arXiv - QuanBio - Genomics最新文献_第2页

Gene and RNA Editing: Methods, Enabling Technologies, Applications, and Future Directions 基因和 RNA 编辑：方法、赋能技术、应用和未来方向

arXiv - QuanBio - Genomics Pub Date : 2024-09-01 DOI: arxiv-2409.09057

Mohammed Aledhari, Mohamed Rahouti

{"title":"Gene and RNA Editing: Methods, Enabling Technologies, Applications, and Future Directions","authors":"Mohammed Aledhari, Mohamed Rahouti","doi":"arxiv-2409.09057","DOIUrl":"https://doi.org/arxiv-2409.09057","url":null,"abstract":"Gene and RNA editing methods, technologies, and applications are emerging as\u0000innovative forms of therapy and medicine, offering more efficient\u0000implementation compared to traditional pharmaceutical treatments. Current\u0000trends emphasize the urgent need for advanced methods and technologies to\u0000detect public health threats, including diseases and viral agents. Gene and RNA\u0000editing techniques enhance the ability to identify, modify, and ameliorate the\u0000effects of genetic diseases, disorders, and disabilities. Viral detection and\u0000identification methods present numerous opportunities for enabling\u0000technologies, such as CRISPR, applicable to both RNA and gene editing through\u0000the use of specific Cas proteins. This article explores the distinctions and\u0000benefits of RNA and gene editing processes, emphasizing their contributions to\u0000the future of medical treatment. CRISPR technology, particularly its adaptation\u0000via the Cas13 protein for RNA editing, is a significant advancement in gene\u0000editing. The article will delve into RNA and gene editing methodologies,\u0000focusing on techniques that alter and modify genetic coding. A-to-I and C-to-U\u0000editing are currently the most predominant methods of RNA modification. CRISPR\u0000stands out as the most cost-effective and customizable technology for both RNA\u0000and gene editing. Unlike permanent changes induced by cutting an individual's\u0000DNA genetic code, RNA editing offers temporary modifications by altering\u0000nucleoside bases in RNA strands, which can then attach to DNA strands as\u0000temporary modifiers.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis 用于单细胞 ATAC-seq 数据生成和分析的多功能信息扩散模型

arXiv - QuanBio - Genomics Pub Date : 2024-08-27 DOI: arxiv-2408.14801

Lei Huang, Lei Xiong, Na Sun, Zunpeng Liu, Ka-Chun Wong, Manolis Kellis

{"title":"A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis","authors":"Lei Huang, Lei Xiong, Na Sun, Zunpeng Liu, Ka-Chun Wong, Manolis Kellis","doi":"arxiv-2408.14801","DOIUrl":"https://doi.org/arxiv-2408.14801","url":null,"abstract":"The rapid advancement of single-cell ATAC sequencing (scATAC-seq)\u0000technologies holds great promise for investigating the heterogeneity of\u0000epigenetic landscapes at the cellular level. The amplification process in\u0000scATAC-seq experiments often introduces noise due to dropout events, which\u0000results in extreme sparsity that hinders accurate analysis. Consequently, there\u0000is a significant demand for the generation of high-quality scATAC-seq data in\u0000silico. Furthermore, current methodologies are typically task-specific, lacking\u0000a versatile framework capable of handling multiple tasks within a single model.\u0000In this work, we propose ATAC-Diff, a versatile framework, which is based on a\u0000latent diffusion model conditioned on the latent auxiliary variables to adapt\u0000for various tasks. ATAC-Diff is the first diffusion model for the scATAC-seq\u0000data generation and analysis, composed of auxiliary modules encoding the latent\u0000high-level variables to enable the model to learn the semantic information to\u0000sample high-quality data. Gaussian Mixture Model (GMM) as the latent prior and\u0000auxiliary decoder, the yield variables reserve the refined genomic information\u0000beneficial for downstream analyses. Another innovation is the incorporation of\u0000mutual information between observed and hidden variables as a regularization\u0000term to prevent the model from decoupling from latent variables. Through\u0000extensive experiments, we demonstrate that ATAC-Diff achieves high performance\u0000in both generation and analysis tasks, outperforming state-of-the-art models.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HEK-Omics: The promise of omics to optimize HEK293 for recombinant adeno-associated virus (rAAV) gene therapy manufacturing HEK-Omics：omics有望优化用于重组腺相关病毒（rAAV）基因治疗生产的HEK293

arXiv - QuanBio - Genomics Pub Date : 2024-08-23 DOI: arxiv-2408.13374

Sai Guna Ranjan Gurazada, Hannah M. Kennedy, Richard D. Braatz, Steven J. Mehrman, Shawn W. Polson, Irene T. Rombel

{"title":"HEK-Omics: The promise of omics to optimize HEK293 for recombinant adeno-associated virus (rAAV) gene therapy manufacturing","authors":"Sai Guna Ranjan Gurazada, Hannah M. Kennedy, Richard D. Braatz, Steven J. Mehrman, Shawn W. Polson, Irene T. Rombel","doi":"arxiv-2408.13374","DOIUrl":"https://doi.org/arxiv-2408.13374","url":null,"abstract":"Gene therapy is poised to transition from niche to mainstream medicine, with\u0000recombinant adeno-associated virus (rAAV) as the vector of choice. However,\u0000this requires robust, scalable, industrialized production to meet demand and\u0000provide affordable patient access, which has thus far failed to materialize.\u0000Closing the chasm between demand and supply requires innovation in\u0000biomanufacturing to achieve the essential step change in rAAV product yield and\u0000quality. Omics provides a rich source of mechanistic knowledge that can be\u0000applied to HEK293, the prevailing cell line for rAAV production. In this\u0000review, the findings from a growing number of disparate studies that apply\u0000genomics, epigenomics, transcriptomics, proteomics, and metabolomics to HEK293\u0000bioproduction are explored. Learnings from CHO-Omics, application of omics\u0000approaches to improve CHO bioproduction, provide context for the potential of\u0000\"HEK-Omics\" as a multiomics-informed approach providing actionable mechanistic\u0000insights for improved transient and stable production of rAAV and other\u0000recombinant products in HEK293.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Superimposed Hi-C: A Solution Proposed for Identifying Single Cell's Chromosomal Interactions 叠加 Hi-C：为识别单细胞染色体相互作用而提出的解决方案

arXiv - QuanBio - Genomics Pub Date : 2024-08-23 DOI: arxiv-2408.13039

Jia Zhang, Li Xiao, Peng Qi, Yaling Zeng, Xumeng Chen, Duan-fang Liao, Kai Li

引用次数: 0

Wave-LSTM: Multi-scale analysis of somatic whole genome copy number profiles Wave-LSTM：体细胞全基因组拷贝数图谱的多尺度分析

arXiv - QuanBio - Genomics Pub Date : 2024-08-22 DOI: arxiv-2408.12636

Charles Gadd, Christopher Yau

引用次数: 0

Comparison of algorithms used in single-cell transcriptomic data analysis 单细胞转录组数据分析所用算法的比较

arXiv - QuanBio - Genomics Pub Date : 2024-08-21 DOI: arxiv-2408.12031

Jafar Isbarov, Elmir Mahammadov

{"title":"Comparison of algorithms used in single-cell transcriptomic data analysis","authors":"Jafar Isbarov, Elmir Mahammadov","doi":"arxiv-2408.12031","DOIUrl":"https://doi.org/arxiv-2408.12031","url":null,"abstract":"Single-cell analysis is an increasingly relevant approach in \"omics''\u0000studies. In the last decade, it has been applied to various fields, including\u0000cancer biology, neuroscience, and, especially, developmental biology. This rise\u0000in popularity has been accompanied with creation of modern software,\u0000development of new pipelines and design of new algorithms. Many established\u0000algorithms have also been applied with varying levels of effectiveness.\u0000Currently, there is an abundance of algorithms for all steps of the general\u0000workflow. While some scientists use ready-made pipelines (such as Seurat),\u0000manual analysis is popular, too, as it allows more flexibility. Scientists who\u0000perform their own analysis face multiple options when it comes to the choice of\u0000algorithms. We have used two different datasets to test some of the most\u0000widely-used algorithms. In this paper, we are going to report the main\u0000differences between them, suggest a minimal number of algorithms for each step,\u0000and explain our suggestions. In certain stages, it is impossible to make a\u0000clear choice without further context. In these cases, we are going to explore\u0000the major possibilities, and make suggestions for each one of them.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-cell Curriculum Learning-based Deep Graph Embedding Clustering 基于单细胞课程学习的深度图嵌入式聚类

arXiv - QuanBio - Genomics Pub Date : 2024-08-20 DOI: arxiv-2408.10511

Huifa Li, Jie Fu, Xinpeng Ling, Zhiyu Sun, Kuncan Wang, Zhili Chen

{"title":"Single-cell Curriculum Learning-based Deep Graph Embedding Clustering","authors":"Huifa Li, Jie Fu, Xinpeng Ling, Zhiyu Sun, Kuncan Wang, Zhili Chen","doi":"arxiv-2408.10511","DOIUrl":"https://doi.org/arxiv-2408.10511","url":null,"abstract":"The swift advancement of single-cell RNA sequencing (scRNA-seq) technologies\u0000enables the investigation of cellular-level tissue heterogeneity. Cell\u0000annotation significantly contributes to the extensive downstream analysis of\u0000scRNA-seq data. However, The analysis of scRNA-seq for biological inference\u0000presents challenges owing to its intricate and indeterminate data distribution,\u0000characterized by a substantial volume and a high frequency of dropout events.\u0000Furthermore, the quality of training samples varies greatly, and the\u0000performance of the popular scRNA-seq data clustering solution GNN could be\u0000harmed by two types of low-quality training nodes: 1) nodes on the boundary; 2)\u0000nodes that contribute little additional information to the graph. To address\u0000these problems, we propose a single-cell curriculum learning-based deep graph\u0000embedding clustering (scCLG). We first propose a Chebyshev graph convolutional\u0000autoencoder with multi-decoder (ChebAE) that combines three optimization\u0000objectives corresponding to three decoders, including topology reconstruction\u0000loss of cell graphs, zero-inflated negative binomial (ZINB) loss, and\u0000clustering loss, to learn cell-cell topology representation. Meanwhile, we\u0000employ a selective training strategy to train GNN based on the features and\u0000entropy of nodes and prune the difficult nodes based on the difficulty scores\u0000to keep the high-quality graph. Empirical results on a variety of gene\u0000expression datasets show that our model outperforms state-of-the-art methods.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection 对增强型基因表达谱进行元学习以提高肺癌检测能力

arXiv - QuanBio - Genomics Pub Date : 2024-08-19 DOI: arxiv-2408.09635

Arya Hadizadeh Moghaddam, Mohsen Nayebi Kerdabadi, Cuncong Zhong, Zijun Yao

{"title":"Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection","authors":"Arya Hadizadeh Moghaddam, Mohsen Nayebi Kerdabadi, Cuncong Zhong, Zijun Yao","doi":"arxiv-2408.09635","DOIUrl":"https://doi.org/arxiv-2408.09635","url":null,"abstract":"Gene expression profiles obtained through DNA microarray have proven\u0000successful in providing critical information for cancer detection classifiers.\u0000However, the limited number of samples in these datasets poses a challenge to\u0000employ complex methodologies such as deep neural networks for sophisticated\u0000analysis. To address this \"small data\" dilemma, Meta-Learning has been\u0000introduced as a solution to enhance the optimization of machine learning models\u0000by utilizing similar datasets, thereby facilitating a quicker adaptation to\u0000target datasets without the requirement of sufficient samples. In this study,\u0000we present a meta-learning-based approach for predicting lung cancer from gene\u0000expression profiles. We apply this framework to well-established deep learning\u0000methodologies and employ four distinct datasets for the meta-learning tasks,\u0000where one as the target dataset and the rest as source datasets. Our approach\u0000is evaluated against both traditional and deep learning methodologies, and the\u0000results show the superior performance of meta-learning on augmented source data\u0000compared to the baselines trained on single datasets. Moreover, we conduct the\u0000comparative analysis between meta-learning and transfer learning methodologies\u0000to highlight the efficiency of the proposed approach in addressing the\u0000challenges associated with limited sample sizes. Finally, we incorporate the\u0000explainability study to illustrate the distinctiveness of decisions made by\u0000meta-learning.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum Annealing for Enhanced Feature Selection in Single-Cell RNA Sequencing Data Analysis 在单细胞 RNA 测序数据分析中增强特征选择的量子退火法

arXiv - QuanBio - Genomics Pub Date : 2024-08-16 DOI: arxiv-2408.08867

Selim Romero, Shreyan Gupta, Victoria Gatlin, Robert S. Chapkin, James J. Cai

{"title":"Quantum Annealing for Enhanced Feature Selection in Single-Cell RNA Sequencing Data Analysis","authors":"Selim Romero, Shreyan Gupta, Victoria Gatlin, Robert S. Chapkin, James J. Cai","doi":"arxiv-2408.08867","DOIUrl":"https://doi.org/arxiv-2408.08867","url":null,"abstract":"Feature selection is vital for identifying relevant variables in\u0000classification and regression models, especially in single-cell RNA sequencing\u0000(scRNA-seq) data analysis. Traditional methods like LASSO often struggle with\u0000the nonlinearities and multicollinearities in scRNA-seq data due to complex\u0000gene expression and extensive gene interactions. Quantum annealing, a form of\u0000quantum computing, offers a promising solution. In this study, we apply quantum\u0000annealing-empowered quadratic unconstrained binary optimization (QUBO) for\u0000feature selection in scRNA-seq data. Using data from a human cell\u0000differentiation system, we show that QUBO identifies genes with nonlinear\u0000expression patterns related to differentiation time, many of which play roles\u0000in the differentiation process. In contrast, LASSO tends to select genes with\u0000more linear expression changes. Our findings suggest that the QUBO method,\u0000powered by quantum annealing, can reveal complex gene expression patterns that\u0000traditional methods might overlook, enhancing scRNA-seq data analysis and\u0000interpretation.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks 通过 scRNA-seq 发现泛癌症基因组，优化基于深度学习的下游任务

arXiv - QuanBio - Genomics Pub Date : 2024-08-13 DOI: arxiv-2408.07233

Jong Hyun Kim, Jongseong Jang

{"title":"Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks","authors":"Jong Hyun Kim, Jongseong Jang","doi":"arxiv-2408.07233","DOIUrl":"https://doi.org/arxiv-2408.07233","url":null,"abstract":"The application of machine learning to transcriptomics data has led to\u0000significant advances in cancer research. However, the high dimensionality and\u0000complexity of RNA sequencing (RNA-seq) data pose significant challenges in\u0000pan-cancer studies. This study hypothesizes that gene sets derived from\u0000single-cell RNA sequencing (scRNA-seq) data will outperform those selected\u0000using bulk RNA-seq in pan-cancer downstream tasks. We analyzed scRNA-seq data\u0000from 181 tumor biopsies across 13 cancer types. High-dimensional weighted gene\u0000co-expression network analysis (hdWGCNA) was performed to identify relevant\u0000gene sets, which were further refined using XGBoost for feature selection.\u0000These gene sets were applied to downstream tasks using TCGA pan-cancer RNA-seq\u0000data and compared to six reference gene sets and oncogenes from OncoKB\u0000evaluated with deep learning models, including multilayer perceptrons (MLPs)\u0000and graph neural networks (GNNs). The XGBoost-refined hdWGCNA gene set\u0000demonstrated higher performance in most tasks, including tumor mutation burden\u0000assessment, microsatellite instability classification, mutation prediction,\u0000cancer subtyping, and grading. In particular, genes such as DPM1, BAD, and\u0000FKBP4 emerged as important pan-cancer biomarkers, with DPM1 consistently\u0000significant across tasks. This study presents a robust approach for feature\u0000selection in cancer genomics by integrating scRNA-seq data and advanced\u0000analysis techniques, offering a promising avenue for improving predictive\u0000accuracy in cancer research.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0