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Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification.

Molecular & cellular proteomics : MCP

Jiang W, Wen B, Li K, Zeng WF, da Veiga Leprevost F, Moon J, Petyuk VA, Edwards NJ, Liu T, Nesvizhskii AI, Zhang B.
PMID: 34737085
Mol Cell Proteomics. 2021 Nov 01;20:100171. doi: 10.1016/j.mcpro.2021.100171. Epub 2021 Nov 01.

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported...

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Jiang W, Wen B, Li K, et al. Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification. Mol Cell Proteomics. 2021;20:100171doi: 10.1016/j.mcpro.2021.100171.
Jiang, W., Wen, B., Li, K., Zeng, W. F., da Veiga Leprevost, F., Moon, J., Petyuk, V. A., Edwards, N. J., Liu, T., Nesvizhskii, A. I., & Zhang, B. (2021). Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification. Molecular & cellular proteomics : MCP, 20100171. https://doi.org/10.1016/j.mcpro.2021.100171
Jiang, Wen, et al. "Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification." Molecular & cellular proteomics : MCP vol. 20 (2021): 100171. doi: https://doi.org/10.1016/j.mcpro.2021.100171
Jiang W, Wen B, Li K, Zeng WF, da Veiga Leprevost F, Moon J, Petyuk VA, Edwards NJ, Liu T, Nesvizhskii AI, Zhang B. Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification. Mol Cell Proteomics. 2021 Nov 01;20:100171. doi: 10.1016/j.mcpro.2021.100171. Epub 2021 Nov 01. PMID: 34737085; PMCID: PMC8609164.
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Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems.

Molecular & cellular proteomics : MCP

Zhang B, Kuster B.
PMID: 31399542
Mol Cell Proteomics. 2019 Aug 09;18(8):S1-S4. doi: 10.1074/mcp.E119.001693.

No abstract available.

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Zhang B, Kuster B. Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems. Mol Cell Proteomics. 2019;18(8):S1-S4doi: 10.1074/mcp.E119.001693.
Zhang, B., & Kuster, B. (2019). Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems. Molecular & cellular proteomics : MCP, 18(8), S1-S4. https://doi.org/10.1074/mcp.E119.001693
Zhang, Bing, and Kuster, Bernhard. "Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems." Molecular & cellular proteomics : MCP vol. 18,8 (2019): S1-S4. doi: https://doi.org/10.1074/mcp.E119.001693
Zhang B, Kuster B. Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems. Mol Cell Proteomics. 2019 Aug 09;18(8):S1-S4. doi: 10.1074/mcp.E119.001693. PMID: 31399542; PMCID: PMC6692779.
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Proteogenomic characterization of pancreatic ductal adenocarcinoma.

Cell

Cao L, Huang C, Cui Zhou D, Hu Y, Lih TM, Savage SR, Krug K, Clark DJ, Schnaubelt M, Chen L, da Veiga Leprevost F, Eguez RV, Yang W, Pan J, Wen B, Dou Y, Jiang W, Liao Y, Shi Z, Terekhanova NV, Cao S, Lu RJ, Li Y, Liu R, Zhu H, Ronning P, Wu Y, Wyczalkowski MA, Easwaran H, Danilova L, Mer AS, Yoo S, Wang JM, Liu W, Haibe-Kains B, Thiagarajan M, Jewell SD, Hostetter G, Newton CJ, Li QK, Roehrl MH, Fenyö D, Wang P, Nesvizhskii AI, Mani DR, Omenn GS, Boja ES, Mesri M, Robles AI, Rodriguez H, Bathe OF, Chan DW, Hruban RH, Ding L, Zhang B, Zhang H.
PMID: 34534465
Cell. 2021 Sep 16;184(19):5031-5052.e26. doi: 10.1016/j.cell.2021.08.023.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal...

Cite
Cao L, Huang C, Cui Zhou D, et al. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell. 2021;184(19):5031-5052.e26doi: 10.1016/j.cell.2021.08.023.
Cao, L., Huang, C., Cui Zhou, D., Hu, Y., Lih, T. M., Savage, S. R., Krug, K., Clark, D. J., Schnaubelt, M., Chen, L., da Veiga Leprevost, F., Eguez, R. V., Yang, W., Pan, J., Wen, B., Dou, Y., Jiang, W., Liao, Y., Shi, Z., Terekhanova, N. V., Cao, S., Lu, R. J., Li, Y., Liu, R., Zhu, H., Ronning, P., Wu, Y., Wyczalkowski, M. A., Easwaran, H., Danilova, L., Mer, A. S., Yoo, S., Wang, J. M., Liu, W., Haibe-Kains, B., Thiagarajan, M., Jewell, S. D., Hostetter, G., Newton, C. J., Li, Q. K., Roehrl, M. H., Fenyö, D., Wang, P., Nesvizhskii, A. I., Mani, D. R., Omenn, G. S., Boja, E. S., Mesri, M., Robles, A. I., Rodriguez, H., Bathe, O. F., Chan, D. W., Hruban, R. H., Ding, L., Zhang, B., Zhang, H. (2021). Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell, 184(19), 5031-5052.e26. https://doi.org/10.1016/j.cell.2021.08.023
Cao, Liwei, et al. "Proteogenomic characterization of pancreatic ductal adenocarcinoma." Cell vol. 184,19 (2021): 5031-5052.e26. doi: https://doi.org/10.1016/j.cell.2021.08.023
Cao L, Huang C, Cui Zhou D, Hu Y, Lih TM, Savage SR, Krug K, Clark DJ, Schnaubelt M, Chen L, da Veiga Leprevost F, Eguez RV, Yang W, Pan J, Wen B, Dou Y, Jiang W, Liao Y, Shi Z, Terekhanova NV, Cao S, Lu RJ, Li Y, Liu R, Zhu H, Ronning P, Wu Y, Wyczalkowski MA, Easwaran H, Danilova L, Mer AS, Yoo S, Wang JM, Liu W, Haibe-Kains B, Thiagarajan M, Jewell SD, Hostetter G, Newton CJ, Li QK, Roehrl MH, Fenyö D, Wang P, Nesvizhskii AI, Mani DR, Omenn GS, Boja ES, Mesri M, Robles AI, Rodriguez H, Bathe OF, Chan DW, Hruban RH, Ding L, Zhang B, Zhang H. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell. 2021 Sep 16;184(19):5031-5052.e26. doi: 10.1016/j.cell.2021.08.023. PMID: 34534465; PMCID: PMC8654574.
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Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes.

Nature communications

Lehmann BD, Colaprico A, Silva TC, Chen J, An H, Ban Y, Huang H, Wang L, James JL, Balko JM, Gonzalez-Ericsson PI, Sanders ME, Zhang B, Pietenpol JA, Chen XS.
PMID: 34725325
Nat Commun. 2021 Nov 01;12(1):6276. doi: 10.1038/s41467-021-26502-6.

Triple-negative breast cancer (TNBC) is a collection of biologically diverse cancers characterized by distinct transcriptional patterns, biology, and immune composition. TNBCs subtypes include two basal-like (BL1, BL2), a mesenchymal (M) and a luminal androgen receptor (LAR) subtype. Through a...

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Lehmann BD, Colaprico A, Silva TC, et al. Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes. Nat Commun. 2021;12(1):6276doi: 10.1038/s41467-021-26502-6.
Lehmann, B. D., Colaprico, A., Silva, T. C., Chen, J., An, H., Ban, Y., Huang, H., Wang, L., James, J. L., Balko, J. M., Gonzalez-Ericsson, P. I., Sanders, M. E., Zhang, B., Pietenpol, J. A., & Chen, X. S. (2021). Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes. Nature communications, 12(1), 6276. https://doi.org/10.1038/s41467-021-26502-6
Lehmann, Brian D, et al. "Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes." Nature communications vol. 12,1 (2021): 6276. doi: https://doi.org/10.1038/s41467-021-26502-6
Lehmann BD, Colaprico A, Silva TC, Chen J, An H, Ban Y, Huang H, Wang L, James JL, Balko JM, Gonzalez-Ericsson PI, Sanders ME, Zhang B, Pietenpol JA, Chen XS. Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes. Nat Commun. 2021 Nov 01;12(1):6276. doi: 10.1038/s41467-021-26502-6. PMID: 34725325; PMCID: PMC8560912.
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A proteogenomic portrait of lung squamous cell carcinoma.

Cell

Satpathy S, Krug K, Jean Beltran PM, Savage SR, Petralia F, Kumar-Sinha C, Dou Y, Reva B, Kane MH, Avanessian SC, Vasaikar SV, Krek A, Lei JT, Jaehnig EJ, Omelchenko T, Geffen Y, Bergstrom EJ, Stathias V, Christianson KE, Heiman DI, Cieslik MP, Cao S, Song X, Ji J, Liu W, Li K, Wen B, Li Y, Gümüş ZH, Selvan ME, Soundararajan R, Visal TH, Raso MG, Parra ER, Babur Ö, Vats P, Anand S, Schraink T, Cornwell M, Rodrigues FM, Zhu H, Mo CK, Zhang Y, da Veiga Leprevost F, Huang C, Chinnaiyan AM, Wyczalkowski MA, Omenn GS, Newton CJ, Schurer S, Ruggles KV, Fenyö D, Jewell SD, Thiagarajan M, Mesri M, Rodriguez H, Mani SA, Udeshi ND, Getz G, Suh J, Li QK, Hostetter G, Paik PK, Dhanasekaran SM, Govindan R, Ding L, Robles AI, Clauser KR, Nesvizhskii AI, Wang P, Carr SA, Zhang B, Mani DR, Gillette MA.
PMID: 34358469
Cell. 2021 Aug 05;184(16):4348-4371.e40. doi: 10.1016/j.cell.2021.07.016.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an...

Cite
Satpathy S, Krug K, Jean Beltran PM, et al. A proteogenomic portrait of lung squamous cell carcinoma. Cell. 2021;184(16):4348-4371.e40doi: 10.1016/j.cell.2021.07.016.
Satpathy, S., Krug, K., Jean Beltran, P. M., Savage, S. R., Petralia, F., Kumar-Sinha, C., Dou, Y., Reva, B., Kane, M. H., Avanessian, S. C., Vasaikar, S. V., Krek, A., Lei, J. T., Jaehnig, E. J., Omelchenko, T., Geffen, Y., Bergstrom, E. J., Stathias, V., Christianson, K. E., Heiman, D. I., Cieslik, M. P., Cao, S., Song, X., Ji, J., Liu, W., Li, K., Wen, B., Li, Y., Gümüş, Z. H., Selvan, M. E., Soundararajan, R., Visal, T. H., Raso, M. G., Parra, E. R., Babur, �. �., Vats, P., Anand, S., Schraink, T., Cornwell, M., Rodrigues, F. M., Zhu, H., Mo, C. K., Zhang, Y., da Veiga Leprevost, F., Huang, C., Chinnaiyan, A. M., Wyczalkowski, M. A., Omenn, G. S., Newton, C. J., Schurer, S., Ruggles, K. V., Fenyö, D., Jewell, S. D., Thiagarajan, M., Mesri, M., Rodriguez, H., Mani, S. A., Udeshi, N. D., Getz, G., Suh, J., Li, Q. K., Hostetter, G., Paik, P. K., Dhanasekaran, S. M., Govindan, R., Ding, L., Robles, A. I., Clauser, K. R., Nesvizhskii, A. I., Wang, P., Carr, S. A., Zhang, B., Mani, D. R., Gillette, M. A. (2021). A proteogenomic portrait of lung squamous cell carcinoma. Cell, 184(16), 4348-4371.e40. https://doi.org/10.1016/j.cell.2021.07.016
Satpathy, Shankha, et al. "A proteogenomic portrait of lung squamous cell carcinoma." Cell vol. 184,16 (2021): 4348-4371.e40. doi: https://doi.org/10.1016/j.cell.2021.07.016
Satpathy S, Krug K, Jean Beltran PM, Savage SR, Petralia F, Kumar-Sinha C, Dou Y, Reva B, Kane MH, Avanessian SC, Vasaikar SV, Krek A, Lei JT, Jaehnig EJ, Omelchenko T, Geffen Y, Bergstrom EJ, Stathias V, Christianson KE, Heiman DI, Cieslik MP, Cao S, Song X, Ji J, Liu W, Li K, Wen B, Li Y, Gümüş ZH, Selvan ME, Soundararajan R, Visal TH, Raso MG, Parra ER, Babur Ö, Vats P, Anand S, Schraink T, Cornwell M, Rodrigues FM, Zhu H, Mo CK, Zhang Y, da Veiga Leprevost F, Huang C, Chinnaiyan AM, Wyczalkowski MA, Omenn GS, Newton CJ, Schurer S, Ruggles KV, Fenyö D, Jewell SD, Thiagarajan M, Mesri M, Rodriguez H, Mani SA, Udeshi ND, Getz G, Suh J, Li QK, Hostetter G, Paik PK, Dhanasekaran SM, Govindan R, Ding L, Robles AI, Clauser KR, Nesvizhskii AI, Wang P, Carr SA, Zhang B, Mani DR, Gillette MA. A proteogenomic portrait of lung squamous cell carcinoma. Cell. 2021 Aug 05;184(16):4348-4371.e40. doi: 10.1016/j.cell.2021.07.016. PMID: 34358469; PMCID: PMC8475722.
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WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs.

Nucleic acids research

Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B.
PMID: 31114916
Nucleic Acids Res. 2019 Jul 02;47:W199-W205. doi: 10.1093/nar/gkz401.

WebGestalt is a popular tool for the interpretation of gene lists derived from large scale -omics studies. In the 2019 update, WebGestalt supports 12 organisms, 342 gene identifiers and 155 175 functional categories, as well as user-uploaded functional databases....

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Liao Y, Wang J, Jaehnig EJ, et al. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019;47:W199-W205doi: 10.1093/nar/gkz401.
Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z., & Zhang, B. (2019). WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic acids research, 47W199-W205. https://doi.org/10.1093/nar/gkz401
Liao, Yuxing, et al. "WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs." Nucleic acids research vol. 47 (2019): W199-W205. doi: https://doi.org/10.1093/nar/gkz401
Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019 Jul 02;47:W199-W205. doi: 10.1093/nar/gkz401. PMID: 31114916; PMCID: PMC6602449.
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ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies.

Proteomics

Calinawan AP, Song X, Ji J, Dhanasekaran SM, Petralia F, Wang P, Reva B.
PMID: 32510176
Proteomics. 2020 Nov;20(21):e1900359. doi: 10.1002/pmic.201900359. Epub 2020 Aug 06.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive multi-omics data resources of deep proteogenomic profiles for multiple cancer types. To enable the broader community of biological and medical researchers to intuitively query, explore, and download data...

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Calinawan AP, Song X, Ji J, et al. ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies. Proteomics. 2020;20(21):e1900359doi: 10.1002/pmic.201900359.
Calinawan, A. P., Song, X., Ji, J., Dhanasekaran, S. M., Petralia, F., Wang, P., & Reva, B. (2020). ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies. Proteomics, 20(21), e1900359. https://doi.org/10.1002/pmic.201900359
Calinawan, Anna Pamela, et al. "ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies." Proteomics vol. 20,21 (2020): e1900359. doi: https://doi.org/10.1002/pmic.201900359
Calinawan AP, Song X, Ji J, Dhanasekaran SM, Petralia F, Wang P, Reva B. ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies. Proteomics. 2020 Nov;20(21):e1900359. doi: 10.1002/pmic.201900359. Epub 2020 Aug 06. PMID: 32510176; PMCID: PMC7677200.
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Methods, Tools and Current Perspectives in Proteogenomics.

Molecular & cellular proteomics : MCP

Ruggles KV, Krug K, Wang X, Clauser KR, Wang J, Payne SH, Fenyö D, Zhang B, Mani DR.
PMID: 28456751
Mol Cell Proteomics. 2017 Jun;16(6):959-981. doi: 10.1074/mcp.MR117.000024. Epub 2017 Apr 29.

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics,

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Ruggles KV, Krug K, Wang X, et al. Methods, Tools and Current Perspectives in Proteogenomics. Mol Cell Proteomics. 2017;16(6):959-981doi: 10.1074/mcp.MR117.000024.
Ruggles, K. V., Krug, K., Wang, X., Clauser, K. R., Wang, J., Payne, S. H., Fenyö, D., Zhang, B., & Mani, D. R. (2017). Methods, Tools and Current Perspectives in Proteogenomics. Molecular & cellular proteomics : MCP, 16(6), 959-981. https://doi.org/10.1074/mcp.MR117.000024
Ruggles, Kelly V, et al. "Methods, Tools and Current Perspectives in Proteogenomics." Molecular & cellular proteomics : MCP vol. 16,6 (2017): 959-981. doi: https://doi.org/10.1074/mcp.MR117.000024
Ruggles KV, Krug K, Wang X, Clauser KR, Wang J, Payne SH, Fenyö D, Zhang B, Mani DR. Methods, Tools and Current Perspectives in Proteogenomics. Mol Cell Proteomics. 2017 Jun;16(6):959-981. doi: 10.1074/mcp.MR117.000024. Epub 2017 Apr 29. PMID: 28456751; PMCID: PMC5461547.
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Proteogenomic characterization of pancreatic ductal adenocarcinoma.

Cell

Cao L, Huang C, Cui Zhou D, Hu Y, Lih TM, Savage SR, Krug K, Clark DJ, Schnaubelt M, Chen L, da Veiga Leprevost F, Eguez RV, Yang W, Pan J, Wen B, Dou Y, Jiang W, Liao Y, Shi Z, Terekhanova NV, Cao S, Lu RJ, Li Y, Liu R, Zhu H, Ronning P, Wu Y, Wyczalkowski MA, Easwaran H, Danilova L, Mer AS, Yoo S, Wang JM, Liu W, Haibe-Kains B, Thiagarajan M, Jewell SD, Hostetter G, Newton CJ, Li QK, Roehrl MH, Fenyö D, Wang P, Nesvizhskii AI, Mani DR, Omenn GS, Boja ES, Mesri M, Robles AI, Rodriguez H, Bathe OF, Chan DW, Hruban RH, Ding L, Zhang B, Zhang H.
PMID: 34534465
Cell. 2021 Sep 16;184(19):5031-5052.e26. doi: 10.1016/j.cell.2021.08.023.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal...

Cite
Cao L, Huang C, Cui Zhou D, et al. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell. 2021;184(19):5031-5052.e26doi: 10.1016/j.cell.2021.08.023.
Cao, L., Huang, C., Cui Zhou, D., Hu, Y., Lih, T. M., Savage, S. R., Krug, K., Clark, D. J., Schnaubelt, M., Chen, L., da Veiga Leprevost, F., Eguez, R. V., Yang, W., Pan, J., Wen, B., Dou, Y., Jiang, W., Liao, Y., Shi, Z., Terekhanova, N. V., Cao, S., Lu, R. J., Li, Y., Liu, R., Zhu, H., Ronning, P., Wu, Y., Wyczalkowski, M. A., Easwaran, H., Danilova, L., Mer, A. S., Yoo, S., Wang, J. M., Liu, W., Haibe-Kains, B., Thiagarajan, M., Jewell, S. D., Hostetter, G., Newton, C. J., Li, Q. K., Roehrl, M. H., Fenyö, D., Wang, P., Nesvizhskii, A. I., Mani, D. R., Omenn, G. S., Boja, E. S., Mesri, M., Robles, A. I., Rodriguez, H., Bathe, O. F., Chan, D. W., Hruban, R. H., Ding, L., Zhang, B., Zhang, H. (2021). Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell, 184(19), 5031-5052.e26. https://doi.org/10.1016/j.cell.2021.08.023
Cao, Liwei, et al. "Proteogenomic characterization of pancreatic ductal adenocarcinoma." Cell vol. 184,19 (2021): 5031-5052.e26. doi: https://doi.org/10.1016/j.cell.2021.08.023
Cao L, Huang C, Cui Zhou D, Hu Y, Lih TM, Savage SR, Krug K, Clark DJ, Schnaubelt M, Chen L, da Veiga Leprevost F, Eguez RV, Yang W, Pan J, Wen B, Dou Y, Jiang W, Liao Y, Shi Z, Terekhanova NV, Cao S, Lu RJ, Li Y, Liu R, Zhu H, Ronning P, Wu Y, Wyczalkowski MA, Easwaran H, Danilova L, Mer AS, Yoo S, Wang JM, Liu W, Haibe-Kains B, Thiagarajan M, Jewell SD, Hostetter G, Newton CJ, Li QK, Roehrl MH, Fenyö D, Wang P, Nesvizhskii AI, Mani DR, Omenn GS, Boja ES, Mesri M, Robles AI, Rodriguez H, Bathe OF, Chan DW, Hruban RH, Ding L, Zhang B, Zhang H. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell. 2021 Sep 16;184(19):5031-5052.e26. doi: 10.1016/j.cell.2021.08.023. PMID: 34534465; PMCID: PMC8654574.
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A proteogenomic portrait of lung squamous cell carcinoma.

Cell

Satpathy S, Krug K, Jean Beltran PM, Savage SR, Petralia F, Kumar-Sinha C, Dou Y, Reva B, Kane MH, Avanessian SC, Vasaikar SV, Krek A, Lei JT, Jaehnig EJ, Omelchenko T, Geffen Y, Bergstrom EJ, Stathias V, Christianson KE, Heiman DI, Cieslik MP, Cao S, Song X, Ji J, Liu W, Li K, Wen B, Li Y, Gümüş ZH, Selvan ME, Soundararajan R, Visal TH, Raso MG, Parra ER, Babur Ö, Vats P, Anand S, Schraink T, Cornwell M, Rodrigues FM, Zhu H, Mo CK, Zhang Y, da Veiga Leprevost F, Huang C, Chinnaiyan AM, Wyczalkowski MA, Omenn GS, Newton CJ, Schurer S, Ruggles KV, Fenyö D, Jewell SD, Thiagarajan M, Mesri M, Rodriguez H, Mani SA, Udeshi ND, Getz G, Suh J, Li QK, Hostetter G, Paik PK, Dhanasekaran SM, Govindan R, Ding L, Robles AI, Clauser KR, Nesvizhskii AI, Wang P, Carr SA, Zhang B, Mani DR, Gillette MA.
PMID: 34358469
Cell. 2021 Aug 05;184(16):4348-4371.e40. doi: 10.1016/j.cell.2021.07.016.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an...

Cite
Satpathy S, Krug K, Jean Beltran PM, et al. A proteogenomic portrait of lung squamous cell carcinoma. Cell. 2021;184(16):4348-4371.e40doi: 10.1016/j.cell.2021.07.016.
Satpathy, S., Krug, K., Jean Beltran, P. M., Savage, S. R., Petralia, F., Kumar-Sinha, C., Dou, Y., Reva, B., Kane, M. H., Avanessian, S. C., Vasaikar, S. V., Krek, A., Lei, J. T., Jaehnig, E. J., Omelchenko, T., Geffen, Y., Bergstrom, E. J., Stathias, V., Christianson, K. E., Heiman, D. I., Cieslik, M. P., Cao, S., Song, X., Ji, J., Liu, W., Li, K., Wen, B., Li, Y., Gümüş, Z. H., Selvan, M. E., Soundararajan, R., Visal, T. H., Raso, M. G., Parra, E. R., Babur, �. �., Vats, P., Anand, S., Schraink, T., Cornwell, M., Rodrigues, F. M., Zhu, H., Mo, C. K., Zhang, Y., da Veiga Leprevost, F., Huang, C., Chinnaiyan, A. M., Wyczalkowski, M. A., Omenn, G. S., Newton, C. J., Schurer, S., Ruggles, K. V., Fenyö, D., Jewell, S. D., Thiagarajan, M., Mesri, M., Rodriguez, H., Mani, S. A., Udeshi, N. D., Getz, G., Suh, J., Li, Q. K., Hostetter, G., Paik, P. K., Dhanasekaran, S. M., Govindan, R., Ding, L., Robles, A. I., Clauser, K. R., Nesvizhskii, A. I., Wang, P., Carr, S. A., Zhang, B., Mani, D. R., Gillette, M. A. (2021). A proteogenomic portrait of lung squamous cell carcinoma. Cell, 184(16), 4348-4371.e40. https://doi.org/10.1016/j.cell.2021.07.016
Satpathy, Shankha, et al. "A proteogenomic portrait of lung squamous cell carcinoma." Cell vol. 184,16 (2021): 4348-4371.e40. doi: https://doi.org/10.1016/j.cell.2021.07.016
Satpathy S, Krug K, Jean Beltran PM, Savage SR, Petralia F, Kumar-Sinha C, Dou Y, Reva B, Kane MH, Avanessian SC, Vasaikar SV, Krek A, Lei JT, Jaehnig EJ, Omelchenko T, Geffen Y, Bergstrom EJ, Stathias V, Christianson KE, Heiman DI, Cieslik MP, Cao S, Song X, Ji J, Liu W, Li K, Wen B, Li Y, Gümüş ZH, Selvan ME, Soundararajan R, Visal TH, Raso MG, Parra ER, Babur Ö, Vats P, Anand S, Schraink T, Cornwell M, Rodrigues FM, Zhu H, Mo CK, Zhang Y, da Veiga Leprevost F, Huang C, Chinnaiyan AM, Wyczalkowski MA, Omenn GS, Newton CJ, Schurer S, Ruggles KV, Fenyö D, Jewell SD, Thiagarajan M, Mesri M, Rodriguez H, Mani SA, Udeshi ND, Getz G, Suh J, Li QK, Hostetter G, Paik PK, Dhanasekaran SM, Govindan R, Ding L, Robles AI, Clauser KR, Nesvizhskii AI, Wang P, Carr SA, Zhang B, Mani DR, Gillette MA. A proteogenomic portrait of lung squamous cell carcinoma. Cell. 2021 Aug 05;184(16):4348-4371.e40. doi: 10.1016/j.cell.2021.07.016. PMID: 34358469; PMCID: PMC8475722.
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PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data.

Proteomics

Odom GJ, Ban Y, Colaprico A, Liu L, Silva TC, Sun X, Pico AR, Zhang B, Wang L, Chen X.
PMID: 32430990
Proteomics. 2020 Nov;20(21):e1900409. doi: 10.1002/pmic.201900409. Epub 2020 Jul 02.

The authors present pathwayPCA, an R/Bioconductor package for integrative pathway analysis that utilizes modern statistical methodology, including supervised and adaptive, elastic-net, sparse principal component analysis. pathwayPCA can be applied to continuous, binary, and survival outcomes in studies with multiple...

Cite
Odom GJ, Ban Y, Colaprico A, et al. PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data. Proteomics. 2020;20(21):e1900409doi: 10.1002/pmic.201900409.
Odom, G. J., Ban, Y., Colaprico, A., Liu, L., Silva, T. C., Sun, X., Pico, A. R., Zhang, B., Wang, L., & Chen, X. (2020). PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data. Proteomics, 20(21), e1900409. https://doi.org/10.1002/pmic.201900409
Odom, Gabriel J, et al. "PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data." Proteomics vol. 20,21 (2020): e1900409. doi: https://doi.org/10.1002/pmic.201900409
Odom GJ, Ban Y, Colaprico A, Liu L, Silva TC, Sun X, Pico AR, Zhang B, Wang L, Chen X. PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data. Proteomics. 2020 Nov;20(21):e1900409. doi: 10.1002/pmic.201900409. Epub 2020 Jul 02. PMID: 32430990; PMCID: PMC7677175.
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Computational Proteomics: Focus on Deep Learning.

Proteomics

Wen B, Zhang B.
PMID: 33210458
Proteomics. 2020 Nov;20(21):e2000258. doi: 10.1002/pmic.202000258.

No abstract available.

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Wen B, Zhang B. Computational Proteomics: Focus on Deep Learning. Proteomics. 2020;20(21):e2000258doi: 10.1002/pmic.202000258.
Wen, B., & Zhang, B. (2020). Computational Proteomics: Focus on Deep Learning. Proteomics, 20(21), e2000258. https://doi.org/10.1002/pmic.202000258
Wen, Bo, and Zhang, Bing. "Computational Proteomics: Focus on Deep Learning." Proteomics vol. 20,21 (2020): e2000258. doi: https://doi.org/10.1002/pmic.202000258
Wen B, Zhang B. Computational Proteomics: Focus on Deep Learning. Proteomics. 2020 Nov;20(21):e2000258. doi: 10.1002/pmic.202000258. PMID: 33210458.
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