来自杂志 Briefings in Bioinformatics 的文献。
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1.
徐炳祥 (2025-08-29 16:01):
#paper doi: 10.1093/bib/bbaf074 Briefings in Bioinformatics, 2025, A comprehensive review and benchmark of differential analysis tools for Hi-C data。Hi-C作为研究染色质空间构象的主流技术,其差异分析对研究染色质空间构象在基因表达调控过程中的作用至关重要,然而目前缺乏得到广泛认可的此类工具。本文对10种Hi-C数据差异分析工具进行了全面的综述与性能评估,重点关注它们在识别不同条件下染色质互作差异方面的统计方法、可用性和实际表现。研究通过半模拟数据和真实CTCF缺失实验进行测试,发现工具在预处理过滤、标准化步骤上存在显著差异,且多数工具难以有效控制错误发现率(FDR)。结果表明,diffHic 在整体性能上表现最佳,能较好地控制I类错误并保持较高的检测效能;而基于二维空间结构的工具(如FIND、Selfish)并未显示出预期优势。文章强调预处理步骤对结果影响巨大,并指出当前工具尚不支持复杂实验设计(如配对或重复测量数据),呼吁开发更稳健且灵活的分析方法。
Abstract:
Abstract Motivation The 3D organization of the genome plays a crucial role in various biological processes. Hi-C technology is widely used to investigate chromosome structures by quantifying 3D proximity between … >>>
Abstract Motivation The 3D organization of the genome plays a crucial role in various biological processes. Hi-C technology is widely used to investigate chromosome structures by quantifying 3D proximity between genomic regions. While numerous computational tools exist for detecting differences in Hi-C data between conditions, a comprehensive review and benchmark comparing their effectiveness is lacking. Results This study offers a comprehensive review and benchmark of 10 generic tools for differential analysis of Hi-C matrices at the interaction count level. The benchmark assesses the statistical methods, usability, and performance (in terms of precision and power) of these tools, using both real and simulated Hi-C data. Results reveal a striking variability in performance among the tools, highlighting the substantial impact of preprocessing filters and the difficulty all tools encounter in effectively controlling the false discovery rate across varying resolutions and chromosome sizes. Availability The complete benchmark is available at https://forgemia.inra.fr/scales/replication-chrocodiff using processed data deposited at https://doi.org/10.57745/LR0W9R. Contact nathalie.vialaneix@inrae.fr <<<
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前进 (2024-11-30 23:26):
# paper DOI: [10.1093/bib/bbv088](https://doi.org/10.1093/bib/bbv088) A Comparison of Base-calling Algorithms for Illumina Sequencing Technology 这篇论文主要讲述了Illumina测序技术中用于basecalling的不同算法的性能比较。文章提供了一个综合的比较分析,涵盖了多种最近开发的bascalling算法,并提出了一个统一的统计模型,该模型能够涵盖大多数现有的basecall算法。研究的目的在于通过比较这些算法在处理Illumina平台产生的测序数据时的准确性和效率,来帮助科研人员选择最适合他们需求的basecall工具。论文中提到的算法包括Bustard、Srfim、AYB、Ibis和freeIbis等,并通过实验数据评估了它们的对齐率、错误率和区分能力。通过这些比较,论文旨在为高通量测序数据分析中basecall步骤提供指导和建议。
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