SAINTexpress: improvements and additional features in Significance Analysis of Interactome software

By Guoci Teo, Guomin Liu, Jianping Zhang, Alexey I. Nesvizhskii, A. Gingras, Hyungwon Choi. Journal of Proteomics, 2013

612 citations recorded by the source corpus.

Significance Analysis of INTeractome (SAINT) is a statistical method for probabilistically scoring protein-protein interaction data from affinity purification-mass spectrometry (AP-MS) experiments. The utility of the software has been demonstrated in many protein-protein interaction mapping studies, yet the extensive testing also revealed some practical drawbacks. In this paper, we present a new implementation, SAINTexpress, with a simpler statistical model and a quicker scoring algorithm, leading to significant improvements in computational speed and sensitivity of scoring. SAINTexpress also incorporates external interaction data to compute a supplemental topology-based score to improve the likelihood of identifying co-purifying protein complexes in a probabilistically objective manner. Overall, these changes are expected to improve the performance and user experience of SAINT across various types of high quality datasets.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/0e34a2422f151658918b6974e255dd76f8e231de

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Spectroscopy

Citations in the corpus

The functional landscape of the human phosphoproteome (2019, methodology): “Raw data was searched using MaxQuant (Cox and Mann 2008), and high confidence protein-protein interactions were scored using SAINTexpress (Teo et al. 2014).”
High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies. (2018, methodology): “SAINTexpress (Teo et al., 2014) was used to define high-confidence proximity interactions for each of the bait proteins (STAR Methods).”
Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. (2020, positive): “Each protein construct was transfected into HEK293T cells and affinity purified, and high-confidence interactors were identified by mass spectrometry (MS) and scored using SAINTexpress (significance analysis of interactome) and MiST (mass spectrometry interaction statistics) scoring algorithms (6, 7”