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首页> 外文会议>2013 8th International Symposium on Health Informatics and Bioinformatics >Ranking tandem mass spectra: And the impact of database size and scoring function on peptide spectrum matches
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Ranking tandem mass spectra: And the impact of database size and scoring function on peptide spectrum matches

机译：对串联质谱进行排名：数据库大小和评分功能对肽谱匹配的影响

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摘要

Proteomics is currently driven by mass spectrometry. For the analysis of tandem mass spectra many computational algorithms have been proposed. There are two approaches, one which assigns a peptide sequence to a tandem mass spectrum directly and one which employs a sequence database for looking up possible solutions. The former method needs high quality spectra while the latter can tolerate lower quality spectra. Since both methods are computationally expensive, it is sensible to establish spectral quality using an independent fast algorithm. In this study, we first establish proper settings for database search algorithms for the analysis of spectra in our gold benchmark dataset and then analyze the performance of ScanRanker, an algorithm for quality assessment of tandem MS spectra, on this ground truth data. We found that OMSSA and MSGFDB have limitations in their scoring functions but were able to form a proper consensus prediction using majority vote for our benchmark data. Unfortunately, ScanRanker's results do not correlate well with the consensus and ScanRanker is also too slow to be used in the capacity it is supposed to be used.
机译：蛋白质组学目前由质谱驱动。为了分析串联质谱，已经提出了许多计算算法。有两种方法，一种是将肽序列直接分配给串联质谱，另一种是使用序列数据库查找可能的溶液。前一种方法需要高质量的光谱，而后者可以忍受较低质量的光谱。由于两种方法的计算量都很大，因此使用独立的快速算法来建立频谱质量是明智的。在这项研究中，我们首先为数据库搜索算法建立适当的设置，以分析黄金基准数据集中的光谱，然后根据该地面真实数据分析ScanRanker（一种用于串联MS质谱图质量评估的算法）的性能。我们发现OMSSA和MSGFDB在评分功能上有局限性，但能够使用多数票作为基准数据来形成适当的共识预测。不幸的是，ScanRanker的结果与共识并没有很好的关联，并且ScanRanker的速度也太慢而无法使用。

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