使用MapReduce在多个细胞系中进行基因表达建模
Gene expression modelling across multiple cell-lines with MapReduce
摘要：
随着近年来大规模联合体产生的大量高通量测序数据，预测性基因表达建模已成为转录组学和表观遗传学数据综合分析的重要工具。然而，测序数据集通常很大，并且以前的建模框架通常效率低下，无法利用多核或分布式处理架构。在这项研究中，我们详细介绍了一个高效和并行MapReduce实现的基因表达建模。我们利用该框架的计算效率，对各种癌细胞系和非癌细胞系的50多个组蛋白修饰数据集进行综合分析。我们的研究结果表明，组蛋白修饰和mRNA转录之间的全基因组关系是谱系，组织和核型不变的，并且在来自非癌细胞系的匹配表观遗传/转录组数据上训练的模型能够以等同的全基因组保真度预测癌性表达。
英文摘要：
With the wealth of high-throughput sequencing data generated by recent large-scale consortia, predictive gene expression modelling has become an important tool for integrative analysis of transcriptomic and epigenetic data. However, sequencing data-sets are characteristically large, and previously modelling frameworks are typically inefficient and unable to leverage multi-core or distributed processing architectures. In this study, we detail an efficient and parallelised MapReduce implementation of gene expression modelling. We leverage the computational efficiency of this framework to provide an integrative analysis of over fifty histone modification data-sets across a variety of cancerous and non-cancerous cell-lines. Our results demonstrate that the genome-wide relationships between histone modifications and mRNA transcription are lineage, tissue and karyotype-invariant, and that models trained on matched epigenetic/transcriptomic data from non-cancerous cell-lines are able to predict cancerous expression with equivalent genome-wide fidelity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
