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py_ped_sim: a flexible forward pedigree and genetic simulator for complex family pedigree analysis

BMC Bioinformatics 2025年第1期26卷 1-18页

作者： Guardado, Miguel Perez, Cynthia Campana, Sthen Chavez Rojas, Berenice Magaña, Joaquín Jackson, Shalom Samperio, Emily Hernandez, Selena Syas, Kaela Hernandez, Ryan D. Zavala, Elena I. Rohlfs, Rori V. Department of Mathematics San Francisco State University San FranciscoCA94132 United States Biological and Medical Informatics Graduate Program University of California San Francisco San FranciscoCA94158 United States Bioengineering and Therapeutic Sciences University of California San FranciscoCA94134 United States Department of Data Science University of Oregon EugeneOR97403 United States Department of Biology San Francisco State University San FranciscoCA94132 United States Department of Molecular and Cell Biology University of California Berkeley BerkeleyCA94720 United States

Background: Large-scale family pedigrees are commonly used across medical, evolutionary, and forensic genetics. These pedigrees are tools for identifying genetic disorders, tracking evolutionary patterns, and establishing familial relationships via forensic genetic identification. However, there is a lack of software to accurately simulate different pedigree structures along with genomes corresponding to those individuals in a family pedigree. This limits simulation-based evaluations of methods that use pedigrees. Results: We have developed a python command-line-based tool called py_ped_sim that facilitates the simulation of pedigree structures and the genomes of individuals in a pedigree. py_ped_sim represents pedigrees as directed acyclic graphs, enabling conversion between standard pedigree formats and integration with the forward population genetic simulator, SLiM. Notably, py_ped_sim allows the simulation of varying numbers of offspring for a set of parents, with the capacity to shift the distribution of sibship sizes over generations. We additionally add simulations for events of misattributed paternity, which offers a way to simulate half-sibling relationships, and simulations to extend the breadth of a family pedigree. We validated the accuracy of both our genome simulator and pedigree simulator. We show that we can simulate genomes onto family pedigrees with levels of expected kinship. Conclusions: py_ped_sim is a user-friendly and open-source solution for simulating pedigree structures and conducting pedigree genome simulations. It empowers medical, forensic, and evolutionary genetics researchers to gain deeper insights into the dynamics of genetic inheritance and relatedness within families. © The Author(s) 2025.

关键词： Open source software

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Robustness tests for biomedical foundation models should tailor to specification

arXiv

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arXiv 2025年

作者： Xian, R. Patrick Baker, Noah R. David, Tom Cui, Qiming Holmgren, A. Jay Bauer, Stefan Sushil, Madhumita Abbasi-Asl, Reza Weill Institute for Neurosciences University of California San Francisco 1651 4th Street San FranciscoCA94158 United States Biological and Medical Informatics Graduate Program University of California San Francisco 550 16th Street 3rd Floor San FranciscoCA94158 United States PRISM Eval 10 Rue de Penthièvre Paris75008 France Department of Bioengineering University of California Berkeley 306 Stanley Hall University Drive BerkeleyCA94720 United States Division of Clinical Informatics and Digital Transformation University of California San Francisco 10 Koret Way San FranciscoCA94117 United States School of Computation Information and Technology Technical University of Munich & Helmholtz AI Arcisstrasse 21 Munich80333 Germany Bakar Computational Health Sciences Institute University of California San Francisco 490 Illinois Street San FranciscoCA94158 United States

Existing regulatory frameworks for biomedical AI include robustness as a key component but lack detailed implementational guidance. The recent rise of biomedical foundation models creates new hurdles in testing and certification given their broad capabilities and susceptibility to complex distribution shifts. To balance test feasibility and effectiveness, we suggest a priority-based, task-oriented approach to tailor robustness evaluation objectives to a predefined specification. We urge concrete policies to adopt a granular categorization of robustness concepts in the specification. Our approach promotes the standardization of risk assessment and monitoring, which guides technical developments and mitigation efforts. © 2025, CC BY-NC-SA.

关键词： Specifications

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Translational Bioinformatics to Enable Precision Medicine for All: Elevating Equity across Molecular, Clinical, and Digital Realms

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Yearbook of medical informatics 2022年第1期31卷 106-115页

作者： Tang, Alice Woldemariam, Sarah Roger, Jacquelyn Sirota, Marina Bakar Computational Health Sciences Institute UCSF San Francisco CA United States Graduate Program in Bioengineering UCSF San Francisco CA United States School of Medicine UCSF San Francisco CA United States Graduate Program in Biological and Medical Informatics UCSF San Francisco CA United States Department of Pediatrics UCSF San Francisco CA United States

Objectives: Over the past few years, challenges from the pandemic have led to an explosion of data sharing and algorithmic development efforts in the areas of molecular measurements, clinical data, and digital health. We aim to characterize and describe recent advanced computational approaches in translational bioinformatics across these domains in the context of issues or progress related to equity and inclusion. Methods: We conducted a literature assessment of the trends and approaches in translational bioinformatics in the past few years. Results: We present a review of recent computational approaches across molecular, clinical, and digital realms. We discuss applications of phenotyping, disease subtype characterization, predictive modeling, biomarker discovery, and treatment selection. We consider these methods and applications through the lens of equity and inclusion in biomedicine. Conclusion: Equity and inclusion should be incorporated at every step of translational bioinformatics projects, including project design, data collection, model creation, and clinical implementation. These considerations, coupled with the exciting breakthroughs in big data and machine learning, are pivotal to reach the goals of precision medicine for all. © 2022 IMIA and Georg Thieme Verlag KG.

关键词： Bioinformatics genomics health equity precision medicine

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Mitochondrial DNA variation in Alzheimer's disease reveals a unique microprotein called SHMOOSE(jan, 10.1038/s41380-023-01956-w, 2023)

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MOLECULAR PSYCHIATRY 2023年第4期28卷 1827-1827页

作者： Miller, Brendan Kim, Su-Jeong Mehta, Hemal H. Cao, Kevin Kumagai, Hiroshi Thumaty, Neehar Leelaprachakul, Naphada Braniff, Regina Gonzalez Jiao, Henry Vaughan, Joan Diedrich, Jolene Saghatelian, Alan Arpawong, Thalida E. Crimmins, Eileen M. Ertekin-Taner, Niluefer Tubi, Meral A. Hare, Evan T. Braskie, Meredith N. Decarie-Spain, Lea Kanoski, Scott E. Grodstein, Francine Bennett, David A. Zhao, Lu Toga, Arthur W. Wan, Junxiang Yen, Kelvin Cohen, Pinchas Leonard Davis School of Gerontology University of Southern California Los Angeles CA USA Neuroscience Graduate Program University of Southern California Los Angeles CA USA Clayton Foundation Laboratories for Peptide Biology The Salk Institute for Biological Studies La Jolla CA USA Mayo Clinic Department of Neuroscience Jacksonville FL USA Imaging Genetics Center Institute for Neuroimaging and Informatics University of Southern California Los Angeles CA USA Department of Neurology University of Southern California Los Angeles CA USA Human and Evolutionary Biology Section Department of Biological Sciences Dornsife College of Letters Arts and Sciences University of Southern California Los Angeles CA USA Rush Alzheimer’s Disease Center Rush University Medical Center Chicago IL USA Laboratory of Neuro Imaging USC Stevens Neuroimaging and Informatics Institute Keck School of Medicine of University of Southern California Los Angeles CA USA

Mitochondrial DNA variants have previously associated with disease, but the underlying mechanisms have been largely elusive. Here, we report that mitochondrial SNP rs2853499 associated with Alzheimer’s disease (AD), neuroimaging, and transcriptomics. We mapped rs2853499 to a novel mitochondrial small open reading frame called SHMOOSE with microprotein encoding potential. Indeed, we detected two unique SHMOOSE-derived peptide fragments in mitochondria by using mass spectrometry—the first unique mass spectrometry-based detection of a mitochondrial-encoded microprotein to date. Furthermore, cerebrospinal fluid (CSF) SHMOOSE levels in humans correlated with age, CSF tau, and brain white matter volume. We followed up on these genetic and biochemical findings by carrying out a series of functional experiments. SHMOOSE acted on the brain following intracerebroventricular administration, differentiated mitochondrial gene expression in multiple models, localized to mitochondria, bound the inner mitochondrial membrane protein mitofilin, and boosted mitochondrial oxygen consumption. Altogether, SHMOOSE has vast implications for the fields of neurobiology, Alzheimer’s disease, and microproteins.

关键词： Genetics Neuroscience Psychiatric disorders

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Metabolomics as molecular analysis tool in cell culture engineering

Metabolomics as molecular analysis tool in cell culture engi...

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作者： Vernardis, S.I. Chrysanthopoulos, P.K. Goudar, C.T. Klapa, M.I. Foundation for Research and Technology-Hellas Metabolic Engineering and Systems Biology Laboratory Institute of Chemical Engineering and High-Temperature Chemical Processes Patras GR- 265 04 Greece Cell Culture Development Global Biological Development Bayer HealthCare 800 Dwight Way Berkeley CA 94710 United States Interdepartmental Graduate Program in Informatics in Life Sciences Medical School University of Patras Greece Departments of Chemical and Biomolecular Engineering and Bioengineering University of Maryland College Park MD 20742 United States

ISBN: (纸本)9783902661708

So far, cell culture engineering has used the high-throughput transcriptional and more recently protein profiling techniques attempting to further understand and resolve significant issues regarding the cell culture performance and its optimization. At the metabolic level, flux analysis has been used to monitor changes in the metabolic state that could affect the successful outcome of cell cultures. Metabolomics, referring to the simultaneous quantification of the (relative) concentration of the free small metabolites, is the most recently introduced high-throughput method for the measurement of the metabolic fingerprint of a biological system. Considering the role of metabolism in the context of the overall cellular function, it is easily understandable why quantifying a complete and accurate metabolomic profile is foreseen to have a major positive impact in cell culture engineering research. We will present application of Gas Chromatorgaphy-Mass Spectrometry metabolomics to monitor and analyze mammalian cell culture systems. Specifically, two bioreactor processes will be discussed in which baby hamster kidney cells were cultivated in high-cell density perfusion reactors at both laboratory and manufacturing scales and samples were analyzed over the course of the cultivation. Metabolomics enabled the differentiation between cell cultures based on bioreactor type, and stage of cell growth. Multivariate statistical analysis enabled the identification of the metabolites whose change in concentration was characteristic of the modification in any of these macroscopic parameters. The acquired results support the usefulness of metabolomics as a molecular analysis tool in cell culture engineering. © 2010 IFAC.

关键词： Bioreactors

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Metabolomics and network biology for sensitive monitoring of how growth environment changes affect the physiology of industrial-scale perfusion cultures

Metabolomics and network biology for sensitive monitoring of...

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12th IFAC Symposium on Computer Applications in Biotechnology, CAB 2013

作者： Vernardis, S.I. Goudar, C.T. Klapa, M.I. Metabolic Engineering and Systems Biology Laboratory Institute of Chemical Engineering Foundation for Research and Technology-Hellas Patras GR-265 04 Greece Cell Culture Development Global Biological Development Bayer HealthCare 800 Dwight Way Berkeley CA 94710 United States Interdepartmental Graduate Program in Informatics in Life Sciences Medical School University of Patras Greece Cell Science and Technology AMGEN Inc. Thousand Oaks CA 91320 United States Depts. of Chemical and Biomolecular Eng. and Bioengineering University of Maryland College Park MD 20742 United States

ISBN: (纸本)9783902823588

Metabolomic analysis can indeed enhance the prime variable dataset for the monitoring of perfusion cultures by providing a higher resolution view of the metabolic state. Metabolic profiles can capture physiological state shifts over the course of the perfusion cultures and indicate a metabolic "signature" of the phase transitions, which is not observable from prime variable data. Notably, metabolomics provides orthogonal (to prime variables) evidence that all cultures follow this same metabolic state shift with cell age, independently of bioreactor scale. Additionally, this analysis can increase the information content of process development experiments by helping better understand the impact of changes in bioreactor operating conditions on cell physiology. In this context, metabolic profiling could be integrated into the monitoring of cell physiology in perfusion cultures. © IFAC.

关键词： Bioreactors

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Domain-guided data augmentation for deep learning on medical imaging

arXiv

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arXiv 2022年

作者： Athalye, Chinmayee Arnaout, Rima Department of Medicine Division of Cardiology Bakar Computational Health Sciences Institute University of California San Francisco United States Department of Medicine Division of Cardiology Bakar Computational Health Sciences Institute Chan Zuckerberg Biohub Intercampus Research Award Investigator Biological and Medical Informatics Graduate Program University of California San Francisco United States

While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Here, we test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found that using a context-preserving cut-paste strategy, we could create valid training data as measured by performance of the resulting trained model on the benchmark test dataset. When used in an online fashion, models trained on this data performed similarly to those trained using traditional data augmentation (FETAL-125 F-score 85.33 ± 0.24 vs 86.89 ± 0.60, p-value 0.0139;OB-125 F-score 74.60 ± 0.11 vs 72.43 ± 0.62, p-value 0.0039). Furthermore, the ability to perform augmentations during training time, as well as the ability to apply chosen augmentations equally across data classes, are important considerations in designing a bespoke data augmentation. Finally, we provide open-source code to facilitate running bespoke data augmentations in an online fashion. Taken together, this work expands the ability to design and apply domain-guided data augmentations for medical imaging tasks. © 2022, CC BY-NC-ND.

关键词： medical imaging

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Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

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Biomedical Data Science 1000年第1期5卷 293-320页

作者： Ying Wang Kristin Tsuo Masahiro Kanai Benjamin M. Neale Alicia R. Martin 2Stanley Center for Psychiatric Research and Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge Massachusetts USA 1Analytic and Translational Genetics Unit Massachusetts General Hospital Boston Massachusetts USA email: armartin@*** 3Biological and Biomedical Sciences Harvard Medical School Boston Massachusetts USA 4Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA 5Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan

Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.

关键词： polygenic risk scores (PRS) genetic risk prediction diverse ancestry populations PRS generalizability clinical translation of PRS

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Computational Methods for Single-Cell Proteomics

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Biomedical Data Science 1000年第1期6卷 47-71页

作者： Sophia M. Guldberg Trine Line Hauge Okholm Elizabeth E. McCarthy Matthew H. Spitzer 3Gladstone-UCSF Institute for Genomic Immunology San Francisco California USA 2Biomedical Sciences Graduate Program University of California San Francisco California USA 1Department of Otolaryngology–Head and Neck Surgery and Department of Microbiology and Immunology University of California San Francisco California USA email: matthew.spitzer@ucsf.edu 4Helen Diller Family Comprehensive Cancer Center University of California San Francisco California USA 5Institute for Human Genetics Division of Rheumatology Department of Medicine Medical Scientist Training Program and Biological and Medical Informatics Graduate Program University of California San Francisco California USA 6Parker Institute for Cancer Immunotherapy San Francisco California USA 7Chan Zuckerberg Biohub San Francisco California USA

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

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