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Automatic classification of single-molecule force spectroscopy traces from heterogeneous samples.
Bioinformatics ( IF 5.8 ) Pub Date : 2020-07-11 , DOI: 10.1093/bioinformatics/btaa626
Nina I Ilieva 1 , Nicola Galvanetto 1 , Michele Allegra 1, 2 , Marco Brucale 3 , Alessandro Laio 1, 4
Affiliation  

Single-molecule force spectroscopy (SMFS) experiments pose the challenge of analyzing protein unfolding data (traces) coming from preparations with heterogeneous composition (e.g. where different proteins are present in the sample). An automatic procedure able to distinguish the unfolding patterns of the proteins is needed. Here, we introduce a data analysis pipeline able to recognize in such datasets traces with recurrent patterns (clusters).

中文翻译:

自动分类来自异质样品的单分子力谱痕迹。

单分子力谱(SMFS)实验带来了挑战,分析来自具有不同组成的样品(例如样品中存在不同蛋白质的地方)的蛋白质展开数据(痕量)。需要能够区分蛋白质的展开模式的自动程序。在这里,我们介绍了一种数据分析管道,该管道能够在此类数据集中识别具有重复模式(簇)的迹线。
更新日期:2020-07-13
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