SAINT: probabilistic scoring of affinity purification-mass spectrometry data

被引：439

作者：

Choi, Hyungwon ^{[2
]}

Larsen, Brett ^{[1
]}

Lin, Zhen-Yuan ^{[1
]}

Breitkreutz, Ashton ^{[1
]}

Mellacheruvu, Dattatreya ^{[2
]}

Fermin, Damian ^{[2
]}

Qin, Zhaohui S. ^{[3
,4
]}

Tyers, Mike ^{[1
,5
,6
,7
]}

Gingras, Anne-Claude ^{[1
,5
]}

Nesvizhskii, Alexey I. ^{[2
,8
]}

机构：

[1] Mt Sinai Hosp, Samuel Lunenfeld Res Inst, Ctr Syst Biol, Toronto, ON M5G 1X5, Canada

[2] Univ Michigan, Dept Pathol, Ann Arbor, MI 48109 USA

[3] Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA

[4] Univ Michigan, Ctr Stat Genet, Ann Arbor, MI 48109 USA

[5] Univ Toronto, Dept Mol Genet, Toronto, ON, Canada

[6] Univ Edinburgh, Wellcome Trust Ctr Cell Biol, Edinburgh, Midlothian, Scotland

[7] Univ Edinburgh, Sch Biol Sci, Ctr Syst Biol, Edinburgh, Midlothian, Scotland

[8] Univ Michigan, Ctr Computat Med & Bioinformat, Ann Arbor, MI 48109 USA

来源：

NATURE METHODS | 2011年 / 8卷 / 01期

基金：

美国国家卫生研究院;

关键词：

PROTEIN-INTERACTION NETWORKS; QUANTITATIVE PROTEOMICS; LABEL-FREE; REVEALS; YEAST;

D O I：

10.1038/NMETH.1541

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We present 'significance analysis of interactome' (SAINT), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity purification-mass spectrometry (AP-MS). The method uses label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We show that SAINT is applicable to data of different scales and protein connectivity and allows transparent analysis of AP-MS data.

引用

页码：70 / U100

页数：6

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