Gygi Lab Software

The Gygi lab has developed a number of software packages that we share with the community to aid proteomics research at large. Below you will find many of these programs/applications. If you do not see what you're looking for please contact us.

Progress in science depends on new techniques, new discoveries and new ideas, probably in that order.

-Sydney Brenner



The Real-Time Search** (RTS-MS3) provides real-time (<5 ms / spectrum) spectral identification and triggers SPS-MS3 scans that utilize assigned and pure fragment ions for accurate quantitation.Time consuming SPS-MS3 spectra are only acquired after confident peptide identification, greatly increasing the number of peptides interrogated and reducing the effects of isobaric interference. Try it!


The Tomahto software provides real-time instrument control and decision making. Tomahto enables simplied implementation of TOMAHAQ targeted assay. It provides an array of functionalities including MS1 peak detection, MS2 real-time peak matching (RTPM), MS2 fragmentation pattern match, SPS ion purity filter, MS3 automatic gain control (AGC), MS3 quant scan insertion, and target peptide close-out. In addition to controlling data acquisition, it also allows real-time data visualization and post-acquisition analysis. Try it!


The GoDig software is a real-time analytics platform that enables next-generation TMT-based multiplexed targeted proteomics. It obviates the need for making internal peptide standards and tedious method curation. As a result, it essentially makes targeting nearly any prviously detected peptides possible. One only needs a list of peptides and GoDig will perform real-time elution calibration and spectral matching to identify the targets, and prompt SPS-MS3 scans to quantify them down to attomole level. Try it!


  1. Erickson, B.K., Mintseris, J., Schweppe, D.K., Navarrete-Perea, J., Erickson, A.R., Nusinow, D.P., Paulo, J.A., and Gygi, S.P. (2019). Active Instrument Engagement Combined with a Real-Time Database Search for Improved Performance of Sample Multiplexing Workflows. Read it!
  2. Schweppe, D.K., Eng, J.K., Bailey, D., Rad, R., Yu, Q., Navarrete-Perea, J., Huttlin, E.L., Erickson, B.K., Paulo, J.A., and Gygi, S.P. (2019). Full-featured, real-time database searching platform enables fast and accurate multiplexed quantitative proteomics.  Read it!
  3. Yu Q., Xiao H., Jedrychowski M.P., Schweppe D.K., Navarrete-Perea J., Knott J., Rogers J., Chouchani E.T., and Gygi S.P. (2020). Sample multiplexing for targeted pathway proteomics in aging mice. Read it!
  4. Yu, Q.; Liu, X.; Keller, M. P.; Navarrete-Perea, J.; Zhang, T.; Fu, S.; Vaites, L. P.; Shuken, S. R.; Schmid, E.; Keele, G. R.; Li, J.; Huttlin, E. L.; Rashan, E. H.; Simcox, J.; Churchill, G. A.; Schweppe, D. K.; Attie, A. D.; Paulo, J. A.; Gygi, S. P. (2023). Sample Multiplexing-Based Targeted Pathway Proteomics with Real-Time Analytics Reveals the Impact of Genetic Variation on Protein Expression. Read it!


Cross-linking mass spectrometry (XLMS) has become an established approach to complement the more common techniques for protein structure elucidation for those molecular machines where the traditional techniques are unable to obtain sufficient resolution. The success of XLMS relies on the ability to obtain high coverage of cross-linked peptides which are then used as restraints to guide modeling efforts. Here we describe advances in chemical and computational methods to obtain density of 1 cross-link per 7 amino acids of protein sequence. We show that this level of coverage is sufficient to produce a medium-resolution model a large protein subcomplex without using structural information for the subunits, as well as to define specific interfaces at higher resolution.

Mintseris J. and Gygi S.P. (2020). High-density chemical cross-linking for modeling protein interactions. Read it!

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BioPlex Explorer

Since 2012, we have been profiling protein interactions in human cells via affinity-purification mass spectrometry and systematically analyzing interactions for all accessible human proteins at proteome scale. Leveraging the clones available in the human ORFeome (v. 8.1) developed by Marc Vidal and David Hill at the Dana Farber Cancer Center, we have been expressing C-terminally HA-FLAG-tagged versions of each human protein for immunopurification and LC-MS identification of binding partners. We have then been combining these interaction profiles by the thousands to create a series of models of the human interactome with steadily increasing scale. While the first, BioPlex 1.0 – HEK293T v1.0, included ~24,000 interactions among 8,000 proteins, BioPlex 2.0 – HEK293T v2.0 expanded coverage to ~57,000 interactions among 11,000 proteins. The most recent, BioPlex 3.0 – HEK293T v3.0 & HCT116 v1.0, includes nearly 120,000 interactions among nearly 15,000 proteins and is the most comprehensive experimentally derived model of the human interactome to date. Because each protein’s network position reflects its subcellular localization, biological function, and disease association, these networks have been powerful tools for study of thousands of uncharacterized proteins. They have also provided myriad insights into interactome modularity and organization.


HEK293T (v3.0) & HCT116 (v1.0): Huttlin et. al. bioRxiv (2020)
HEK293T (v2.0): Huttlin et. al. Nature (2017)
HEK293T (v1.0): Huttlin et. al. Cell (2015)
BioPlex Explorer: Schweppe et. al. JPR (2018)


Accurate assignment of monoisotopic peaks is essential for the identification of peptides in bottom-up proteomics. Misassignment or inaccurate attribution of peptidic ions leads to lower sensitivity and fewer total peptide identifications. In the present work, we present a performant, open-source, cross-platform algorithm, Monocle, for the rapid reassignment of instrument-assigned precursor peaks to monoisotopic peptide assignments. We demonstrate that the present algorithm can be integrated into many common proteomic pipelines and provides rapid conversion from multiple data source types. Finally, we show that our monoisotopic peak assignment results in up to a twofold increase in total peptide identifications compared to analyses lacking monoisotopic correction and a 44% improvement over previous monoisotopic peak correction algorithms.

Rad R. et al. (2021) Improved Monoisotopic Mass Estimation for Deeper Proteome Coverage. Read it!

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Search algorithms like Sequest or Mascot often successfully identify the proper peptide sequence, but fail to provide information about the presence or absence of site-determining ions. As a result, users must manually inspect each spectrum to confirm proper site localization. Here, we present a probability-based score, named the Ascore, which measures the probability of correct phosphorylation site localization based on the presence and intensity of site-determining ions in MS/MS spectra.

Beausoleil SA, Villen J, Gerber SA, Rush J, and Gygi S.P. (2020). A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Read it!

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Motif-x (short for motif extractor) is a software tool designed to extract over-represented patterns from any sequence data set. The algorithm is an iterative strategy which builds successive motifs through comparison to a dynamic statistical background.

Schwartz, D. & Gygi, S.P. (2005). An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Read it!

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Analysis of Independent Differences (AID) for Thermal Proteome Profiling. AID examines the differences between the fractions of non-denatured protein in order to predict the most likely shifted proteins from thermal proteome profiling experiments.

Panov, A. & Gygi, S.P. (2019). Analysis of Independent Differences (AID) detects complex thermal proteome profiles independent of shape and identifies candidate panobinostat targets. Read it!

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