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            <title><![CDATA[Recent developments of fluorescent proteins and biosensors driven by cross-disciplinary insights]]></title>
            <link>https://www.nature.com/articles/s41592-026-03268-7</link>
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                <![CDATA[<p>Nature Methods, Published online: 16 October 2026; <a href="https://www.nature.com/articles/s41592-026-03268-7">doi:10.1038/s41592-026-03268-7</a></p>This Review summarizes recent developments in fluorescent protein technology, including the engineering of fluorescent biosensors.]]></content:encoded>
            <dc:title><![CDATA[Recent developments of fluorescent proteins and biosensors driven by cross-disciplinary insights]]></dc:title>
            <dc:creator>Anneliese M. M. Gest</dc:creator><dc:creator>Sohum Mehta</dc:creator><dc:creator>Jin Zhang</dc:creator>
            <dc:identifier>doi:10.1038/s41592-026-03268-7</dc:identifier>
            <dc:source>Nature Methods, Published online: 2026-10-16; | doi:10.1038/s41592-026-03268-7</dc:source>
            <dc:date>2026-10-16</dc:date>
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        <item rdf:about="https://www.nature.com/articles/s41592-026-03264-x">
            <title><![CDATA[AmpliconSuite: an end-to-end workflow for analyzing focal amplifications in cancer genomes]]></title>
            <link>https://www.nature.com/articles/s41592-026-03264-x</link>
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                <![CDATA[<p>Nature Methods, Published online: 16 October 2026; <a href="https://www.nature.com/articles/s41592-026-03264-x">doi:10.1038/s41592-026-03264-x</a></p>AmpliconSuite is a toolkit that enables the analysis of focal amplifications from cancer whole-genome sequencing data.]]></content:encoded>
            <dc:title><![CDATA[AmpliconSuite: an end-to-end workflow for analyzing focal amplifications in cancer genomes]]></dc:title>
            <dc:creator>Jens Luebeck</dc:creator><dc:creator>Ted Liefeld</dc:creator><dc:creator>Ivy Tsz-Lo Wong</dc:creator><dc:creator>Edwin Huang</dc:creator><dc:creator>Forrest Kim</dc:creator><dc:creator>Bhargavi Dameracharla</dc:creator><dc:creator>Michael Chan</dc:creator><dc:creator>Rohil Ahuja</dc:creator><dc:creator>Kyra Fetter</dc:creator><dc:creator>Dhruv Khatri</dc:creator><dc:creator>Kaiyuan Zhu</dc:creator><dc:creator>Thorin Tabor</dc:creator><dc:creator>Daniel Schreyer</dc:creator><dc:creator>Gino Prasad</dc:creator><dc:creator>Aditi Gnanasekar</dc:creator><dc:creator>Siavash Raeisi Dehkordi</dc:creator><dc:creator>Simone Spandau</dc:creator><dc:creator>Michał Adamaszek</dc:creator><dc:creator>Rishaan Kenkre</dc:creator><dc:creator>Tushar Agashe</dc:creator><dc:creator>Devika Torvi</dc:creator><dc:creator>Mădălina Giurgiu-Kraljič</dc:creator><dc:creator>Soyeon Kim</dc:creator><dc:creator>Hoon Kim</dc:creator><dc:creator>Peter Bailey</dc:creator><dc:creator>Roel G. W. Verhaak</dc:creator><dc:creator>Viraj Deshpande</dc:creator><dc:creator>Michael Reich</dc:creator><dc:creator>Paul S. Mischel</dc:creator><dc:creator>Jill P. Mesirov</dc:creator><dc:creator>Vineet Bafna</dc:creator>
            <dc:identifier>doi:10.1038/s41592-026-03264-x</dc:identifier>
            <dc:source>Nature Methods, Published online: 2026-10-16; | doi:10.1038/s41592-026-03264-x</dc:source>
            <dc:date>2026-10-16</dc:date>
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            <prism:doi>10.1038/s41592-026-03264-x</prism:doi>
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        <item rdf:about="https://www.nature.com/articles/s41592-026-03266-9">
            <title><![CDATA[Integrating sex as a biological variable into one’s science]]></title>
            <link>https://www.nature.com/articles/s41592-026-03266-9</link>
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                <![CDATA[<p>Nature Methods, Published online: 14 October 2026; <a href="https://www.nature.com/articles/s41592-026-03266-9">doi:10.1038/s41592-026-03266-9</a></p>Addressing sex-related variation might seem like an experimental or data analysis time-sink. It’s not.]]></content:encoded>
            <dc:title><![CDATA[Integrating sex as a biological variable into one’s science]]></dc:title>
            <dc:creator>Vivien Marx</dc:creator>
            <dc:identifier>doi:10.1038/s41592-026-03266-9</dc:identifier>
            <dc:source>Nature Methods, Published online: 2026-10-14; | doi:10.1038/s41592-026-03266-9</dc:source>
            <dc:date>2026-10-14</dc:date>
            <prism:publicationName>Nature Methods</prism:publicationName>
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        <item rdf:about="https://www.nature.com/articles/s41592-026-03249-w">
            <title><![CDATA[LoL-align: sensitive and fast probabilistic protein structure alignment]]></title>
            <link>https://www.nature.com/articles/s41592-026-03249-w</link>
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                <![CDATA[<p>Nature Methods, Published online: 14 October 2026; <a href="https://www.nature.com/articles/s41592-026-03249-w">doi:10.1038/s41592-026-03249-w</a></p>LoL-align is a probabilistic, distance-based protein structure alignment algorithm that iteratively optimizes a local log-odds score, achieving higher sensitivity for remote homologs and better alignment quality than available methods.]]></content:encoded>
            <dc:title><![CDATA[LoL-align: sensitive and fast probabilistic protein structure alignment]]></dc:title>
            <dc:creator>Lasse Reifenrath</dc:creator><dc:creator>Michel van Kempen</dc:creator><dc:creator>Gyuri Kim</dc:creator><dc:creator>Soo Hyun Kim</dc:creator><dc:creator>Mohammadreza Radnezhad</dc:creator><dc:creator>Cameron L. M. Gilchrist</dc:creator><dc:creator>Milot Mirdita</dc:creator><dc:creator>Martin Steinegger</dc:creator><dc:creator>Johannes Söding</dc:creator>
            <dc:identifier>doi:10.1038/s41592-026-03249-w</dc:identifier>
            <dc:source>Nature Methods, Published online: 2026-10-14; | doi:10.1038/s41592-026-03249-w</dc:source>
            <dc:date>2026-10-14</dc:date>
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            <title><![CDATA[MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics]]></title>
            <link>https://www.nature.com/articles/s41592-026-03247-y</link>
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                <![CDATA[<p>Nature Methods, Published online: 14 October 2026; <a href="https://www.nature.com/articles/s41592-026-03247-y">doi:10.1038/s41592-026-03247-y</a></p>This Article presents MassNet, a large-scale AI-ready corpus of 1.5 billion MS/MS spectra spanning 35 species, paired with a standardized storage format called Mass Spectrometry DDA Tensor (MSDT). Built on this resource, XuanjiNovo is a nonautoregressive transformer for de novo peptide sequencing that substantially outperforms previous methods.]]></content:encoded>
            <dc:title><![CDATA[MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics]]></dc:title>
            <dc:creator>Jun A</dc:creator><dc:creator>Xiang Zhang</dc:creator><dc:creator>Xiaofan Zhang</dc:creator><dc:creator>Jiaqi Wei</dc:creator><dc:creator>Te Zhang</dc:creator><dc:creator>Yamin Deng</dc:creator><dc:creator>Pu Liu</dc:creator><dc:creator>Zongxiang Nie</dc:creator><dc:creator>Yiwen Yu</dc:creator><dc:creator>Yi Chen</dc:creator><dc:creator>Nanqing Dong</dc:creator><dc:creator>Zhiqiang Gao</dc:creator><dc:creator>Siqi Sun</dc:creator><dc:creator>Tiannan Guo</dc:creator>
            <dc:identifier>doi:10.1038/s41592-026-03247-y</dc:identifier>
            <dc:source>Nature Methods, Published online: 2026-10-14; | doi:10.1038/s41592-026-03247-y</dc:source>
            <dc:date>2026-10-14</dc:date>
            <prism:publicationName>Nature Methods</prism:publicationName>
            <prism:doi>10.1038/s41592-026-03247-y</prism:doi>
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        <item rdf:about="https://www.nature.com/articles/s41592-026-03276-7">
            <title><![CDATA[The companion website]]></title>
            <link>https://www.nature.com/articles/s41592-026-03276-7</link>
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                <![CDATA[<p>Nature Methods, Published online: 13 October 2026; <a href="https://www.nature.com/articles/s41592-026-03276-7">doi:10.1038/s41592-026-03276-7</a></p>How biologists can make the most of supplementary materials for impact.]]></content:encoded>
            <dc:title><![CDATA[The companion website]]></dc:title>
            <dc:creator>Andreas Bueckle</dc:creator><dc:creator>Matthias Stahl</dc:creator><dc:creator>Kelly Krause</dc:creator><dc:creator>Torsten Möller</dc:creator><dc:creator>Helena Klara Jambor</dc:creator>
            <dc:identifier>doi:10.1038/s41592-026-03276-7</dc:identifier>
            <dc:source>Nature Methods, Published online: 2026-10-13; | doi:10.1038/s41592-026-03276-7</dc:source>
            <dc:date>2026-10-13</dc:date>
            <prism:publicationName>Nature Methods</prism:publicationName>
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        <item rdf:about="https://www.nature.com/articles/s41592-026-03255-y">
            <title><![CDATA[Near-infrared photochromic miniprotein enables photoacoustic molecular imaging of acute inflammation and intracellular antigens]]></title>
            <link>https://www.nature.com/articles/s41592-026-03255-y</link>
            <content:encoded>
                <![CDATA[<p>Nature Methods, Published online: 13 October 2026; <a href="https://www.nature.com/articles/s41592-026-03255-y">doi:10.1038/s41592-026-03255-y</a></p>A small, photoswitchable and genetically encodable protein operating in the near-infrared window is reported for photoacoustic tomography applications.]]></content:encoded>
            <dc:title><![CDATA[Near-infrared photochromic miniprotein enables photoacoustic molecular imaging of acute inflammation and intracellular antigens]]></dc:title>
            <dc:creator>Olena S. Oliinyk</dc:creator><dc:creator>Yirui Xu</dc:creator><dc:creator>Jinhuan Luo</dc:creator><dc:creator>Kateryna O. Tokarchuk</dc:creator><dc:creator>Kyrylo Yu. Manoilov</dc:creator><dc:creator>Josie van de Klashorst</dc:creator><dc:creator>Matthew P. Eyring</dc:creator><dc:creator>Mu-en Wang</dc:creator><dc:creator>Ming Chen</dc:creator><dc:creator>Pengfei Song</dc:creator><dc:creator>John W. Hickey</dc:creator><dc:creator>Junjie Yao</dc:creator><dc:creator>Vladislav V. Verkhusha</dc:creator>
            <dc:identifier>doi:10.1038/s41592-026-03255-y</dc:identifier>
            <dc:source>Nature Methods, Published online: 2026-10-13; | doi:10.1038/s41592-026-03255-y</dc:source>
            <dc:date>2026-10-13</dc:date>
            <prism:publicationName>Nature Methods</prism:publicationName>
            <prism:doi>10.1038/s41592-026-03255-y</prism:doi>
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        <item rdf:about="https://www.nature.com/articles/s41592-026-03267-8">
            <title><![CDATA[Early-career balancing act: training, moving, relationships and language learning]]></title>
            <link>https://www.nature.com/articles/s41592-026-03267-8</link>
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                <![CDATA[<p>Nature Methods, Published online: 12 October 2026; <a href="https://www.nature.com/articles/s41592-026-03267-8">doi:10.1038/s41592-026-03267-8</a></p>These early career scientists in Europe share challenges and advice about finding the ‘right’ lab and lab–life blend.]]></content:encoded>
            <dc:title><![CDATA[Early-career balancing act: training, moving, relationships and language learning]]></dc:title>
            <dc:creator>Vivien Marx</dc:creator>
            <dc:identifier>doi:10.1038/s41592-026-03267-8</dc:identifier>
            <dc:source>Nature Methods, Published online: 2026-10-12; | doi:10.1038/s41592-026-03267-8</dc:source>
            <dc:date>2026-10-12</dc:date>
            <prism:publicationName>Nature Methods</prism:publicationName>
            <prism:doi>10.1038/s41592-026-03267-8</prism:doi>
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