Ruhr University Bochum , Medical Faculty, Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany.
EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus , Hinxton, Cambridge , United Kingdom.
J Proteome Res. 2019 Feb 1;18(2):741-747. doi: 10.1021/acs.jproteome.8b00723. Epub 2018 Dec 5.
Proteomics using LC-MS/MS has become one of the main methods to analyze the proteins in biological samples in high-throughput. But the existing mass-spectrometry instruments are still limited with respect to resolution and measurable mass ranges, which is one of the main reasons why shotgun proteomics is the major approach. Here proteins are digested, which leads to the identification and quantification of peptides instead. While often neglected, the important step of protein inference needs to be conducted to infer from the identified peptides to the actual proteins in the original sample. In this work, we highlight some of the previously published and newly added features of the tool PIA - Protein Inference Algorithms, which helps the user with the protein inference of measured samples. We also highlight the importance of the usage of PSI standard file formats, as PIA is the only current software supporting all available standards used for spectrum identification and protein inference. Additionally, we briefly describe the benefits of working with workflow environments for proteomics analyses and show the new features of the PIA nodes for the KNIME Analytics Platform. Finally, we benchmark PIA against a recently published data set for isoform detection. PIA is open source and available for download on GitHub ( https://github.com/mpc-bioinformatics/pia ) or directly via the community extensions inside the KNIME analytics platform.
基于 LC-MS/MS 的蛋白质组学已经成为高通量分析生物样品中蛋白质的主要方法之一。但现有的质谱仪器在分辨率和可测量的质量范围方面仍然有限,这也是 shotgun 蛋白质组学成为主要方法的主要原因之一。在这里,蛋白质被消化,从而导致肽的鉴定和定量,而这一重要步骤往往被忽视,需要进行蛋白质推断,从鉴定的肽推断到原始样品中的实际蛋白质。在这项工作中,我们强调了工具 PIA - Protein Inference Algorithms 的一些先前发表的和新添加的功能,这有助于用户对测量样本进行蛋白质推断。我们还强调了使用 PSI 标准文件格式的重要性,因为 PIA 是唯一支持用于光谱识别和蛋白质推断的所有可用标准的当前软件。此外,我们简要描述了在蛋白质组学分析中使用工作流环境的好处,并展示了 KNIME Analytics Platform 中 PIA 节点的新功能。最后,我们针对最近发表的用于异构体检测的数据集对 PIA 进行了基准测试。PIA 是开源的,可在 GitHub(https://github.com/mpc-bioinformatics/pia)上下载,也可直接在 KNIME analytics 平台内的社区扩展中下载。