上周我们公布了,蛋白质组学习小组起飞啦! 短短几天就获得了250多小伙伴的支持,让我们也更有信心的带领大家掌握一个蛋白质组学数据处理的实战,前面两期我们分享的是:
导入数据 设置 Group Experment fraction
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(1)数据库位置 ftp://massive.ucsd.edu/MSV000079904/sequence/
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(2)将上述两个fasta文件合并成一个文件,格式仍然是fasta格式
(3)在Maxquant中加库:
除了设置了LFQ,其他都是采用默认参数。参考文章 The MaxQuant computational platform for mass spectrometry–based shotgun proteomics 已经放在微云盘
(1)Precursor mass tolerance was set to 4.5 ppm in the main search, and fragment mass tolerance was set to 20 ppm. 质控
(2)Digestion enzyme specificity was set to Trypsin/P with a maximum of 2 missed cleav-ages. 做实验时用了什么酶切,漏切越小越好,越准确
(3)A minimum peptide length of 7 residues was required for identification. 最小的肽段长度为7,太短的假阳性率高
(4)Up to 5 modifications per peptide were allowed; acety-lation (protein N-terminal) and oxidation (Met) were set as variable modifications, and carbamidomethyl (Cys) was set as a fixed modifi-cation. 本文中加的都是肽段常有修饰,不针对其他修饰分析
(5)No Andromeda score threshold was set for unmodified peptides. A minimum Andromeda score of 40 was required for modified peptides. 每一个鉴定到的修饰都有一个打分Andromeda score最小40, 越大越好
(6)Peptide and protein false discovery rates (FDR) were both set to 1% based off a target-decoy reverse database. 假阳性率
(7)“Match between runs” based on accurate m/z and retention time was enabled with a 0.7 min match time window and 20 min alignment time window 。样本和样本之间每一个峰的洗脱时间尽量平行
(8)参数设置好,点击开始
(1)Windows 下 设置参数后,文件开始后会在文件存放的路径生成 一个数据文件夹,一个参数文件。
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(2)linux 的同学,mqpar 文件可以导入到Linux下,更改文件路径,直接运行
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