PhosFox: a bioinformatics tool for peptide-level processing of LC-MS/MS-based phosphoproteomic data
- Sandra Söderholm†1,
- Petteri Hintsanen†2,
- Tiina Öhman1,
- Tero Aittokallio2 and
- Tuula A Nyman1Email author
© Söderholm et al.; licensee BioMed Central Ltd. 2014
Received: 26 March 2014
Accepted: 19 June 2014
Published: 26 June 2014
It is possible to identify thousands of phosphopeptides and –proteins in a single experiment with mass spectrometry-based phosphoproteomics. However, a current bottleneck is the downstream data analysis which is often laborious and requires a number of manual steps.
Toward automating the analysis steps, we have developed and implemented a software, PhosFox, which enables peptide-level processing of phosphoproteomic data generated by multiple protein identification search algorithms, including Mascot, Sequest, and Paragon, as well as cross-comparison of their identification results. The software supports both qualitative and quantitative phosphoproteomics studies, as well as multiple between-group comparisons. Importantly, PhosFox detects uniquely phosphorylated peptides and proteins in one sample compared to another. It also distinguishes differences in phosphorylation sites between phosphorylated proteins in different samples. Using two case study examples, a qualitative phosphoproteome dataset from human keratinocytes and a quantitative phosphoproteome dataset from rat kidney inner medulla, we demonstrate here how PhosFox facilitates an efficient and in-depth phosphoproteome data analysis. PhosFox was implemented in the Perl programming language and it can be run on most common operating systems. Due to its flexible interface and open source distribution, the users can easily incorporate the program into their MS data analysis workflows and extend the program with new features. PhosFox source code, implementation and user instructions are freely available from https://bitbucket.org/phintsan/phosfox.
PhosFox facilitates efficient and more in-depth comparisons between phosphoproteins in case–control settings. The open source implementation is easily extendable to accommodate additional features for widespread application use cases.
The human proteome is estimated to include up to 500,000 phosphorylation sites , but only a fraction of the potential phosphorylation sites have been identified so far. The advances in phosphopeptide enrichment procedures and high-throughput mass spectrometry instrumentation have led to rapid development of MS-based phosphoproteomics during the last few years, and currently thousands of phosphorylation sites can be detected from a single sample. In MS-based phosphoproteomics, protein identification and phosphopeptide mapping relies on database search engines, including Mascot , Sequest , X!Tandem , OMSSA , Andromeda/Maxquant , and Paragon . However, the user is often limited with the choice of search engine(s) to those that are compatible with the raw data from the MS-instrument used.
There are several software solutions and bioinformatic tools designed for managing and extracting information from phosphoproteomics experiments, such as ArMone , ProteoConnections , PhosphoSiteAnalyzer , and PeptideDepot . Additionally, there are protein modification site localization algorithms which are integrated in search engines and interfaces, for example Mascot delta  and PhosphoRS . While these software solutions can be successfully used in certain applications, to our knowledge, there are no software solutions for directly comparing phosphoproteomic results on the phosphopeptide level between multiple different database search engines and/or between stimulated versus control samples. We have previously developed a tool named Compid  to integrate and compare proteomics data from Mascot and Paragon, but this software does not take into account modifications, such as phosphorylation, and cannot thus distinguish between phosphorylated proteins and peptides or their non-phosphorylated counterparts.
To meet these limitations, we developed and implemented a software tool, PhosFox, which enables peptide-level processing of phosphoproteomic data generated by several protein identification search algorithms (including Mascot, Sequest, and Paragon), as well as between-algorithm comparisons and multiple between-group comparisons. Moreover, adding support for other post-translational modifications is possible with the current implementation of PhosFox, and to demonstrate this we have included the possibility to process also acetylation with PhosFox. The open source and efficient implementation is easily extendable to promote its wide application to large-scale phosphopeptide analyses.
Results and Discussion
Top-ranked canonical pathways and networks after dsRNA-stimulation of human keratinocytes and control samples
Role of BRCA1 in DNA damage
DNA methylation and transcriptional repression signaling
Cell cycle: G2/M DNA damage checkpoint regulation
Cyclins and cell cycle regulation
Mismatch repair in eukaryotes
Role of BRCA1 in DNA damage
Phosphatidylethanolamine biosynthesis III
Endometrial cancer signaling
DNA damage-induced 14-3-3σ signaling
Cell death and survival, cell cycle, nervous system development and function
Cellular assembly and organization, cellular compromise, cell death and survival
Cell cycle, DNA replication, recombination and repair, cell death and survival
Gene expression, cell signaling, post-translational modification
RNA post-transcriptional modification, cell morphology, cellular compromise
Cell cycle, cellular movement, gene expression
Epithelial adherens junction signaling
Remodeling of epithelial adherens junctions
DNA damage-induced 14-3-3σ signaling
Sertoli cell-sertoli cell junction signaling
Role of CHK proteins in cell cycle checkpoint control
Cell cycle: G2/M DNA damage checkpoint regulation
Germ cell-sertoli cell junction signaling
Cell cycle, DNA replication, recombination, and repair, gene expression
Cell morphology, cellular function and maintenance, cell cycle
RNA post-transcriptional modification, cell cycle, cellular movement
Cellular assembly and organization, DNA replication, recombination, and repair, cell morphology
Cellular development, cellular movement, connective tissue disorders
Organismal survival, organ morphology, respiratory system development and function
Viruses are able to manipulate a variety of host-cell signal transduction pathways. The biological impact of the dsRNA stimulation versus no stimulation was studied in more detail with KEA, a kinase enrichment analysis tool . The proteins with unique phosphorylation sites in the control and case samples were analyzed separately, and the best ranked kinases, kinase families, and kinase classes for these datasets are shown in Additional file 8: Table S7. Out of the 445 different kinases included in the KEA knowledgebase, substrates for 140 kinases were identified in the case dataset and substrates for 160 kinases in the control dataset. The dsRNA-stimulated dataset included more significantly enriched substrates for MAPK3 (ERK1) and MAPK8 (JNK1), compared to the control dataset. Moreover, substrates for kinases with known roles in regulation of infection were enriched in the case dataset, but not in the control dataset (p-value < 0.01). One of these kinases was protein kinase C beta type (PRKCB1), which is involved in immunity, apoptosis and NFκB signaling pathways . Serine/threonine-protein kinase MARK1 is active in cell polarity, microtubule dynamics and Wnt signaling  and PRKDC (DNA-dependent protein kinase catalytic subunit) is known as a molecular sensor of DNA damage .
MAPK and PI3K/Akt signaling events regulate responses to extracellular stimuli including viral infections, but also various cellular activities such as cell metabolism and proliferation [27, 28]. Cyclins and cell cycle regulation is included as one of the top-ranked pathways in the case dataset (Table 1A). Also other phosphoproteomic studies with focus on viral infections have demonstrated alterations in phosphorylation of proteins included in these signaling pathways [20, 29]–, suggesting that these pathways have an important role in host-response against viral infection.
At present, thousands of phosphopeptides and –proteins can be identified in a single experiment with high-throughput LC-MS/MS. However, a major difficulty in these studies is the downstream data analysis which is often laborious; in particular, the comparative analysis of the identified phosphopeptides between different samples and the comparison of identification results from different search engines often requires multiple, partially manual steps. To this end, we have developed PhosFox, which enables an automated and integrated phosphoproteomic data analysis. PhosFox compares phosphopeptide results generated with various database search engines across multiple sample groups, such as those with different treatments or time points. PhosFox supports both quantitative and qualitative phosphoproteomic data, and includes special features such as categorization of such phosphopeptides that are unique either to control or case group, or common to both groups. In conclusion, PhosFox facilitates efficient and more in-depth comparisons between phosphoproteins in case–control settings. The open source implementation is easily extendable to accommodate additional features for widespread application use cases, such as a motif-finding option, which would provide valuable information about the kinases that are phosphorylating the identified phosphorylation sites, leading to greater understanding of the functional impacts that these modifications have on cellular processes.
PhosFox software tool
The phosphopeptide data analysis program PhosFox was implemented in the Perl programming language. PhosFox is free software and can be run on most common operating systems, including Windows. Due to its flexible interface and open source distribution, the users can easily incorporate the program into their MS data analysis workflows and extend the program with new features. PhosFox source code, implementation and user instructions are available at https://bitbucket.org/phintsan/phosfox.
PhosFox has been designed so that the user can directly import the database search results as its input. The input data is imported in plain text format, either as comma-separated values (CSV) or tab-separated values (TSV). PhosFox supports the most common file formats and contents exported by the proteomic software being used for analyzing the MS results and performing the database searches. For example, the Mascot search results can directly be converted to CSV files. Paragon and Sequest generated peptide search results can be saved as TSV files. Moreover, if the database searches have been carried out through the Proteome Discoverer (Thermo Scientific) interface, the peptide spectral matches (PSMs) can be exported as plain text format files.
The user can import an arbitrary number of input files (peptide lists) for the analysis. Every input file is defined as either “case” (e.g. stimulated sample) or “control” (e.g. nonstimulated sample). If multiple search result files are added, the files are grouped and processed as one batch. The user can specify cut-off values for multiple quality scores, such as Mascot ion score or Paragon peptide confidence level. PhosFox does not test the confidence of phosphorylation assignments to particular amino acids in the peptide sequence matches, but the user can set a threshold for scores generated by modification site localization algorithms incorporated in search engines, such as Mascot delta  or PhosphoRS .PhosFox detects phosphorylated and acetylated amino acid residues for each peptide, which have been defined in the settings file (by default serine, threonine, tyrosine, and lysine), using the post-translational modification field in the corresponding input file. Non-phosphorylated and non-acetylated peptides are discarded from further processing. As a unique feature of the tool, each phosphorylated (or acetylated) peptide is examined whether it is uniquely modified in the case or the control sample (see Figure 1A for details). A ‘uniquely phosphorylated peptide’ is a phosphopeptide that has a unique phosphorylation or phosphorylations either in the case or control sample.
Peptides in quantitative datasets are treated similarly to peptides in qualitative sets: each peptide is checked for “enrichment” in either case or control sample by comparing the relative amount of detected peptides against a user-specified threshold. For example, the user can specify that if a phosphopeptide has more than a two-fold difference in the case sample, relative to the control sample, it is considered as enriched in the case sample. Such peptides are treated by PhosFox as if they were identified in a (qualitative) case sample. Similar strategy is used for identifying peptides enriched in the control sample.
In cases where multiple search engines are used, PhosFox can also compare similarities and differences between the results from the different search engines. Each input file is attributed to a specific search engine. If there are multiple search engines specified, a peptide is deemed uniquely phosphorylated in the case sample (resp. control) only if it has been detected in the case (control) sample by at least one search engine and not detected in the control (case) sample by any search engine. Furthermore, PhosFox divides unique phosphoproteins into different search engine-specific files, thus facilitating the extraction of either supporting or complementary information about identifications between the different search engines.
Finally, PhosFox reports novel phosphorylations (and acetylations) to the user by comparing the identified sites against those reported in the UniProt, PHOSIDA and PhosphoSitePlus databases. The program outputs HTML reports with lists of peptides, including their modification differences between control and case groups, as well as between the database search engines.
PhosFox is freely available online at: https://bitbucket.org/phintsan/phosfox. The homepage provides installation instructions and a user manual, and here it is possible to download and extract the distribution package, which includes all the required Perl modules. PhosFox is platform independent, but requires Perl version 5.6 or newer. This is already installed in most Unixes and unix-like operating systems (GNU/Linux, BSDs, OS X). For Microsoft Windows, we recommend Strawberry Perl, or the precompiled binary executable (see instructions on the homepage). Protein sequences in FASTA format are needed, and at the moment UniProt and NCBI RefSeq FASTA formats are supported. The detected peptide lists can be imported in plain text format (see the manual for details for supported file types), and a minimum of one case file and one control file is required. PhosFox can optionally detect acetylations and phosphorylations that have been described in the literature before. This feature is enabled by downloading and installing separate database files from the PhosFox web site. PhosFox is free software and requires no licensing either from academic or non-academic users. The source code can be redistributed and/or modified under the GNU General Public License or Artistic License.
Qualitative phosphoproteome samples
Human keratinocytes, HaCaT cells (from ATCC) were transfected with 7 μg/ml dsRNA-analogue polyinosinic-polycytidylic acid (poly I:C) (Sigma-Aldrich) using Lipofectamine™ 2000 (Invitrogen) for 1 h or left untreated. The cells were collected and washed with PBS before they were lysed with HEPES lysis buffer (50 mM HEPES, 150 mM NaCl, 1 mM EDTA, 1% NP-40, pH 7.4) including protease and phosphatase inhibitor cocktails (Sigma-Aldrich). The cell lysates were centrifuged 11,686 × g for 15 min at 4°C and the protein content was measured with Bio-Rad DC™ protein assay (Bio-Rad). For the samples, 8 mg of protein was used. The proteins were precipitated with 10% TCA/acetone and resuspended in 1 ml of urea buffer (8 M urea, 400 mM NH4CO3, 20 mM DL-dithiotheitol, 1 mM EDTA, pH 8.5). The proteins were reduced, alkylated and enzymatically digested in-solution with lysyl endopeptidase (7.5 μg/sample, rLys-C Mass Spec Grade, Promega) for 2 hrs, which after the samples were diluted with 7 ml of destilled water, followed by digestion with trypsin (20 μg/sample, Sequencing Grade Modified Trypsin, Promega) for 16 hrs. Undigested proteins and cell debris were removed, and the samples were desalted on Sep-Pak Vac RP C18 cartridges (Waters). The peptides were fractionated by SCX-HPLC, using an ÄKTApurifier™ instrument (Amersham Biosciences). The peptides were separated on a 200 × 4.6 mm, 5 μm, 200 Å PolySULFOETHYL A™ column (PolyLC) by applying a gradient run with increasing salt concentration. The A buffer contained 10 mM KH2PO4, 20% acetonitrile, with a pH < 3. The gradient was set to 0–50% buffer B (buffer A + 0.4 M KCl) in 25 min, followed by 50–100% buffer B in 15 min. The flow rate was 1 ml/min and 1 ml fractions were collected by an autosampler. The SCX-fractions containing phosphopeptides were collected and desalted. Phosphopeptide enrichment was performed with IMAC using PHOS-Select™ Iron Affinity Gel (Sigma Aldrich) and SigmaPrep™ spin columns according to the manufacturer’s instruction. The enriched phosphopeptides were vacuum-dried and dissolved in 0.1% TFA, which after analyzed by nanoLC-MS/MS using an Ultimate 3000 nano-LC (Dionex) coupled to a QSTAR Elite hybrid quadrupole TOF-MS (Applied Biosystems/MDS Sciex) with nano-ESI ionization as previously described [17, 32]. The samples were loaded on a ProteCol C18-Trap column (SGE) and separated on a PepMap C18 analytical column (15 cm × 75 μm, 5 μm, 100 Å) (LC Packings/Dionex) at 200 nl/min with a linear gradient of 0–40% acetonitrile in 120 min. The MS data was acquired with Analyst QS 2.0 software. Information-dependent acquisition method consisted of a 0.5 s TOF-MS survey scan of m/z 400–1400. From every survey scan two most abundant ions with charge states +2 to +4 were selected for product ion scans, and each selected target ion was dynamically excluded for 60 s. Smart IDA was activated with automatic collision energy and automatic MS/MS accumulation. The LC-MS/MS data were submitted through the ProteinPilot 4.0 interface (Applied Biosystems/MDS Sciex) to an in-house Mascot database search engine version 4.0 (Matrix Science), and to the ProteinPilot algorithm Paragon. The data were searched against the human canonical sequences in the Swiss-Prot database (version 01032013 with 539,616 sequences for the Mascot searches and version 01042013 with 539,829 sequences for the Paragon searches). Similar search criteria for both Mascot and Paragon were used and the criteria are listed, together with the original searches, as Additional file 1: Table S1. For additional confidence of the peptide identifications, the Mascot search results were filtered with an ion score expected cut-off value of 0.01, and the Paragon search results with a peptide confidence level of 99%. The raw data, together with the original Mascot and Paragon searches, has been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository  with the dataset identifier PXD000577.
Quantitative phosphoproteome samples
Published iTRAQ datasets  were used for the implementation and testing of the quantitative data support in PhosFox. As described in the original publication , rat inner medullary collecting duct samples were incubated with or without dDAVP, a V2 receptor-analog of vasopressin, at four different time points (0.5, 2, 5 and 15 min). The proteins were enzymatically digested and each peptide sample was labeled with 8-plex iTRAQ reagent. The labeled samples were combined into a single sample before SCX fractionation, Ga3+ IMAC, and LC-MS/MS analysis with a LTQ Orbitrap Velos mass spectrometer (Thermo Scientific). The MS/MS data was searched with the Sequest algorithm through the Proteome Discoverer platform (Thermo Scientific) on a concatenated database of the Rat Refseq Database (NCBI, March 3, 2010, 30,734 entries), and the abundance ratios (dDAVP/control) for the four time points were calculated. The 15 min time point from one of the three biological replicates was analyzed with PhosFox and compared to the original results (Additional file 7: Figure S1).
The authors thank Dr. Mark Knepper and Dr. Jason Hoffert for providing the quantitative phosphoproteomic datasets. This work was supported by the Academy of Finland [grant numbers 135628, 140950, 255842, 272931, 133227, 269862, 134020, and 218310]; the Sigrid Jusélius foundation, and the Integrative Life Science Doctoral Program (ILS) at the University of Helsinki.
- Lemeer S, Heck AJ: The phosphoproteomics data explosion. Curr Opin Chem Biol 2009,13(4):414–420.View ArticlePubMedGoogle Scholar
- Perkins DN, Pappin DJ, Creasy DM, Cottrell JS: Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999,20(18):3551–3567.View ArticlePubMedGoogle Scholar
- Eng JK, McCormack AL, Yates JRI: An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 1994, 5: 976–989.View ArticlePubMedGoogle Scholar
- Craig R, Beavis RC: TANDEM: matching proteins with tandem mass spectra. Bioinformatics 2004,20(9):1466–1467.View ArticlePubMedGoogle Scholar
- Geer LY, Markey SP, Kowalak JA, Wagner L, Xu M, Maynard DM, Yang X, Shi W, Bryant SH: Open mass spectrometry search algorithm. J Proteome Res 2004,3(5):958–964.View ArticlePubMedGoogle Scholar
- Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M: Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 2011,10(4):1794–1805.View ArticlePubMedGoogle Scholar
- Shilov IV, Seymour SL, Patel AA, Loboda A, Tang WH, Keating SP, Hunter CL, Nuwaysir LM, Schaeffer DA: The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol Cell Proteomics 2007,6(9):1638–1655.View ArticlePubMedGoogle Scholar
- Jiang X, Ye M, Cheng K, Zou H: ArMone: a software suite specially designed for processing and analysis of phosphoproteome data. J Proteome Res 2010,9(5):2743–2751.View ArticlePubMedGoogle Scholar
- Courcelles M, Lemieux S, Voisin L, Meloche S, Thibault P: ProteoConnections: a bioinformatics platform to facilitate proteome and phosphoproteome analyses. Proteomics 2011,11(13):2654–2671.View ArticlePubMedGoogle Scholar
- Bennetzen MV, Cox J, Mann M, Andersen JS: PhosphoSiteAnalyzer: a bioinformatic platform for deciphering phospho proteomes using kinase predictions retrieved from NetworKIN. J Proteome Res 2012,11(6):3480–3486.View ArticlePubMedGoogle Scholar
- Yu K, Salomon AR: PeptideDepot: flexible relational database for visual analysis of quantitative proteomic data and integration of existing protein information. Proteomics 2009,9(23):5350–5358.PubMed CentralView ArticlePubMedGoogle Scholar
- Savitski MM, Lemeer S, Boesche M, Lang M, Mathieson T, Bantscheff M, Kuster B: Confident phosphorylation site localization using the Mascot Delta Score. Mol Cell Proteomics 2011,10(2):M110.003830.PubMed CentralView ArticlePubMedGoogle Scholar
- Taus T, Kocher T, Pichler P, Paschke C, Schmidt A, Henrich C, Mechtler K: Universal and confident phosphorylation site localization using phosphoRS. J Proteome Res 2011,10(12):5354–5362.View ArticlePubMedGoogle Scholar
- Lietzen N, Natri L, Nevalainen OS, Salmi J, Nyman TA: Compid: a new software tool to integrate and compare MS/MS based protein identification results from Mascot and Paragon. J Proteome Res 2010,9(12):6795–6800.View ArticlePubMedGoogle Scholar
- Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, Purkayastha S, Juhasz P, Martin S, Bartlet-Jones M, He F, Jacobson A, Pappin DJ: Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 2004,3(12):1154–1169.View ArticlePubMedGoogle Scholar
- Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M: Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 2002,1(5):376–386.View ArticlePubMedGoogle Scholar
- Ohman T, Lietzen N, Valimaki E, Melchjorsen J, Matikainen S, Nyman TA: Cytosolic RNA recognition pathway activates 14–3-3 protein mediated signaling and caspase-dependent disruption of cytokeratin network in human keratinocytes. J Proteome Res 2010,9(3):1549–1564.View ArticlePubMedGoogle Scholar
- Villen J, Gygi SP: The SCX/IMAC enrichment approach for global phosphorylation analysis by mass spectrometry. Nat Protoc 2008,3(10):1630–1638.PubMed CentralView ArticlePubMedGoogle Scholar
- Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M: Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 2006,127(3):635–648.View ArticlePubMedGoogle Scholar
- Luo R, Fang L, Jin H, Wang D, An K, Xu N, Chen H, Xiao S: Label-free quantitative phosphoproteomic analysis reveals differentially regulated proteins and pathway in PRRSV-infected pulmonary alveolar macrophages. J Proteome Res 2014,13(3):1270–1280.View ArticlePubMedGoogle Scholar
- Hoffert JD, Pisitkun T, Saeed F, Song JH, Chou CL, Knepper MA: Dynamics of the G protein-coupled vasopressin V2 receptor signaling network revealed by quantitative phosphoproteomics. Mol Cell Proteomics 2012,11(2):M111.014613.PubMed CentralView ArticlePubMedGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003,13(11):2498–2504.PubMed CentralView ArticlePubMedGoogle Scholar
- Lachmann A, Ma'ayan A: KEA: kinase enrichment analysis. Bioinformatics 2009,25(5):684–686.PubMed CentralView ArticlePubMedGoogle Scholar
- Kawakami T, Kawakami Y, Kitaura J: Protein kinase C beta (PKC beta): normal functions and diseases. J Biochem 2002,132(5):677–682.View ArticlePubMedGoogle Scholar
- Timm T, Marx A, Panneerselvam S, Mandelkow E, Mandelkow EM: Structure and regulation of MARK, a kinase involved in abnormal phosphorylation of Tau protein. BMC Neurosci 2008,9(Suppl 2):S9. http://www.ncbi.nlm.nih.gov/pubmed/19090997PubMed CentralView ArticlePubMedGoogle Scholar
- Davis AJ, Lee KJ, Chen DJ: The N-terminal region of the DNA-dependent protein kinase catalytic subunit is required for its DNA double-stranded break-mediated activation. J Biol Chem 2013,288(10):7037–7046.PubMed CentralView ArticlePubMedGoogle Scholar
- Melchjorsen J, Sorensen LN, Paludan SR: Expression and function of chemokines during viral infections: from molecular mechanisms to in vivo function. J Leukoc Biol 2003,74(3):331–343.View ArticlePubMedGoogle Scholar
- Mikkelsen SS, Jensen SB, Chiliveru S, Melchjorsen J, Julkunen I, Gaestel M, Arthur JS, Flavell RA, Ghosh S, Paludan SR: RIG-I-mediated activation of p38 MAPK is essential for viral induction of interferon and activation of dendritic cells: dependence on TRAF2 and TAK1. J Biol Chem 2009,284(16):10774–10782.PubMed CentralView ArticlePubMedGoogle Scholar
- Stahl JA, Chavan SS, Sifford JM, Macleod V, Voth DE, Edmondson RD, Forrest JC: Phosphoproteomic analyses reveal signaling pathways that facilitate lytic gammaherpesvirus replication. PLoS Pathog 2013,9(9):e1003583.PubMed CentralView ArticlePubMedGoogle Scholar
- Wojcechowskyj JA, Didigu CA, Lee JY, Parrish NF, Sinha R, Hahn BH, Bushman FD, Jensen ST, Seeholzer SH, Doms RW: Quantitative phosphoproteomics reveals extensive cellular reprogramming during HIV-1 entry. Cell Host Microbe 2013,13(5):613–623.PubMed CentralView ArticlePubMedGoogle Scholar
- Popova TG, Turell MJ, Espina V, Kehn-Hall K, Kidd J, Narayanan A, Liotta L, Petricoin EF 3rd, Kashanchi F, Bailey C, Popov SG: Reverse-phase phosphoproteome analysis of signaling pathways induced by Rift valley fever virus in human small airway epithelial cells. PLoS One 2010,5(11):e13805.PubMed CentralView ArticlePubMedGoogle Scholar
- Lietzen N, Ohman T, Rintahaka J, Julkunen I, Aittokallio T, Matikainen S, Nyman TA: Quantitative subcellular proteome and secretome profiling of influenza A virus-infected human primary macrophages. PLoS Pathog 2011,7(5):e1001340.PubMed CentralView ArticlePubMedGoogle Scholar
- Vizcaino JA, Cote RG, Csordas A, Dianes JA, Fabregat A, Foster JM, Griss J, Alpi E, Birim M, Contell J, O’Kelly G, Schoenegger A, Ovelleiro D, Perez-Riverol Y, Reisinger F, Rios D, Wang R, Hermjakob H: The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res 2013,41(Database issue):D1063-D1069.PubMed CentralView ArticlePubMedGoogle Scholar
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