arXiv - QuanBio - Genomics最新文献

Allium Vegetables Intake and Digestive System Cancer Risk: A Study Based on Mendelian Randomization, Network Pharmacology and Molecular Docking 葱类蔬菜摄入量与消化系统癌症风险：基于孟德尔随机化、网络药理学和分子对接的研究

arXiv - QuanBio - Genomics Pub Date : 2024-09-16 DOI: arxiv-2409.11187

Shuhao Li, Jingwen Lou, Yelina Mulatihan, Yuhang Xiong, Yao Li, Qi Xu

{"title":"Allium Vegetables Intake and Digestive System Cancer Risk: A Study Based on Mendelian Randomization, Network Pharmacology and Molecular Docking","authors":"Shuhao Li, Jingwen Lou, Yelina Mulatihan, Yuhang Xiong, Yao Li, Qi Xu","doi":"arxiv-2409.11187","DOIUrl":"https://doi.org/arxiv-2409.11187","url":null,"abstract":"Background: Allium vegetables (garlic and onion) are one of the flavorings in\u0000people's daily diets. Observational studies suggest that intake of allium\u0000vegetables may be correlated with a lower incidence of digestive system\u0000cancers. However, the existence of a causal relationship is still controversial\u0000due to confounding factors and reverse causation. Therefore, we explored the\u0000causal relationship between intake of allium vegetables and digestive system\u0000cancers using Mendelian randomization approach. Methods: First, we performed\u0000Mendelian randomization analyses using inverse variance weighting (IVW),\u0000weighted median, and MR-Egger approaches, and demonstrated the reliability of\u0000the results in the sensitivity step. Second, Multivariable Mendelian\u0000randomization was applied to adjust for smoking and alcohol consumption. Third,\u0000we explored the molecular mechanisms behind the positive results through\u0000network pharmacology and molecular docking methods. Results: The study suggests\u0000that increased intake of garlic reduced gastric cancer risk. However, onion\u0000intake was not statistically associated with digestive system cancer.\u0000Conclusion: Garlic may have a protective effect against gastric cancer.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255500","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

wgatools: an ultrafast toolkit for manipulating whole genome alignments wgatools：操作全基因组比对的超快工具包

arXiv - QuanBio - Genomics Pub Date : 2024-09-13 DOI: arxiv-2409.08569

Wenjie Wei, Songtao Gui, Jian Yang, Erik Garrison, Jianbing Yan, Hai-Jun Liu

引用次数: 0

Selecting Differential Splicing Methods: Practical Considerations 选择差分剪接方法：实际考虑因素

arXiv - QuanBio - Genomics Pub Date : 2024-09-09 DOI: arxiv-2409.05458

Ben J Draper, Mark J Dunning, David C James

{"title":"Selecting Differential Splicing Methods: Practical Considerations","authors":"Ben J Draper, Mark J Dunning, David C James","doi":"arxiv-2409.05458","DOIUrl":"https://doi.org/arxiv-2409.05458","url":null,"abstract":"Alternative splicing is crucial in gene regulation, with significant\u0000implications in clinical settings and biotechnology. This review article\u0000compiles bioinformatics RNA-seq tools for investigating differential splicing;\u0000offering a detailed examination of their statistical methods, case\u0000applications, and benefits. A total of 22 tools are categorised by their\u0000statistical family (parametric, non-parametric, and probabilistic) and level of\u0000analysis (transcript, exon, and event). The central challenges in quantifying\u0000alternative splicing include correct splice site identification and accurate\u0000isoform deconvolution of transcripts. Benchmarking studies show no consensus on\u0000tool performance, revealing considerable variability across different\u0000scenarios. Tools with high citation frequency and continued developer\u0000maintenance, such as DEXSeq and rMATS, are recommended for prospective\u0000researchers. To aid in tool selection, a guide schematic is proposed based on\u0000variations in data input and the required level of analysis. Additionally,\u0000advancements in long-read RNA sequencing are expected to drive the evolution of\u0000differential splicing tools, reducing the need for isoform deconvolution and\u0000prompting further innovation.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":"114 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180948","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

Advancements in practical k-mer sets: essentials for the curious 实用 k-mer 集的进展：好奇者的必备知识

arXiv - QuanBio - Genomics Pub Date : 2024-09-08 DOI: arxiv-2409.05210

Camille Marchet

引用次数: 0

Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration 基于机器学习预测与年龄相关性黄斑变性小鼠模型中视网膜下病变严重程度相关的关键基因

arXiv - QuanBio - Genomics Pub Date : 2024-09-08 DOI: arxiv-2409.05047

Kuan Yan, Yue Zeng, Dai Shi, Ting Zhang, Dmytro Matsypura, Mark C. Gillies, Ling Zhu, Junbin Gao

{"title":"Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration","authors":"Kuan Yan, Yue Zeng, Dai Shi, Ting Zhang, Dmytro Matsypura, Mark C. Gillies, Ling Zhu, Junbin Gao","doi":"arxiv-2409.05047","DOIUrl":"https://doi.org/arxiv-2409.05047","url":null,"abstract":"Age-related macular degeneration (AMD) is a major cause of blindness in older\u0000adults, severely affecting vision and quality of life. Despite advances in\u0000understanding AMD, the molecular factors driving the severity of subretinal\u0000scarring (fibrosis) remain elusive, hampering the development of effective\u0000therapies. This study introduces a machine learning-based framework to predict\u0000key genes that are strongly correlated with lesion severity and to identify\u0000potential therapeutic targets to prevent subretinal fibrosis in AMD. Using an\u0000original RNA sequencing (RNA-seq) dataset from the diseased retinas of JR5558\u0000mice, we developed a novel and specific feature engineering technique,\u0000including pathway-based dimensionality reduction and gene-based feature\u0000expansion, to enhance prediction accuracy. Two iterative experiments were\u0000conducted by leveraging Ridge and ElasticNet regression models to assess\u0000biological relevance and gene impact. The results highlight the biological\u0000significance of several key genes and demonstrate the framework's effectiveness\u0000in identifying novel therapeutic targets. The key findings provide valuable\u0000insights for advancing drug discovery efforts and improving treatment\u0000strategies for AMD, with the potential to enhance patient outcomes by targeting\u0000the underlying genetic mechanisms of subretinal lesion development.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180951","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

Advancements in colored k-mer sets: essentials for the curious 彩色 k-mer 集的进展：好奇者的必备知识

arXiv - QuanBio - Genomics Pub Date : 2024-09-08 DOI: arxiv-2409.05214

Camille Marchet

引用次数: 0

Nearest Neighbor CCP-Based Molecular Sequence Analysis 基于近邻 CCP 的分子序列分析

arXiv - QuanBio - Genomics Pub Date : 2024-09-07 DOI: arxiv-2409.04922

Sarwan Ali, Prakash Chourasia, Bipin Koirala, Murray Patterson

{"title":"Nearest Neighbor CCP-Based Molecular Sequence Analysis","authors":"Sarwan Ali, Prakash Chourasia, Bipin Koirala, Murray Patterson","doi":"arxiv-2409.04922","DOIUrl":"https://doi.org/arxiv-2409.04922","url":null,"abstract":"Molecular sequence analysis is crucial for comprehending several biological\u0000processes, including protein-protein interactions, functional annotation, and\u0000disease classification. The large number of sequences and the inherently\u0000complicated nature of protein structures make it challenging to analyze such\u0000data. Finding patterns and enhancing subsequent research requires the use of\u0000dimensionality reduction and feature selection approaches. Recently, a method\u0000called Correlated Clustering and Projection (CCP) has been proposed as an\u0000effective method for biological sequencing data. The CCP technique is still\u0000costly to compute even though it is effective for sequence visualization.\u0000Furthermore, its utility for classifying molecular sequences is still\u0000uncertain. To solve these two problems, we present a Nearest Neighbor\u0000Correlated Clustering and Projection (CCP-NN)-based technique for efficiently\u0000preprocessing molecular sequence data. To group related molecular sequences and\u0000produce representative supersequences, CCP makes use of sequence-to-sequence\u0000correlations. As opposed to conventional methods, CCP doesn't rely on matrix\u0000diagonalization, therefore it can be applied to a range of machine-learning\u0000problems. We estimate the density map and compute the correlation using a\u0000nearest-neighbor search technique. We performed molecular sequence\u0000classification using CCP and CCP-NN representations to assess the efficacy of\u0000our proposed approach. Our findings show that CCP-NN considerably improves\u0000classification task accuracy as well as significantly outperforms CCP in terms\u0000of computational runtime.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":"2017 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180952","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

Toward Capturing Genetic Epistasis From Multivariate Genome-Wide Association Studies Using Mixed-Precision Kernel Ridge Regression 利用混合精度核岭回归从多变量全基因组关联研究中捕捉遗传外显性

arXiv - QuanBio - Genomics Pub Date : 2024-09-03 DOI: arxiv-2409.01712

Hatem Ltaief, Rabab Alomairy, Qinglei Cao, Jie Ren, Lotfi Slim, Thorsten Kurth, Benedikt Dorschner, Salim Bougouffa, Rached Abdelkhalak, David E. Keyes

{"title":"Toward Capturing Genetic Epistasis From Multivariate Genome-Wide Association Studies Using Mixed-Precision Kernel Ridge Regression","authors":"Hatem Ltaief, Rabab Alomairy, Qinglei Cao, Jie Ren, Lotfi Slim, Thorsten Kurth, Benedikt Dorschner, Salim Bougouffa, Rached Abdelkhalak, David E. Keyes","doi":"arxiv-2409.01712","DOIUrl":"https://doi.org/arxiv-2409.01712","url":null,"abstract":"We exploit the widening margin in tensor-core performance between\u0000[FP64/FP32/FP16/INT8,FP64/FP32/FP16/FP8/INT8] on NVIDIA [Ampere,Hopper] GPUs to\u0000boost the performance of output accuracy-preserving mixed-precision computation\u0000of Genome-Wide Association Studies (GWAS) of 305K patients from the UK BioBank,\u0000the largest-ever GWAS cohort studied for genetic epistasis using a multivariate\u0000approach. Tile-centric adaptive-precision linear algebraic techniques motivated\u0000by reducing data motion gain enhanced significance with low-precision GPU\u0000arithmetic. At the core of Kernel Ridge Regression (KRR) techniques for GWAS\u0000lie compute-bound cubic-complexity matrix operations that inhibit scaling to\u0000aspirational dimensions of the population, genotypes, and phenotypes. We\u0000accelerate KRR matrix generation by redesigning the computation for Euclidean\u0000distances to engage INT8 tensor cores while exploiting symmetry.We accelerate\u0000solution of the regularized KRR systems by deploying a new four-precision\u0000Cholesky-based solver, which, at 1.805 mixed-precision ExaOp/s on a nearly full\u0000Alps system, outperforms the state-of-the-art CPU-only REGENIE GWAS software by\u0000five orders of magnitude.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180954","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

CMOB: Large-Scale Cancer Multi-Omics Benchmark with Open Datasets, Tasks, and Baselines CMOB：具有开放数据集、任务和基线的大规模癌症多指标基准测试

arXiv - QuanBio - Genomics Pub Date : 2024-09-02 DOI: arxiv-2409.02143

Ziwei Yang, Rikuto Kotoge, Zheng Chen, Xihao Piao, Yasuko Matsubara, Yasushi Sakurai

{"title":"CMOB: Large-Scale Cancer Multi-Omics Benchmark with Open Datasets, Tasks, and Baselines","authors":"Ziwei Yang, Rikuto Kotoge, Zheng Chen, Xihao Piao, Yasuko Matsubara, Yasushi Sakurai","doi":"arxiv-2409.02143","DOIUrl":"https://doi.org/arxiv-2409.02143","url":null,"abstract":"Machine learning has shown great potential in the field of cancer multi-omics\u0000studies, offering incredible opportunities for advancing precision medicine.\u0000However, the challenges associated with dataset curation and task formulation\u0000pose significant hurdles, especially for researchers lacking a biomedical\u0000background. Here, we introduce the CMOB, the first large-scale cancer\u0000multi-omics benchmark integrates the TCGA platform, making data resources\u0000accessible and usable for machine learning researchers without significant\u0000preparation and expertise.To date, CMOB includes a collection of 20 cancer\u0000multi-omics datasets covering 32 cancers, accompanied by a systematic data\u0000processing pipeline. CMOB provides well-processed dataset versions to support\u000020 meaningful tasks in four studies, with a collection of benchmarks. We also\u0000integrate CMOB with two complementary resources and various biological tools to\u0000explore broader research avenues.All resources are open-accessible with\u0000user-friendly and compatible integration scripts that enable non-experts to\u0000easily incorporate this complementary information for various tasks. We conduct\u0000extensive experiments on selected datasets to offer recommendations on suitable\u0000machine learning baselines for specific applications. Through CMOB, we aim to\u0000facilitate algorithmic advances and hasten the development, validation, and\u0000clinical translation of machine-learning models for personalized cancer\u0000treatments. CMOB is available on GitHub\u0000(url{https://github.com/chenzRG/Cancer-Multi-Omics-Benchmark}).","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180953","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

BWT construction and search at the terabase scale 在太基准尺度上构建和搜索 BWT

arXiv - QuanBio - Genomics Pub Date : 2024-09-01 DOI: arxiv-2409.00613

Heng Li

引用次数: 0