Overcoming the Limitations Posed by TCR-beta Repertoire Modeling through a GPU-Based In-Silico DNA Recombination Algorithm

Gregory M. Striemer, Harsha Krovi, A. Akoglu, B. Vincent, Benjamin Hopson, J. Frelinger, Adam Buntzman
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引用次数: 3

Abstract

The DNA recombination process known as V(D)J recombination is the central mechanism for generating diversity among antigen receptors such as T-cell receptors (TCRs). This diversity is crucial for the development of the adaptive immune system. However, modeling of all the α β TCR sequences is encumbered by the enormity of the potential repertoire, which has been predicted to exceed 1015 sequences. Prior modeling efforts have, therefore, been limited to extrapolations based on the analysis of minor subsets of the overall TCRbeta repertoire. In this study, we map the recombination process completely onto the graphics processing unit (GPU) hardware architecture using the CUDA programming environment to circumvent prior limitations. For the first time, we present a model of the mouse TCRbeta repertoire to an extent which enabled us to evaluate the Convergent Recombination Hypothesis (CRH) comprehensively at peta-scale level on a single GPU.
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基于gpu的DNA重组算法克服TCR-beta库建模的局限性
被称为V(D)J重组的DNA重组过程是抗原受体如t细胞受体(tcr)之间产生多样性的主要机制。这种多样性对适应性免疫系统的发展至关重要。然而,所有α β TCR序列的建模都受到潜在库的巨大影响,预计超过1015个序列。因此,先前的建模工作仅限于基于整体TCRbeta曲目的次要子集的分析的外推。在本研究中,我们使用CUDA编程环境将重组过程完全映射到图形处理单元(GPU)硬件架构上,以绕过先前的限制。我们首次提出了小鼠TCRbeta库的模型,使我们能够在单个GPU上全面评估收敛重组假说(CRH)的peta级水平。
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