Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Institut Pasteur, Structural Mass Spectrometry and Proteomics Unit, Paris, France.
Bioinformatics. 2017 Jun 15;33(12):1883-1885. doi: 10.1093/bioinformatics/btx093.
Around 75% of all mass spectra remain unidentified by widely adopted proteomic strategies. We present DiagnoProt, an integrated computational environment that can efficiently cluster millions of spectra and use machine learning to shortlist high-quality unidentified mass spectra that are discriminative of different biological conditions.
We exemplify the use of DiagnoProt by shortlisting 4366 high-quality unidentified tandem mass spectra that are discriminative of different types of the Aspergillus fungus.
DiagnoProt, a demonstration video and a user tutorial are available at http://patternlabforproteomics.org/diagnoprot .
andrerfsilva@gmail.com or paulo@pcarvalho.com.
Supplementary data are available at Bioinformatics online.
约 75%的所有质谱仍然无法通过广泛采用的蛋白质组学策略进行鉴定。我们提出了 DiagnoProt，这是一个集成的计算环境，可以有效地对数百万个光谱进行聚类，并使用机器学习来筛选出高质量的、可区分不同生物条件的未识别质谱。
我们通过筛选出 4366 条可区分不同类型的曲霉菌的高质量未识别串联质谱来举例说明 DiagnoProt 的使用。
DiagnoProt、演示视频和用户教程可在 http://patternlabforproteomics.org/diagnoprot 上获得。
andrerfsilva@gmail.com 或 paulo@pcarvalho.com。
补充数据可在 Bioinformatics 在线获得。
