MSBlender statistics

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MSBlender specifications


Unique identifier OMICS_19891
Name MSBlender
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS
Programming languages Python
Computer skills Advanced
Stability Stable
gcc, GNU Scientific Library, matplotlib
Maintained Yes



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  • person_outline Edward Marcotte <>
  • person_outline Alexey Nesvizhskii <>
  • person_outline Hyungwon Choi <>
  • person_outline Taejoon Kwon <>

Additional information

Publication for MSBlender

MSBlender in publications

PMCID: 5016792
PMID: 27609711
DOI: 10.1038/srep33038

[…] from mass spectrometry-based proteomic analysis were searched against the human ensembl grch37.75 protein database. several database search algorithms have complementary sensitivity and specificity. msblender software increases the number of peptides identified by statistically combining search scores from multiple algorithms. we integrated the database search scores from three algorithms […]

PMCID: 4838525
PMID: 27103885
DOI: 10.5808/GI.2016.14.1.2

[…] the integration of paragon and mascot () [] by assigning the peptides with higher scores from the two search engines to the ms/ms spectra, which can leads to unreliable false positive rates. also, msblender integrates the search scores from the search engines into a probability score for every possible psm and then estimates fdrs for the psms in a reliable manner []. this method identifies […]

PMCID: 4738005
PMID: 26870755
DOI: 10.1016/j.dib.2015.11.062

[…] mass spectra were searched using 3 search engines: tide, inspect and msgfdb each employing a different search methodology, and the spectral counts were integrated probabilistically using msblender . we found we were able to increase the total peptide-spectral matches and proteins identified by 20–60% depending on the sample compared to using sequest alone, with a false discovery rate […]

PMCID: 4558836
PMID: 26335531
DOI: 10.1186/s12859-015-0714-x

[…] or a combination of these. several groups have also published efforts in combining multiple algorithms for peptide-spectrum matching, for instance the framework developed by searle et al. [], the msblender software from kwon et al. [] or the fdranalysis algorithm of wedge et al. []. recently, in de bruin et al. [] and mohammed et al. [] we have shown how some of these algorithms […]

PMCID: 4012837
PMID: 24742327
DOI: 10.1021/pr4011684

[…] the same search parameters as described previously except that ms-gfdb was newly added for the current study (with the settings –t 300 ppm –c13 1 –nnet 0 –n 2). then, we combined these results with msblender and considered peptide−spectrum matches with an estimated fdr less than 0.01. subsequently, we calculated apex scores, with weighted spectral counts per protein (using a fdr < 0.01 […]

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MSBlender institution(s)
Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA; Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA; Department of Pathology and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Chemistry and Biochemistry, University of Texas at Austin, Austin, TX, USA
MSBlender funding source(s)
Supported grants from the NIH, NSF, the Welch (F1515) & Packard Foundations, and NIH grants R01- GM094231 and R01-CA126239.

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