PDF&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Mon, 05 Jun 2017 00:00:00 GMT</pubDate>
      <dc:creator>Vladimír Boža</dc:creator>
      <dc:creator>Broňa Brejová</dc:creator>
      <dc:creator>Tomáš Vinař</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Three decades of nanopore sequencing</title>
      <link>https://reading.michaelmvh.com/article/97f054433926a6c539d5234bb9fe903e5a774087487d1e4790ba4d5ab40db213/</link>
      <guid isPermaLink="false">urn:sha256:97f054433926a6c539d5234bb9fe903e5a774087487d1e4790ba4d5ab40db213</guid>
      <description>&lt;p&gt;No abstract is available.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2016-05-01&quot; title=&quot;Source date; precision not specified&quot;&gt;May 1, 2016&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Nature Biotechnology&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Foundations · Review&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1038/nbt.3423&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Sun, 01 May 2016 00:00:00 GMT</pubDate>
      <dc:creator>David W. Deamer</dc:creator>
      <dc:creator>Mark A. Akeson</dc:creator>
      <dc:creator>Daniel Branton</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Analysis of nanopore data using hidden Markov models</title>
      <link>https://reading.michaelmvh.com/article/a9a68cab24e62a29c03bb189f1aaa7f38ce100071b3fa4d97724eabeba312364/</link>
      <guid isPermaLink="false">urn:sha256:a9a68cab24e62a29c03bb189f1aaa7f38ce100071b3fa4d97724eabeba312364</guid>
      <description>&lt;p&gt;MOTIVATION: Nanopore-based sequencing techniques can reconstruct properties of biosequences by analyzing the sequence-dependent ionic current steps produced as biomolecules pass through a pore. Typically this involves alignment of new data to a reference, where both reference construction and alignment have been performed by hand. RESULTS: We propose an automated method for aligning nanopore data to a reference through the use of hidden Markov models. Several features that arise from prior processing steps and from the class of enzyme used can be simply incorporated into the model. Previously, the M2MspA nanopore was shown to be sensitive enough to distinguish between cytosine, methylcytosine and hydroxymethylcytosine. We validated our automated methodology on a subset of that data by automatically calculating an error rate for the distinction between the three cytosine variants and show that the automated methodology produces a 2-3% error rate, lower than the 10% error rate from previous manual segmentation and alignment. AVAILABILITY AND IMPLEMENTATION: The data, output, scripts and tutorials replicating the analysis are available at https://github.com/UCSCNanopore/Data/tree/master/Automation.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2015-02-03&quot; title=&quot;Source date; precision not specified&quot;&gt;February 3, 2015&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Bioinformatics&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Hidden Markov models · Signal analysis&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1093/bioinformatics/btv046&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://academic.oup.com/bioinformatics/article-pdf/31/12/1897/49014256/bioinformatics_31_12_1897.pdf&quot;&gt;Open PDF&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Tue, 03 Feb 2015 00:00:00 GMT</pubDate>
      <dc:creator>Jacob Matthew Schreiber</dc:creator>
      <dc:creator>Kevin J. Karplus</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Segmentation of Noisy Signals Generated By a Nanopore</title>
      <link>https://reading.michaelmvh.com/article/c8b1bd48428ec5535cd8f4287c103f4626858fe34d5b55abb4c6ec2917d48e9c/</link>
      <guid isPermaLink="false">urn:sha256:c8b1bd48428ec5535cd8f4287c103f4626858fe34d5b55abb4c6ec2917d48e9c</guid>
      <description>&lt;p&gt;ABSTRACT Nanopore-based single-molecule sequencing techniques exploit ionic current steps produced as biomolecules pass through a pore to reconstruct properties of the sequence. A key task in analyzing complex nanopore data is discovering the boundaries between these steps, which has traditionally been done in research labs by hand. We present an automated method of analyzing nanopore data, by detecting regions of ionic current corresponding to the translocation of a biomolecule, and then segmenting the region. The segmenter uses a divide-and-conquer method to recursively discover boundary points, with an implementation that works several times faster than real time and that can handle low-pass filtered signals.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2015-01-23&quot; title=&quot;Source date; precision not specified&quot;&gt;January 23, 2015&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; bioRxiv (Cold Spring Harbor Laboratory)&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Preprint · Segmentation · Signal analysis&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1101/014258&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.biorxiv.org/content/biorxiv/early/2015/01/23/014258.full.pdf&quot;&gt;Open PDF&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Fri, 23 Jan 2015 00:00:00 GMT</pubDate>
      <dc:creator>Jacob Matthew Schreiber</dc:creator>
      <dc:creator>Kevin J. Karplus</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Discrimination among Protein Variants Using an Unfoldase-Coupled Nanopore</title>
      <link>https://reading.michaelmvh.com/article/8fdda0a3f380da0ca5978e5684b67c4ef88839a1a6647d01403e1db98dbc305d/</link>
      <guid isPermaLink="false">urn:sha256:8fdda0a3f380da0ca5978e5684b67c4ef88839a1a6647d01403e1db98dbc305d</guid>
      <description>&lt;p&gt;Previously we showed that the protein unfoldase ClpX could facilitate translocation of individual proteins through the α-hemolysin nanopore. This results in ionic current fluctuations that correlate with unfolding and passage of intact protein strands through the pore lumen. It is plausible that this technology could be used to identify protein domains and structural modifications at the single-molecule level that arise from subtle changes in primary amino acid sequence (e.g., point mutations). As a test, we engineered proteins bearing well-characterized domains connected in series along an ∼700 amino acid strand. Point mutations in a titin immunoglobulin domain (titin I27) and point mutations, proteolytic cleavage, and rearrangement of beta-strands in green fluorescent protein (GFP), caused ionic current pattern changes for single strands predicted by bulk phase and force spectroscopy experiments. Among these variants, individual proteins could be classified at 86-99% accuracy using standard machine learning tools. We conclude that a ClpXP-nanopore device can discriminate among distinct protein domains, and that sequence-dependent variations within those domains are detectable.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2014-11-17&quot; title=&quot;Source date; precision not specified&quot;&gt;November 17, 2014&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; ACS Nano&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Protein analysis · Unfoldase-driven transport · Variant discrimination&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1021/nn5049987&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Mon, 17 Nov 2014 00:00:00 GMT</pubDate>
      <dc:creator>Jeff Nivala</dc:creator>
      <dc:creator>Logan Mulroney</dc:creator>
      <dc:creator>Gabriel Li</dc:creator>
      <dc:creator>Jacob Matthew Schreiber</dc:creator>
      <dc:creator>Mark A. Akeson</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
    <item>
      <title>Unfoldase-mediated protein translocation through an α-hemolysin nanopore</title>
      <link>https://reading.michaelmvh.com/article/0d96c018acef4e5b866b983b95c600962396ed49f47f558bedb96437ce6e6627/</link>
      <guid isPermaLink="false">urn:sha256:0d96c018acef4e5b866b983b95c600962396ed49f47f558bedb96437ce6e6627</guid>
      <description>&lt;p&gt;No abstract is available.&lt;/p&gt;&lt;p class=&quot;metadata&quot;&gt;&lt;time datetime=&quot;2013-02-03&quot; title=&quot;Source date; precision not specified&quot;&gt;February 3, 2013&lt;/time&gt; &lt;span aria-hidden=&quot;true&quot;&gt; · &lt;/span&gt; Nature Biotechnology&lt;/p&gt;&lt;p class=&quot;provenance&quot;&gt;Curated by &lt;a href=&quot;https://github.com/Michaelmvh/scholarly-rss-feed/blob/main/src/curated/nanopore-foundations.json&quot; rel=&quot;external&quot;&gt;Nanopore Foundations &amp;amp; Signal Analysis&lt;/a&gt; · Landmark · Protein analysis · Unfoldase-driven transport&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://doi.org/10.1038/nbt.2503&quot;&gt;Original publication&lt;/a&gt;&lt;/p&gt;</description>
      <pubDate>Sun, 03 Feb 2013 00:00:00 GMT</pubDate>
      <dc:creator>Jeff Nivala</dc:creator>
      <dc:creator>Douglas B Marks</dc:creator>
      <dc:creator>Mark A. Akeson</dc:creator>
      <category>Nanopore Foundations &amp; Signal Analysis</category>
    </item>
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