@article{chen_song_williams_shuford_liu_wang_li_shi_gokce_ducoste_et al._2013, title={Monolignol Pathway 4-Coumaric Acid: Coenzyme A Ligases in Populus trichocarpa: Novel Specificity, Metabolic Regulation, and Simulation of Coenzyme A Ligation Fluxes}, volume={161}, ISSN={["0032-0889"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84874626790&partnerID=MN8TOARS}, DOI={10.1104/pp.112.210971}, abstractNote={Abstract}, number={3}, journal={PLANT PHYSIOLOGY}, author={Chen, Hsi-Chuan and Song, Jina and Williams, Cranos M. and Shuford, Christopher M. and Liu, Jie and Wang, Jack P. and Li, Quanzi and Shi, Rui and Gokce, Emine and Ducoste, Joel and et al.}, year={2013}, month={Mar}, pages={1501–1516} }
 @article{gokce_franck_oh_dean_muddiman_2012, title={In-Depth Analysis of the Magnaporthe oryzae Conidial Proteome}, volume={11}, ISSN={["1535-3907"]}, DOI={10.1021/pr300604s}, abstractNote={The filamentous fungus Magnaporthe oryzae (M. oryzae) is the causative agent of rice blast disease and presents a significant threat to worldwide rice production. To establish the groundwork for future research on the pathogenic development of M. oryzae, a global proteomic study of conidia was performed. The filter aided sample preparation method (FASP) and anion StageTip fractionation combined with long, optimized shallow 210 min nanoLC gradients prior to mass spectrometry analysis on an Orbitrap XL was applied, which resulted in a doubling of protein identifications in comparison to our previous GeLC analysis. Herein, we report the identification of 2912 conidial proteins at a 1% protein false discovery rate (FDR) and we present the most extensive study performed on M. oryzae conidia to date. A similar distribution between identified proteins and the predicted proteome was observed when subcellular localization analysis was performed, suggesting the detected proteins build a representative portion of the predicted proteome. A higher percentage of cytoplasmic proteins (associated with translation, energy, and metabolism) were observed in the conidial proteome relative to the whole predicted proteome. Conversely, nuclear and extracellular proteins were less well represented in the conidial proteome. Further analysis by gene ontology revealed biological insights into identified proteins important for central metabolic processes and the physiology of conidia.}, number={12}, journal={JOURNAL OF PROTEOME RESEARCH}, author={Gokce, Emine and Franck, William L. and Oh, Yeonyee and Dean, Ralph A. and Muddiman, David C.}, year={2012}, month={Dec}, pages={5827–5835} }
 @article{gokce_shuford_franck_dean_muddiman_2011, title={Evaluation of Normalization Methods on GeLC-MS/MS Label-Free Spectral Counting Data to Correct for Variation during Proteomic Workflows}, volume={22}, ISSN={["1879-1123"]}, DOI={10.1007/s13361-011-0237-2}, abstractNote={Normalization of spectral counts (SpCs) in label-free shotgun proteomic approaches is important to achieve reliable relative quantification. Three different SpC normalization methods, total spectral count (TSpC) normalization, normalized spectral abundance factor (NSAF) normalization, and normalization to selected proteins (NSP) were evaluated based on their ability to correct for day-to-day variation between gel-based sample preparation and chromatographic performance. Three spectral counting data sets obtained from the same biological conidia sample of the rice blast fungus Magnaporthe oryzae were analyzed by 1D gel and liquid chromatography-tandem mass spectrometry (GeLC-MS/MS). Equine myoglobin and chicken ovalbumin were spiked into the protein extracts prior to 1D-SDS- PAGE as internal protein standards for NSP. The correlation between SpCs of the same proteins across the different data sets was investigated. We report that TSpC normalization and NSAF normalization yielded almost ideal slopes of unity for normalized SpC versus average normalized SpC plots, while NSP did not afford effective corrections of the unnormalized data. Furthermore, when utilizing TSpC normalization prior to relative protein quantification, t-testing and fold-change revealed the cutoff limits for determining real biological change to be a function of the absolute number of SpCs. For instance, we observed the variance decreased as the number of SpCs increased, which resulted in a higher propensity for detecting statistically significant, yet artificial, change for highly abundant proteins. Thus, we suggest applying higher confidence level and lower fold-change cutoffs for proteins with higher SpCs, rather than using a single criterion for the entire data set. By choosing appropriate cutoff values to maintain a constant false positive rate across different protein levels (i.e., SpC levels), it is expected this will reduce the overall false negative rate, particularly for proteins with higher SpCs.}, number={12}, journal={JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY}, author={Gokce, Emine and Shuford, Christopher M. and Franck, William L. and Dean, Ralph A. and Muddiman, David C.}, year={2011}, month={Dec}, pages={2199–2208} }
 @article{gokce_andrews_dean_muddiman_2011, title={Increasing proteome coverage with offline RP HPLC coupled to online RP nanoLC-MS}, volume={879}, ISSN={["1873-376X"]}, DOI={10.1016/j.jchromb.2011.01.032}, abstractNote={Fractionation prior to mass spectrometry is an indispensable step in proteomics. In this paper we report the success of performing offline reversed phase high pressure liquid chromatography (HPLC) fractionation on a C18 2.0 mm×150 mm column at the peptide level with microliter per minute flow rates prior to online nano-flow reversed phase liquid chromatography mass spectrometry (nanoLC-MS) using the well-studied fungus Saccharomyces cerevisiae. A C18 75 μm×150 mm column was used online and the online elution gradients for each fraction were adjusted in order to obtain well resolved separation. Comparing this method directly to only performing nanoLC-MS we observed a 61.6% increase in the number of identified proteins. At a 1% false discovery rate 1028 proteins were identified using two dimensions of RPLC versus 636 proteins identified in a single nano-flow separation. The majority of proteins identified by one dimension of nano-LC were present in the proteins identified in our two dimensional strategy. Although increasing analysis time, this non-orthogonal and facile pre-fractionation method affords a more comprehensive examination of the proteome.}, number={9-10}, journal={JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES}, author={Gokce, Emine and Andrews, Genna L. and Dean, Ralph A. and Muddiman, David C.}, year={2011}, month={Mar}, pages={610–614} }