Search Results - "Nature methods"

ColabFold: making protein folding accessible to all by Mirdita, Milot, Schütze, Konstantin, Moriwaki, Yoshitaka, Heo, Lim, Ovchinnikov, Sergey, Steinegger, Martin

“…ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold…”
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nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation by Isensee, Fabian, Jaeger, Paul F., Kohl, Simon A. A., Petersen, Jens, Maier-Hein, Klaus H.

“…Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While…”
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SciPy 1.0: fundamental algorithms for scientific computing in Python by Virtanen, Pauli, Gommers, Ralf, Oliphant, Travis E., Haberland, Matt, Reddy, Tyler, Cournapeau, David, Burovski, Evgeni, Peterson, Pearu, Weckesser, Warren, Bright, Jonathan, van der Walt, Stéfan J., Brett, Matthew, Wilson, Joshua, Millman, K. Jarrod, Mayorov, Nikolay, Nelson, Andrew R. J., Jones, Eric, Kern, Robert, Larson, Eric, Carey, C J, Polat, İlhan, Feng, Yu, Moore, Eric W., VanderPlas, Jake, Laxalde, Denis, Perktold, Josef, Cimrman, Robert, Henriksen, Ian, Quintero, E. A., Harris, Charles R., Archibald, Anne M., Ribeiro, Antônio H., Pedregosa, Fabian, van Mulbregt, Paul

“…SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto…”
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Sensitive protein alignments at tree-of-life scale using DIAMOND by Buchfink, Benjamin, Reuter, Klaus, Drost, Hajk-Georg

“…We are at the beginning of a genomic revolution in which all known species are planned to be sequenced. Accessing such data for comparative analyses is crucial…”
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Cellpose: a generalist algorithm for cellular segmentation by Stringer, Carsen, Wang, Tim, Michaelos, Michalis, Pachitariu, Marius

“…Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on…”
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Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm by Cheng, Haoyu, Concepcion, Gregory T., Feng, Xiaowen, Zhang, Haowen, Li, Heng

“…Haplotype-resolved de novo assembly is the ultimate solution to the study of sequence variations in a genome. However, existing algorithms either collapse…”
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Fast, sensitive and accurate integration of single-cell data with Harmony by Korsunsky, Ilya, Millard, Nghia, Fan, Jean, Slowikowski, Kamil, Zhang, Fan, Wei, Kevin, Baglaenko, Yuriy, Brenner, Michael, Loh, Po-ru, Raychaudhuri, Soumya

“…The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological…”
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ilastik: interactive machine learning for (bio)image analysis by Berg, Stuart, Kutra, Dominik, Kroeger, Thorben, Straehle, Christoph N., Kausler, Bernhard X., Haubold, Carsten, Schiegg, Martin, Ales, Janez, Beier, Thorsten, Rudy, Markus, Eren, Kemal, Cervantes, Jaime I, Xu, Buote, Beuttenmueller, Fynn, Wolny, Adrian, Zhang, Chong, Koethe, Ullrich, Hamprecht, Fred A., Kreshuk, Anna

“…We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational…”
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Martini 3: a general purpose force field for coarse-grained molecular dynamics by Souza, Paulo C. T., Alessandri, Riccardo, Barnoud, Jonathan, Thallmair, Sebastian, Faustino, Ignacio, Grünewald, Fabian, Patmanidis, Ilias, Abdizadeh, Haleh, Bruininks, Bart M. H., Wassenaar, Tsjerk A., Kroon, Peter C., Melcr, Josef, Nieto, Vincent, Corradi, Valentina, Khan, Hanif M., Domański, Jan, Javanainen, Matti, Martinez-Seara, Hector, Reuter, Nathalie, Best, Robert B., Vattulainen, Ilpo, Monticelli, Luca, Periole, Xavier, Tieleman, D. Peter, de Vries, Alex H., Marrink, Siewert J.

“…The coarse-grained Martini force field is widely used in biomolecular simulations. Here we present the refined model, Martini 3 ( http://cgmartini.nl ), with…”
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Single-cell chromatin state analysis with Signac by Stuart, Tim, Srivastava, Avi, Madad, Shaista, Lareau, Caleb A., Satija, Rahul

“…The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of…”
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Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction by Punjani, Ali, Zhang, Haowei, Fleet, David J.

“…Cryogenic electron microscopy (cryo-EM) is widely used to study biological macromolecules that comprise regions with disorder, flexibility or partial…”
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Feature-based molecular networking in the GNPS analysis environment by Nothias, Louis-Félix, Petras, Daniel, Schmid, Robin, Dührkop, Kai, Rainer, Johannes, Sarvepalli, Abinesh, Protsyuk, Ivan, Ernst, Madeleine, Tsugawa, Hiroshi, Fleischauer, Markus, Aicheler, Fabian, Aksenov, Alexander A., Alka, Oliver, Allard, Pierre-Marie, Barsch, Aiko, Cachet, Xavier, Caraballo-Rodriguez, Andres Mauricio, Da Silva, Ricardo R., Dang, Tam, Garg, Neha, Gauglitz, Julia M., Gurevich, Alexey, Isaac, Giorgis, Jarmusch, Alan K., Kameník, Zdeněk, Kang, Kyo Bin, Kessler, Nikolas, Koester, Irina, Korf, Ansgar, Le Gouellec, Audrey, Ludwig, Marcus, Martin H., Christian, McCall, Laura-Isobel, McSayles, Jonathan, Meyer, Sven W., Mohimani, Hosein, Morsy, Mustafa, Moyne, Oriane, Neumann, Steffen, Neuweger, Heiko, Nguyen, Ngoc Hung, Nothias-Esposito, Melissa, Paolini, Julien, Phelan, Vanessa V., Pluskal, Tomáš, Quinn, Robert A., Rogers, Simon, Shrestha, Bindesh, Tripathi, Anupriya, van der Hooft, Justin J. J., Vargas, Fernando, Weldon, Kelly C., Witting, Michael, Yang, Heejung, Zhang, Zheng, Zubeil, Florian, Kohlbacher, Oliver, Böcker, Sebastian, Alexandrov, Theodore, Bandeira, Nuno, Wang, Mingxun, Dorrestein, Pieter C.

“…Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based…”
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Museum of spatial transcriptomics by Moses, Lambda, Pachter, Lior

“…The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In…”
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Benchmarking atlas-level data integration in single-cell genomics by Luecken, Malte D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., Theis, Fabian J.

“…Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint…”
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Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices by Alseekh, Saleh, Aharoni, Asaph, Brotman, Yariv, Contrepois, Kévin, D’Auria, John, Ewald, Jan, C. Ewald, Jennifer, Fraser, Paul D., Giavalisco, Patrick, Hall, Robert D., Heinemann, Matthias, Link, Hannes, Luo, Jie, Neumann, Steffen, Nielsen, Jens, Perez de Souza, Leonardo, Saito, Kazuki, Sauer, Uwe, Schroeder, Frank C., Schuster, Stefan, Siuzdak, Gary, Skirycz, Aleksandra, Sumner, Lloyd W., Snyder, Michael P., Tang, Huiru, Tohge, Takayuki, Wang, Yulan, Wen, Weiwei, Wu, Si, Xu, Guowang, Zamboni, Nicola, Fernie, Alisdair R.

“…Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However,…”
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NicheNet: modeling intercellular communication by linking ligands to target genes by Browaeys, Robin, Saelens, Wouter, Saeys, Yvan

“…Computational methods that model how gene expression of a cell is influenced by interacting cells are lacking. We present NicheNet (…”
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fMRIPrep: a robust preprocessing pipeline for functional MRI by Esteban, Oscar, Markiewicz, Christopher J., Blair, Ross W., Moodie, Craig A., Isik, A. Ilkay, Erramuzpe, Asier, Kent, James D., Goncalves, Mathias, DuPre, Elizabeth, Snyder, Madeleine, Oya, Hiroyuki, Ghosh, Satrajit S., Wright, Jessey, Durnez, Joke, Poldrack, Russell A., Gorgolewski, Krzysztof J.

“…Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally,…”
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Fast and accurate long-read assembly with wtdbg2 by Ruan, Jue, Li, Heng

“…Existing long-read assemblers require thousands of central processing unit hours to assemble a human genome and are being outpaced by sequencing technologies…”
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DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput by Demichev, Vadim, Messner, Christoph B., Vernardis, Spyros I., Lilley, Kathryn S., Ralser, Markus

“…We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the…”
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U-Net: deep learning for cell counting, detection, and morphometry by Falk, Thorsten, Mai, Dominic, Bensch, Robert, Çiçek, Özgün, Abdulkadir, Ahmed, Marrakchi, Yassine, Böhm, Anton, Deubner, Jan, Jäckel, Zoe, Seiwald, Katharina, Dovzhenko, Alexander, Tietz, Olaf, Dal Bosco, Cristina, Walsh, Sean, Saltukoglu, Deniz, Tay, Tuan Leng, Prinz, Marco, Palme, Klaus, Simons, Matias, Diester, Ilka, Brox, Thomas, Ronneberger, Olaf

“…U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data…”
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