Aoshima Ken, Takahashi Kentaro, Ikawa Masayuki, Kimura Takayuki, Fukuda Mitsuru, Tanaka Satoshi, Parry Howell E, Fujita Yuichiro, Yoshizawa Akiyasu C, Utsunomiya Shin-Ichi, Kajihara Shigeki, Tanaka Koichi, Oda Yoshiya
Eisai Co., Ltd., Tsukuba, Ibaraki, 300-2635, Japan.
iBio Tech Co., Ltd., Tsukuba, Ibaraki, 305-0031, Japan.
BMC Bioinformatics. 2014 Nov 25;15(1):376. doi: 10.1186/s12859-014-0376-0.
Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study.
We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools.
AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.
质谱数据的无标记定量是蛋白质和代谢物差异表达谱分析中最简单且成本最低的方法之一。在生物标志物和药物发现研究领域,对高精度和高性能的无标记计算定量方法的需求仍然很高。然而,最近最先进的液相色谱 - 质谱联用仪(LC-MS)以高扫描速度、高精度和分辨率产生大量分析数据,这通常无法手动解读。此外,最近的无标记方法仍有有待改进的问题,例如如何减少候选峰的假阳性/假阴性、如何扩展可扩展性以及如何增强和自动化数据处理。AB3D(一种使用LC-MS在诊断和药物发现中进行生物标志物发现的简单无标记定量算法)解决了这些问题,并具有使用MS1进行蛋白质组学研究的无标记定量能力。
我们开发了一种名为AB3D的算法,这是一种使用MS1光谱数据的无标记峰检测和定量算法。为了测试我们的算法,使用3个数据集评估了AB3D在LC-MS数据集上的实际应用。然后使用相同的LC-MS数据集,在广泛使用的软件工具(如MZmine 2、MSight、SuperHirn、OpenMS)和我们的算法AB3D之间进行比较。所有定量结果均经过人工确认,我们发现与其他四个现有软件工具相比,使用标准肽混合物或五日热巴尔通体(菌株JK31)的真实复杂生物样品时,AB3D能够以更少的假阳性和假阴性正确识别和定量已知肽段。此外,通过比较使用HeLa和BSA样品的复杂肽混合物的两个技术重复之间的变异性,AB3D显示出最佳的可靠性。在性能方面,AB3D算法比其他四个现有软件工具快约1.2至15倍。
AB3D是一种简单快速的算法,用于使用MS1质谱数据进行无标记定量,可用于大规模LC-MS数据分析,具有较高的真阳性率和合理的假阳性率。此外,AB3D表现出最佳的重现性,并且比现有的4个软件工具快约1.2至15倍。