Proteogenomic Landscape of Squamous Cell Lung Cancer

Stewart, Paul A.; Slebos, Robbert J.C.; Welsh, Eric A.; Cen, Ling; Zhang, Y.; Chen, Z.; Cheng, Chia Ho; Pettersson, Fredrik; Berglund, Anders; Zhang, G.; Fang, Bin; Izumi, Victoria; Yoder, Sean; Fellows, Katherine; Chen, Y.A.; Teer, Jamie K.; Eschrich, Steven A.; Koomen, John M.; Haura, Eric B.

doi:10.1016/j.jtho.2017.06.037

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To further compare the different protein inference methods, they were finally tested on two sets of less engineered data, sets without known relative abundance differences. Data were downloaded from Stewart et al, a set consisting of DIA data contrasting lung squamous cell carcinomas to adjacent tissues, 14 and from Gao et al, a set comparing primary to metastatic cell lines. 15 Peptide abundances were derived with the nf-core quantms pipeline followed by our protein summarization strategies and plotted the number of differentially abundant proteins as a function of each method's estimated quantitative protein-level FDR or q value.…”

Section: Benchmark On Cancer Proteomics-related Datamentioning

confidence: 99%

“…The first set consists of data from four frozen lung squamous cell carcinomas and four adjacent tissues harvested with a Thermo QExactive Plus. 14 The second set consists of data from a comparison of three primary cell lines (SK-MEL-1, A375, and G-361) to three metastatic (RPMI-7951, SH-4, and SK-MEL-3) cell lines analyzed (two samples per cell line) by a Thermo Orbitrap Fusion Lumos Tribrid. 15…”

Section: Cancer Proteomics Datamentioning

confidence: 99%

See 1 more Smart Citation

Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry

2023

View full text Add to dashboard Cite

A frequent goal, or subgoal, when processing data from a quantitative shotgun proteomics experiment is a list of proteins that are differentially abundant under the examined experimental conditions. Unfortunately, obtaining such a list is a challenging process, as the mass spectrometer analyzes the proteolytic peptides of a protein rather than the proteins themselves. We have previously designed a Bayesian hierarchical probabilistic model, Triqler, for combining peptide identification and quantification errors into probabilities of proteins being differentially abundant. However, the model was developed for data from data-dependent acquisition. Here, we show that Triqler is also compatible with data-independent acquisition data after applying minor alterations for the missing value distribution. Furthermore, we find that it has better performance than a set of compared state-of-the-art protein summarization tools when evaluated on data-independent acquisition data.

show abstract

Section: Benchmark On Cancer Proteomics-related Datamentioning

confidence: 99%

Section: Cancer Proteomics Datamentioning

confidence: 99%