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1.  Data Management, Analysis, Standardization and Reproducibility in a ProteoRed Multicentric Quantitative Proteomics Study with the OmicsHub Proteomics Software Tool 
RP-47
The ProteoRed multicentric experiment was designed to test each laboratory abilities to perform quantitative proteomic analysis, compare methodologies and inter-lab reproducibility for relative quantitative analysis of proteomes and to evaluate data reporting and data sharing tools (MIAPE documents, standard formats, public repositories). The experiment consist in analyzing two different samples (A and B), which contain an identical matrix of E. Coli proteins plus four standard proteins (CYC_HORSE, MYG_HORSE, ALDOA_RABIT, BSA_BOVIN), spiked in different amounts. The samples are designed primarily to be analyzed by LC-MS, although DIGE analysis could be also possible. Each lab will have the choice to test their preferred method for quantitative comparison of the two samples. However, to set as much standardization and reproducibility as possible in terms of data analysis, data sharing, protocols information, results and reporting we propose the OmicsHub Proteomics server to be the single platform to integrate the protein identification steps of the MS multicentric experiment and serve as a web repository. After the “In-lab” analysis is performed, with the laboratory's own tools, every lab is able to load its experiment (protocols, parameters, instruments, etc.) and import its spectrum data via web into the OmicsHub Proteomics analysis and managment server. Every experiment in OmicsHub is automatically stored following the PRIDE standard format. The OmicsHub Proteomics software tool performs the workflow tasks of Protein identification (using the search engines Mascot and Phenyx), Protein annotation, Protein grouping, FDR filtering (allowing the use of a local decoy database, designed ad hoc for this experiment) and Reporting of the protein identification results in a systematic and centralized manner. The OmicsHub Proteomics allows the researchers at ProteoRed consortium to perform its multicentric study with full reproducibility, standardization and experiment comparison; reducing time and data management complexity prior to the final quantification analysis.
PMCID: PMC2918095
2.  Multi-Site Assessment of ProteoRed Plasma Reference Sample for Benchmarking LC-MS Platform Performance 
One of the missions of the Spanish Proteomics Network (ProteoRed ISCIII) is to assist its proteomics core facilities in evaluating their capabilities to perform qualitative and quantitative proteomics analysis. In 2010, the ProteoRed's Sample Collection and Handling Group designed a moderately complex plasma standard reference sample primarily to be used for routine quality assurance monitoring of laboratory instrumentation, as well as inter-laboratory performance assessment, and development and validation of novel technologies. The ProteoRed Plasma Reference (PPR) sample is a subset of highly abundant well-characterized human plasma proteins with a number of isoforms, in addition to 4 spiked-in proteins, altogether distributed over 5 orders of magnitude in concentration. The PPR sample was recently stress tested in the latest ProteoRed Multicenter Experiment (PME6) that counted with the participation of 17 proteomics facilities using a wide range of LC-MS platforms. Although each laboratory was allowed to use its own favorite methodology, we requested the sample be analyzed in a single LC-MS run in experimental triplicate (3 different digestions).Evaluation of the results submitted by the study participants revealed moderate discrepancies at the peptide identification level, and poor overlap at the protein identification level. In an attempt to identify the source of such irreproducibility, raw data of 8 laboratories (24 LC-MS runs) were reanalyzed centrally using a standardized data analysis pipeline, which included protein inference using ProteinProphet software. We found that the majority of protein identification discrepancies across submitted reports of these 8 laboratories were due to inconsistencies on how data analysts and computational tools group and/or infer proteins. Immunoglobulin variable chain identifications were particularly conflicting throughout identification lists, even in the centralized analysis. Using a series of LC-MS performance metrics, we benchmarked the performance of 8 LC-MS instruments (Orbitraps) and identified system components that vary the most across laboratories.
PMCID: PMC3186592
3.  A Multi-Laboratory Study Assessing Robustness and Reproducibility of Plasma Reference Sample for Benchmarking LC-MS Platform Performance 
An increasingly common request for proteomics core facilities is determining qualitative and quantitative differences among clinical samples such as plasma, CSF, or urine. One of the missions of the Spanish Network of Proteomics Facilities (ProteoRed-ISCIII) is to assist its proteomics core facilities in evaluating their capabilities to perform qualitative and quantitative proteomics analysis. This year, in an attempt to represent a realistic experiment scenario that might be requested to a proteomics core facility, we provided a moderately complex plasma standard reference sample to be used for routine QC monitoring of laboratory instrumentation. The ProteoRed Plasma Reference (PPR) sample is a subset of highly abundant well-characterized human plasma proteins with a number of isoforms, in addition to 4 spiked-in proteins, altogether distributed over 5 orders of magnitude in concentration. The PPR sample was recently stress tested in the latest ProteoRed Multicenter Experiment (PME6) that counted with the participation of 17 proteomics facilities using a wide range of LC-MS platforms. We requested the sample be analyzed in a single LC-MS run in experimental triplicate (3 different digestions). Evaluation of the results submitted by the study participants revealed moderate discrepancies at the peptide identification level, and poor overlap at the protein identification level. In an attempt to identify the source of such irreproducibility, raw data of 8 laboratories (24 LC-MS runs) were reanalyzed centrally using a standardized data analysis pipeline, which included protein inference using ProteinProphet software. We found that the majority of protein identification discrepancies across submitted reports of these 8 laboratories were due to inconsistencies on how data analysis and computational tools group and/or infer proteins. Immunoglobulin variable chain identifications were particularly conflicting throughout identification lists, even in the centralized analysis. Using a series of LC-MS performance metrics, we benchmarked the performance of 8 LC-MS instruments (Orbitraps) and identified system components that vary the most across laboratories.
PMCID: PMC3186614
4.  ProteoRed Multicenter Experiment for Long-term Quality Control Evaluation of Proteomics Core Facilities 
Quality control (QC) is becoming increasingly important in proteomic experiments in order to guarantee the quality of research results. Deployment of QC metrics helps in monitoring stability, overall performance and reproducibility of analytical techniques. In an attempt to dispel some of the notions that LC-MS-based proteomics is poorly reproducible, the proteomics community has demonstrated increasingly concerns about the quality of proteomics data made publicly available. Here we describe the ProteoRed Multicenter Experiment for Quality Control (PMEQC), a longitudinal QC multicenter study involving 12 institutions, to assess the repeatability of LC-MS/MS proteomics data within a specific site, the reproducibility across multiple sites and across multiple platforms. Our experimental design also provided a unique opportunity to assess the repeatability of protein sample preparation within a specific site.
The main study was divided into 2 sub studies (Study A and B) that evaluate separately inter- and intra-laboratory variability. Each participant received two sample vials of trypsin-digested yeast proteins (Study A) and the same undigested protein sample (Study B). All participants were requested to follow a strict LC-MS/MS guideline for sample injection amounts and LC gradient. To enable inter-laboratory comparisons, data analysis was centralized and performed under standard procedures using a common workflow that includes well-known software tools for proteomics analysis such as msconvert.exe, X!Tandem, PeptideProphet, OpenMS and R programming language.
Here, we summarize the key findings of the PMEQC project and provide technical insights to better understand and pinpoint the main sources of variability and other issues faced by proteomics core facilities. Our study reveals that the overall performance regarding reproducibility, sensitivity, dynamic range, among other metrics, is directly related to laboratory staff expertise, and less dependent on instrumentation. Furthermore, the present study provides a rich data set of metrics against which other laboratories can benchmark their performance.
PMCID: PMC3630681
5.  ABRF Affiliates and Chapters 
The mission of the ABRF is to advance life sciences core facilities and biotechnology laboratories through research, communication, and education. To facilitate this mission, the ABRF has implemented ABRF Affiliates and Chapters and the ABRF Affiliates and Chapters Committee for the following purposes: a) To encourage the establishment, support the operations, and facilitate the coordination of new regional and special interest groups that have goals related to those of the ABRF in support of life sciences shared resources; b) To establish partnerships and collaborate with other existing organizations that have goals related to those of the ABRF in support of life sciences shared resources; c) To promote the technologies, research support and administration of biomolecular resource facilities; d) To promote the development and applications of biotechnologies as shared research resources and to facilitate the advancement of life sciences research; e) To play a leadership role in networking core laboratories, researchers, and students, matching those with similar and complementary interests and skills; e) To enhance communication on the regional, national and international level regarding ABRF activities; to enhance the visibility of the ABRF in the scientific community; to educate the scientific community about the value of the ABRF; and to broaden the number and diversity of core laboratories and biotechnology laboratories that take advantage of the ABRF research group studies and ABRF membership networking opportunities; and f) To enhance the visibility of the ABRF with funding agencies that support the development, acquisition and application of core facility shared research resources. ABRF Chapters are special interest groups which may be formed based on common interests and/or geographical boundaries and support grassroots networks of individuals who wish to help advance ABRF goals and promote the mission of shared resource facilities and biomolecular resources. ABRF Affiliates are special interest organizations that are autonomous from the ABRF, have common and complementary interests with the ABRF, and have the goal of developing a collaborative relationship with the ABRF. Please join us for the ABRF Affiliates and Chapters Open Mic Session from 6:00 pm to 6:45 pm on Sunday.
PMCID: PMC3186469
6.  A DIGE study on the effects of salbutamol on the rat muscle proteome - an exemplar of best practice for data sharing in proteomics 
BMC Research Notes  2011;4:86.
Background
Proteomic techniques allow researchers to perform detailed analyses of cellular states and many studies are published each year, which highlight large numbers of proteins quantified in different samples. However, currently few data sets make it into public databases with sufficient metadata to allow other groups to verify findings, perform data mining or integrate different data sets. The Proteomics Standards Initiative has released a series of "Minimum Information About a Proteomics Experiment" guideline documents (MIAPE modules) and accompanying data exchange formats. This article focuses on proteomic studies based on gel electrophoresis and demonstrates how the corresponding MIAPE modules can be fulfilled and data deposited in public databases, using a new experimental data set as an example.
Findings
We have performed a study of the effects of an anabolic agent (salbutamol) at two different time points on the protein complement of rat skeletal muscle cells, quantified by difference gel electrophoresis. In the DIGE study, a total of 31 non-redundant proteins were identified as being potentially modulated at 24 h post treatment and 110 non redundant proteins at 96 h post-treatment. Several categories of function have been highlighted as strongly enriched, providing candidate proteins for further study. We also use the study as an example of best practice for data deposition.
Conclusions
We have deposited all data sets from this study in public databases for further analysis by the community. We also describe more generally how gel-based protein identification data sets can now be deposited in the PRoteomics IDEntifications database (PRIDE), using a new software tool, the PRIDESpotMapper, which we developed to work in conjunction with the PRIDE Converter application. We also demonstrate how the ProteoRed MIAPE generator tool can be used to create and share a complete and compliant set of MIAPE reports for this experiment and others.
doi:10.1186/1756-0500-4-86
PMCID: PMC3080311  PMID: 21443781
7.  Mass Spectrometry Data Collection in Parallel at Multiple Core Facilities Operating TripleTOF 5600 and Orbitrap Elite/Velos Pro/Q Exactive Mass Spectrometers 
Proteomic research can benefit from simultaneous access to multiple cutting-edge mass spectrometers. 18 core facilities responded to our investigators seeking service through the ABRF Discussion Forum. Five of the facilities selected completed four plasma proteomics experiments as routine fee-for-service. Each biological experiment entailed an iTRAQ 4-plex proteome comparison of immunodepleted plasma provided as 30 labeled-peptide fractions. Identical samples were analyzed by two AB SCIEX TripleTOF 5600 and three Thermo Orbitrap (Elite/Velos Pro/Q Exactive) instruments. 480 LC-MS/MS runs delivered >250 GB of data over two months.
We compare herein routine service analyses of three peptide fractions of different peptide abundance. Data files from each instrument were studied to develop optimal analysis parameters to compare with default parameters in Mascot Distiller 2.4, ProteinPilot 4.5 beta, AB Sciex MS Data Converter 1.3 beta, and Proteome Discover 1.3. Peak-picking for TripleTOFs was best by ProteinPilot 4.5 beta while Mascot Distiller and Proteome Discoverer were comparable for the Orbitraps.
We compared protein identification and quantitation in SwissProt 2012_07 database by Mascot Server 2.4.01 versus ProteinPilot. By all search methods, more proteins, up to two fold, were identified using the Q Exactive than others. Q Exactive excelled also at the number of unique significant peptide ion sequences. However, software-dependent impact on subsequent interpretation, due to peptide modifications, can be critical. These findings may have special implications for iTRAQ plasma proteomics. For the low abundance peptide ions, the slope of the dynamic range drop-off in the plasma proteome is uniquely sharp compared with cell lysates. Our study provides data for testable improvements in the operation of these mass spectrometers. More importantly, we have demonstrated a new affordable expedient workflow for investigators to perform proteomic experiments through the ABRF infrastructure. (We acknowledge John Cottrell for optimizing the peak-picking parameters for Mascot Distiller).
PMCID: PMC3635246
8.  ABRF-sPRG 2013 Study: Development and Characterization of a Proteomics Normalization Standard Consisting of 1000 Stable Isotope Labeled Peptides and a Qualitative Stability Study of Peptides from the ABRF-sPRG 2012 Study 
The Proteomics Standards Research Group (sPRG) is reporting the first year progress in a two-year sPRG 2012-2013 study which focuses on the generation of a standard that can be used for interassay, interspecies, and interlaboratory normalization in both label-free and stable isotope label-based quantitative proteomics analysis. The standard has been formulated as two mixtures: 1000 stable isotope 13C/15N-labeled synthetic tryptic peptides alone, and peptides mixed with a tryptic digest from a HEK 293 cell lysate. The sequences of the synthetic peptides were derived from approximately 400 proteins and were conserved across proteomes of the most commonly analyzed species: Homo sapiens, Mus musculus and Rattus norvegicus. The selected peptides represent the full range of hydrophobicities and isoelectric points typical to tryptic peptides from complex proteomic samples. The standard was designed to represent proteins of various concentrations, spanning three orders of magnitude. This year we focused our efforts on selection of appropriate protein and peptide candidates, peptide synthesis, quality assessment and LC-MS evaluation by several sPRG member laboratories. The sPRG study design and initial results of a thorough characterization of the standard using a variety of instrumental configurations and bioinformatics approaches will be presented in this talk.
The sPRG is hopeful that the designed formulation will become a valuable resource in various mass spectrometry-based proteomic applications, including quantitative and differential profiling, as well as general benchmarking (e.g. chromatographic retention time). The sPRG plans to start recruiting study participants in April 2013, complete the study by the end of the year 2013, and present the results at the ABRF 2014 meeting. The sPRG encourages proteomics laboratories to participate in the study and sign in at www.abrf.org/sprg.
The second half of the session will discuss the qualitative stability study performed using purified synthetic peptides containing a variety of modifications selected from the 2012 sPRG ABRF sample. The stability of the selected synthetic peptides was evaluated by the sPRG using different storage conditions over a three-month period. After storage at either at room temperature, +4°C or −80°C for one week, one month, or three months. Quantitative LC-MS/MS analysis was used to monitor the stability and degradation of the peptides, and to determine the effect of modifications and storage conditions on peptide degradation rates. The data presented have been built on the quantitative study that was presented at both the 2012 ABRF and ASMS conferences. All forms of degraded peptides were separated and identified using nano-LC tandem mass spectrometry on a Thermo Scientific Q-Exactive hybrid mass spectrometer. Integrated extracted ion chromatograms were used to measure relative amounts of degradation to identify which pathways are most prevalent during storage.
PMCID: PMC3635292
9.  [No title available] 
The Proteomics Standards Research Group (sPRG) is reporting the first year progress in a two-year sPRG 2012–2013 study which focuses on the generation of a standard that can be used for interassay, interspecies, and interlaboratory normalization in both label-free and stable isotope label-based quantitative proteomics analysis. The standard has been formulated as two mixtures: 1000 stable isotope 13C/15N-labeled synthetic tryptic peptides alone, and peptides mixed with a tryptic digest from a HEK 293 cell lysate. The sequences of the synthetic peptides were derived from approximately 400 proteins and were conserved across proteomes of the most commonly analyzed species: Homo sapiens, Mus musculus and Rattus norvegicus. The selected peptides represent the full range of hydrophobicities and isoelectric points typical to tryptic peptides from complex proteomic samples. The standard was designed to represent proteins of various concentrations, spanning three orders of magnitude. This year we focused our efforts on selection of appropriate protein and peptide candidates, peptide synthesis, quality assessment and LC-MS evaluation by several sPRG member laboratories. The sPRG study design and initial results of a thorough characterization of the standard using a variety of instrumental configurations and bioinformatics approaches will be presented in this talk.
The sPRG is hopeful that the designed formulation will become a valuable resource in various mass spectrometry-based proteomic applications, including quantitative and differential profiling, as well as general benchmarking (e.g. chromatographic retention time). The sPRG plans to start recruiting study participants in April 2013, complete the study by the end of the year 2013, and present the results at the ABRF 2014 meeting. The sPRG encourages proteomics laboratories to participate in the study and sign in at www.abrf.org/sprg.
The second half of the session will discuss the qualitative stability study performed using purified synthetic peptides containing a variety of modifications selected from the 2012 sPRG ABRF sample. The stability of the selected synthetic peptides was evaluated by the sPRG using different storage conditions over a three-month period. After storage at either at room temperature, +4°C or -80°C for one week, one month, or three months. Quantitative LC-MS/MS analysis was used to monitor the stability and degradation of the peptides, and to determine the effect of modifications and storage conditions on peptide degradation rates. The data presented have been built on the quantitative study that was presented at both the 2012 ABRF and ASMS conferences. All forms of degraded peptides were separated and identified using nano-LC tandem mass spectrometry on a Thermo Scientific Q-Exactive hybrid mass spectrometer. Integrated extracted ion chromatograms were used to measure relative amounts of degradation to identify which pathways are most prevalent during storage.
PMCID: PMC3635369
10.  Label-free Differential Proteomics Using a Combination of MS Profiling and Targeted MS/MS 
RP-105
Differential proteomic analysis is an essential tool in the effort to elucidate the biochemical mechanisms that underlie phenotypes in mouse models of human diseases. There are many methods for differential proteomic analysis of complex samples, such as mouse tissue lysates, including two dimensional gel electrophoresis, iTRAQ and label-free methods. The Association for Biomolecular Resource Facilities proteomic research group study from 2007 (ABRF PRG07) evaluated multiple laboratories and methodologies for performing differential proteomics. Overall, label-free mass spectrometry (MS)-based methodologies were the most sensitive and most accurate for identifying and quantifying differentially expressed/spiked proteins. In an effort to evaluate a label-free method for (MS) based differential proteomic analysis, a combination of MS profiling and targeted MS/MS experiments have been used to identify tryptic peptides derived three proteins that were differentially spiked into the Sigma Proteomics Dynamic Range Standard Set (UPS2). LC/MS analyses were done on a microfluidic-based nanoflow LC coupled to a quadrupole time-of-flight (Q-TOF) mass spectrometer. The MS profiling data was processed using differential analysis software. Features that were differentially-spiked were subjected to targeted MS/MS and the peptides were identified using database search software. Our results illustrate the effectiveness of this approach, as all three unknown differentially-spiked proteins were successfully identified and quantitated. Beyond the QTOF itself, this study demonstrates the importance of both reproducible high resolution HPLC and appropriate software as being critical components of the described label-free method.
PMCID: PMC2918089
11.  [No title available] 
The goals of the ABRF Next Generation Sequencing (ABRF-NGS) study are to evaluate the performance of all available NGS platforms and to identify optimal methods and best practices across sites. The study is a coordinated effort of five ABRF Research Groups, involving over 20 core facility laboratories. The ABRF-NGS study currently includes the Illumina HiSeq, Roche 454, Life Technologies Ion Torrent PGM and Pacific Biosciences PacBio RS platforms. The first phase of the study is focused on transcriptome analysis using RNA reference samples from the Microarray Quality Control (MAQC) study together with spike-in controls developed by the External RNA Controls Consortium (ERCC). The aim of this first phase is to assess sequencing accuracy, absolute and relative expression levels, and differential expression detection. The ABRF-NGS study is not intended to be a “bake-off” but rather is an effort to establish a reference data set for each platform to help sites improve their methods. Future phases of the study will include evaluation of results with degraded RNA and DNA, microRNA profiling, DNA and RNA sequencing of a HapMap trio, and DNA sequencing of reference sets of samples with well defined “difficult-to-sequence” regions. The long-term goals of the ABRF-NGS study are to optimize the detection of genetic variation with the latest sequencing tools and to establish a community resource for self-evaluation and self-improvement that will allow users of next generation sequencing technologies to readily compare their own performance data as instruments and protocols change. This is a key feature of an evaluation resource given the rapid pace of development of NGS technologies. This session will present the ABRF-NGS project design and participants and the current status of data collection and analysis.
PMCID: PMC3630658
12.  PRG-2011: Defining the Interaction between Users and Suppliers of Proteomics Services 
Over the last ten years the Proteomics Research Group (PRG) has undertaken technical studies that have covered a wide range of issues unique to the rapidly developing field of proteomics and have included a range of qualitative and quantitative experiments. The PRG studies have resulted in a great deal of attention not only within the ABRF community but also outside as is evident from numerous articles dealing with proteomics methods, procedures and standardization. As the field continues to develop, the diversity of instrumentation and laboratory workflows have grown in tandem. Therefore, in the PRG2011 study it seemed especially useful to perform a survey to help the PRG define future studies based on the current blend of sample types and technologies and obtain a view of emerging trends. A survey was created to ascertain three main insights into core facility function: 1) How labs interact with their clients, 2) The capacity of labs to meet the demands of their clients, and 3) The blend of experimental techniques offered to and requested by clients. Survey questions were designed to obtain information from both users of core facilities and the directors and personnel of core facilities. Questions covered such topics as the type and age of instruments in use, how data is analyzed and presented to client, sources of funding, and emerging proteomics trends. Results are compiled en masse and presented without regard to institution. Early results reveal that about 2/3 of the responders are not ABRF members, and at least one lab still has an operational mass spectrometer that was acquired in 1990!
PMCID: PMC3186496
13.  A Multi-Centric Study To Assess Reproducibility of Protein Quantification By SRM LC-MS Proteomic Analysis 
In order to evaluate the robustness and reproducibility, within and across laboratories, of the SRM quantification methodology, we set up a multi-centric study (PME7) carried out at 9 laboratories, most of them members of the ProteoRed-ISCIII network of proteomics facilities in Spain.
Each participant laboratory received identical samples of a “Quantitative Proteomics Sample Set” prepared by Sigma-Aldrich. The sample set consisted of 5 different samples A-E, containing tryptic digests of 9 human proteins, spiked in different amounts to a yeast lysate digest. The amounts of these proteins are distributed in three different concentration “Tiers”: Tier 1: 3 protein digests in the range 25-2500 fmol / microgram of yeast lysate; Tier 2: 3 protein digests in the range 2.5- 1250 fmol / microgram of yeast lysate; Tier 3: 3 protein digests in the range 0.25-25 fmol / microgram of yeast lysate. In addition, each sample contains identical amount (250 fmol) of a tenth protein digest, for normalization purposes. Finally, two different labeled AQUA peptides per protein were added to each sample in defined amounts.
The five samples were analyzed in triplicate by SRM at the different laboratories, using similar, but not strictly identical, chromatographic and spectrometric conditions, and with different instruments. Each laboratory reported results on relative quantification (fold changes between A-E samples) and absolute quantification based on the AQUA peptide standards.
The results demonstrate a good degree of reproducibility of targeted quantification measurements by SRM at different laboratories, irrespective of the method of analysis and the spectrometer used. The average %CV of the measured absolute protein amounts ranges from less than 10% for Tier 1 proteins, to 40-60% for the proteins at the lowest concentrations.
The results obtained at each laboratory allow the assessment of the limitations in sensitivity and limits of quantification under the diverse analytical conditions used.
PMCID: PMC3630565
14.  ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining 
BMC Bioinformatics  2008;9(Suppl 9):S5.
Background
New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed.
Results
We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network.
Conclusion
The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies.
doi:10.1186/1471-2105-9-S9-S5
PMCID: PMC2537576  PMID: 18793469
15.  PRG-2011: Defining the Interaction Between Users and Suppliers of Proteomics Services/Facilities 
Over the last ten years the Proteomics Research Group (PRG) has undertaken technical studies that have covered a wide range of issues unique to the rapidly developing field of proteomics. These studies have included a range of qualitative and quantitative experiments. The PRG studies have resulted in a great deal of attention not only within the ABRF community but also outside as is evident from numerous articles dealing with proteomics methods, procedures and standardization. As the field continues to develop, the diversity of instrumentation and laboratory workflows have grown in tandem. Therefore, for the PRG2011 study it seemed especially useful to perform a survey to help define future studies based on the current blend of sample types and technologies and obtain a view of emerging trends. A survey was created to ascertain three main insights into core facility function: 1) How labs interact with their clients, 2) The capacity of labs to meet the demands of their clients, and 3) The blend of experimental techniques offered to and requested by clients. Survey questions were designed to obtain information from both users of core facilities and the directors and personnel of core facilities. Questions covered such topics as the type and age of instruments in use, how data is analyzed and presented to client, sources of funding, and emerging proteomics trends. Results are compiled en masse and presented without regard to institution.
PMCID: PMC3186500
16.  (s9) Proteomics Standardization 
The needs and issues related to establishing inter-laboratory standardization in quantitative proteomics will be highlighted by presentations from three international initiatives. By establishing the need for standardization, each group will also highlight the tremendous amount of instrument and experimental variation that is also measured when trying to determine real biological changes. The Spanish-based ProteoRed consortium addresses multiple proteomics platforms, ranging from MS-based to gel-based. The NIH CPTAC network is focused on MS-based studies, and the UK-based FixingProteomics initiative covers mostly gel-based methods. Results from these studies will demonstrate complement and overlap in technology and approaches used between these groups, all motivated by the universal goal of standardizing these complex technologies across laboratories.
PMCID: PMC3186634
17.  The Establishment and Growth of the Vermont Genetics Network Proteomics Facility 
CF-18
The Vermont Genetics Network (VGN) Proteomics Facility supports biological and biomedical research primarily throughout Vermont, but also welcomes sample submission from investigators outside of Vermont. The facility uses state-of-the-art mass spectrometry for analyzing proteins and peptides. Facility personnel acquire and analyze proteomic data and conduct training in proteomic methods and experimental design. Pre-submission and post-data acquisition consultations are arranged with facility directors. Details can be found at www.uvm.edu/∼vgn/proteomics. The facility is led by Co-Directors Dr. Bryan Ballif and Dr. Dwight Matthews. Facility manager, Dr. Bin Deng interacts with investigators and runs proteomics analyses. Dr. Jim Vincent, VGN Bioinformatics Core director, provides bioinformatics support and Dr. Janet Murray coordinates the outreach to Vermont Baccalaureate Partner Institutions. The Proteomics Facility has three mass spectrometers each coupled to nanoflow high performance liquid chromatography (nano-HPLC). The mass spectrometers are two Thermo-Finnigan LTQ linear quadrupole ion trap mass spectrometers and one Thermo-Finnigan LTQ-Orbitrap hybrid mass spectrometer. Since last year over 2000 samples have been analyzed, twelve peer-reviewed papers have been published and 17 grant proposals have been supported by the proteomics facility. The user base for the proteomics facility is growing and includes over 34 faculty/post doctorate/staff, 28 graduate students and 7 undergraduates. At least 11 seminars and 11 poster presentations have been presented by users/staff that have presented VGN proteomic data. It is an exciting time for proteomics in Vermont. The facility is funded by the VGN through NIH grant P20 RR16462 from the INBRE Program of the National Center for Research Resources.
PMCID: PMC2918110
18.  Help Us Help You! The Cytometry Interest Research Group Bridging the Gap Between Sample Purification and Downstream Applications 
Cytometry is a critical tool applied throughout life sciences research. For example, the ability to identify and isolate cells with desired characteristics is required for subsequent analysis using downstream applications such as proteomics and DNA sequencing. Most research institutions have a shared cytometry resource facility within their organization. Recently, a group of cytometrists affiliated with the Association of Biomolecular Research Facilities (ABRF) have come together to discuss the formation of a new research group (RG) within ABRF. Unlike other organizations, such as the International Society for the Advancement of Cytometry (ISAC) which focuces on technology issues related to flow cytometry, our goal is to create a group that serves as a bridge between cytometry and other RG's such as proteomics, nucleic acids, and light microscopy. Some examples of important areas to be explored include: filling in the gaps between the transfer of samples from cytometry to other cores, creating synergistic relationships between cores and enhancing overall data output. This can be accomplished by creating a deeper relationship with other RGs to determine specific areas that need improvement within the sample processing pipeline. Pilot/validation studies can be created to pinpoint and resolve these issues. As institutional research core facilities become more centralized, there is an added value when cores cooperate with each other to streamline the processing pipeline, and optimize results with the least amount of material. The Cytometry RG organizing committee is interested in your input and would like other RG group members to share their ideas with us to help shape this group. Preparative cytometry is a hub from which many other cores acquire their samples and our ultimate goal is to create better service, samples and data for end users.
PMCID: PMC3630556
19.  Human Proteinpedia: a unified discovery resource for proteomics research 
Nucleic Acids Research  2008;37(Database issue):D773-D781.
Sharing proteomic data with the biomedical community through a unified proteomic resource, especially in the context of individual proteins, is a challenging prospect. We have developed a community portal, designated as Human Proteinpedia (http://www.humanproteinpedia.org/), for sharing both unpublished and published human proteomic data through the use of a distributed annotation system designed specifically for this purpose. This system allows laboratories to contribute and maintain protein annotations, which are also mapped to the corresponding proteins through the Human Protein Reference Database (HPRD; http://www.hprd.org/). Thus, it is possible to visualize data pertaining to experimentally validated posttranslational modifications (PTMs), protein isoforms, protein–protein interactions (PPIs), tissue expression, expression in cell lines, subcellular localization and enzyme substrates in the context of individual proteins. With enthusiastic participation of the proteomics community, the past 15 months have witnessed data contributions from more than 75 labs around the world including 2710 distinct experiments, >1.9 million peptides, >4.8 million MS/MS spectra, 150 368 protein expression annotations, 17 410 PTMs, 34 624 PPIs and 2906 subcellular localization annotations. Human Proteinpedia should serve as an integrated platform to store, integrate and disseminate such proteomic data and is inching towards evolving into a unified human proteomics resource.
doi:10.1093/nar/gkn701
PMCID: PMC2686511  PMID: 18948298
20.  A semantic proteomics dashboard (SemPoD) for data management in translational research 
BMC Systems Biology  2012;6(Suppl 3):S20.
Background
One of the primary challenges in translational research data management is breaking down the barriers between the multiple data silos and the integration of 'omics data with clinical information to complete the cycle from the bench to the bedside. The role of contextual metadata, also called provenance information, is a key factor ineffective data integration, reproducibility of results, correct attribution of original source, and answering research queries involving "What", "Where", "When", "Which", "Who", "How", and "Why" (also known as the W7 model). But, at present there is limited or no effective approach to managing and leveraging provenance information for integrating data across studies or projects. Hence, there is an urgent need for a paradigm shift in creating a "provenance-aware" informatics platform to address this challenge. We introduce an ontology-driven, intuitive Semantic Proteomics Dashboard (SemPoD) that uses provenance together with domain information (semantic provenance) to enable researchers to query, compare, and correlate different types of data across multiple projects, and allow integration with legacy data to support their ongoing research.
Results
The SemPoD platform, currently in use at the Case Center for Proteomics and Bioinformatics (CPB), consists of three components: (a) Ontology-driven Visual Query Composer, (b) Result Explorer, and (c) Query Manager. Currently, SemPoD allows provenance-aware querying of 1153 mass-spectrometry experiments from 20 different projects. SemPod uses the systems molecular biology provenance ontology (SysPro) to support a dynamic query composition interface, which automatically updates the components of the query interface based on previous user selections and efficientlyprunes the result set usinga "smart filtering" approach. The SysPro ontology re-uses terms from the PROV-ontology (PROV-O) being developed by the World Wide Web Consortium (W3C) provenance working group, the minimum information required for reporting a molecular interaction experiment (MIMIx), and the minimum information about a proteomics experiment (MIAPE) guidelines. The SemPoD was evaluated both in terms of user feedback and as scalability of the system.
Conclusions
SemPoD is an intuitive and powerful provenance ontology-driven data access and query platform that uses the MIAPE and MIMIx metadata guideline to create an integrated view over large-scale systems molecular biology datasets. SemPoD leverages the SysPro ontology to create an intuitive dashboard for biologists to compose queries, explore the results, and use a query manager for storing queries for later use. SemPoD can be deployed over many existing database applications storing 'omics data, including, as illustrated here, the LabKey data-management system. The initial user feedback evaluating the usability and functionality of SemPoD has been very positive and it is being considered for wider deployment beyond the proteomics domain, and in other 'omics' centers.
doi:10.1186/1752-0509-6-S3-S20
PMCID: PMC3524316  PMID: 23282161
21.  Access Guide to Human Proteinpedia 
Human Proteinpedia (http://www.humanproteinpedia.org) is a publicly available proteome repository for sharing human protein data derived from multiple experimental platforms. It incorporates diverse features of human proteome including protein-protein interactions, enzyme-substrate relationships, PTMs, subcellular localization, expression of proteins in various human tissues and cell lines in diverse biological conditions including diseases. Through a public distributed annotation system developed especially for proteomic data, investigators across the globe can upload, view and edit proteomic data even before they are published. Inclusion of information on investigators and laboratories that generated the data, visualization of tandem mass spectra, stained tissue sections, protein/peptide microarrays, fluorescent micrographs and Western blots ensure quality of proteomic data assimilated in Human Proteinpedia. Many of the protein annotations submitted to Human Proteinpedia have also been made available to the scientific community through Human Protein Reference Database (http://www.hprd.org), another resource developed by our group. In this protocol, we describe how to submit, edit and retrieve proteomic data in Human Proteinpedia.
doi:10.1002/0471250953.bi0121s41
PMCID: PMC3664228  PMID: 23504933
mass spectrometry; tissue microarrays; biomarkers; disease proteomics; HPRD; proteotypic peptides; multiple reaction monitoring
22.  Structural and functional protein network analyses predict novel signaling functions for rhodopsin 
Proteomic analyses, literature mining, and structural data were combined to generate an extensive signaling network linked to the visual G protein-coupled receptor rhodopsin. Network analysis suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking.
Using a shotgun proteomic approach, we identified the protein inventory of the light sensing outer segment of the mammalian photoreceptor.These data, combined with literature mining, structural modeling, and computational analysis, offer a comprehensive view of signal transduction downstream of the visual G protein-coupled receptor rhodopsin.The network suggests novel signaling branches downstream of rhodopsin to cytoskeleton dynamics and vesicular trafficking.The network serves as a basis for elucidating physiological principles of photoreceptor function and suggests potential disease-associated proteins.
Photoreceptor cells are neurons capable of converting light into electrical signals. The rod outer segment (ROS) region of the photoreceptor cells is a cellular structure made of a stack of around 800 closed membrane disks loaded with rhodopsin (Liang et al, 2003; Nickell et al, 2007). In disc membranes, rhodopsin arranges itself into paracrystalline dimer arrays, enabling optimal association with the heterotrimeric G protein transducin as well as additional regulatory components (Ciarkowski et al, 2005). Disruption of these highly regulated structures and processes by germline mutations is the cause of severe blinding diseases such as retinitis pigmentosa, macular degeneration, or congenital stationary night blindness (Berger et al, 2010).
Traditionally, signal transduction networks have been studied by combining biochemical and genetic experiments addressing the relations among a small number of components. More recently, large throughput experiments using different techniques like two hybrid or co-immunoprecipitation coupled to mass spectrometry have added a new level of complexity (Ito et al, 2001; Gavin et al, 2002, 2006; Ho et al, 2002; Rual et al, 2005; Stelzl et al, 2005). However, in these studies, space, time, and the fact that many interactions detected for a particular protein are not compatible, are not taken into consideration. Structural information can help discriminate between direct and indirect interactions and more importantly it can determine if two or more predicted partners of any given protein or complex can simultaneously bind a target or rather compete for the same interaction surface (Kim et al, 2006).
In this work, we build a functional and dynamic interaction network centered on rhodopsin on a systems level, using six steps: In step 1, we experimentally identified the proteomic inventory of the porcine ROS, and we compared our data set with a recent proteomic study from bovine ROS (Kwok et al, 2008). The union of the two data sets was defined as the ‘initial experimental ROS proteome'. After removal of contaminants and applying filtering methods, a ‘core ROS proteome', consisting of 355 proteins, was defined.
In step 2, proteins of the core ROS proteome were assigned to six functional modules: (1) vision, signaling, transporters, and channels; (2) outer segment structure and morphogenesis; (3) housekeeping; (4) cytoskeleton and polarity; (5) vesicles formation and trafficking, and (6) metabolism.
In step 3, a protein-protein interaction network was constructed based on the literature mining. Since for most of the interactions experimental evidence was co-immunoprecipitation, or pull-down experiments, and in addition many of the edges in the network are supported by single experimental evidence, often derived from high-throughput approaches, we refer to this network, as ‘fuzzy ROS interactome'. Structural information was used to predict binary interactions, based on the finding that similar domain pairs are likely to interact in a similar way (‘nature repeats itself') (Aloy and Russell, 2002). To increase the confidence in the resulting network, edges supported by a single evidence not coming from yeast two-hybrid experiments were removed, exception being interactions where the evidence was the existence of a three-dimensional structure of the complex itself, or of a highly homologous complex. This curated static network (‘high-confidence ROS interactome') comprises 660 edges linking the majority of the nodes. By considering only edges supported by at least one evidence of direct binary interaction, we end up with a ‘high-confidence binary ROS interactome'. We next extended the published core pathway (Dell'Orco et al, 2009) using evidence from our high-confidence network. We find several new direct binary links to different cellular functional processes (Figure 4): the active rhodopsin interacts with Rac1 and the GTP form of Rho. There is also a connection between active rhodopsin and Arf4, as well as PDEδ with Rab13 and the GTP-bound form of Arl3 that links the vision cycle to vesicle trafficking and structure. We see a connection between PDEδ with prenyl-modified proteins, such as several small GTPases, as well as with rhodopsin kinase. Further, our network reveals several direct binary connections between Ca2+-regulated proteins and cytoskeleton proteins; these are CaMK2A with actinin, calmodulin with GAP43 and S1008, and PKC with 14-3-3 family members.
In step 4, part of the network was experimentally validated using three different approaches to identify physical protein associations that would occur under physiological conditions: (i) Co-segregation/co-sedimentation experiments, (ii) immunoprecipitations combined with mass spectrometry and/or subsequent immunoblotting, and (iii) utilizing the glycosylated N-terminus of rhodopsin to isolate its associated protein partners by Concanavalin A affinity purification. In total, 60 co-purification and co-elution experiments supported interactions that were already in our literature network, and new evidence from 175 co-IP experiments in this work was added. Next, we aimed to provide additional independent experimental confirmation for two of the novel networks and functional links proposed based on the network analysis: (i) the proposed complex between Rac1/RhoA/CRMP-2/tubulin/and ROCK II in ROS was investigated by culturing retinal explants in the presence of an ROCK II-specific inhibitor (Figure 6). While morphology of the retinas treated with ROCK II inhibitor appeared normal, immunohistochemistry analyses revealed several alterations on the protein level. (ii) We supported the hypothesis that PDEδ could function as a GDI for Rac1 in ROS, by demonstrating that PDEδ and Rac1 co localize in ROS and that PDEδ could dissociate Rac1 from ROS membranes in vitro.
In step 5, we use structural information to distinguish between mutually compatible (‘AND') or excluded (‘XOR') interactions. This enables breaking a network of nodes and edges into functional machines or sub-networks/modules. In the vision branch, both ‘AND' and ‘XOR' gates synergize. This may allow dynamic tuning of light and dark states. However, all connections from the vision module to other modules are ‘XOR' connections suggesting that competition, in connection with local protein concentration changes, could be important for transmitting signals from the core vision module.
In the last step, we map and functionally characterize the known mutations that produce blindness.
In summary, this represents the first comprehensive, dynamic, and integrative rhodopsin signaling network, which can be the basis for integrating and mapping newly discovered disease mutants, to guide protein or signaling branch-specific therapies.
Orchestration of signaling, photoreceptor structural integrity, and maintenance needed for mammalian vision remain enigmatic. By integrating three proteomic data sets, literature mining, computational analyses, and structural information, we have generated a multiscale signal transduction network linked to the visual G protein-coupled receptor (GPCR) rhodopsin, the major protein component of rod outer segments. This network was complemented by domain decomposition of protein–protein interactions and then qualified for mutually exclusive or mutually compatible interactions and ternary complex formation using structural data. The resulting information not only offers a comprehensive view of signal transduction induced by this GPCR but also suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking, predicting an important level of regulation through small GTPases. Further, it demonstrates a specific disease susceptibility of the core visual pathway due to the uniqueness of its components present mainly in the eye. As a comprehensive multiscale network, it can serve as a basis to elucidate the physiological principles of photoreceptor function, identify potential disease-associated genes and proteins, and guide the development of therapies that target specific branches of the signaling pathway.
doi:10.1038/msb.2011.83
PMCID: PMC3261702  PMID: 22108793
protein interaction network; rhodopsin signaling; structural modeling
23.  Absolute quantification of microbial proteomes at different states by directed mass spectrometry 
The developed, directed mass spectrometry workflow allows to generate consistent and system-wide quantitative maps of microbial proteomes in a single analysis. Application to the human pathogen L. interrogans revealed mechanistic proteome changes over time involved in pathogenic progression and antibiotic defense, and new insights about the regulation of absolute protein abundances within operons.
The developed, directed proteomic approach allowed consistent detection and absolute quantification of 1680 proteins of the human pathogen L. interrogans in a single LC–MS/MS experiment.The comparison of 25 extensive, consistent and quantitative proteome maps revealed new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans, and about the regulation of protein abundances within operons.The generated time-resolved data sets are compatible with pattern analysis algorithms developed for transcriptomics, including hierarchical clustering and functional enrichment analysis of the detected profile clusters.This is the first study that describes the absolute quantitative behavior of any proteome over multiple states and represents the most comprehensive proteome abundance pattern comparison for any organism to date.
Over the last decade, mass spectrometry (MS)-based proteomics has evolved as the method of choice for system-wide proteome studies and now allows for the characterization of several thousands of proteins in a single sample. Despite these great advances, redundant monitoring of protein levels over large sample numbers in a high-throughput manner remains a challenging task. New directed MS strategies have shown to overcome some of the current limitations, thereby enabling the acquisition of consistent and system-wide data sets of proteomes with low-to-moderate complexity at high throughput.
In this study, we applied this integrated, two-stage MS strategy to investigate global proteome changes in the human pathogen L. interrogans. In the initial discovery phase, 1680 proteins (out of around 3600 gene products) could be identified (Schmidt et al, 2008) and, by focusing precious MS-sequencing time on the most dominant, specific peptides per protein, all proteins could be accurately and consistently monitored over 25 different samples within a few days of instrument time in the following scoring phase (Figure 1). Additionally, the co-analysis of heavy reference peptides enabled us to obtain absolute protein concentration estimates for all identified proteins in each perturbation (Malmström et al, 2009). The detected proteins did not show any biases against functional groups or protein classes, including membrane proteins, and span an abundance range of more than three orders of magnitude, a range that is expected to cover most of the L. interrogans proteome (Malmström et al, 2009).
To elucidate mechanistic proteome changes over time involved in pathogenic progression and antibiotic defense of L. interrogans, we generated time-resolved proteome maps of cells perturbed with serum and three different antibiotics at sublethal concentrations that are currently used to treat Leptospirosis. This yielded an information-rich proteomic data set that describes, for the first time, the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date. Using this unique property of the data set, we could quantify protein components of entire pathways across several time points and subject the data sets to cluster analysis, a tool that was previously limited to the transcript level due to incomplete sampling on protein level (Figure 4). Based on these analyses, we could demonstrate that Leptospira cells adjust the cellular abundance of a certain subset of proteins and pathways as a general response to stress while other parts of the proteome respond highly specific. The cells furthermore react to individual treatments by ‘fine tuning' the abundance of certain proteins and pathways in order to cope with the specific cause of stress. Intriguingly, the most specific and significant expression changes were observed for proteins involved in motility, tissue penetration and virulence after serum treatment where we tried to simulate the host environment. While many of the detected protein changes demonstrate good agreement with available transcriptomics data, most proteins showed a poor correlation. This includes potential virulence factors, like Loa22 or OmpL1, with confirmed expression in vivo that were significantly up-regulated on the protein level, but not on the mRNA level, strengthening the importance of proteomic studies. The high resolution and coverage of the proteome data set enabled us to further investigate protein abundance changes of co-regulated genes within operons. This suggests that although most proteins within an operon respond to regulation synchronously, bacterial cells seem to have subtle means to adjust the levels of individual proteins or protein groups outside of the general trend, a phenomena that was recently also observed on the transcript level of other bacteria (Güell et al, 2009).
The method can be implemented with standard high-resolution mass spectrometers and software tools that are readily available in the majority of proteomics laboratories. It is scalable to any proteome of low-to-medium complexity and can be extended to post-translational modifications or peptide-labeling strategies for quantification. We therefore expect the approach outlined here to become a cornerstone for microbial systems biology.
Over the past decade, liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) has evolved into the main proteome discovery technology. Up to several thousand proteins can now be reliably identified from a sample and the relative abundance of the identified proteins can be determined across samples. However, the remeasurement of substantially similar proteomes, for example those generated by perturbation experiments in systems biology, at high reproducibility and throughput remains challenging. Here, we apply a directed MS strategy to detect and quantify sets of pre-determined peptides in tryptic digests of cells of the human pathogen Leptospira interrogans at 25 different states. We show that in a single LC–MS/MS experiment around 5000 peptides, covering 1680 L. interrogans proteins, can be consistently detected and their absolute expression levels estimated, revealing new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans. This is the first study that describes the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date.
doi:10.1038/msb.2011.37
PMCID: PMC3159967  PMID: 21772258
absolute quantification; directed mass spectrometry; Leptospira interrogans; microbiology; proteomics
24.  P5-M Proteinscape—Software Platform for Managing Proteomics Data 
Proteomics inherently deals with huge amounts of data. Current mass spectrometers acquire hundreds of thousands of spectra within a single project. Thus, data management and data analysis are a challenge. We have developed a software platform (Proteinscape) that stores all relevant proteomics data efficiently and allows fast access and correlation analysis within proteomics projects.
The software is based on a relational database system using Web-based server-client architecture with intra- and Internet access.
Proteinscape stores relevant data from all steps of proteomics projects—study design, sample treatment, separation techniques (e.g., gel electrophoresis or liquid chromatography), protein digestion, mass spectrometry, and protein database search results. Gel spot data can be imported directly from several 2DE-gel image analysis software packages as well as spot-picking robots. Spectra (MS and MS/MS) are imported automatically during acquisition from MALDI and ESI mass spectrometers.
Many algorithms for automated spectra and search result processing are integrated. PMF spectra are calibrated and filtered for contaminant and polymer peaks (Score-booster). A single non-redundant protein list—containing only proteins that can be distinguished by the MS/MS data—can be generated from MS/MS search results (ProteinExtractor). This algorithm can combine data from different search algorithms or different experiments (MALDI/ESI, or acquisition repetitions) into a single protein list.
Navigation within the database is possible either by using the hierarchy of project, sample, protein/peptide separation, spectrum, and identification results, or by using a gel viewer plug-in. Available features include zooming, annotations (protein, spot name, etc.), export of the annotated image, and links to spot, spectrum, and protein data.
Proteinscape includes sophisticated query tools that allow data retrieval for typical questions in proteome projects. Here we present the benefit and power of usage of 6 years of continuous use of the software in over 70 proteome projects managed in house.
PMCID: PMC2291826
25.  ABRF Affiliates and Chapters 
The mission of the ABRF is to advance core facilities and biotechnology laboratories through research, communication, and education. To achieve this mission, the ABRF has implemented both the ABRF Affiliates and Chapters Program and the ABRF Affiliates and Chapters Committee for the following purposes: a) To encourage the formation and coordinate the activities of new regional and special interest groups whose goals are aligned with those of the ABRF.b) To support the operations of and establish collaborative partnerships with these groups.c) To promote biomolecular resource facilities by fostering both technical and administrative excellence.d) To enhance and extend our leadership role in the networking of core laboratories by connecting researchers, students and other stakeholders with synergistic interests and skill sets.e) To enhance communication on the regional, national and international level.f) To enlighten the scientific community concerning the value of the ABRF.g) To increase the number and diversity of laboratories that benefit from ABRF sponsored by activities.
The ABRF Affiliates and Chapters initiative is now in its second year and, to date, has been very successful! Our poster will present details on current Affiliates and Chapters, procedures and protocols on how to apply for Chapter or Affiliate status, relevant resources, ongoing and future projects and more.
PMCID: PMC3630597

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