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Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry.

Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence

Halloran JT, Bilmes JA, Noble WS.
PMID: 25298752
Uncertain Artif Intell. 2014;30:320-329.

We present a peptide-spectrum alignment strategy that employs a dynamic Bayesian network (DBN) for the identification of spectra produced by tandem mass spectrometry (MS/MS). Our method is fundamentally generative in that it models peptide fragmentation in MS/MS as a...

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Halloran JT, Bilmes JA, Noble WS. Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry. Uncertain Artif Intell. 2014;30:320-329
Halloran, J. T., Bilmes, J. A., & Noble, W. S. (2014). Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry. Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence, 30320-329.
Halloran, John T, et al. "Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry." Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence vol. 30 (2014): 320-329.
Halloran JT, Bilmes JA, Noble WS. Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry. Uncertain Artif Intell. 2014;30:320-329. PMID: 25298752; PMCID: PMC4185971.
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Mitochondrial Isolation and Purification from Mouse Spinal Cord.

Bio-protocol

Parone PA, Da Cruz S, Cleveland DW.
PMID: 27819013
Bio Protoc. 2013 Nov 05;3(21). doi: 10.21769/BioProtoc.961.

Mitochondria are eukaryotic organelles that play a crucial role in several cellular processes, including energy production, β-oxidation of fatty acids and regulation of calcium homeostasis. In the last 20 years there has been a hightened interest in the study...

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Parone PA, Da Cruz S, Cleveland DW. Mitochondrial Isolation and Purification from Mouse Spinal Cord. Bio Protoc. 2013;3(21)doi: 10.21769/BioProtoc.961.
Parone, P. A., Da Cruz, S., & Cleveland, D. W. (2013). Mitochondrial Isolation and Purification from Mouse Spinal Cord. Bio-protocol, 3(21), . https://doi.org/10.21769/BioProtoc.961
Parone, Philippe A, et al. "Mitochondrial Isolation and Purification from Mouse Spinal Cord." Bio-protocol vol. 3,21 (2013). doi: https://doi.org/10.21769/BioProtoc.961
Parone PA, Da Cruz S, Cleveland DW. Mitochondrial Isolation and Purification from Mouse Spinal Cord. Bio Protoc. 2013 Nov 05;3(21). doi: 10.21769/BioProtoc.961. PMID: 27819013; PMCID: PMC5096646.
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Post analysis data acquisition for the iterative MS/MS sampling of proteomics mixtures.

Journal of proteome research

Hoopmann MR, Merrihew GE, von Haller PD, MacCoss MJ.
PMID: 19256536
J Proteome Res. 2009 Apr;8(4):1870-5. doi: 10.1021/pr800828p.

The identification of peptides by microcapillary liquid chromatography-tandem mass spectrometry (microLC-MS/MS) has become routine because of the development of fast scanning mass spectrometers, data-dependent acquisition, and database searching algorithms. However, many peptides within the detection limit of the mass...

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Hoopmann MR, Merrihew GE, von Haller PD, et al. Post analysis data acquisition for the iterative MS/MS sampling of proteomics mixtures. J Proteome Res. 2009;8(4):1870-5doi: 10.1021/pr800828p.
Hoopmann, M. R., Merrihew, G. E., von Haller, P. D., & MacCoss, M. J. (2009). Post analysis data acquisition for the iterative MS/MS sampling of proteomics mixtures. Journal of proteome research, 8(4), 1870-5. https://doi.org/10.1021/pr800828p
Hoopmann, Michael R, et al. "Post analysis data acquisition for the iterative MS/MS sampling of proteomics mixtures." Journal of proteome research vol. 8,4 (2009): 1870-5. doi: https://doi.org/10.1021/pr800828p
Hoopmann MR, Merrihew GE, von Haller PD, MacCoss MJ. Post analysis data acquisition for the iterative MS/MS sampling of proteomics mixtures. J Proteome Res. 2009 Apr;8(4):1870-5. doi: 10.1021/pr800828p. PMID: 19256536; PMCID: PMC2671646.
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Systematic comparison of the human saliva and plasma proteomes.

Proteomics. Clinical applications

Yan W, Apweiler R, Balgley BM, Boontheung P, Bundy JL, Cargile BJ, Cole S, Fang X, Gonzalez-Begne M, Griffin TJ, Hagen F, Hu S, Wolinsky LE, Lee CS, Malamud D, Melvin JE, Menon R, Mueller M, Qiao R, Rhodus NL, Sevinsky JR, States D, Stephenson JL, Than S, Yates JR, Yu W, Xie H, Xie Y, Omenn GS, Loo JA, Wong DT.
PMID: 19898684
Proteomics Clin Appl. 2009 Jan 01;3(1):116-134. doi: 10.1002/prca.200800140.

The proteome of human salivary fluid has the potential to open new doors for disease biomarker discovery. A recent study to comprehensively identify and catalog the human ductal salivary proteome led to the compilation of 1166 proteins. The protein...

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Yan W, Apweiler R, Balgley BM, et al. Systematic comparison of the human saliva and plasma proteomes. Proteomics Clin Appl. 2009;3(1):116-134doi: 10.1002/prca.200800140.
Yan, W., Apweiler, R., Balgley, B. M., Boontheung, P., Bundy, J. L., Cargile, B. J., Cole, S., Fang, X., Gonzalez-Begne, M., Griffin, T. J., Hagen, F., Hu, S., Wolinsky, L. E., Lee, C. S., Malamud, D., Melvin, J. E., Menon, R., Mueller, M., Qiao, R., Rhodus, N. L., Sevinsky, J. R., States, D., Stephenson, J. L., Than, S., Yates, J. R., Yu, W., Xie, H., Xie, Y., Omenn, G. S., Loo, J. A., & Wong, D. T. (2009). Systematic comparison of the human saliva and plasma proteomes. Proteomics. Clinical applications, 3(1), 116-134. https://doi.org/10.1002/prca.200800140
Yan, Weihong, et al. "Systematic comparison of the human saliva and plasma proteomes." Proteomics. Clinical applications vol. 3,1 (2009): 116-134. doi: https://doi.org/10.1002/prca.200800140
Yan W, Apweiler R, Balgley BM, Boontheung P, Bundy JL, Cargile BJ, Cole S, Fang X, Gonzalez-Begne M, Griffin TJ, Hagen F, Hu S, Wolinsky LE, Lee CS, Malamud D, Melvin JE, Menon R, Mueller M, Qiao R, Rhodus NL, Sevinsky JR, States D, Stephenson JL, Than S, Yates JR, Yu W, Xie H, Xie Y, Omenn GS, Loo JA, Wong DT. Systematic comparison of the human saliva and plasma proteomes. Proteomics Clin Appl. 2009 Jan 01;3(1):116-134. doi: 10.1002/prca.200800140. PMID: 19898684; PMCID: PMC2773554.
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Brain Proteome Changes Induced by Olfactory Learning in Drosophila.

Journal of proteome research

Zhang Y, Shan B, Boyle M, Liu J, Liao L, Xu T, Yates JR.
PMID: 24983411
J Proteome Res. 2014 Aug 01;13(8):3763-3770. doi: 10.1021/pr500325q. Epub 2014 Jul 10.

For more than 30 years, the study of learning and memory in Drosophila melanogaster (fruit fly) has used an olfactory learning paradigm and has resulted in the discovery of many genes involved in memory formation. By varying learning programs,...

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Zhang Y, Shan B, Boyle M, et al. Brain Proteome Changes Induced by Olfactory Learning in Drosophila. J Proteome Res. 2014;13(8):3763-3770doi: 10.1021/pr500325q.
Zhang, Y., Shan, B., Boyle, M., Liu, J., Liao, L., Xu, T., & Yates, J. R. (2014). Brain Proteome Changes Induced by Olfactory Learning in Drosophila. Journal of proteome research, 13(8), 3763-3770. https://doi.org/10.1021/pr500325q
Zhang, Yaoyang, et al. "Brain Proteome Changes Induced by Olfactory Learning in Drosophila." Journal of proteome research vol. 13,8 (2014): 3763-3770. doi: https://doi.org/10.1021/pr500325q
Zhang Y, Shan B, Boyle M, Liu J, Liao L, Xu T, Yates JR. Brain Proteome Changes Induced by Olfactory Learning in Drosophila. J Proteome Res. 2014 Aug 01;13(8):3763-3770. doi: 10.1021/pr500325q. Epub 2014 Jul 10. PMID: 24983411; PMCID: PMC4412804.
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NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity.

Bioinformatics (Oxford, England)

Barot M, Gligorijević V, Cho K, Bonneau R.
PMID: 33576802
Bioinformatics. 2021 Feb 12; doi: 10.1093/bioinformatics/btab098. Epub 2021 Feb 12.

MOTIVATION: Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to protein functional annotation use sequence similarity to transfer...

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Barot M, Gligorijević V, Cho K, et al. NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity. Bioinformatics. 2021;doi: 10.1093/bioinformatics/btab098.
Barot, M., Gligorijević, V., Cho, K., & Bonneau, R. (2021). NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity. Bioinformatics (Oxford, England), . https://doi.org/10.1093/bioinformatics/btab098
Barot, Meet, et al. "NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity." Bioinformatics (Oxford, England) vol. (2021). doi: https://doi.org/10.1093/bioinformatics/btab098
Barot M, Gligorijević V, Cho K, Bonneau R. NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity. Bioinformatics. 2021 Feb 12; doi: 10.1093/bioinformatics/btab098. Epub 2021 Feb 12. PMID: 33576802; PMCID: PMC8388039.
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Molecular mechanisms of MYCN-dependent apoptosis and the MDM2-p53 pathway: an Achille's heel to be exploited for the therapy of MYCN-amplified neuroblastoma.

Frontiers in oncology

Petroni M, Veschi V, Gulino A, Giannini G.
PMID: 23091802
Front Oncol. 2012 Oct 12;2:141. doi: 10.3389/fonc.2012.00141. eCollection 2012.

The p53 oncosuppressor is very seldom mutated in neuroblastoma, but several mechanisms cooperate to its functional inactivation in this tumor. Increased MDM2 levels, due to genetic amplification or constitutive inhibition of p14( ARF), significantly contribute to this event highlighting...

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Petroni M, Veschi V, Gulino A, et al. Molecular mechanisms of MYCN-dependent apoptosis and the MDM2-p53 pathway: an Achille's heel to be exploited for the therapy of MYCN-amplified neuroblastoma. Front Oncol. 2012;2:141doi: 10.3389/fonc.2012.00141.
Petroni, M., Veschi, V., Gulino, A., & Giannini, G. (2012). Molecular mechanisms of MYCN-dependent apoptosis and the MDM2-p53 pathway: an Achille's heel to be exploited for the therapy of MYCN-amplified neuroblastoma. Frontiers in oncology, 2141. https://doi.org/10.3389/fonc.2012.00141
Petroni, Marialaura, et al. "Molecular mechanisms of MYCN-dependent apoptosis and the MDM2-p53 pathway: an Achille's heel to be exploited for the therapy of MYCN-amplified neuroblastoma." Frontiers in oncology vol. 2 (2012): 141. doi: https://doi.org/10.3389/fonc.2012.00141
Petroni M, Veschi V, Gulino A, Giannini G. Molecular mechanisms of MYCN-dependent apoptosis and the MDM2-p53 pathway: an Achille's heel to be exploited for the therapy of MYCN-amplified neuroblastoma. Front Oncol. 2012 Oct 12;2:141. doi: 10.3389/fonc.2012.00141. eCollection 2012. PMID: 23091802; PMCID: PMC3470040.
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GO Explorer: A gene-ontology tool to aid in the interpretation of shotgun proteomics data.

Proteome science

Carvalho PC, Fischer JS, Chen EI, Domont GB, Carvalho MG, Degrave WM, Yates JR, Barbosa VC.
PMID: 19239707
Proteome Sci. 2009 Feb 24;7:6. doi: 10.1186/1477-5956-7-6.

BACKGROUND: Spectral counting is a shotgun proteomics approach comprising the identification and relative quantitation of thousands of proteins in complex mixtures. However, this strategy generates bewildering amounts of data whose biological interpretation is a challenge.RESULTS: Here we present a...

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Carvalho PC, Fischer JS, Chen EI, et al. GO Explorer: A gene-ontology tool to aid in the interpretation of shotgun proteomics data. Proteome Sci. 2009;7:6doi: 10.1186/1477-5956-7-6.
Carvalho, P. C., Fischer, J. S., Chen, E. I., Domont, G. B., Carvalho, M. G., Degrave, W. M., Yates, J. R., & Barbosa, V. C. (2009). GO Explorer: A gene-ontology tool to aid in the interpretation of shotgun proteomics data. Proteome science, 76. https://doi.org/10.1186/1477-5956-7-6
Carvalho, Paulo C, et al. "GO Explorer: A gene-ontology tool to aid in the interpretation of shotgun proteomics data." Proteome science vol. 7 (2009): 6. doi: https://doi.org/10.1186/1477-5956-7-6
Carvalho PC, Fischer JS, Chen EI, Domont GB, Carvalho MG, Degrave WM, Yates JR, Barbosa VC. GO Explorer: A gene-ontology tool to aid in the interpretation of shotgun proteomics data. Proteome Sci. 2009 Feb 24;7:6. doi: 10.1186/1477-5956-7-6. PMID: 19239707; PMCID: PMC2652440.
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A statistical approach to peptide identification from clustered tandem mass spectrometry data.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine

Ryu S, Goodlett DR, Noble WS, Minin VN.
PMID: 23828149
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2012 Oct 04;648-653. doi: 10.1109/BIBMW.2012.6470214.

Tandem mass spectrometry experiments generate from thousands to millions of spectra. These spectra can be used to identify the presence of proteins in biological samples. In this work, we propose a new method to identify peptides, substrings of proteins,...

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Ryu S, Goodlett DR, Noble WS, et al. A statistical approach to peptide identification from clustered tandem mass spectrometry data. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2012;648-653doi: 10.1109/BIBMW.2012.6470214.
Ryu, S., Goodlett, D. R., Noble, W. S., & Minin, V. N. (2012). A statistical approach to peptide identification from clustered tandem mass spectrometry data. Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 648-653. https://doi.org/10.1109/BIBMW.2012.6470214
Ryu, Soyoung, et al. "A statistical approach to peptide identification from clustered tandem mass spectrometry data." Proceedings. IEEE International Conference on Bioinformatics and Biomedicine vol. (2012): 648-653. doi: https://doi.org/10.1109/BIBMW.2012.6470214
Ryu S, Goodlett DR, Noble WS, Minin VN. A statistical approach to peptide identification from clustered tandem mass spectrometry data. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2012 Oct 04;648-653. doi: 10.1109/BIBMW.2012.6470214. PMID: 23828149; PMCID: PMC3698614.
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Can the false-discovery rate be misleading?.

Proteomics

Barboza R, Cociorva D, Xu T, Barbosa VC, Perales J, Valente RH, França FM, Yates JR, Carvalho PC.
PMID: 21834134
Proteomics. 2011 Oct;11(20):4105-8. doi: 10.1002/pmic.201100297. Epub 2011 Sep 06.

The decoy-database approach is currently the gold standard for assessing the confidence of identifications in shotgun proteomic experiments. Here, we demonstrate that what might appear to be a good result under the decoy-database approach for a given false-discovery rate...

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Barboza R, Cociorva D, Xu T, et al. Can the false-discovery rate be misleading?. Proteomics. 2011;11(20):4105-8doi: 10.1002/pmic.201100297.
Barboza, R., Cociorva, D., Xu, T., Barbosa, V. C., Perales, J., Valente, R. H., França, F. M., Yates, J. R., & Carvalho, P. C. (2011). Can the false-discovery rate be misleading?. Proteomics, 11(20), 4105-8. https://doi.org/10.1002/pmic.201100297
Barboza, Rodrigo, et al. "Can the false-discovery rate be misleading?." Proteomics vol. 11,20 (2011): 4105-8. doi: https://doi.org/10.1002/pmic.201100297
Barboza R, Cociorva D, Xu T, Barbosa VC, Perales J, Valente RH, França FM, Yates JR, Carvalho PC. Can the false-discovery rate be misleading?. Proteomics. 2011 Oct;11(20):4105-8. doi: 10.1002/pmic.201100297. Epub 2011 Sep 06. PMID: 21834134; PMCID: PMC3313620.
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Showing 1 to 10 of 10 entries