7. For term enrichment analysis, there is a balance between selecting the appropriate number or ontologies, the p-value threshold, and the ability to visualize and interpret the results. For
example, a large number of enriched terms might be useful to
understand and explore the results but might be difficult to
meaningfully display in a publication.
Acknowledgments
This work was supported by the National Institutes of Health
(NIH) through a training grant (T15 LM007359) and a subcontract development project award (P30 AG062715).
References
1. Muntel J, Gandhi T, Verbeke L, Bernhardt
OM, Treiber T, Bruderer R, Reiter L (2019)
Surpassing 10 000 identified and quantified
proteins in a single run by optimizing current
LC-MS instrumentation and data analysis strategy. Mol Omics 15:348–360
2. Elias JE, Gygi SP (2007) Target-decoy search
strategy for increased confidence in large-scale
protein identifications by mass spectrometry.
Nat Methods 4:207–214
3. Choi H, Nesvizhskii AI (2008) False discovery
rates and related statistical concepts in mass
spectrometry-based proteomics. J Proteome
Res 7:47–50
4. Audain E, Uszkoreit J, Sachsenberg T,
Pfeuffer J, Liang X, Hermjakob H,
Sanchez A, Eisenacher M, Reinert K, Tabb
DL, Kohlbacher O, Perez-Riverol Y (2017)
In-depth analysis of protein inference algorithms using multiple search engines and welldefined metrics. J Proteome 150:170–182
5. Nesvizhskii AI, Keller A, Kolker E, Aebersold R
(2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem
75:4646–4658
6. Schilling B, Rardin MJ, MacLean BX,
Zawadzka AM, Frewen BE, Cusack MP, Sorensen DJ, Bereman MS, Jing E, Wu CC,
Verdin E, Kahn CR, Maccoss MJ, Gibson BW
(2012) Platform-independent and label-free
quantitation of proteomic data using MS1
extracted ion chromatograms in skyline: application to protein acetylation and phosphorylation. Mol Cell Proteomics 11:202–214
7. Guergues J, Wohlfahrt J, Zhang P, Liu B, Stevens SM (2020) Deep proteome profiling
reveals novel pathways associated with
pro-inflammatory and alcohol-induced microglial activation phenotypes. J Proteome
220:103753
8. Perez-Riverol Y, Csordas A, Bai J, BernalLlinares M, Hewapathirana S, Kundu DJ,
Inuganti A, Griss J, Mayer G, Eisenacher M,
Pe ´rez
E,
Uszkoreit
J,
Pfeuffer
J,
Sachsenberg T, Yilmaz S, Tiwary S, Cox J,
Audain E, Walzer M, Jarnuczak AF,
Ternent T, Brazma A, Vizcaı ´no JA (2019)
The PRIDE database and related tools and
resources in 2019: improving support for
quantification data. Nucleic Acids Res 47:
D442–D450
9. Chambers MC, Maclean B, Burke R,
Amodei D, Ruderman DL, Neumann S,
Gatto L, Fischer B, Pratt B, Egertson J,
Hoff K, Kessner D, Tasman N, Shulman N,
Frewen B, Baker TA, Brusniak MY, Paulse C,
Creasy D, Flashner L, Kani K, Moulding C,
Seymour SL, Nuwaysir LM, Lefebvre B,
Kuhlmann F, Roark J, Rainer P, Detlev S,
Hemenway T, Huhmer A, Langridge J,
Connolly B, Chadick T, Holly K, Eckels J,
Deutsch EW, Moritz RL, Katz JE, Agus DB,
MacCoss M, Tabb DL, Mallick P (2012) A
cross-platform toolkit for mass spectrometry
and proteomics. Nat Biotechnol 30:918–920
10. Kong AT, Leprevost FV, Avtonomov DM,
Mellacheruvu D, Nesvizhskii AI (2017)
MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based
proteomics. Nat Methods 14:513–520
11. Shteynberg D, Deutsch EW, Lam H, Eng JK,
Sun Z, Tasman N, Mendoza L, Moritz RL,
Aebersold R, Nesvizhskii AI (2011) iProphet:
multi-level integrative analysis of shotgun
Qualitative and Quantitative Shotgun Proteomics Data Analysis. . .
307
example, a large number of enriched terms might be useful to
understand and explore the results but might be difficult to
meaningfully display in a publication.
Acknowledgments
This work was supported by the National Institutes of Health
(NIH) through a training grant (T15 LM007359) and a subcontract development project award (P30 AG062715).
References
1. Muntel J, Gandhi T, Verbeke L, Bernhardt
OM, Treiber T, Bruderer R, Reiter L (2019)
Surpassing 10 000 identified and quantified
proteins in a single run by optimizing current
LC-MS instrumentation and data analysis strategy. Mol Omics 15:348–360
2. Elias JE, Gygi SP (2007) Target-decoy search
strategy for increased confidence in large-scale
protein identifications by mass spectrometry.
Nat Methods 4:207–214
3. Choi H, Nesvizhskii AI (2008) False discovery
rates and related statistical concepts in mass
spectrometry-based proteomics. J Proteome
Res 7:47–50
4. Audain E, Uszkoreit J, Sachsenberg T,
Pfeuffer J, Liang X, Hermjakob H,
Sanchez A, Eisenacher M, Reinert K, Tabb
DL, Kohlbacher O, Perez-Riverol Y (2017)
In-depth analysis of protein inference algorithms using multiple search engines and welldefined metrics. J Proteome 150:170–182
5. Nesvizhskii AI, Keller A, Kolker E, Aebersold R
(2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem
75:4646–4658
6. Schilling B, Rardin MJ, MacLean BX,
Zawadzka AM, Frewen BE, Cusack MP, Sorensen DJ, Bereman MS, Jing E, Wu CC,
Verdin E, Kahn CR, Maccoss MJ, Gibson BW
(2012) Platform-independent and label-free
quantitation of proteomic data using MS1
extracted ion chromatograms in skyline: application to protein acetylation and phosphorylation. Mol Cell Proteomics 11:202–214
7. Guergues J, Wohlfahrt J, Zhang P, Liu B, Stevens SM (2020) Deep proteome profiling
reveals novel pathways associated with
pro-inflammatory and alcohol-induced microglial activation phenotypes. J Proteome
220:103753
8. Perez-Riverol Y, Csordas A, Bai J, BernalLlinares M, Hewapathirana S, Kundu DJ,
Inuganti A, Griss J, Mayer G, Eisenacher M,
Pe ´rez
E,
Uszkoreit
J,
Pfeuffer
J,
Sachsenberg T, Yilmaz S, Tiwary S, Cox J,
Audain E, Walzer M, Jarnuczak AF,
Ternent T, Brazma A, Vizcaı ´no JA (2019)
The PRIDE database and related tools and
resources in 2019: improving support for
quantification data. Nucleic Acids Res 47:
D442–D450
9. Chambers MC, Maclean B, Burke R,
Amodei D, Ruderman DL, Neumann S,
Gatto L, Fischer B, Pratt B, Egertson J,
Hoff K, Kessner D, Tasman N, Shulman N,
Frewen B, Baker TA, Brusniak MY, Paulse C,
Creasy D, Flashner L, Kani K, Moulding C,
Seymour SL, Nuwaysir LM, Lefebvre B,
Kuhlmann F, Roark J, Rainer P, Detlev S,
Hemenway T, Huhmer A, Langridge J,
Connolly B, Chadick T, Holly K, Eckels J,
Deutsch EW, Moritz RL, Katz JE, Agus DB,
MacCoss M, Tabb DL, Mallick P (2012) A
cross-platform toolkit for mass spectrometry
and proteomics. Nat Biotechnol 30:918–920
10. Kong AT, Leprevost FV, Avtonomov DM,
Mellacheruvu D, Nesvizhskii AI (2017)
MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based
proteomics. Nat Methods 14:513–520
11. Shteynberg D, Deutsch EW, Lam H, Eng JK,
Sun Z, Tasman N, Mendoza L, Moritz RL,
Aebersold R, Nesvizhskii AI (2011) iProphet:
multi-level integrative analysis of shotgun
Qualitative and Quantitative Shotgun Proteomics Data Analysis. . .
307