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arXiv 2019年

作者： Kopf, Andreas Fortuin, Vincent Somnath, Vignesh Ram Claassen, Manfred Institute of Molecular Systems Biology Department of Biology ETH Zürich Switzerland Biomedical Informatics Group Department of Computer Science ETH Zürich Switzerland Switzerland Life Science Graduate School Zurich PhD Program Systems Biology Switzerland

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering gained popularity due to the non-linearity of neural networks, which allows for flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on synthetic data, the MNIST benchmark data set, and a challenging real-world task of defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines and we show that the MoE architecture in the decoder reduces the computational cost of sampling specific data modes with high fidelity. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Computationally designed proteins mimic antibody immune evasion in viral evolution

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Immunity 2025年

作者： Youssef, Noor Gurev, Sarah Ghantous, Fadi Brock, Kelly P. Jaimes, Javier A. Thadani, Nicole N. Dauphin, Ann Sherman, Amy C. Yurkovetskiy, Leonid Soto, Daria Estanboulieh, Ralph Kotzen, Ben Notin, Pascal Kollasch, Aaron W. Cohen, Alexander A. Dross, Sandra E. Erasmus, Jesse Fuller, Deborah H. Bjorkman, Pamela J. Lemieux, Jacob E. Luban, Jeremy Seaman, Michael S. Marks, Debora S. Department of Systems Biology Harvard Medical School Boston 02115 MA United States Broad Institute of Harvard and MIT Cambridge 02139 MA United States Department of Electrical Engineering and Computer Science MIT Cambridge 02139 MA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Boston 02215 MA United States Program in Molecular Medicine University of Massachusetts Chan Medical School Worcester 01655 MA United States Division of Infectious Diseases Department of Medicine Brigham and Women's Hospital Boston 02115 MA United States Massachusetts General Hospital Boston 02114 MA United States Division of Biology and Biological Engineering California Institute of Technology Pasadena 91125 CA United States Department of Microbiology University of Washington Seattle 98195 WA United States National Primate Research Center Seattle 98109 WA United States HDT Bio Seattle 98109 WA United States Massachusetts Consortium on Pathogen Readiness Boston 02115 MA United States Ragon Institute of MGH MIT and Harvard Cambridge 02139 MA United States

Recurrent waves of viral infection necessitate vaccines and therapeutics that remain effective against emerging viruses. Our ability to evaluate interventions is currently limited to assessments against past or circulating variants, which likely differ in their immune escape potential compared with future variants. To address this, we developed EVE-Vax, a computational method for designing antigens that foreshadow immune escape observed in future viral variants. We designed 83 SARS-CoV-2 spike proteins that transduced ACE2-positive cells and displayed neutralization resistance comparable to variants that emerged up to 12 months later in the COVID-19 pandemic. Designed spikes foretold antibody escape from B.1-BA.4/5 bivalent booster sera seen in later variants. The designed constructs also highlighted the increased neutralization breadth elicited by nanoparticle-based, compared with mRNA-based, boosters in non-human primates. Our approach offers targeted panels of synthetic proteins that map the immune landscape for early vaccine and therapeutic evaluation against future viral strains. © 2025 The Authors

关键词： antibody escape generative protein models immune landscape machine learning protein design vaccine evaluation viral evolution

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SeqScreen: a biocuration platform for robust taxonomic and biological process characterization of nucleic acid sequences of interest

SeqScreen: a biocuration platform for robust taxonomic and b...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Dreycey Albin Dan Nasko R. A. Leo Elworth Jacob Lu Advait Balaji Christian Diaz Nidhi Shah Jeremy Selengut Chris Hulme-Lowe Pravin Muthu Gene Godbold Mikael Lindvall Madeline Diep Adam Porter Mihai Pop Krista Ternus Todd J. Treangen Program in Systems Synthetic and Physical Biology Rice University Center for Bioinformatics and Computational Biology University of Maryland Department of Computer Science Rice University Signature Science LLC Fraunhofer Center for Experimental Software Engineering Department of Computer Science University of Maryland

ISBN: (纸本)9781728118680

Rapid advancements in synthetic biology and nucleic acid synthesis, in particular concerns about its intentional or accidental misuse, call for more sophisticated screening tools to identify genes of interest within short sequence fragments. One major gap in predicting genes of concern is the inadequacy of current tools and ontologies to describe the specific biological processes of pathogenic proteins. The objective of this work is to design software that sensitively assigns taxonomic classifications, functional annotations, and biological processes of interest to short nucleotide sequences of unknown origin (50bp-1,000bp). The overarching goal is to perform sensitive characterization of short sequences and highlight specific pathogenic biological processes of interest (BPoIs). The SeqScreen software executes these tasks in analytical workflows with Nextflow and outputs results in a tab-delimited report. Local and global alignments differentiate hits to taxonomically-related sequences from similar but unrelated sequences, and an ensemble approach leverages multiple tools and databases to assign a variety of functional terms to each query sequence. Final biological process assessments are made from the predicted functional annotations, which leverage information in pre-existing databases, as well as new custom biocurations. Machine learning models predict each biological process of interest on large protein databases before incorporation into the SeqScreen framework to streamline computational efficiency, ensure reproducible results, allow for version control, and facilitate the review of the automated predictions by expert biocurators. The SeqScreen source code is available at .

关键词： Biological Process interest Base Sequence Nucleic acids Version control protein database Database synthetic biology ANNOTATIONS Leverage

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An integrative systems-biology approach defines mechanisms of Alzheimer’s disease neurodegeneration

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Nature Communications 2025年第1期16卷 1-21页

作者： Leventhal, Matthew J. Zanella, Camila A. Kang, Byunguk Peng, Jiajie Gritsch, David Liao, Zhixiang Bukhari, Hassan Wang, Tao Pao, Ping-Chieh Danquah, Serwah Benetatos, Joseph Nehme, Ralda Farhi, Samouil Tsai, Li-Huei Dong, Xianjun Scherzer, Clemens R. Feany, Mel B. Fraenkel, Ernest MIT Ph.D. Program in Computational and Systems Biology Cambridge MA United States Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA United States Broad Institute of Harvard and MIT Cambridge MA United States Department of Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA United States Spatial Technology Platform Broad Institute of Harvard and MIT Cambridge MA United States Precision Neurology Program Brigham and Women’s Hospital and Harvard Medical school Boston MA United States APDA Center for Advanced Parkinson’s Disease Research Brigham and Women’s Hospital and Harvard Medical School Boston MA United States Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA United States Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA United States School of Computer Science Northwestern Polytechnical University Xi’an China Stephen and Denise Adams Center of Yale School of Medicine New Haven CT United States

Despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer’s disease, remain incompletely understood. To define relevant pathways, we conducted an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in Drosophila. We also measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer’s disease and identified Alzheimer’s genetic variants that modify gene expression in disease-vulnerable neurons in humans. We then used a network model to integrate these data with previously published Alzheimer’s disease proteomics, lipidomics and genomics. Here, we computationally predict and experimentally confirm how HNRNPA2B1 and MEPCE enhance toxicity of the tau protein, a pathological feature of Alzheimer’s disease. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate DNA damage in Drosophila and human stem cell-derived neural progenitor cells. Our study identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer’s disease. © The Author(s) 2025.

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The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

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Cell 2025年

作者： Pekar, Jonathan E. Lytras, Spyros Ghafari, Mahan Magee, Andrew F. Parker, Edyth Wang, Yu Ji, Xiang Havens, Jennifer L. Katzourakis, Aris Vasylyeva, Tetyana I. Suchard, Marc A. Hughes, Alice C. Hughes, Joseph Rambaut, Andrew Robertson, David L. Dellicour, Simon Worobey, Michael Wertheim, Joel O. Lemey, Philippe Institute of Ecology and Evolution University of Edinburgh Edinburgh United Kingdom Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla 92093 CA United States Department of Biomedical Informatics University of California San Diego La Jolla 92093 CA United States Department of Medicine University of California San Diego La Jolla 92093 CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Medical Research Council University of Glasgow Centre for Virus Research Glasgow United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States Department of Immunology and Microbiology The Scripps Research Institute La Jolla 92037 CA United States Institute of Genomics and Global Health Redeemer's University Osun State Ede Nigeria Department of Computer Science and Engineering University of California San Diego La Jolla 92093 CA United States Department of Mathematics School of Science and Engineering Tulane University New Orleans LA United States Department of Population Health and Disease Prevention University of California Irvine Irvine 92617 CA United States Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles 90095 CA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States School of Biological Sciences University of Hong Kong Hong Kong Hong Kong China Biodiversity Green Development Foundation Beijing China Spatial Epidemiology Lab (SpELL) Université Libre de Bruxelles CP160/12 50 av. FD Roosevelt Bruxelles 1050 Belgium Department of Microbiology Immunology and Transplantation Rega Institute

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses. © 2025 The Authors

关键词： molecular epidemiology phylogeography sarbecoviruses virus evolution

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Rewriting regulatory DNA to dissect and reprogram gene expression

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Cell 2025年

作者： Martyn, Gabriella E. Montgomery, Michael T. Jones, Hank Guo, Katherine Doughty, Benjamin R. Linder, Johannes Bisht, Deepa Xia, Fan Cai, Xiangmeng S. Chen, Ziwei Cochran, Kelly Lawrence, Kathryn A. Munson, Glen Pampari, Anusri Fulco, Charles P. Sahni, Nidhi Kelley, David R. Lander, Eric S. Kundaje, Anshul Engreitz, Jesse M. Department of Genetics Stanford University School of Medicine Stanford 94305 CA United States Basic Science and Engineering Initiative Stanford Children's Health Betty Irene Moore Children's Heart Center Stanford 94305 CA United States Calico Life Sciences LLC South San Francisco 94080 CA United States Department of Genitourinary Medical Oncology Division of Cancer Medicine The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Genomic Medicine The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Epigenetics and Molecular Carcinogenesis The University of Texas MD Anderson Cancer Center Houston 77230 TX United States Department of Bioengineering Stanford University Stanford 94305 CA United States Department of Computer Science Stanford University Stanford 94305 CA United States Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard Cambridge 02142 MA United States Gene Regulation Observatory Broad Institute of MIT and Harvard Cambridge 02142 MA United States Broad Institute of MIT and Harvard Cambridge 02142 MA United States Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston 77230 TX United States Quantitative and Computational Biosciences Program Baylor College of Medicine Houston 77030 TX United States Department of Biology MIT Cambridge 02139 MA United States Department of Systems Biology Harvard Medical School Boston 02115 MA United States Stanford Cardiovascular Institute Stanford University Stanford 94305 CA United States

Regulatory DNA provides a platform for transcription factor binding to encode cell-type-specific patterns of gene expression. However, the effects and programmability of regulatory DNA sequences remain difficult to map or predict. Here, we develop variant effects from flow-sorting experiments with CRISPR targeting screens (Variant-EFFECTS) to introduce hundreds of designed edits to endogenous regulatory DNA and quantify their effects on gene expression. We systematically dissect and reprogram 3 regulatory elements for 2 genes in 2 cell types. These data reveal endogenous binding sites with effects specific to genomic context, transcription factor motifs with cell-type-specific activities, and limitations of computational models for predicting the effect sizes of variants. We identify small edits that can tune gene expression over a large dynamic range, suggesting new possibilities for prime-editing-based therapeutics targeting regulatory DNA. Variant-EFFECTS provides a generalizable tool to dissect regulatory DNA and to identify genome editing reagents that tune gene expression in an endogenous context. © 2025 Elsevier Inc.

关键词： CRISPR enhancers gene regulation high-throughput screening non-coding variants predictive models prime editing RNA FlowFISH sequence design transcription factors

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Orchard: building large cancer phylogenies using stochastic combinatorial search

arXiv

引用

arXiv 2023年

作者： Kulman, Ethan Kuang, Rui Morris, Quaid Computational and Systems Biology Program Sloan Kettering Institute New YorkNY United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN United States

Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point mutations detected with bulk DNA sequencing. However, these methods become inaccurate when reconstructing phylogenies with more than 30 mutations, or, in some cases, fail to recover a phylogeny altogether. Here, we introduce Orchard, a cancer phylogeny reconstruction algorithm that is fast and accurate using up to 1000 mutations. Orchard samples without replacement from a factorized approximation of the posterior distribution over phylogenies, a novel result derived in this paper. Each factor in this approximate posterior corresponds to a conditional distribution for adding a new mutation to a partially built phylogeny. Orchard optimizes each factor sequentially, generating a sequence of incrementally larger phylogenies that ultimately culminate in a complete tree containing all mutations. Our evaluations demonstrate that Orchard outperforms state-of-the-art cancer phylogeny reconstruction methods in reconstructing more plausible phylogenies across 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). Remarkably, Orchard accurately reconstructs cancer phylogenies using up to 1,000 mutations. Additionally, we demonstrate that the large and accurate phylogenies reconstructed by Orchard are useful for identifying patterns of somatic mutations and genetic variations among distinct cancer cell subpopulations. © 2023, CC BY.

关键词： Plant diseases

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Dental Disease Detection based on CNN for Panoramic Dental Radiographs 11

Dental Disease Detection based on CNN for Panoramic Dental R...

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11th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2023

作者： El Bagoury, Manar Al-Shetairy, Mirna Ahmed, Omar Mehanny, Samah S Hamdy, Tassneem Omar, Gihan Akram, Marwan Mohamed, Rana Amr, Hebatullah Gad, Walaa Ain Shams University Faculty of Computer and Information Sciences Bioinformatics Program Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Faculty of Dentistry Cairo and Galala Universities Oral Biology Department Cairo Egypt Cairo University Faculty of Dentistry Oral and Maxillofacial Radiology Department Cairo Egypt Future University in Egypt Faculty of Oral and Dental Medicine Oral Radiology Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

Panoramic dental radiography is one of the examination tools frequently performed in dental clinics and hospitals as it captures a wide range of the dental region and its surroundings, but due to the complex structure of the dental area and lack of time, dentists focus on tiny parts of the images. With the help of Machine Learning, dentists can detect dental defects and anomalies faster and more accurately. In this study, a data set of 733 panoramic radiographs of adult patients was collected from the faculty of Oral and Dental Medicine, Future University, Egypt. Using a combination of pre-trained CNN architecture and a finetuned network, the collected panoramic radiographs were used to determine the optimal architecture. The purpose of this research was to develop a computer-aided detection system based on a finetuned CNN algorithm, and to evaluate the accuracy and usefulness of this system for the detection of proximal and occlusal caries, interdental and inter radicular alveolar bone loss, periapical lesions, and impacted teeth in panoramic radiographs. Different object detection models were tested to detect selected dental diseases that were found in the panoramic radiographs, and after a few experiments, the YOLOv5 architecture was proved to be the most optimal after achieving 0.61 mAP@0.5 and 0.28 mAP@ [0.5-0.95] for the total six classes. This is the best-achieved results to our knowledge. © 2023 IEEE.

关键词： Object detection

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COULD DROPPING A FEW CELLS CHANGE THE TAKEAWAYS FROM DIFFERENTIAL EXPRESSION?

arXiv

引用

arXiv 2023年

作者： Shiffman, Miriam Giordano, Ryan Broderick, Tamara MIT Computational & Systems Biology Program United States MIT Dept. of Electrical Engineering & Computer Science United States UC Berkeley Dept. of Statistics United States

Analysis of differential gene expression plays a fundamental role in biology toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While any analysis is run on particular cells or samples, the intent is to generalize to future occurrences of the treatment or disease. Implicitly, generalization is justified under the assumption that present and future samples are independent and identically distributed from the same population. Though this assumption is always false, we might hope that any deviation from the assumption is small enough that A) fundamental conclusions of the analysis still hold, and B) standard tools like standard error, significance, and power still reflect generalizability. Conversely, we might worry about these deviations, and reliance on standard statistical tools, if conclusions could be substantively changed by dropping a very small fraction of observations. While checking every small fraction is computationally intractable, recent work develops an approximation to identify when such an influential subset exists. Building on this work, we develop a metric for dropping-data robustness of differential expression;namely, we cast the analysis in a differentiable form suitable to the approximation, extend the approximation to models whose hyperparameters depend on the full dataset, and extend the notion of a data point from a single cell to a pseudobulk observation. We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets. We use our tool to assess the robustness of differential expression for published single-cell RNA-seq data, and discover that thousands of genes can have their results flipped by dropping Copyright © 2023, The Authors. All rights reserved.

关键词： Diseases

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Latent space temporal model of microbial abundance to predict domination and bacteremia

arXiv

引用

arXiv 2018年

作者： Zhong, Ruiqi Joseph, Tyler Xavier, Joao B. Pe'Er, Itsik Department of Computer Science Columbia University New YorkNY United States Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New YorkNY United States

Gut microbial composition has been linked to multiple health outcomes. Yet, temporal analysis of this composition had been limited to deterministic models. In this paper, we introduce a probabilistic model for the dynamics of intestinal microbiomes that takes into account interaction among bacteria as well as external effects such as antibiotics. The model successfully deals with pragmatic issues such as random measurement error and varying time intervals between measurements through latent space modelling. We demonstrate utility of the model by using latent state features to predict the clinical events of intestinal domination and bacteremia, improving accuracy over existing methods. We further leverage this framework to validate known links between antibiotics and clinical outcomes, while discovering new ones. Copyright © 2018, The Authors. All rights reserved.

关键词： Antibiotics

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