Pub 2020-2021

2021-07-26

  1. Ploy N Pratanwanich, Fei Yao, Ying Chen, Casslynn WQ Koh, Yuk Kei Wan, Christopher Hendra, Polly Poon, Yeek Teck Goh, Phoebe ML Yap, Jing Yuan Chooi, and others. Identification of differential rna modifications from nanopore direct rna sequencing with xpore. Nature Biotechnology, pages 1–9, 2021. URL: https://www.nature.com/articles/s41587-021-00949-w.

  2. Nathanael Andrews, Jason T Serviss, Natalie Geyer, Agneta B Andersson, Ewa Dzwonkowska, Iva Šutevski, Rosan Heijboer, Ninib Baryawno, Marco Gerling, and Martin Enge. An unsupervised method for physical cell interaction profiling of complex tissues. Nature Methods, pages 1–9, 2021. URL: https://www.nature.com/articles/s41592-021-01196-2.

  3. Leah L Weber, Palash Sashittal, and Mohammed El-Kebir. Doubletd: detecting doublets in single-cell dna sequencing data. Bioinformatics, 37(Supplement_1):i214–i221, 2021. URL: https://doi.org/10.1093/bioinformatics/btab266.

  4. Ariya Shajii, Ibrahim Numanagić, Alexander T Leighton, Haley Greenyer, Saman Amarasinghe, and Bonnie Berger. A python-based programming language for high-performance computational genomics. Nature Biotechnology, pages 1–2, 2021. URL: https://www.nature.com/articles/s41587-021-00985-6.

  5. David M. Kurtz, Joanne Soo, Lyron Co Ting Keh, Stefan Alig, Jacob J. Chabon, Brian J. Sworder, Andre Schultz, Michael C. Jin, Florian Scherer, Andrea Garofalo, Charles W. Macaulay, Emily G. Hamilton, Binbin Chen, Mari Olsen, Joseph G. Schroers-Martin, Alexander F. M. Craig, Everett J. Moding, Mohammad S. Esfahani, Chih Long Liu, Ulrich Dührsen, Andreas Hüttmann, René-Olivier Casasnovas, Jason R. Westin, Mark Roschewski, Wyndham H. Wilson, Gianluca Gaidano, Davide Rossi, Maximilian Diehn, and Ash A. Alizadeh. Enhanced detection of minimal residual disease by targeted sequencing of phased variants in circulating tumor dna. Nature Biotechnology, Jul 2021. URL: https://doi.org/10.1038/s41587-021-00981-w, doi:10.1038/s41587-021-00981-w.

  6. Ricard Argelaguet, Anna SE Cuomo, Oliver Stegle, and John C Marioni. Computational principles and challenges in single-cell data integration. Nature Biotechnology, pages 1–14, 2021. URL: https://www.nature.com/articles/s41587-021-00895-7.

  7. Xiaoyang Jing and Jinbo Xu. Fast and effective protein model refinement using deep graph neural networks. Nature Computational Science, pages 1–8, 2021. URL: https://www.nature.com/articles/s43588-021-00098-9.

  8. Naozumi Hiranuma, Hahnbeom Park, Minkyung Baek, Ivan Anishchenko, Justas Dauparas, and David Baker. Improved protein structure refinement guided by deep learning based accuracy estimation. Nature communications, 12(1):1–11, 2021. URL: https://www.nature.com/articles/s41467-021-21511-x.

  9. Linhua Wang and Zhandong Liu. Missing-value imputation and in-silico region detection for spatially resolved transcriptomics. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/05/17/2021.05.14.443446, arXiv:https://www.biorxiv.org/content/early/2021/05/17/2021.05.14.443446.full.pdf, doi:10.1101/2021.05.14.443446.

  10. Andrew Erickson, Emelie Berglund, Mengxiao He, Maja Marklund, Reza Mirzazadeh, Niklas Schultz, Ludvig Bergenstråhle, Linda Kvastad, Alma Andersson, Joseph Bergenstråhle, Ludvig Larsson, Alia Shamikh, Elisa Basmaci, Teresita Diaz De Ståhl, Timothy Rajakumar, Kim Thrane, Andrew L Ji, Paul A Khavari, Firaz Tarish, Anna Tanoglidi, Jonas Maaskola, Richard Colling, Tuomas Mirtti, Freddie C Hamdy, Dan J Woodcock, Thomas Helleday, Ian G. Mills, Alastair D Lamb, and Joakim Lundeberg. The spatial landscape of clonal somatic mutations in benign and malignant tissue. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/07/12/2021.07.12.452018, arXiv:https://www.biorxiv.org/content/early/2021/07/12/2021.07.12.452018.full.pdf, doi:10.1101/2021.07.12.452018.

  11. Saket Choudhary and Rahul Satija. Comparison and evaluation of statistical error models for scrna-seq. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.07.07.451498v1.

2021-07-12

  1. Yasushi Okochi, Shunta Sakaguchi, Ken Nakae, Takefumi Kondo, and Honda Naoki. Model-based prediction of spatial gene expression via generative linear mapping. Nature Communications, 12(1):1–13, 2021. URL: https://www.nature.com/articles/s41467-021-24014-x.

  2. Brendan Francis Miller, Lyla Atta, Arpan Sahoo, Feiyang Huang, and Jean Fan. Reference-free cell-type deconvolution of pixel-resolution spatially resolved transcriptomics data. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.06.15.448381v1.

  3. Hananeh Aliee and Fabian J Theis. AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution. Cell Systems, 2021. URL: https://www.sciencedirect.com/science/article/pii/S2405471221001927.

  4. Lily Zheng, Laura Wood, Rachel Karchin, and Robert Scharpf. Estimation of cancer cell fractions and clone trees from multi-region sequencing of tumors. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.06.12.448194v1.

  5. Peter Carbonetto, Abhishek Sarkar, Zihao Wang, and Matthew Stephens. Non-negative matrix factorization algorithms greatly improve topic model fits. arXiv preprint arXiv:2105.13440, 2021. URL: https://arxiv.org/abs/2105.13440.

  6. Sohrab Salehi, Farhia Kabeer, Nicholas Ceglia, Mirela Andronescu, Marc J Williams, Kieran R Campbell, Tehmina Masud, Beixi Wang, Justina Biele, Jazmine Brimhall, and others. Clonal fitness inferred from time-series modelling of single-cell cancer genomes. Nature, pages 1–6, 2021. URL: https://www.nature.com/articles/s41586-021-03648-3.

  7. Livius Penter, Satyen H Gohil, Caleb Lareau, Leif S Ludwig, Erin M Parry, Teddy Huang, Shuqiang Li, Wandi Zhang, Dimitri Livitz, Ignaty Leshchiner, and others. Longitudinal single-cell dynamics of chromatin accessibility and mitochondrial mutations in chronic lymphocytic leukemia mirror disease history. Cancer Discovery, 2021. URL: https://cancerdiscovery.aacrjournals.org/content/early/2021/06/10/2159-8290.CD-21-0276.

  8. Junyan Lu, Ester Cannizzaro, Fabienne Meier-Abt, Sebastian Scheinost, Peter-Martin Bruch, Holly AR Giles, Almut Lütge, Jennifer Hüllein, Lena Wagner, Brian Giacopelli, and others. Multi-omics reveals clinically relevant proliferative drive associated with mtor-myc-oxphos activity in chronic lymphocytic leukemia. Nature Cancer, pages 1–12, 2021. URL: https://www.nature.com/articles/s43018-021-00216-6.

  9. Chenchen Zhu, Jingyan Wu, Han Sun, Francesca Briganti, Benjamin Meder, Wu Wei, and Lars M Steinmetz. Single-molecule, full-length transcript isoform sequencing reveals disease mutation-associated rna isoforms in cardiomyocytes. Nature Communications, 2021. URL: https://www.nature.com/articles/s41467-021-24484-z.

  10. Chong Chu, Rebeca Borges-Monroy, Vinayak V Viswanadham, Soohyun Lee, Heng Li, Eunjung Alice Lee, and Peter J Park. Comprehensive identification of transposable element insertions using multiple sequencing technologies. Nature Communications, 12(1):1–12, 2021.

  11. Peter V Kharchenko. The triumphs and limitations of computational methods for scrna-seq. Nature Methods, pages 1–10, 2021.

  12. Carter Allen, Yuzhou Chang, Brian Neelon, Won Chang, Hang J. Kim, Zihai Li, Qin Ma, and Dongjun Chung. A bayesian multivariate mixture model for spatial transcriptomics data. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/06/24/2021.06.23.449615, arXiv:https://www.biorxiv.org/content/early/2021/06/24/2021.06.23.449615.full.pdf, doi:10.1101/2021.06.23.449615.

  13. Jun Zhao, Ariel Jaffe, Henry Li, Ofir Lindenbaum, Esen Sefik, Ruaidhrí Jackson, Xiuyuan Cheng, Richard A Flavell, and Yuval Kluger. Detection of differentially abundant cell subpopulations in scrna-seq data. Proceedings of the National Academy of Sciences, 2021. URL: https://www.pnas.org/content/118/22/e2100293118.

  14. Shang-Hua Gao, Qi Han, Duo Li, Pai Peng, Ming-Ming Cheng, and Pai Peng. Representative batch normalization with feature calibration. In CVPR. 2021.

  15. Yue You, Luyi Tian, Shian Su, Xueyi Dong, Jafar S Jabbari, Peter F Hickey, and Matthew E Ritchie. Benchmarking umi-based single cell rna-sequencing preprocessing workflows. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/06/17/2021.06.17.448895, arXiv:https://www.biorxiv.org/content/early/2021/06/17/2021.06.17.448895.full.pdf, doi:10.1101/2021.06.17.448895.

  16. Dongze He, Mohsen Zakeri, Hirak Sarkar, Charlotte Soneson, Avi Srivastava, and Rob Patro. Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell rna-seq data. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/07/01/2021.06.29.450377, arXiv:https://www.biorxiv.org/content/early/2021/07/01/2021.06.29.450377.full.pdf, doi:10.1101/2021.06.29.450377.

  17. Alexander S Garruss, Katherine M Collins, and George M Church. Deep representation learning improves prediction of laci-mediated transcriptional repression. Proceedings of the National Academy of Sciences, 2021. URL: https://www.pnas.org/content/118/27/e2022838118.

2021-05-31

  1. Stefanie Warnat-Herresthal, Hartmut Schultze, Krishna Prasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Haendler, Peter Pickkers, N Ahmad Aziz, and others. Swarm Learning as a privacy-preserving machine learning approach for disease classification. Nature, 2021. URL: https://www.nature.com/articles/s41586-021-03583-3.

  2. Ming Y Lu, Tiffany Y Chen, Drew FK Williamson, Melissa Zhao, Maha Shady, Jana Lipkova, and Faisal Mahmood. AI-based pathology predicts origins for cancers of unknown primary. Nature, pages 1–5, 2021. URL: https://www.nature.com/articles/s41586-021-03512-4.

  3. Michael Spencer Chapman, Anna Maria Ranzoni, Brynelle Myers, Nicholas Williams, Tim HH Coorens, Emily Mitchell, Timothy Butler, Kevin J Dawson, Yvette Hooks, Luiza Moore, and others. Lineage tracing of human development through somatic mutations. Nature, pages 1–6, 2021. URL: https://www.nature.com/articles/s41586-021-03548-6.

  4. Paolo Marangio, Ka Ying Toby Law, Guido Sanguinetti, and Sander Granneman. DiffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data. Genome Biology, 2021. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02379-y.

  5. Abhishek Sarkar and Matthew Stephens. Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis. Nature Genetics, May 2021. URL: https://doi.org/10.1038/s41588-021-00873-4, doi:10.1038/s41588-021-00873-4.

  6. Tianyi Sun, Dongyuan Song, Wei Vivian Li, and Jingyi Jessica Li. Scdesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured. Genome Biology, 22(1):1–37, 2021.

  7. Carolyn Shasha, Yuan Tian, Florian Mair, Helen E.R. Miller, and Raphael Gottardo. Superscan: supervised single-cell annotation. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/05/22/2021.05.20.445014, arXiv:https://www.biorxiv.org/content/early/2021/05/22/2021.05.20.445014.full.pdf, doi:10.1101/2021.05.20.445014.

  8. Massimo Andreatta, Jesus Corria-Osorio, Sören Müller, Rafael Cubas, George Coukos, and Santiago J. Carmona. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nature Communications, 12(1):2965, May 2021. URL: https://doi.org/10.1038/s41467-021-23324-4, doi:10.1038/s41467-021-23324-4.

  9. Jonathan R. Belyeu, Murad Chowdhury, Joseph Brown, Brent S. Pedersen, Michael J. Cormier, Aaron R. Quinlan, and Ryan M. Layer. Samplot: a platform for structural variant visual validation and automated filtering. Genome Biology, 22(1):161, May 2021. URL: https://doi.org/10.1186/s13059-021-02380-5, doi:10.1186/s13059-021-02380-5.

  10. Lucille Lopez-Delisle and Jean-Baptiste Delisle. Baredsc: bayesian approach to retrieve expression distribution of single-cell. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/05/26/2021.05.26.445740.1, arXiv:https://www.biorxiv.org/content/early/2021/05/26/2021.05.26.445740.1.full.pdf, doi:10.1101/2021.05.26.445740.

  11. Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, and Eric Xing. Explaining a black-box using deep variational information bottleneck approach. In AAAI 2021. AAAI, 2021. URL: https://arxiv.org/pdf/1902.06918.pdf.

  12. Magda Markowska, Tomasz Cąkała, Błażej Miasojedow, Dilafruz Juraeva, Johanna Mazur, Edith Ross, Eike Staub, and Ewa Szczurek. Conet: copy number event tree model of evolutionary tumor history for single-cell data. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.04.23.441204v1.

  13. Chi-Yun Wu, Billy T Lau, Heon Seok Kim, Anuja Sathe, Susan M Grimes, Hanlee P Ji, and Nancy R Zhang. Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer. Nature Biotechnology, pages 1–11, 2021. URL: https://www.nature.com/articles/s41587-021-00911-w.

2021-05-03

  1. Jérémie Breda, Mihaela Zavolan, and Erik van Nimwegen. Bayesian inference of gene expression states from single-cell RNA-seq data. Nature biotech, 2021. URL: https://www.nature.com/articles/s41587-021-00875-x.

  2. Akira Cortal, Loredana Martignetti, Emmanuelle Six, and Antonio Rausell. Cell-ID: gene signature extraction and cell identity recognition at individual cell level. Nature biotech, 2020. URL: https://www.nature.com/articles/s41587-021-00896-6.

  3. Chao Gao, Jialin Liu, April R Kriebel, Sebastian Preissl, Chongyuan Luo, Rosa Castanon, Justin Sandoval, Angeline Rivkin, Joseph R Nery, Margarita M Behrens, and others. Iterative single-cell multi-omic integration using online learning. Nature Biotechnology, pages 1–8, 2021. URL: https://www.nature.com/articles/s41587-021-00867-x.

  4. Osvaldo D Rivera, Michael J Mallory, Mathieu Quesnel-Vallières, Rakesh Chatrikhi, David C Schultz, Martin Carroll, Yoseph Barash, Sara Cherry, and Kristen W Lynch. Alternative splicing redefines landscape of commonly mutated genes in acute myeloid leukemia. Proceedings of the National Academy of Sciences, 2021. URL: https://www.pnas.org/content/118/15/e2014967118.

  5. Chantriolnt-Andreas Kapourani, Ricard Argelaguet, Guido Sanguinetti, and Catalina A Vallejos. scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution. Genome Biology, 22(1):1–21, 2021. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02329-8.

  6. Akira Cortal, Loredana Martignetti, Emmanuelle Six, and Antonio Rausell. Cell-ID: gene signature extraction and cell identity recognition at individual cell level. Nature biotech, 2020. URL: https://www.nature.com/articles/s41587-021-00896-6.

  7. Youjin Lee, Derek Bogdanoff, Yutong Wang, George C Hartoularos, Jonathan M Woo, Cody T Mowery, Hunter M Nisonoff, David S Lee, Yang Sun, James Lee, and others. Xyzeq: spatially resolved single-cell rna sequencing reveals expression heterogeneity in the tumor microenvironment. Science Advances, 7(17):eabg4755, 2021. URL: https://advances.sciencemag.org/content/7/17/eabg4755?rss=1.

  8. Iain C Clark, Cristina Gutiérrez-Vázquez, Michael A Wheeler, Zhaorong Li, Veit Rothhammer, Mathias Linnerbauer, Liliana M Sanmarco, Lydia Guo, Manon Blain, Stephanie EJ Zandee, and others. Barcoded viral tracing of single-cell interactions in central nervous system inflammation. Science, 2021. URL: https://science.sciencemag.org/content/372/6540/eabf1230?rss=1.

  9. Alexander N. Gorelick, Minsoo Kim, Walid K. Chatila, Konnor La, A. Ari Hakimi, Michael F. Berger, Barry S. Taylor, Payam A. Gammage, and Ed Reznik. Respiratory complex and tissue lineage drive recurrent mutations in tumour mtDNA. Nature Metabolism, 3(4):558–570, April 2021. URL: https://doi.org/10.1038/s42255-021-00378-8, doi:10.1038/s42255-021-00378-8.

  10. Tal Ashuach, Daniel A Reidenbach, Adam Gayoso, and Nir Yosef. Peakvi: a deep generative model for single cell chromatin accessibility analysis. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/04/30/2021.04.29.442020, arXiv:https://www.biorxiv.org/content/early/2021/04/30/2021.04.29.442020.full.pdf, doi:10.1101/2021.04.29.442020.

  11. Wendell Jones, Binsheng Gong, Natalia Novoradovskaya, Dan Li, Rebecca Kusko, Todd A. Richmond, Donald J. Johann, Halil Bisgin, Sayed Mohammad Ebrahim Sahraeian, Pierre R. Bushel, and et al. A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency. Genome Biology, Apr 2021. URL: https://doi.org/10.1186/s13059-021-02316-z.

  12. Magda Markowska, Tomasz Cąkała, Błażej Miasojedow, Dilafruz Juraeva, Johanna Mazur, Edith Ross, Eike Staub, and Ewa Szczurek. Conet: copy number event tree model of evolutionary tumor history for single-cell data. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.04.23.441204v1.full.

  13. M Gordian Adam, Georg Beyer, Nicole Christiansen, Beate Kamlage, Christian Pilarsky, Marius Distler, Tim Falbusch, Ansgar Chromik, Fritz Klein, Marcus Bahra, and others. Identification and validation of a multivariable prediction model based on blood plasma and serum metabolomics for the distinction of chronic pancreatitis subjects from non-pancreas disease control subjects. Gut, 2021. URL: https://gut.bmj.com/content/early/2021/02/18/gutjnl-2020-320723.abstract.

2021-04-12

  1. Qiao Liu, Jiaze Xu, Rui Jiang, and Wing Hung Wong. Density estimation using deep generative neural networks. Proceedings of the National Academy of Sciences, 2021. URL: https://www.pnas.org/content/118/15/e2101344118, arXiv:https://www.pnas.org/content/118/15/e2101344118.full.pdf, doi:10.1073/pnas.2101344118.

  2. Daniel P Cooke, David C Wedge, and Gerton Lunter. A unified haplotype-based method for accurate and comprehensive variant calling. Nature Biotechnology, pages 1–8, 2021. URL: https://www.nature.com/articles/s41587-021-00861-3.

  3. Surojit Biswas, Grigory Khimulya, Ethan C Alley, Kevin M Esvelt, and George M Church. Low-n protein engineering with data-efficient deep learning. BioRxiv, 2020. URL: https://www.nature.com/articles/s41592-021-01100-y.

  4. Matteo Borella, Graziano Martello, Davide Risso, and Chiara Romualdi. PsiNorm: a scalable normalization for single-cell RNA-seq data. BioRxiv, 2021. URL: https://doi.org/10.1101/2021.04.07.438822, doi:10.1101/2021.04.07.438822.

  5. Alexey Kolesnikov, Sidharth Goel, Maria Nattestad, Taedong Yun, Gunjan Baid, Howard Yang, Cory Y McLean, Pi-Chuan Chang, and Andrew Carroll. Deeptrio: variant calling in families using deep learning. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/04/06/2021.04.05.438434, arXiv:https://www.biorxiv.org/content/early/2021/04/06/2021.04.05.438434.full.pdf, doi:10.1101/2021.04.05.438434.

  6. Michael E Nelson, Simone G Riva, and Ann Cvejic. Smash: a scalable, general marker gene identification framework for single-cell rna sequencing and spatial transcriptomics. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/04/09/2021.04.08.438978, arXiv:https://www.biorxiv.org/content/early/2021/04/09/2021.04.08.438978.full.pdf, doi:10.1101/2021.04.08.438978.

  7. Jonas A. Sibbesen, Jordan M. Eizenga, Adam M. Novak, Jouni Sirén, Xian Chang, Erik Garrison, and Benedict Paten. Haplotype-aware pantranscriptome analyses using spliced pangenome graphs. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/03/28/2021.03.26.437240, arXiv:https://www.biorxiv.org/content/early/2021/03/28/2021.03.26.437240.full.pdf, doi:10.1101/2021.03.26.437240.

  8. Zhiyuan Hu, Ahmed Ashour Ahmed, and Christopher Yau. An interpretable meta-clustering framework for single-cell rna-seq data integration and evaluation. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.03.29.437525v1.full.

  9. Zhe Wang, Shiyi Yang, Yusuke Koga, Sean E Corbett, W Evan Johnson, Masanao Yajima, and Joshua D Campbell. Celda: a bayesian model to perform bi-clustering of genes into modules and cells into subpopulations using single-cell rna-seq data. bioRxiv, 2020. URL: https://www.biorxiv.org/content/10.1101/2020.11.16.373274v2.

  10. Pablo Moreno, Ni Huang, Jonathan R Manning, Suhaib Mohammed, Andrey Solovyev, Krzysztof Polanski, Wendi Bacon, Ruben Chazarra, Carlos Talavera-López, Maria A Doyle, and others. User-friendly, scalable tools and workflows for single-cell rna-seq analysis. Nature Methods, pages 1–2, 2021. URL: https://www.nature.com/articles/s41592-021-01102-w.

2021-03-29

  1. Wanze Chen, Orane Guillaume-Gentil, Riccardo Dainese, Pernille Yde Rainer, Magda Zachara, Christoph G. Gäbelein, Julia A. Vorholt, and Bart Deplancke. Genome-wide molecular recording using live-seq. bioRxiv, mar 2021. URL: https://doi.org/10.1101/2021.03.24.436752, doi:10.1101/2021.03.24.436752.

  2. Juexin Wang, Anjun Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Ren Qi, Cankun Wang, Hongjun Fu, Qin Ma, and Dong Xu. scGNN is a novel graph neural network framework for single-cell RNA-seq analyses. Nature Communications, mar 2021. URL: https://doi.org/10.1038/s41467-021-22197-x, doi:10.1038/s41467-021-22197-x.

  3. Christoffer Norn, Basile IM Wicky, David Juergens, Sirui Liu, David Kim, Doug Tischer, Brian Koepnick, Ivan Anishchenko, David Baker, and Sergey Ovchinnikov. Protein sequence design by conformational landscape optimization. Proceedings of the National Academy of Sciences, 2021. URL: https://www.pnas.org/content/118/11/e2017228118.

  4. Yang Liu, Tao Wang, bin Zhou, and Deyou Zheng. Robust integration of multiple single-cell rna sequencing datasets using a single reference space. Nature biotechnology, 2021. URL: https://www.nature.com/articles/s41587-021-00859-x.

  5. Karthik A Jagadeesh, Kushal K Dey, Daniel T Montoro, Steven Gazal, Jesse M Engreitz, Ramnik J Xavier, Alkes L Price, and Aviv Regev. Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.03.19.436212v1.

  6. He Zhao, Piyush Rai, Lan Du, Wray Buntine, Dinh Phung, and Mingyuan Zhou. Variational autoencoders for sparse and overdispersed discrete data. In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, 1684–1694. PMLR, 26–28 Aug 2020. URL: http://proceedings.mlr.press/v108/zhao20c.html.

  7. Suoqin Jin, Christian F Guerrero-Juarez, Lihua Zhang, Ivan Chang, Raul Ramos, Chen-Hsiang Kuan, Peggy Myung, Maksim V Plikus, and Qing Nie. Inference and analysis of cell-cell communication using cellchat. Nature communications, 12(1):1–20, 2021. URL: https://www.nature.com/articles/s41467-021-21246-9.

  8. Daniel N. Baker, Nathan Dyjack, Vladimir Braverman, Stephanie C. Hicks, and Ben Langmead. Minicore: fast scrna-seq clustering with various distances. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/03/25/2021.03.24.436859, arXiv:https://www.biorxiv.org/content/early/2021/03/25/2021.03.24.436859.full.pdf, doi:10.1101/2021.03.24.436859.

  9. Tatsuhiko Naito, Ken Suzuki, Jun Hirata, Yoichiro Kamatani, Koichi Matsuda, Tatsushi Toda, and Yukinori Okada. A deep learning method for hla imputation and trans-ethnic mhc fine-mapping of type 1 diabetes. Nature Communications, 12(1):1–14, 2021.

  10. Galadriel Briere, Elodie Darbo, Patricia Thebault, and Raluca Uricaru. Consensus clustering applied to multi-omic disease subtyping. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/03/25/2020.10.19.345389, arXiv:https://www.biorxiv.org/content/early/2021/03/25/2020.10.19.345389.full.pdf, doi:10.1101/2020.10.19.345389.

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2021-03-15

  1. Marc Jan Bonder, Craig Smail, Michael J Gloudemans, Laure Frésard, David Jakubosky, Matteo D’Antonio, Xin Li, Nicole M Ferraro, Ivan Carcamo-Orive, Bogdan Mirauta, and others. Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics. Nature genetics, 53(3):313–321, 2021. URL: https://www.nature.com/articles/s41588-021-00800-7.

  2. Julie Jerber, Daniel D Seaton, Anna SE Cuomo, Natsuhiko Kumasaka, James Haldane, Juliette Steer, Minal Patel, Daniel Pearce, Malin Andersson, Marc Jan Bonder, and others. Population-scale single-cell rna-seq profiling across dopaminergic neuron differentiation. Nature genetics, pages 1–9, 2021. URL: https://www.nature.com/articles/s41588-021-00801-6.

  3. John-William Sidhom, H Benjamin Larman, Drew M Pardoll, and Alexander S Baras. Deeptcr: a deep learning framework for revealing structural concepts within tcr repertoire. Nature communications, 2021. URL: https://www.nature.com/articles/s41467-021-21879-w.

  4. Pawel F. Przytycki and Katherine S. Pollard. Cellwalker integrates single-cell and bulk data to resolve regulatory elements across cell types in complex tissues. Genome Biology, 2021. URL: https://doi.org/10.1186/s13059-021-02279-1, doi:10.1186/s13059-021-02279-1.

  5. Mor Nitzan and Michael P. Brenner. Revealing lineage-related signals in single-cell gene expression using random matrix theory. Proceedings of the National Academy of Sciences, 2021. URL: https://www.pnas.org/content/118/11/e1913931118, arXiv:https://www.pnas.org/content/118/11/e1913931118.full.pdf, doi:10.1073/pnas.1913931118.

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  7. Eddie Park, Yan Jiang, Lili Hao, Jingyi Hui, and Yi Xing. Genetic variation and microrna targeting of a-to-i rna editing fine tune human tissue transcriptomes. Genome Biology, 22(1):77, Mar 2021. URL: https://doi.org/10.1186/s13059-021-02287-1, doi:10.1186/s13059-021-02287-1.

  8. Changcai Huang, Guangyu Li, Jiayu Wu, Junbo Liang, and Xiaoyue Wang. Identification of pathogenic variants in cancer genes using base editing screens with editing efficiency correction. Genome Biology, 22(1):80, Mar 2021. URL: https://doi.org/10.1186/s13059-021-02305-2, doi:10.1186/s13059-021-02305-2.

  9. Bianca Dumitrascu, Soledad Villar, Dustin G Mixon, and Barbara E Engelhardt. Optimal marker gene selection for cell type discrimination in single cell analyses. Nature communications, 12(1):1–8, 2021. URL: https://www.nature.com/articles/s41467-021-21453-4.

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  17. Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, and others. Bootstrap your own latent: a new approach to self-supervised learning. arXiv preprint arXiv:2006.07733, 2020. URL: https://arxiv.org/abs/2006.07733.

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2021-03-01

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  2. Benjamin L Emert, Christopher J Cote, Eduardo A Torre, Ian P Dardani, Connie L Jiang, Naveen Jain, Sydney M Shaffer, and Arjun Raj. Variability within rare cell states enables multiple paths toward drug resistance. Nature Biotechnology, pages 1–12, 2021. URL: https://www.nature.com/articles/s41587-021-00837-3.

  3. Dylan M Cable, Evan Murray, Luli S Zou, Aleksandrina Goeva, Evan Z Macosko, Fei Chen, and Rafael A Irizarry. Robust decomposition of cell type mixtures in spatial transcriptomics. Nature Biotechnology, pages 1–10, 2021. URL: https://www.nature.com/articles/s41587-021-00830-w.

  4. Fang Wang, Qihan Wang, Vakul Mohanty, Shaoheng Liang, Jinzhuang Dou, Jincheng Han, Darlan Conterno Minussi, Ruli Gao, Li Ding, Nicholas Navin, and others. Medalt: single-cell copy number lineage tracing enabling gene discovery. Genome Biology, 22(1):1–22, 2021. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02291-5.

  5. Hongyu Guo and Jun Li. Scsorter: assigning cells to known cell types according to marker genes. Genome Biology, 22(1):1–18, 2021. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02281-7.

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  17. Bobby Ranjan, Florian Schmidt, Wenjie Sun, Jinyu Park, Mohammad Amin Honardoost, Joanna Tan, Nirmala Arul Rayan, and Shyam Prabhakar. Scconsensus: combining supervised and unsupervised clustering for cell type identification in single-cell rna sequencing data. bioRxiv, 2020.

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2021-02-01

  1. Brian Hie, Ellen D Zhong, Bonnie Berger, and Bryan Bryson. Learning the language of viral evolution and escape. Science, 371(6526):284–288, 2021. URL: https://science.sciencemag.org/content/371/6526/284.

  2. Ashwin Narayan, Bonnie Berger, and Hyunghoon Cho. Assessing single-cell transcriptomic variability through density-preserving data visualization. Nature Biotechnology, pages 1–10, 2021. URL: https://www.nature.com/articles/s41587-020-00801-7.

  3. Lyla Atta and Jean Fan. Veloviz: rna-velocity informed 2d embeddings for visualizing cellular trajectories. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.01.28.425293v1.

  4. Karren Dai Yang, Anastasiya Belyaeva, Saradha Venkatachalapathy, Karthik Damodaran, Abigail Katcoff, Adityanarayanan Radhakrishnan, GV Shivashankar, and Caroline Uhler. Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Nature Communications, 12(1):1–10, 2021. URL: https://www.nature.com/articles/s41467-020-20249-2.

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  6. Anna S.E. Cuomo, Giordano Alvari, Christina B. Azodi, Davis J. McCarthy, and Marc Jan Bonder. Optimising expression quantitative trait locus mapping workflows for single-cell studies. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/01/21/2021.01.20.427401, arXiv:https://www.biorxiv.org/content/early/2021/01/21/2021.01.20.427401.full.pdf, doi:10.1101/2021.01.20.427401.

  7. Yang Xu, Priyojit Das, and Rachel Patton McCord. Smile: mutual information learning for integration of single cell omics data. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/01/29/2021.01.28.428619, arXiv:https://www.biorxiv.org/content/early/2021/01/29/2021.01.28.428619.full.pdf, doi:10.1101/2021.01.28.428619.

  8. Jian Yan, Yunjiang Qiu, André M Ribeiro Dos Santos, Yimeng Yin, Yang E Li, Nick Vinckier, Naoki Nariai, Paola Benaglio, Anugraha Raman, Xiaoyu Li, and others. Systematic analysis of binding of transcription factors to noncoding variants. Nature, pages 1–5, 2021. URL: https://www.nature.com/articles/s41586-021-03211-0.

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2021-01-18

  1. Rens van de Schoot, Sarah Depaoli, Ruth King, Bianca Kramer, Kaspar Märtens, Mahlet G. Tadesse, Marina Vannucci, Andrew Gelman, Duco Veen, Joukje Willemsen, and Christopher Yau. Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1):1–26, 2021. URL: https://www.nature.com/articles/s43586-020-00001-2, doi:10.1038/s43586-020-00001-2.

  2. Ze Zhang, Danyi Xiong, Xinlei Wang, Hongyu Liu, and Tao Wang. Mapping the functional landscape of t cell receptor repertoires by single-t cell transcriptomics. Nature Methods, 18(1):92–99, 2021. URL: https://www.nature.com/articles/s41592-020-01020-3.

  3. Ronen Sadeh, Israa Sharkia, Gavriel Fialkoff, Ayelet Rahat, Jenia Gutin, Alon Chappleboim, Mor Nitzan, Ilana Fox-Fisher, Daniel Neiman, Guy Meler, and others. Chip-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells of origin. Nature Biotechnology, pages 1–13, 2021. URL: https://www.nature.com/articles/s41587-020-00775-6.

  4. Ruiping Wang, Minghao Dang, Kazuto Harada, Guangchun Han, Fang Wang, Melissa Pool Pizzi, Meina Zhao, Ghia Tatlonghari, Shaojun Zhang, Dapeng Hao, and others. Single-cell dissection of intratumoral heterogeneity and lineage diversity in metastatic gastric adenocarcinoma. Nature Medicine, pages 1–11, 2021. URL: https://www.nature.com/articles/s41591-020-1125-8.

  5. David Fawkner-Corbett, Agne Antanaviciute, Kaushal Parikh, Marta Jagielowicz, Ana Sousa Gerós, Tarun Gupta, Neil Ashley, Doran Khamis, Darren Fowler, Edward Morrissey, and others. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell, 2021. URL: https://www.cell.com/cell/fulltext/S0092-8674(20)31686-X.

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2020-12-14

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  2. Robert R Stickels, Evan Murray, Pawan Kumar, Jilong Li, Jamie L Marshall, Daniela J Di Bella, Paola Arlotta, Evan Z Macosko, and Fei Chen. Highly sensitive spatial transcriptomics at near-cellular resolution with slide-seqv2. Nature Biotechnology, 2020. URL: https://www.nature.com/articles/s41587-020-0739-1.

  3. Jonathan R Bowles, Caroline Hoppe, Hilary L Ashe, and Magnus Rattray. Scalable inference of transcriptional kinetic parameters from ms2 time series data. bioRxiv, 2020. URL: https://www.biorxiv.org/content/10.1101/2020.12.04.412049v1.

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2020-11-30

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2020-11-16

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  2. Santiago Codesido, Mohamed Hanafi, Yoric Gagnebin, Víctor González-Ruiz, Serge Rudaz, and Julien Boccard. Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets. Bioinformatics, 11 2020. btaa954. URL: https://doi.org/10.1093/bioinformatics/btaa954, arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa954/34178616/btaa954.pdf, doi:10.1093/bioinformatics/btaa954.

  3. Yang Liu, Mingyu Yang, Yanxiang Deng, Graham Su, Cindy Guo, Di Zhang, Dongjoo Kim, Zhiliang Bai, Yang Xiao, Stephanie Halene, and Rong Fan. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell, 2020. URL: https://www.cell.com/cell/fulltext/S0092-8674(20)31390-8.

  4. Britta Velten, Jana Muriel Braunger, Damien Arnol, Ricard Argelaguet, and Oliver Stegle. Identifying temporal and spatial patterns of variation from multi-modal data using mefisto. bioRxiv, 2020. URL: https://www.biorxiv.org/content/10.1101/2020.11.03.366674v1.

  5. Lukas M Weber, Ariel A Hippen, Peter F Hickey, Kristofer C Berrett, Jason Gertz, Jennifer Anne Doherty, Casey S Greene, and Stephanie C Hicks. Genetic demultiplexing of pooled single-cell rna-sequencing samples in cancer facilitates effective experimental design. bioRxiv, 2020. URL: https://www.biorxiv.org/content/10.1101/2020.11.06.371963v1.

  6. Erick Armingol, Adam Officer, Olivier Harismendy, and Nathan E Lewis. Deciphering cell-cell interactions and communication from gene expression. Nature Reviews Genetics, pages 1–18, 2020. URL: https://www.nature.com/articles/s41576-020-00292-x.

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  12. Lifei Wang, Rui Nie, Zeyang Yu, Ruyue Xin, Caihong Zheng, Zhang Zhang, Jiang Zhang, and Jun Cai. An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell rna-sequencing data. Nature Machine Intelligence, pages 1–11, 2020. URL: https://www.pnas.org/content/early/2020/10/29/2005990117.

  13. Ingrid C Romero, Shu Kong, Charless C Fowlkes, Carlos Jaramillo, Michael A Urban, Francisca Oboh-Ikuenobe, Carlos D’Apolito, and Surangi W Punyasena. Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy. Proceedings of the National Academy of Sciences, 117(45):28496–28505, 2020. URL: https://www.pnas.org/content/117/45/28496.

  14. Junyue Cao, Diana R. O\textquoteright Day, Hannah A. Pliner, Paul D. Kingsley, Mei Deng, Riza M. Daza, Michael A. Zager, Kimberly A. Aldinger, Ronnie Blecher-Gonen, Fan Zhang, Malte Spielmann, James Palis, Dan Doherty, Frank J. Steemers, Ian A. Glass, Cole Trapnell, and Jay Shendure. A human cell atlas of fetal gene expression. Science, 2020. URL: https://science.sciencemag.org/content/370/6518/eaba7721, arXiv:https://science.sciencemag.org/content/370/6518/eaba7721.full.pdf, doi:10.1126/science.aba7721.

  15. Silvia Domcke, Andrew J. Hill, Riza M. Daza, Junyue Cao, Diana R. O\textquoteright Day, Hannah A. Pliner, Kimberly A. Aldinger, Dmitry Pokholok, Fan Zhang, Jennifer H. Milbank, Michael A. Zager, Ian A. Glass, Frank J. Steemers, Dan Doherty, Cole Trapnell, Darren A. Cusanovich, and Jay Shendure. A human cell atlas of fetal chromatin accessibility. Science, 2020. URL: https://science.sciencemag.org/content/370/6518/eaba7612, arXiv:https://science.sciencemag.org/content/370/6518/eaba7612.full.pdf, doi:10.1126/science.aba7612.

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2020-11-02

  1. Gabriela S Kinker, Alissa C Greenwald, Rotem Tal, Zhanna Orlova, Michael S Cuoco, James M McFarland, Allison Warren, Christopher Rodman, Jennifer A Roth, Samantha A Bender, and others. Pan-cancer single cell rna-seq uncovers recurring programs of cellular heterogeneity. Nature Genetics, pages 807552, 2019.

  2. Paula Nieto, Marc Elosua-Bayes, Juan L. Trincado, Domenica Marchese, Ramon Massoni-Badosa, Maria Salvany, Ana Henriques, Elisabetta Mereu, Catia Moutinho, Sara Ruiz, Patricia Lorden, Vanessa T. Chin, Dominik Kaczorowski, Chia-Ling Chan, Richard Gallagher, Angela Chou, Ester Planas-Rigol, Carlota Rubio-Perez, Ivo Gut, Josep M. Piulats, Joan Seoane, Joseph E. Powell, Eduard Batlle, and Holger Heyn. A single-cell tumor immune atlas for precision oncology. bioRxiv, 2020. URL: https://www.biorxiv.org/content/early/2020/10/26/2020.10.26.354829, arXiv:https://www.biorxiv.org/content/early/2020/10/26/2020.10.26.354829.full.pdf, doi:10.1101/2020.10.26.354829.

  3. Linde A Miles, Robert L Bowman, Tiffany R Merlinsky, Isabelle S Csete, Aik Ooi, Robert Durruthy-Durruthy, Michael Bowman, Christopher Famulare, Minal A Patel, Pedro Mendez, and others. Single-cell mutation analysis of clonal evolution in myeloid malignancies. Nature, 2020. URL: https://www.nature.com/articles/s41586-020-2864-x.

  4. Kiyomi Morita, Feng Wang, Katharina Jahn, Jack Kuipers, Yuanqing Yan, Jairo Matthews, Latasha Little, Curtis Gumbs, Shujuan Chen, Jianhua Zhang, and others. Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics. Nature communications, 2020. URL: https://www.nature.com/articles/s41467-020-19119-8.

  5. C.R. Nowosad, L. Mesin, T.B.R. Castro, and others. Tunable dynamics of b cell selection in gut germinal centres. Nature, 2020. URL: https://www.nature.com/articles/s41586-020-2865-9.

  6. Marius Lange, Volker Bergen, Michal Klein, Manu Setty, Bernhard Reuter, Mostafa Bakhti, Heiko Lickert, Meshal Ansari, Janine Schniering, Herbert B Schiller, and others. Cellrank for directed single-cell fate mapping. bioRxiv, 2020. URL: https://www.biorxiv.org/content/10.1101/2020.10.19.345983v1.

  7. Edward Zhao, Matthew R Stone, Xing Ren, Thomas Pulliam, Paul Nghiem, Jason H Bielas, and Raphael Gottardo. Bayesspace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution. bioRxiv, 2020. URL: https://www.biorxiv.org/content/10.1101/2020.09.04.283812v1.

  8. Viktor Petukhov, Konstantin Khodosevich, Ruslan A Soldatov, and Peter V Kharchenko. Bayesian segmentation of spatially resolved transcriptomics data. bioRxiv, 2020. URL: https://www.biorxiv.org/content/10.1101/2020.10.05.326777v1.

  9. Kentaro Kawata, Hiroyasu Wakida, Toshimichi Yamada, Kenzui Taniue, Han Han, Masahide Seki, Yutaka Suzuki, and Nobuyoshi Akimitsu. Metabolic labeling of rna using multiple ribonucleoside analogs enables the simultaneous evaluation of rna synthesis and degradation rates. Genome research, 30(10):gr.264408.120–1491, 2020.

  10. Tracey W. Chan, Ting Fu, Jae Hoon Bahn, Hyun-Ik Jun, Jae-Hyung Lee, Giovanni Quinones-Valdez, Chonghui Cheng, and Xinshu Xiao. Rna editing in cancer impacts mrna abundance in immune response pathways. Genome Biology, 2020. doi:10.1186/s13059-020-02171-4.

  11. Shengqing Stan Gu, Xiaoqing Wang, Xihao Hu, Peng Jiang, Ziyi Li, Nicole Traugh, Xia Bu, Qin Tang, Chenfei Wang, Zexian Zeng, and et al. Clonal tracing reveals diverse patterns of response to immune checkpoint blockade. Genome Biology, 2020. doi:10.1186/s13059-020-02166-1.

  12. Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, and others. Rethinking attention with performers. arXiv preprint arXiv:2009.14794, 2020. URL: https://arxiv.org/abs/2009.14794.

  13. Thin Nguyen, Hang Le, and Svetha Venkatesh. Graphdta: prediction of drug–target binding affinity using graph convolutional networks. Bioinformatics, 2019. URL: https://www.biorxiv.org/content/early/2019/06/27/684662, arXiv:https://www.biorxiv.org/content/early/2019/06/27/684662.full.pdf, doi:10.1101/684662.

  14. Masaki Asada, Makoto Miwa, and Yutaka Sasaki. Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature. Bioinformatics, 10 2020. btaa907. URL: https://doi.org/10.1093/bioinformatics/btaa907, arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa907/34012017/btaa907.pdf, doi:10.1093/bioinformatics/btaa907.

2020-10-19

  1. Jialin Liu, Chao Gao, Joshua Sodicoff, Velina Kozareva, Evan Z Macosko, and Joshua D Welch. Jointly defining cell types from multiple single-cell datasets using liger. Nature Protocols, pages 1–31, 2020. URL: https://doi.org/10.1038/s41596-020-0391-8.

  2. Shiyi Yang, Sean E Corbett, Yusuke Koga, Zhe Wang, W Evan Johnson, Masanao Yajima, and Joshua D Campbell. Decontamination of ambient rna in single-cell rna-seq with decontx. Genome biology, 21(1):1–15, 2020. URL: https://doi.org/10.1186/s13059-020-1950-6.

  3. Y. Zhang, P. Qu, Y. Ji, and et al. A system hierarchy for brain-inspired computing. Nature, 2020. URL: https://www.nature.com/articles/s41586-020-2782-y.

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  5. Kalliopi Skamaki, Stephane Emond, Matthieu Chodorge, John Andrews, D Gareth Rees, Daniel Cannon, Bojana Popovic, Andrew Buchanan, Ralph Minter, and Florian Hollfelder. In vitro evolution of antibody affinity via insertional mutagenesis scanning of an entire antibody variable region. PNAS, 2020. URL: https://doi.org/10.1073/pnas.2002954117.

  6. Claudio Lorenzi, Sylvain Barriere, Laureline Villemin, Jean-Philippevand Dejardin Bretones, Alban Mancheron, and William Ritchie. Imoka: k-mer based software to anahttps://github.com/statbiomed/hlab-pubmon/blob/master/bibs/m2020-10-19.biblyze large collections of sequencing data. Genome Biology, 21(1):261, Oct 2020. URL: https://doi.org/10.1186/s13059-020-02165-2, doi:10.1186/s13059-020-02165-2.

  7. Shengqing Stan Gu, Xiaoqing Wang, Xihao Hu, Peng Jiang, Ziyi Li, Nicole Traugh, Xia Bu, Qin Tang, Chenfei Wang, Zexian Zeng, Jingxin Fu, Cliff Meyer, Yi Zhang, Paloma Cejas, Klothilda Lim, Jin Wang, Wubing Zhang, Collin Tokheim, Avinash Das Sahu, Xiaofang Xing, Benjamin Kroger, Zhangyi Ouyang, Henry Long, Gordon J. Freeman, Myles Brown, and X. Shirley Liu. Clonal tracing reveals diverse patterns of response to immune checkpoint blockade. Genome Biology, 21(1):263, Oct 2020. URL: https://doi.org/10.1186/s13059-020-02166-1, doi:10.1186/s13059-020-02166-1.

  8. Andres M Cifuentes-Bernal, Vu Vh Pham, Xiaomei Li, Lin Liu, Jiuyong Li, and Thuc Duy Le. A pseudo-temporal causality approach to identifying miRNA-mRNA interactions during biological processes. Bioinformatics, 10 2020. btaa899. URL: https://doi.org/10.1093/bioinformatics/btaa899, arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa899/33937099/btaa899.pdf, doi:10.1093/bioinformatics/btaa899.

  9. Jeffrey N Law, Shiv D Kale, and T M Murali. Accurate and efficient gene function prediction using a Multi-Bacterial network. Bioinformatics, 10 2020. btaa885. URL: https://doi.org/10.1093/bioinformatics/btaa885, arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa885/33894881/btaa885.pdf, doi:10.1093/bioinformatics/btaa885.

  10. K.C. Akdemir, V.T. Le, J.M. Kim, and et al. Somatic mutation distributions in cancer genomes vary with three-dimensional chromatin structure. Nat Genet, 10 2020. btaa885. URL: https://doi.org/10.1038/s41588-020-0708-0, arXiv:https://www.nature.com/articles/s41588-020-0708-0#citeas, doi:10.1038/s41588-020-0708-0.

  11. Kexin Huang, Cao Xiao, Lucas M Glass, and Jimeng Sun. MolTrans: Molecular interaction transformer for drug target interaction prediction. Bioinformatics, 10 2020. btaa880. URL: https://doi.org/10.1093/bioinformatics/btaa880, arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa880/33937102/btaa880.pdf, doi:10.1093/bioinformatics/btaa880.

  12. Julie Jiang, Li-Ping Liu, and Soha Hassoun. Learning graph representations of biochemical networks and its application to enzymatic link prediction. Bioinformatics, 10 2020. btaa881. URL: https://doi.org/10.1093/bioinformatics/btaa881, arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa881/33880019/btaa881.pdf, doi:10.1093/bioinformatics/btaa881.

  13. Gregory W Schwartz, Yeqiao Zhou, Jelena Petrovic, Maria Fasolino, Lanwei Xu, Sydney M Shaffer, Warren S Pear, Golnaz Vahedi, and Robert B Faryabi. Toomanycells identifies and visualizes relationships of single-cell clades. Nature Methods, 17(4):405–413, 2020. URL: https://www-nature-com.eproxy.lib.hku.hk/articles/s41592-020-0748-5.

  14. Geoff Fudenberg, David R Kelley, and Katherine S Pollard. Predicting 3d genome folding from dna sequence with akita. Nature Methods, pages 1–7, 2020. URL: https://www.nature.com/articles/s41592-020-0958-x.

  15. Birgit Kriener, Rishidev Chaudhuri, and Ila R Fiete. Robust parallel decision-making in neural circuits with nonlinear inhibition. Proceedings of the National Academy of Sciences, 117(41):25505–25516, 2020. URL: https://www.pnas.org/content/117/41/25505.short.

2020-10-05

  1. Monika Litviňuková, Carlos Talavera-López, Henrike Maatz, Daniel Reichart, Catherine L Worth, Eric L Lindberg, Masatoshi Kanda, Krzysztof Polanski, Matthias Heinig, Michael Lee, and others. Cells of the adult human heart. Nature, pages 1–10, 2020. URL: https://www.nature.com/articles/s41586-020-2797-4.

  2. Alexandra Maslova, Ricardo N Ramirez, Ke Ma, Hugo Schmutz, Chendi Wang, Curtis Fox, Bernard Ng, Christophe Benoist, Sara Mostafavi, and others. Deep learning of immune cell differentiation. Proceedings of the National Academy of Sciences, 2020. URL: https://www.pnas.org/content/early/2020/09/23/2011795117.

  3. Ziqi Zhang and Xiuwei Zhang. Inference of multiple trajectories in single cell rna-seq data from rna velocity. bioRxiv, 2020. URL: https://www.biorxiv.org/content/early/2020/10/02/2020.09.30.321125, doi:10.1101/2020.09.30.321125.

  4. Simone Ciccolella, Camir Ricketts, Mauricio Soto Gomez, Murray Patterson, Dana Silverbush, Paola Bonizzoni, Iman Hajirasouliha, and Gianluca Della Vedova. Inferring Cancer Progression from Single-Cell Sequencing while Allowing Mutation Losses. Bioinformatics, 08 2020. btaa722. URL: https://doi.org/10.1093/bioinformatics/btaa722, arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa722/33658591/btaa722.pdf, doi:10.1093/bioinformatics/btaa722.

  5. Xin Chen, Zhaowei Yang, Wanqiu Chen, Yongmei Zhao, Andrew Farmer, Bao Tran, Vyacheslav Furtak, Malcolm Moos, Wenming Xiao, and Charles Wang. A multi-center cross-platform single-cell rna sequencing reference dataset. bioRxiv, 2020. URL: https://www.biorxiv.org/content/early/2020/09/21/2020.09.20.305474, arXiv:https://www.biorxiv.org/content/early/2020/09/21/2020.09.20.305474.full.pdf, doi:10.1101/2020.09.20.305474.

  6. Zhisong He, Agnieska Brazovskaja, Sebastian Ebert, J. Gray Camp, and Barbara Treutlein. Css: cluster similarity spectrum integration of single-cell genomics data. Genome biology, 21(1):1–21, 2020.

  7. Nitzan Nissimov, Zivar Hajiyeva, Sebastian Torke, Katja Grondey, Wolfgang Brück, Silke Häusser-Kinzel, and Martin S. Weber. B cells reappear less mature and more activated after their anti-cd20–mediated depletion in multiple sclerosis. Proceedings of the National Academy of Sciences, 2020. URL: https://www.pnas.org/content/early/2020/09/29/2012249117, arXiv:https://www.pnas.org/content/early/2020/09/29/2012249117.full.pdf, doi:10.1073/pnas.2012249117.

  8. Rui Martiniano, Erik Garrison, Eppie R Jones, Andrea Manica, and Richard Durbin. Removing reference bias and improving indel calling in ancient dna data analysis by mapping to a sequence variation graph. Genome biology, 21(1):1–18, 2020.

  9. Yuzhe Yang and Zhi Xu. Rethinking the value of labels for improving class-imbalanced learning. NeurIPS 2020, 2020. arXiv:https://arxiv.org/pdf/2006.07529.

  10. Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. Decoupling representation and classifier for long-tailed recognition. ICLR 2020, 2020. arXiv:https://arxiv.org/pdf/1910.09217.

  11. Dong Zhang, Hanwang Zhang, Jinhui Tang, Xiansheng Hua, and Qianru Sun. Causal intervention for weakly-supervised semantic segmentation. NeurIPS 2020, 2020. arXiv:https://arxiv.org/pdf/2009.12547.

  12. Courtney J Spoerer, Tim C Kietzmann, Johannes Mehrer, Ian Charest, and Nikolaus Kriegeskorte. Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision. bioRxiv, pages 677237, 2020.

  13. Wen Jun Xie, Yifeng Qi, and Bin Zhang. Characterizing chromatin folding coordinate and landscape with deep learning. PLOS Computational Biology, 16(9):e1008262, 2020.

  14. Camila PE de Souza, Mirela Andronescu, Tehmina Masud, Farhia Kabeer, Justina Biele, Emma Laks, Daniel Lai, Patricia Ye, Jazmine Brimhall, Beixi Wang, and others. Epiclomal: probabilistic clustering of sparse single-cell dna methylation data. PLOS Computational Biology, 16(9):e1008270, 2020.

2020-09-21

  1. Xizhi Luo, Fei Qin, Guoshuai Cai, and Feifei Xiao. Integrating Genomic Correlation Structure Improves Copy Number Variations Detection. Bioinformatics, 08 2020. URL: https://doi.org/10.1093/bioinformatics/btaa737.

  2. Ron Zeira and Benjamin J Raphael. Copy number evolution with weighted aberrations in cancer. Bioinformatics, 36(Supplement_1):i344–i352, 07 2020. URL: https://doi.org/10.1093/bioinformatics/btaa470.

  3. Ye Yuan and Ziv Bar-Joseph. Deep learning for inferring gene relationships from single-cell expression data. Proceedings of the National Academy of Sciences, 116(52):27151–27158, 2019.

  4. Thinh N Tran and Gary Bader. Tempora: cell trajectory inference using time-series single-cell rna sequencing data. bioRxiv, pages 846907, 2019.

  5. Nicola F Müller, Ugnė Stolz, Gytis Dudas, Tanja Stadler, and Timothy G Vaughan. Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses. Proceedings of the National Academy of Sciences, 117(29):17104–17111, 2020.

  6. Yan Zhang, Yaru Zhang, Jun Hu, Ji Zhang, Fangjie Guo, Meng Zhou, Guijun Zhang, Fulong Yu, and Jianzhong Su. scTPA: a web tool for single-cell transcriptome analysis of pathway activation signatures. Bioinformatics, 36(14):4217–4219, 05 2020. URL: https://doi.org/10.1093/bioinformatics/btaa532, arXiv:https://academic.oup.com/bioinformatics/article-pdf/36/14/4217/33547302/btaa532.pdf, doi:10.1093/bioinformatics/btaa532.

  7. Lipeng Jin, Chenyao Li, Tao Liu, and Lei Wang. A potential prognostic prediction model of colon adenocarcinoma with recurrence based on prognostic lncrna signatures. Human genomics, 14(1):1–11, 2020.

  8. Lawrence T. C Ong, Grant P Parnell, Ali Afrasiabi, Graeme J Stewart, Sanjay Swaminathan, and David R Booth. Transcribed b lymphocyte genes and multiple sclerosis risk genes are underrepresented in epstein–barr virus hypomethylated regions. Genes and immunity, 21(2):91–99, 2019.

  9. Amrit Dhar, Duncan K. Ralph, Vladimir N. Minin, and Frederick A. Matsen, IV. A bayesian phylogenetic hidden markov model for b cell receptor sequence analysis. PLOS Computational Biology, 16(8):1–27, 08 2020. URL: https://doi.org/10.1371/journal.pcbi.1008030, doi:10.1371/journal.pcbi.1008030.

  10. Long Vo Ngoc, Cassidy Yunjing Huang, California Jack Cassidy, Claudia Medrano, and James T Kadonaga. Identification of the human dpr core promoter element using machine learning. Nature, 2020. URL: https://www.nature.com/articles/s41586-020-2689-7.

  11. Xiaodong Liu, Many Otheres, and Jose M. Polo. Reprogramming roadmap reveals route to human induced trophoblast stem cells. Nature, 2020. URL: https://www.nature.com/articles/s41586-020-2734-6.

  12. Ori Maoz, Gašper Tkacčik, Mohamad Saleh Esteki, Roozbeh Kiani, and Elad Schneidman. Learning probabilistic representations with randomly connected neural circuits. PNAS, 2020. URL: https://doi.org/10.1073/pnas.1912804117.

  13. Guillaume Holley and Páll Melsted. Bifrost: highly parallel construction and indexing of colored and compacted de Bruijn graphs. Genome Biology, 2020.

  14. Yuan He, Surya B Chhetri, Marios Arvanitis, Kaushik Srinivasan, François Aguet, Kristin G Ardlie, Alvaro N Barbeira, Rodrigo Bonazzola, Hae Kyung Im, Christopher D Brown, and others. sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression. Genome Biology, 21(1):1–25, 2020. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02129-6.

  15. Kevin J Gleason, Fan Yang, Brandon L Pierce, Xin He, and Lin S Chen. Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits. Genome Biology, 21(1):1–24, 2020. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02125-w.

  16. Weihua Hu*, Bowen Liu*, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. Strategies for pre-training graph neural networks. In International Conference on Learning Representations. 2020. URL: https://openreview.net/forum?id=HJlWWJSFDH.

  17. Hao Wang and Dit-Yan Yeung. A Survey on Bayesian Deep Learning. arXiv e-prints (To appear in ACM Computing Surveys (CSUR) 2020), pages arXiv:1604.01662, April 2016. arXiv:1604.01662.

  18. Dongjoon Lim and Mathieu Blanchette. EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM. Bioinformatics, 36(Supplement_1):i353–i361, 07 2020. URL: https://doi.org/10.1093/bioinformatics/btaa447, arXiv:https://academic.oup.com/bioinformatics/article-pdf/36/Supplement\_1/i353/33488780/btaa447.pdf, doi:10.1093/bioinformatics/btaa447.

  19. Allen W Zhang and Kieran R Campbell. Computational modelling in single-cell cancer genomics: methods and future directions. arXiv preprint arXiv:2005.01549, 2020. URL: https://arxiv.org/abs/2005.01549.

  20. Chenfei Wang, Dongqing Sun, Xin Huang, Changxin Wan, Ziyi Li, Ya Han, Qian Qin, Jingyu Fan, Xintao Qiu, Yingtian Xie, and others. Integrative analyses of single-cell transcriptome and regulome using maestro. Genome biology, 21(1):1–28, 2020. URL: https://doi.org/10.1186/s13059-020-02116-x.

  21. Sergey Aganezov and Benjamin J Raphael. Reconstruction of clone-and haplotype-specific cancer genome karyotypes from bulk tumor samples. Genome Research, 30(9):1274–1290, 2020. URL: https://genome.cshlp.org/content/30/9/1274.

  22. Simone Zaccaria and Benjamin J Raphael. Characterizing allele-and haplotype-specific copy numbers in single cells with chisel. Nature Biotechnology, pages 1–8, 2020. URL: https://www.nature.com/articles/s41587-020-0661-6.

  23. Volker Bergen, Marius Lange, Stefan Peidli, F Alexander Wolf, and Fabian J Theis. Generalizing rna velocity to transient cell states through dynamical modeling. Nature Biotechnology, pages 1–7, 2020. URL: https://www.nature.com/articles/s41587-020-0591-3.

  24. Junyue Cao, Wei Zhou, Frank Steemers, Cole Trapnell, and Jay Shendure. Sci-fate characterizes the dynamics of gene expression in single cells. Nature Biotechnology, pages 1–9, 2020. URL: https://www.nature.com/articles/s41587-020-0480-9.

  25. Qi Qiu, Peng Hu, Xiaojie Qiu, Kiya W Govek, Pablo G Cámara, and Hao Wu. Massively parallel and time-resolved rna sequencing in single cells with scnt-seq. Nature methods, pages 1–11, 2020. URL: https://www.nature.com/articles/s41592-020-0935-4.

  26. Scott A. Lujan, Matthew J. Longley, Margaret H. Humble, Christopher A. Lavender, Adam Burkholder, Emma L. Blakely, Charlotte L. Alston, Grainne S. Gorman, Doug M. Turnbull, Robert McFarland, Robert W. Taylor, Thomas A. Kunkel, and William C. Copeland. Ultrasensitive deletion detection links mitochondrial dna replication, disease, and aging. Genome Biology, 21(1):248, Sep 2020. URL: https://doi.org/10.1186/s13059-020-02138-5, doi:10.1186/s13059-020-02138-5.

2020-09-07

  1. Alessia Centonze, Shuheng Lin, Elisavet Tika, Alejandro Sifrim, Marco Fioramonti, Milan Malfait, Yura Song, Aline Wuidart, Jens Van Herck, Anne Dannau, and others. Heterotypic cell–cell communication regulates glandular stem cell multipotency. Nature, 584(7822):608–613, 2020. URL: https://www.nature.com/articles/s41586-020-2632-y.

  2. Eiji Miyauchi, Seok-Won Kim, Wataru Suda, Masami Kawasumi, Satoshi Onawa, Naoko Taguchi-Atarashi, Hidetoshi Morita, Todd D Taylor, Masahira Hattori, and Hiroshi Ohno. Gut microorganisms act together to exacerbate inflammation in spinal cords. Nature, pages 1–5, 2020. URL: https://www.nature.com/articles/s41586-020-2634-9.

  3. Amir Ghasemian, Homa Hosseinmardi, Aram Galstyan, Edoardo M. Airoldi, and Aaron Clauset. Stacking models for nearly optimal link prediction in complex networks. Proceedings of the National Academy of Sciences, 2020. URL: https://www.pnas.org/content/early/2020/09/03/1914950117, arXiv:https://www.pnas.org/content/early/2020/09/03/1914950117.full.pdf, doi:10.1073/pnas.1914950117.

  4. Alessandro Didonna, Ester Canto Puig, Qin Ma, Atsuko Matsunaga, Brenda Ho, Stacy J. Caillier, Hengameh Shams, Nicholas Lee, Stephen L. Hauser, Qiumin Tan, Scott S. Zamvil, and Jorge R. Oksenberg. Ataxin-1 regulates b cell function and the severity of autoimmune experimental encephalomyelitis. Proceedings of the National Academy of Sciences, 2020. URL: https://www.pnas.org/content/early/2020/09/01/2003798117, arXiv:https://www.pnas.org/content/early/2020/09/01/2003798117.full.pdf, doi:10.1073/pnas.2003798117.

  5. David Bau, Jun-Yan Zhu, Hendrik Strobelt, Agata Lapedriza, Bolei Zhou, and Antonio Torralba. Understanding the role of individual units in a deep neural network. Proceedings of the National Academy of Sciences, 2020. URL: https://www.pnas.org/content/early/2020/08/31/1907375117.

  6. Akshaya Ramesh, Ryan D Schubert, Ariele L Greenfield, Ravi Dandekar, Rita Loudermilk, Joseph J Sabatino, Matthew T Koelzer, Edwina B Tran, Kanishka Koshal, Kicheol Kim, and others. A pathogenic and clonally expanded b cell transcriptome in active multiple sclerosis. Proceedings of the National Academy of Sciences, 2020. URL: https://www.pnas.org/content/early/2020/08/27/2008523117.

  7. Zhisong He, Agnieska Brazovskaja, Sebastian Ebert, Gray Camp, and Barbara Treutlein. CSS: cluster similarity spectrum integration of single-cell genomics data. Genome Biology, 2020. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02147-4.

  8. Andrew E. Teschendorff, Tianyu Zhu, Charles E. Breeze, and Stephan Beck. Episcore: cell type deconvolution of bulk tissue dna methylomes from single-cell rna-seq data. Genome Biology, 2020. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02126-9.

  9. James M McFarland, Brenton R Paolella, Allison Warren, Kathryn Geiger-Schuller, Tsukasa Shibue, Michael Rothberg, Olena Kuksenko, William N Colgan, Andrew Jones, Emily Chambers, and others. Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action. Nature Communications, 11(1):1–15, 2020. URL: https://www.nature.com/articles/s41467-020-17440-w.

  10. Anna S Nam, Ronan Chaligne, and Dan A Landau. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nature Reviews Genetics, pages 1–16, 2020. URL: https://www.nature.com/articles/s41576-020-0265-5.