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'Isotopo' a database application for facile analysis and management of mass isotopomer data

Database : the journal of biological databases and curation 2014年第2014期2014卷 bau077页

作者： Ahmed, Zeeshan Zeeshan, Saman Huber, Claudia Hensel, Michael Schomburg, Dietmar Münch, Richard Eylert, Eva Eisenreich, Wolfgang Dandekar, Thomas Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Universität München Division of Microbiology University of Osnabrück Department of Bioinformatics and Biochemistry Technical University Braunschweig Institute for Microbiology Biozentrum Technical University Braunschweig Germany and Computational biology and structures program European Molecular Biology Laboratory Germany Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Universität München Division of Microbiology University of Osnabrück Department of Bioinformatics and Biochemistry Technical University Braunschweig Institute for Microbiology Biozentrum Technical University Braunschweig Germany and Computational biology and structures program European Molecular Biology Laboratory 97074 Wuerzburg 97074 Wuerzburg Carl-Neuberg-Str. 1 Lichtenbergstraße 4 Barbarastraße 11 Gebäude 36 49076 Osnabrück Langer Kamp 19B 2. Obergeschoss Spielmannstraße 7 Meyerhofstr. 1 97074 Wuerzburg 97074 Wuerzburg Carl-Neuberg-Str. 1 Lichtenbergstraße 4 Barbarastraße 11 Gebäude 36 49076 Osnabrück Langer Kamp 19B 2. Obergeschoss Spielmannstraße 7 Braunschweig 38106 Hanover Germany Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Univer

UNLABELLED: The composition of stable-isotope labelled isotopologues/isotopomers in metabolic products can be measured by mass spectrometry and supports the analysis of pathways and fluxes. As a prerequisite, the original mass spectra have to be processed, managed and stored to rapidly calculate, analyse and compare isotopomer enrichments to study, for instance, bacterial metabolism in infection. For such applications, we provide here the database application 'Isotopo'. This software package includes (i) a database to store and process isotopomer data, (ii) a parser to upload and translate different data formats for such data and (iii) an improved application to process and convert signal intensities from mass spectra of (13)C-labelled metabolites such as tertbutyldimethylsilyl-derivatives of amino acids. Relative mass intensities and isotopomer distributions are calculated applying a partial least square method with iterative refinement for high precision data. The data output includes formats such as graphs for overall enrichments in amino acids. The package is user-friendly for easy and robust data management of multiple experiments.;AVAILABILITY: The 'Isotopo' software is available at the following web link (section Download): http://***/bioinformatics/isotopo/. The package contains three additional files: software executable setup (installer), one data set file (discussed in this article) and one excel file (which can be used to convert data from excel to '.iso' format). The 'Isotopo' software is compatible only with the Microsoft Windows operating system.;DATABASE URL: http://***/bioinformatics/isotopo/. © The Author(s) 2014. Published by Oxford University Press.

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Leveraging Generative Artificial Intelligence in Diagnosis of Thrombotic Microangiopathies: Focus on Thrombotic Thrombocytopenic Purpura

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Blood 2024年 144卷 2616-2616页

作者： Eunhee Choi Jung-Hyun Lee Robert McDougal William W Lytton Lincoln Medical & Mental Health Center New York NY Department of Neurology State University of New York Downstate Health Sciences University New York NY Program in Computational Biology and Bioinformatics Yale University New haven CT

Introduction Thrombotic microangiopathies (TMA), with etiologies ranging from benign to life-threatening, necessitates rapid and accurate diagnosis, particularly for thrombotic thrombocytopenic purpura (TTP), to initiate timely plasmapheresis preventing severe outcomes. Diagnosing TTP is challenging due to overlapping clinical features with other causes of TMA, such as disseminated intravascular coagulation (DIC), immune thrombocytopenic purpura (ITP), and atypical hemolytic uremic syndrome (aHUS), compounded by that specific diagnostic tests such as biopsies or ADAMTS13 activity assays do not result immediately. This study explored GPT-4's capability in suggesting differential diagnoses for TMA patients and identifying a provisional diagnosis of TTP based on clinical presentation and basic diagnostic workup to determine the need for prompt plasmapheresis, assessing its potential as a diagnostic support tool. Method We utilized open-access case reports from PubMed Central that provided a comprehensive list of cases with diagnosis of TMA. The exclusion criteria included cases with no access, copyright permission issues, preprints, insufficient description, non-case reports, non-English language, and no established diagnosis for TMA. Each case input including only the history and physical examination (H&P) and basic diagnostic workup excluding the confirmatory diagnosis was presented to GPT-4 in three separate trials and was prompted to provide clinical reasoning that both favored or rejected the diagnosis of TTP, create a top three differential diagnoses list selected from a comprehensive list of TMA diagnoses, and determine the necessity of plasmapheresis. Generated results were subsequently compared with the confirmed case diagnosis and management provided within the case report. Result An initial PubMed Central search identified 424 cases; 326 were excluded, resulting in 98 eligible cases. The top three differential diagnoses generated for each case in all three t

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ζ-QVAE: A Quantum Variational Autoencoder utilizing Regularized Mixed-state Latent Representations

arXiv

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arXiv 2024年

作者： Wang, Gaoyuan Warrell, Jonathan Emani, Prashant S. Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Computer Science Yale University New HavenCT06520 United States Department of Statistics & Data Science Yale University New HavenCT06520 United States Department of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States NEC Laboratories America PrincetonNJ08540 United States

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves compressing the original data to manageable dimensions while still representing essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To bridge this gap, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied for both classical and quantum data compression. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full-data density matrix and allow for a"global"training objective. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results consistently indicate that ζ-QVAE exhibits similar or better performance compared to matched classical models. © 2024, CC BY-NC-ND.

关键词： Large datasets

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Hopper flows of deformable particles

arXiv

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arXiv 2022年

作者： Cheng, Yuxuan Treado, John D. Lonial, Ben Habdas, Piotr Weeks, Eric R. Shattuck, Mark D. O'Hern, Corey S. Department of Physics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Physics Emory University AtlantaGA30322 United States Department of Physics Saint Joseph's University PhiladelphiaPA19131 United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY10031 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Numerous experimental and computational studies show that continuous hopper flows of granular materials obey the Beverloo equation that relates the volume flow rate Q and the orifice width w: Q ∼ (w/σavg − k)β, where σavg is the average particle diameter, kσavg is an offset where Q ∼ 0, the power-law scaling exponent β = d − 1/2, and d is the spatial dimension. Recent studies of hopper flows of deformable particles in different background fluids suggest that the particle stiffness and dissipation mechanism can also strongly affect the power-law scaling exponent β. We carry out computational studies of hopper flows of deformable particles with both kinetic friction and background fluid dissipation in two and three dimensions. We show that the exponent β varies continuously with the ratio of the viscous drag to the kinetic friction coefficient, λ = ζ/µ. β = d−1/2 in the λ → 0 limit and d− 3/2 in the λ → ∞ limit, with a midpoint λc that depends on the hopper opening angle θw. We also characterize the spatial structure of the flows and associate changes in spatial structure of the hopper flows to changes in the exponent β. The offset k increases with particle stiffness until k ∼ kmax in the hard-particle limit, where kmax ∼ 3.5 is larger for λ → ∞ compared to that for λ → 0. Finally, we show that the simulations of hopper flows of deformable particles in the λ → ∞ limit recapitulate the experimental results for quasi-2D hopper flows of oil droplets in water. © 2022, CC BY.

关键词： Stiffness

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Cell2Sentence: Teaching Large Language Models the Language of biology 41

Cell2Sentence: Teaching Large Language Models the Language o...

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41st International Conference on Machine Learning, ICML 2024

作者： Levine, Daniel Rizvi, Syed Asad Lévy, Sacha Pallikkavaliyaveetil, Nazreen Zhang, David Chen, Xingyu Ghadermarzi, Sina Wu, Ruiming Zheng, Zihe Vrkic, Ivan Zhong, Anna Raskin, Daphne Han, Insu de Oliveira Fonseca, Antonio Henrique Caro, Josue Ortega Karbasi, Amin Dhodapkar, Rahul M. van Dijk, David Department of Computer Science Yale University New HavenCT United States School of Engineering Applied Science University of Pennsylvania PhiladelphiaPA United States School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Neuroscience Yale School of Medicine New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Google United States Yale Institute for Foundations of Data Science New HavenCT United States Yale School of Engineering and Applied Science New HavenCT United States Roski Eye Institute University of Southern California Los AngelesCA United States Yale School of Medicine New HavenCT United States Cardiovascular Research Center Yale School of Medicine New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into"cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the finetuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S finetuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications. Copyright 2024 by the author(s)

关键词： Gene expression

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Author Correction: Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

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Nature genetics 2024年第6期56卷 1319页

作者： Sneha Mitra Rohan Malik Wilfred Wong Afsana Rahman Alexander J Hartemink Yuri Pritykin Kushal K Dey Christina S Leslie Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. Rye Country Day School Rye NY USA. Tri-Institutional Training Program in Computational Biology and Medicine New York City NY USA. Hunter College City University of New York New York City NY USA. Department of Computer Science Duke University Durham NC USA. Program in Computational Biology and Bioinformatics Duke University Durham NC USA. Center for Genomic and Computational Biology Duke University Durham NC USA. Department of Computer Science Princeton University Princeton NJ USA. Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton NJ USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. deyk@***. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. lesliec@***.

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Grappling with the Gulf

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Science 2009年第5911期323卷 210页

作者： Dov Greenbaum Mark Gerstein Mcdermott Will and Emory LLP 3150 Porter Drive Palo Alto CA 94304 USA. Department of Molecular Biophysics and Biochemistry Program in Computational Biology and Bioinformatics and Department of Computer Science Yale University New Haven CT 06520 USA.

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A Comprehensive Survey of Human Proteome by Analyzing Large Scale Tandem Mass Spectrometry and RNA-sequencing Data

A Comprehensive Survey of Human Proteome by Analyzing Large ...

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第八届中国蛋白质组学大会

作者： Peng Li Jean Thierry-Mieg Baitang Ning James C Fuscoe Qi Lv Pengzhan Hu Ruichao Xue Christopher Mason Altuna Akalin Geng Chen Chen Zhao Weida Tong Huixiao Hong Lei Guo Hong Fang Tao Qing Ying Yu Wenzhong Xiao Danielle Thierry-Mieg Leming Shi Tieliu Shi Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China National Center for Biotechnology InformationNational Library of MedicineNIHBethesdaMD 20894USA National Center for Toxicological ResearchFood and Drug AdministrationJeffersonARUSA Department of PhysiologyBiophysicsand Systems BiologyTri-Institutional Program(CornellSloan-Ketteringand Rockefeller)on Medicine1305 York AveUSA Center for PharmacogenomicsSchool of PharmacyFudan UniversityShanghaiChina Massachusetts General HospitalHarvard Medical School51 Blossom StBostonMA 02114USA National Center for Toxicological ResearchFood and Drug AdministrationJeffersonARUSA Center for Pharmacogenomics School of PharmacyFudan UniversityShanghaiChina

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71 Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape

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Journal of Biomolecular Structure and Dynamics 2013年第SUPP 1期31卷 45-46页

作者： Raluca Gordan[a][b][*] Ning Shen[c] Iris Dror[d] Tianyin Zhou[d] John Horton[b] Remo Rohs[d] & Martha L. Bulyk[e][f] [a] Departments of Biostatistics and Bioinformatics Computer Science and Molecular Genetics and Microbiology Duke University Durham NC USA [b] Institute for Genome Sciences and Policy Duke University Durham NC 27708 USA Phone: Phone: +1 (919) 667-8673 Fax: Phone: +1 (919) 667-8673 [c] Department of Pharmacology and Cancer Biology Duke University Durham NC 27708 USA [d] Molecular and Computational Biology Program Departments of Biological Sciences Chemistry and Physics and Astronomy University of Southern California 1050 Childs Way Los Angeles CA 90089 USA [e] Division of Genetics Departments of Medicine and Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA 02115 USA [f] Harvard-MIT Division of Health Sciences and Technology (HST) Harvard Medical School Boston MA 02115 USA

Transcriptional regulation of gene expression is enacted mainly through binding of transcription factors (TFs) to specific, short DNA sites in cis-regulatory regions of genes. Most TFs are members of protein families that share a common DNA-binding domain and thus recognize similar DNA-binding sequences. It is not well understood why paralogous TFs often bind different genomic target sitesin vivoto effect different regulatory programs, despite apparently recognizing the same sequence motifs. Here, we designed custom protein-binding microarrays (PBMs) to analyze the DNA-binding specificities of twoSaccharomyces cerevisiaebasic helix-loop-helix (bHLH) proteins, Tye7 and Cbf1, as a model system. Our data reveal that E-box DNA-binding sequences (CAnnTG), when tested in the context of their native genomic flanking sequences, are bound differently by Cbf1 and Tye7. computational models of the PBM data indicate that DNA sequence features located in the genomic sequences outside the E-box contribute to DNA-binding specificityin vitro. Our analyses suggest that these flanking regions affect DNA-binding specificity indirectly by influencing the three-dimensional structure of the E-box binding sites. Finally, we show that these subtle differences in intrinsic sequence preferences of Cbf1 and Tye7in vitrohelp to explain their differential DNA-binding preferencesin vivo. Our results provide further evidence that the local shape of DNA-binding sites may be an important feature in distinguishing the DNA-binding preferences among paralogous TFs and thus may play a widespread role in determining how transcriptional regulatory specificity within TF families is achieved.

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A large-scale binding and functional map of human RNA-binding proteins (vol 583, pg 711, 2020)

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NATURE 2021年第7842期589卷 E5-E5页

作者： Van Nostrand, Eric L. Freese, Peter Pratt, Gabriel A. Wang, Xiaofeng Wei, Xintao Xiao, Rui Blue, Steven M. Chen, Jia-Yu Cody, Neal A. L. Dominguez, Daniel Olson, Sara Sundararaman, Balaji Zhan, Lijun Bazile, Cassandra Bouvrette, Louis Philip Benoit Bergalet, Julie Duff, Michael O. Garcia, Keri E. Gelboin-Burkhart, Chelsea Hochman, Myles Lambert, Nicole J. Li, Hairi McGurk, Michael P. Nguyen, Thai B. Palden, Tsultrim Rabano, Ines Sathe, Shashank Stanton, Rebecca Su, Amanda Wang, Ruth Yee, Brian A. Zhou, Bing Louie, Ashley L. Aigner, Stefan Fu, Xiang-Dong Lecuyer, Eric Burge, Christopher B. Graveley, Brenton R. Yeo, Gene W. Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Institute for Genomic Medicine University of California San Diego La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Medical Research Institute Wuhan University Wuhan China Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Institut de Recherches Cliniques de Montréal (IRCM) Montreal Quebec Canada Department of Biochemistry and Molecular Medicine Université de Montréal Montreal Quebec Canada Division of Experimental Medicine McGill University Montreal Quebec Canada Department of Genetics and Genome Sciences Institute for Systems Genomics UConn Health Farmington CT USA

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