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Neutral Theory and the Somatic Evolution of Cancer (vol 35, pg 1308, 2018)

MOLECULAR biology AND EVOLUTION 2018年第6期35卷 1561-1561页

作者： Cannataro, Vincent L. Townsend, Jeffrey P. Department of Biostatistics Yale University New Haven CT Department of Biostatistics Yale University New Haven CT Program in Computational Biology and Bioinformatics Department of Ecology and Evolutionary Biology Yale University New Haven CT Corresponding author: E-mail: jeffrey.townsend@yale.edu. Associate editor

Kimura’s neutral theory argued that positive selection was not responsible for an appreciable fraction of molecular substitutions. Correspondingly, quantitative analysis reveals that the vast majority of substitutions in cancer genomes are not detectably under selection. Insights from the somatic evolution of cancer reveal that beneficial substitutions in cancer constitute a small but important fraction of the molecular variants. The molecular evolution of cancer community will benefit by incorporating the neutral theory of molecular evolution into their understanding and analysis of cancer evolution—and accepting the use of tractable, predictive models, even when there is some evidence that they are not perfect.

关键词： molecular evolution neutral theory cancer tumor genetics distribution of fitness effects somatic mutation

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Bayesian multinomial logistic normal models through marginally latent matrix-T processes

arXiv

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arXiv 2019年

作者： Silverman, Justin D. Roche, Kimberly Holmes, Zachary C. David, Lawrence A. Mukherjee, Sayan Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Medical Scientist Training Program Duke University DurhamNC27708 United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University DurhamNC27708 United States Center for Genomic and Computational Biology Duke University DurhamNC27708 United States Department of Molecular Genetics and Microbiology Duke University DurhamNC27708 United States

Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to handling small data sets due to the non-conjugacy of the multinomial and logistic-normal distributions. We introduce MLN models which can be written as marginally latent matrix-t process (LTP) models. Marginally LTP models describe a flexible class of generalized linear regression, non-linear regression, and time series models. We develop inference schemes for Marginally LTP models and, through application to MLN models, demonstrate that our inference schemes are both highly accurate and often 4-5 orders of magnitude faster than MCMC. Copyright © 2019, The Authors. All rights reserved.

关键词： Multivariant analysis

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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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Essential metadata for 3D BRAIN microscopy

arXiv

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arXiv 2021年

作者： Ropelewski, Alexander J. Rizzo, Megan A. Swedlow, Jason R. Huisken, Jan Osten, Pavel Khanjani, Neda Weiss, Kurt Bakalov, Vesselina Engle, Michelle Gridley, Lauren Krzyzanowski, Michelle Madden, Tom Maiese, Deborah Waterfield, Justin Williams, David Hamilton, Carol Huggins, Wayne Biomedical Applications Group Pittsburgh Supercomputing Center 300 S Craig Street PittsburghPA15213 United States Department of Physiology University of Maryland School of Medicine 660 West Redwood Street BaltimoreMD21201 United States Centre for Gene Regulation & Expression Division of Computational Biology University of Dundee Nethergate DundeeDD1 4HN United Kingdom Morgridge Institute for Research 330 N Orchard Street MadisonWI53715 United States Cold Spring Harbor Laboratory One Bungtown Road Cold Spring HarborNY11724 United States Mark and Mary Stevens Neuroimaging and Informatics Institute Laboratory of Neuro Imaging Keck School of Medicine of University of Southern California 1975 Zonal Avenue Los AngelesCA90033 United States Bioinformatics and Computational Biology Program RTI International 3040 East Cornwallis Road Research Triangle ParkNC27709 United States

Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments’ images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for 3D microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting Essential Metadata for 3D BRAIN Microscopy includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage collaboration. © 2021, CC BY-NC-ND.

关键词： Fluorescence microscopy

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Author Correction: Senotherapeutic peptide treatment reduces biological age and senescence burden in human skin models

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npj aging 2024年第1期10卷 14页

作者： Alessandra Zonari Lear E Brace Kallie Al-Katib William F Porto Daniel Foyt Mylieneth Guiang Edgar Andres Ochoa Cruz Bailey Marshall Melissa Gentz Gabriela Rapozo Guimarães Octavio L Franco Carolina R Oliveira Mariana Boroni Juliana L Carvalho OneSkin Inc. San Francisco CA USA. alessandra@oneskin.co. OneSkin Inc. San Francisco CA USA. Genomic Sciences and Biotechnology Program Catholic University of Brasilia Brasília 70790-160 DF Brazil. Porto Reports Brasília 72236-011 DF Brazil. Bioinformatics and Computational Biology Lab Brazilian National Cancer Institute (INCA) Rio de Janeiro 20231-050 RJ Brazil. Centre of Proteomic Analyses and Biochemistry Genomic Sciences and Biotechnology Program Catholic University of Brasilia Brasilia 70790-160 DF Brazil. S-Inova Biotech Biotechnology Program Catholic University Dom Bosco Campo Grande 79117-010 MS Brazil. Molecular Pathology Program University of Brasilia Brasilia 70.910-900 DF Brazil. Interdisciplinary Biosciences Laboratory Faculty of Medicine University of Brasília Brasília 70.910-900 DF Brazil.

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Lymphatic endothelial mTORC1 instructs metabolic and developmental signaling during lymphangiogenesis

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Developmental Cell 2025年

作者： Han, Fei Simeroth, Summer Zhu, Jie Gryniuk, Irma Pranay, Atul Chen, Weiqing Wang, Yuan Cai, Yuanyuan Shen, Zhiyuan Wang, Guangyu Griffin, Courtney T. Xia, Lijun Yu, Pengchun Cardiovascular Biology Research Program Oklahoma Medical Research Foundation Oklahoma City 73104 OK United States Department of Cell Biology University of Oklahoma Health Sciences Center Oklahoma City 73104 OK United States Aging and Metabolism Research Program Oklahoma Medical Research Foundation Oklahoma City 73104 OK United States Center for Bioinformatics and Computational Biology Houston Methodist Research Institute Houston TX United States Department of Physiology Biophysics & Systems Biology Weill Cornell Graduate School of Medical Science Weill Cornell Medicine Cornell University New York NY United States Department of Radiation Oncology Rutgers Cancer Institute and Robert Wood Johnson Medical School Rutgers University New Brunswick NJ United States Center for Cardiovascular Regeneration Houston Methodist Research Institute Houston TX United States Center for RNA Therapeutics Houston Methodist Research Institute Houston TX United States Department of Cardiothoracic Surgery Weill Cornell Medicine Cornell University New York NY United States Department of Biochemistry and Physiology University of Oklahoma Health Sciences Center Oklahoma City OK United States

The lymphatic vasculature comprises lymphatic capillaries and collecting vessels. To support lymphatic development, lymphatic endothelial cells (LECs) utilize nutrients to fuel lymphangiogenic processes. Meanwhile, LECs maintain constant prospero homeobox 1 (PROX1) expression critical for lymphatic specification. However, molecular mechanisms orchestrating nutrient metabolism while sustaining PROX1 levels in LECs remain unclear. Here, we show that loss of RAPTOR, an indispensable mechanistic target of rapamycin complex 1 (mTORC1) component, downregulates PROX1 and impairs lymphatic capillary growth and differentiation of collecting lymphatics in mice. Mechanistically, mTORC1 inhibition in mouse and human LECs causes Myc reduction, which decreases hexokinase 2 (HK2) and glutaminase (GLS), inhibiting glycolysis and glutaminolysis. Myc or HK2/GLS ablation impedes lymphatic capillary and collecting vessel formation. Interestingly, mTORC1 regulation of PROX1 is independent of Myc-HK2/GLS signaling. Moreover, genetic interaction analysis indicates that Myc and PROX1 play crucial roles in mTORC1-regulated lymphatic development. Collectively, our findings identify mTORC1 as a key regulator of metabolic programs and PROX1 expression during lymphangiogenesis. © 2025 Elsevier Inc.

关键词： glutaminase glutaminolysis glycolysis hexokinase 2 lymphangiogenesis lymphatic endothelial cell lymphatic vessel mTORC1 Myc PROX1

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帕博利珠单抗治疗伴脑转移NSCLC患者的一项非随机、开放Ⅱ期试验的长期随访结果和生物标志物分析

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中国肺癌杂志 2021年第9期24卷 I0007-I0016页

作者： Sarah B GOLDBERG Kurt A SCHALPER Scott N GETTINGER Amit MAHAJAN Roy S HERBST Anne C CHANG Rogerio LILENBAUM Frederick H WILSON Sacit Bulent OMAY James B YU Lucia JILAVEANU Thuy TRAN Kira PAVLIK Elin ROWEN Heather GERRSH Annette KOMLO Richa GUPTA Hailey WYATT Matthew RIBEIRO Yuval KLUGER Geyu ZHOU Wei WEI Veronica L CHANG Harriet M KLUGER 董晓荣(翻译/校对) Department of Medicine(Medical Oncology) Yale School of MedicineNew HavenCTUSA Department of Pathology Yale School of MedicineNew HavenCTUSA Department of Radiology Yale School of MedicineNew HavenCTUSA Department of Neurosurgery Yale School of MedicineNew HavenCTUSA Department of Therapeutic Radiology Yale School of MedicineNew HavenCTUSA Interdepartmental Program in Computational Biology and Bioinformatics Yale School of MedicineNew HavenCTUSA Department of Biostatistics Yale School of Public HealthNew HavenCTUSA 华中科技大学同济医学院附属协和医院胸部肿瘤科

背景与目的我们开展了一项帕博利珠单抗用于伴未治疗脑转移的非小细胞肺癌(non-small cell lung cancer,NSCLC)或黑色素瘤患者的疗效和安全性的II期试验,旨在评估程序性死亡受体1(programmed cell death 1,PD-1)抑制剂在中枢神经系统(ce... 详细信息

背景与目的我们开展了一项帕博利珠单抗用于伴未治疗脑转移的非小细胞肺癌(non-small cell lung cancer,NSCLC)或黑色素瘤患者的疗效和安全性的II期试验,旨在评估程序性死亡受体1(programmed cell death 1,PD-1)抑制剂在中枢神经系统(central nervous system,CNS)中的疗效。中期结果已发表,现报道对NSCLC队列的更新分析结果。方法这是一项开放性、单中心、II期试验。纳入标准:年龄≥18岁,诊断为晚期NSCLC并伴有≥1个5 mm-20 mm脑转移病灶,既往从未治疗或之前放疗后进展,无神经系统症状,不需要激素治疗且美国东部肿瘤协作组(Eastern Cooperative Oncology Group,ECOG)<2分。患者每2周接受一次帕博利珠单抗(10 mg/kg)治疗。队列1为程序性死亡配体1(programmed cell death ligand 1,PD-L1)≥1%的患者,队列2为PD-L1<1%或未评估的患者。主要终点是脑转移患者缓解比例。所有经治患者均纳入疗效与安全性终点的分析。该研究已结束入组,并于***登记注册,注册号为NCT02085070。结果2014年3月31日-2018年5月21日,共42例患者接受治疗。中位随访时间为8.3个月(IQR:4.5个月-26.2个月)。队列1的37例患者中11例有脑转移缓解[29.7%(95%CI:15.9%-47.0%)]。队列2未观察到缓解。治疗相关的3级-4级不良事件(adverse events,AEs)包括2例肺炎、1例全身症状、1例结肠炎、1例肾上腺皮质功能不全、1例高血糖症和1例低钾血症。6例(14%)患者发生了治疗相关的严重不良事件,包括肺炎、急性肾损伤、低钾血症和肾上腺皮质功能不全。没有观察到治疗相关死亡病例。结论帕博利珠单抗治疗PD-L1≥1%的NSCLC伴脑转移患者有效,且对所有纳入的未经治疗的脑转移患者安全。需要进一步探索免疫治疗用于NSCLC合并CNS转移。

关键词：非小细胞肺癌脑转移免疫治疗帕博利珠单抗

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Investigating the Efficacy of Data Mining Techniques for Crowdsourced Biological Datasets

Investigating the Efficacy of Data Mining Techniques for Cro...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Ilona Regan Dany Alkurdi Sarun Paisarnsrisomsuk Carolina Ruiz Elizabeth F. Ryder Rob Gegear Bioinformatics and Computational Biology Program Worcester Polytechnic Institute Worcester MA USA Princeton University Princeton NJ USA Worcester Polytechnic Institute Worcester MA USA University of Massachusetts Dartmouth Dartmouth MA USA

ISBN: (纸本)9781728158198

The rapid worldwide decline of wild pollinators over recent years poses a significant environmental threat due to the critical keystone role that pollinators play in terrestrial ecosystems. In order to gain insight into the major anthropogenic factors causing these declines, researchers have collected large amounts of ecological data. Yet, they often lack the computational tools needed to analyze the information contained in such datasets. We investigate various data analysis techniques for the Beecology Project, which is a citizen science based effort to rapidly collect data on foraging habits of bumblebee species native to Massachusetts. Different data mining approaches were explored, including association analysis, trend analysis, classification, regression, and clustering. It was found that different techniques were more suitable depending on the biological research question. Future work will focus on making tools utilizing these approaches available online through the Beecology website, where they can be used by the public to determine how best to protect our native pollinators and the diverse ecosystems they support.

关键词： Data analysis Conferences Ecosystems Market research Encoding Data mining Biological information theory

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Investigation of RNA-RNA Interactions Using the RISE Database

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Current Protocols in bioinformatics 2018年第1期64卷 e58/1-e58/1页

作者： Ju, Yanyan Gong, Jing Yang, Yucheng T. Zhang, Qiangfeng Cliff MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural Biology Center for Synthetic and Systems Biology Tsinghua-Peking Joint Center for Life Sciences School of Life Sciences Tsinghua University Beijing China Department of Molecular Biophysics and Biochemistry Yale University New Haven CT United States Program in Computational Biology and Bioinformatics Yale University New Haven CT United States

RNA-RNA interactions (RRIs) are essential to understanding the regulatory mechanisms of RNAs. Mapping RRIs in vivo in a transcriptome-wide manner remained challenging until the recent development of several sequencing-based technologies. However, RRIs generated from large-scale studies had not been systematically collected and analyzed before. This article introduces RISE, a database of the RNA Interactome from Sequencing Experiments. RISE provides a comprehensive collection of RRIs in human, mouse, and yeast, derived from transcriptome-wide sequencing experiments, as well as targeted sequencing studies and other public databases/datasets. To facilitate better understanding of the biological roles of these RRIs, RISE also offers rich functional annotations involving RNAs, and an interactive interface to explore the analysis results. Here, we provide a brief description of the RISE website and a step-by-step protocol for using RISE to study RRIs. © 2018 by John Wiley & Sons, Inc. © 2018 John Wiley & Sons, Inc.

关键词： interaction RISE RNA RNA structure RNA-RNA sequencing experiments

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Cell2Sentence: teaching large language models the language of biology 24

Cell2Sentence: teaching large language models the language o...

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Proceedings of the 41st International Conference on Machine Learning

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

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 fine-tuning 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 fine-tuning, 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.

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