| 1. |
- Vizcaíno, Juan Antonio, et al.
(författare)
-
A community proposal to integrate proteomics activities in ELIXIR
- 2017
-
Ingår i: F1000Research. - : F1000 Research Ltd. - 2046-1402. ; 6
-
Tidskriftsartikel (refereegranskat)abstract
- Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on 'The Future of Proteomics in ELIXIR' that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes. These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR's existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper.
|
|
| 2. |
- Aleksic, J., et al.
(författare)
-
Measurement of the Crab Nebula spectrum over three decades in energy with the MAGIC telescopes
- 2015
-
Ingår i: Journal of High Energy Astrophysics. - : Elsevier BV. - 2214-4048 .- 2214-4056. ; 5-6, s. 30-38
-
Tidskriftsartikel (refereegranskat)abstract
- The MAGIC stereoscopic system collected 69 hours of Crab Nebula data between October 2009 and April 2011. Analysis of this data sample using the latest improvements in the MAGIC stereoscopic software provided an unprecedented precision of spectral and night-by-night light curve determination at gamma rays. We derived a differential spectrum with a single instrument from 50 GeV up to almost 30 TeV with 5 bins per energy decade. At low energies, MAGIC results, combined with Fermi-LAT data, show a flat and broad Inverse Compton peak. The overall fit to the data between 1 GeV and 30 TeV is not well described by a log-parabola function. We find that a modified log-parabola function with an exponent of 2.5 instead of 2 provides a good description of the data (chi(2)(red) = 35/26). Using systematic uncertainties of the MAGIC and Fermi-LAT measurements we determine the position of the Inverse Compton peak to be at (53 +/- 3(stat)+ 31(syst)-13(syst)) GeV, which is the most precise estimation up to date and is dominated by the systematic effects. There is no hint of the integral flux variability on daily scales at energies above 300 GeV when systematic uncertainties are included in the flux measurement. We consider three state-of-the-art theoretical models to describe the overall spectral energy distribution of the Crab Nebula. The constant B-field model cannot satisfactorily reproduce the VHE spectral measurements presented in this work, having particular difficulty reproducing the broadness of the observed IC peak. Most probably this implies that the assumption of the homogeneity of the magnetic field inside the nebula is incorrect. On the other hand, the time-dependent 1D spectral model provides a good fit of the new VHE results when considering a 80 mu G magnetic field. However, it fails to match the data when including the morphology of the nebula at lower wavelengths.
|
|
| 3. |
- Audain, Enrique, et al.
(författare)
-
In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics
- 2017
-
Ingår i: Journal of Proteomics. - : Elsevier BV. - 1874-3919. ; 150, s. 170-182
-
Tidskriftsartikel (refereegranskat)abstract
- In mass spectrometry-based shotgun proteomics, protein identifications are usually the desired result. However, most of the analytical methods are based on the identification of reliable peptides and not the direct identification of intact proteins. Thus, assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is a critical step in proteomics research. Currently, different protein inference algorithms and tools are available for the proteomics community. Here, we evaluated five software tools for protein inference (PIA, ProteinProphet, Fido, ProteinLP, MSBayesPro) using three popular database search engines: Mascot, X!Tandem, and MS-GF +. All the algorithms were evaluated using a highly customizable KNIME workflow using four different public datasets with varying complexities (different sample preparation, species and analytical instruments). We defined a set of quality control metrics to evaluate the performance of each combination of search engines, protein inference algorithm, and parameters on each dataset. We show that the results for complex samples vary not only regarding the actual numbers of reported protein groups but also concerning the actual composition of groups. Furthermore, the robustness of reported proteins when using databases of differing complexities is strongly dependant on the applied inference algorithm. Finally, merging the identifications of multiple search engines does not necessarily increase the number of reported proteins, but does increase the number of peptides per protein and thus can generally be recommended. Significance Protein inference is one of the major challenges in MS-based proteomics nowadays. Currently, there are a vast number of protein inference algorithms and implementations available for the proteomics community. Protein assembly impacts in the final results of the research, the quantitation values and the final claims in the research manuscript. Even though protein inference is a crucial step in proteomics data analysis, a comprehensive evaluation of the many different inference methods has never been performed. Previously Journal of proteomics has published multiple studies about other benchmark of bioinformatics algorithms (PMID: 26585461; PMID: 22728601) in proteomics studies making clear the importance of those studies for the proteomics community and the journal audience. This manuscript presents a new bioinformatics solution based on the KNIME/OpenMS platform that aims at providing a fair comparison of protein inference algorithms (https://github.com/KNIME-OMICS). Six different algorithms - ProteinProphet, MSBayesPro, ProteinLP, Fido and PIA- were evaluated using the highly customizable workflow on four public datasets with varying complexities. Five popular database search engines Mascot, X!Tandem, MS-GF + and combinations thereof were evaluated for every protein inference tool. In total > 186 proteins lists were analyzed and carefully compare using three metrics for quality assessments of the protein inference results: 1) the numbers of reported proteins, 2) peptides per protein, and the 3) number of uniquely reported proteins per inference method, to address the quality of each inference method. We also examined how many proteins were reported by choosing each combination of search engines, protein inference algorithms and parameters on each dataset. The results show that using 1) PIA or Fido seems to be a good choice when studying the results of the analyzed workflow, regarding not only the reported proteins and the high-quality identifications, but also the required runtime. 2) Merging the identifications of multiple search engines gives almost always more confident results and increases the number of peptides per protein group. 3) The usage of databases containing not only the canonical, but also known isoforms of proteins has a small impact on the number of reported proteins. The detection of specific isoforms could, concerning the question behind the study, compensate for slightly shorter reports using the parsimonious reports. 4) The current workflow can be easily extended to support new algorithms and search engine combinations.
|
|
| 4. |
- Chen, Jing, et al.
(författare)
-
Low-bias phosphopeptide enrichment from scarce samples using plastic antibodies
- 2015
-
Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 5
-
Tidskriftsartikel (refereegranskat)abstract
- Phosphospecific enrichment techniques and mass spectrometry (MS) are essential tools for comprehending the cellular phosphoproteome. Here, we report a fast and simple approach for low sequence-bias phosphoserine (pS) peptide capture and enrichment that is compatible with low biological or clinical sample input. The approach exploits molecularly imprinted polymers (MIPs, "plastic antibodies") featuring tight neutral binding sites for pS or pY that are capable of cross-reacting with phosphopeptides of protein proteolytic digests. The versatility of the resulting method was demonstrated with small samples of whole-cell lysate from human embryonic kidney (HEK) 293T cells, human neuroblastoma SH-SY5Y cells, mouse brain or human cerebrospinal fluid (CSF). Following pre-fractionation of trypsinized proteins by strong cation exchange (SCX) chromatography, pS-MIP enrichment led to the identification of 924 phosphopeptides in the HEK 293T whole-cell lysate, exceeding the number identified by TiO2-based enrichment (230). Moreover, the phosphopeptides were extracted with low sequence bias and showed no evidence for the characteristic preference of TiO2 for acidic amino acids (aspartic and glutamic acid). Applying the method to human CSF led to the discovery of 47 phosphopeptides belonging to 24 proteins and revealed three previously unknown phosphorylation sites.
|
|
| 5. |
- Eisenacher, Martin, et al.
(författare)
-
Getting a grip on proteomics data - Proteomics Data Collection (ProDaC)
- 2009
-
Ingår i: Proteomics. - : Wiley. - 1615-9861 .- 1615-9853. ; 9:15, s. 3928-3933
-
Tidskriftsartikel (refereegranskat)abstract
- In proteomics, rapid developments in instrumentation led to the acquisition of increasingly large data sets. Correspondingly, ProDaC was founded in 2006 as a Coordination Action project within the 6th European Union Framework Programme to support data sharing and community-wide data collection. The objectives of ProDaC were the development of documentation and storage standards, setup of a standardized data submission pipeline and collection of data. Ending in March 2009, ProDaC has delivered a comprehensive toolbox of standards and computer programs to achieve these goals.
|
|
| 6. |
- Eisenacher, Martin, et al.
(författare)
-
Proteomics Data Collection - 2nd ProDaC Workshop 5 October 2007, Seoul, Korea
- 2008
-
Ingår i: Proteomics. - : Wiley. - 1615-9861 .- 1615-9853. ; 8:7, s. 1326-1330
-
Tidskriftsartikel (refereegranskat)abstract
- Proteomics Data Collection (ProDaC) is an EU-funded Coordination Action within the 6th framework programme. It aims to simplify the publication, dissemination and utilization of proteomics data by establishing standards that will support broad data collection from the research community. As a part of ProDaC, regular workshops are organized on a half-yearly basis to enable communication and discussion of the involved partners and to report on project progress. After the kick-off meeting (October 2006) in Long Beach, CA, USA and the 1st workshop in Lyon, France (April 2007), the 2nd ProDaC workshop took place at the COEX InterContinental Hotel in Seoul, Korea, on 5th October 2007, shortly before the HUPO World Congress. The progress achieved within the first year was presented by the leaders of the work packages. Additionally, a Journal's representative talked about his experiences and future plans concerning Proteomics standards; and two further external speakers presented their research related to data handling and Proteomics repositories.
|
|
| 7. |
- Eisenacher, Martin, et al.
(författare)
-
Proteomics Data Collection - 3rd ProDaC Workshop April 22nd 2008, Toledo, Spain
- 2008
-
Ingår i: Proteomics. - : Wiley. - 1615-9861 .- 1615-9853. ; 8:20, s. 4163-4167
-
Tidskriftsartikel (refereegranskat)abstract
- The "Coordination Action" ProDaC (Proteomics Data Collection) - funded by the EU within the 6(th) framework programme - was created to support the dissemination, utilization and publication of proteomics data. Within this international consortium, standards are developed and maintained to support extensive data collection by the proteomics community. An important part of ProDaC are workshops organized on a regular basis (two per year) to allow discussions and communication between the ProDaC partners and to report on the progress of the project. The kick-off meeting took place in October 2006 in Long Beach, CA, USA. The 1(st) ProDaC workshop was held in Lyon, France (April 2007) and the 2(nd) in Seoul, Korea in October 2007. ProDaC organized the 3(rd) ProDaC workshop at the Beatriz Hotel, Toledo, on 22(nd) April, 2008, directly before the HUPO - PSI spring meeting (Human Proteome Organisation - Proteomics Standards Initiative). The work package coordinators presented talks about the progress achieved during the past six months. Additionally four external speakers presented their work on data conversion and data repositories. The concluding discussion session was chaired by the journal's representative.
|
|
| 8. |
- Eisenacher, Martin, et al.
(författare)
-
Proteomics data collection--4th ProDaC workshop 15 August 2008, Amsterdam, The Netherlands
- 2009
-
Ingår i: Proteomics. - : Wiley. - 1615-9861 .- 1615-9853. ; 9:2, s. 218-222
-
Tidskriftsartikel (refereegranskat)abstract
- ProDaC (Proteomics Data Collection), a Coordination Action within the 6th EU framework programme, was created to support the collection, distribution and public availability of data from proteomics experiments. Within the consortium standards are created and maintained enabling an extensive data collection within the proteomics community. Important elements of ProDaC are workshops held twice a year to allow communication between the ProDaC partners and to report the ongoing progress. The most recent assembly was the 4th ProDaC workshop on August 15th, 2008, in Amsterdam, The Netherlands. It took place directly before the 7th HUPO Annual World Congress (Human Proteome Organisation). Work package coordinators and partners presented the progress achieved since the last meeting. Additionally, an EU official presented funding opportunities for proteomics in the next EU framework programme and five external speakers presented talks about their work in relation to ProDaC.
|
|
| 9. |
- Eisenacher, Martin, et al.
(författare)
-
Proteomics Data Collection-5th ProDaC Workshop
- 2009
-
Ingår i: Proteomics. - : Wiley. - 1615-9861 .- 1615-9853. ; 9:14, s. 3626-3629
-
Tidskriftsartikel (refereegranskat)abstract
- The Proteomics Data Collection (ProDaC) consortium, a "Coordination Action" funded by the 6th EU Framework Programme, started in October 2006. Its aim was to facilitate the collection and distribution of proteomics data and the public availability of data sets from proteomics experiments. Within the consortium standard formats are created and tools are developed to allow extensive data collection within the proteomics community. An important part of ProDaC is the organization of workshops twice a year to inform about the consortium's progress and to stimulate communication between the ProDaC partners and between partners and interested members of the proteomics community. ProDaC ends on March 31, 2009. The most recent (and final) workshop was the 5th ProDaC workshop held on March 4, 2009 in Kolympari, Crete, Greece. The progress since the last meeting and an overall summary was presented by the work package coordinators and partners. Four external speakers presented talks about their work in relation to ProDaC.
|
|
| 10. |
|
|
