| 1. |
- Almeida, Natália, et al.
(författare)
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Mapping the melanoma plasma proteome (MPP) using single-shot proteomics interfaced with the WiMT database
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:24
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Tidskriftsartikel (refereegranskat)abstract
- Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.
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| 2. |
- Audain, Enrique, et al.
(författare)
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In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics
- 2017
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Ingår i: Journal of Proteomics. - : Elsevier BV. - 1874-3919. ; 150, s. 170-182
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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.
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| 3. |
- Deutsch, Eric W., et al.
(författare)
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Expanding the Use of Spectral Libraries in Proteomics
- 2018
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 17:12, s. 4051-4060
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Tidskriftsartikel (refereegranskat)abstract
- The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on ABSTRACT: The 2017 Dagstuhl Seminar on Computational the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.
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| 4. |
- Griss, Johannes, et al.
(författare)
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Response to "Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra"
- 2018
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Ingår i: Journal of Proteome Research. - : AMER CHEMICAL SOC. - 1535-3893 .- 1535-3907. ; 17:5, s. 1993-1996
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Tidskriftsartikel (refereegranskat)abstract
- In the recent benchmarking article entitled "Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra", Rieder et al. compared several different approaches to cluster MS/MS spectra. While we certainly recognize the value of the manuscript, here, we report some shortcomings detected in the original analyses. For most analyses, the authors clustered only single MS/MS runs. In one of the reported analyses, three MS/MS runs were processed together, which already led to computational performance issues in many of the tested approaches. This fact highlights the difficulties of using many of the tested algorithms on the nowadays produced average proteomics data sets. Second, the authors only processed identified spectra when merging MS runs. Thereby, all unidentified spectra that are of lower quality were already removed from the data set and could not influence the clustering results. Next, we found that the authors did not analyze the effect of chimeric spectra on the clustering results. In our analysis, we found that 3% of the spectra in the used data sets were chimeric, and this had marked effects on the behavior of the different clustering algorithms tested. Finally, the authors' choice to evaluate the MS-Cluster and spectra-cluster algorithms using a precursor tolerance of 5 Da for high-resolution Orbitrap data only was, in our opinion, not adequate to assess the performance of MS/MS clustering approaches.
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| 5. |
- Grüning, Björn, et al.
(författare)
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Bioconda: A sustainable and comprehensive software distribution for the life sciences
- 2017
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multi-platform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.
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| 6. |
- Jagtap, Pratik D., et al.
(författare)
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The Association of Biomolecular Resource Facilities Proteome Informatics Research Group Study on Metaproteomics (iPRG-2020)
- 2023
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Ingår i: Journal of biomolecular techniques : JBT. - : Association of Biomolecular Resource Facilities. - 1943-4731. ; 34:3
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Tidskriftsartikel (refereegranskat)abstract
- Metaproteomics research using mass spectrometry data has emerged as a powerful strategy to understand the mechanisms underlying microbiome dynamics and the interaction of microbiomes with their immediate environment. Recent advances in sample preparation, data acquisition, and bioinformatics workflows have greatly contributed to progress in this field. In 2020, the Association of Biomolecular Research Facilities Proteome Informatics Research Group launched a collaborative study to assess the bioinformatics options available for metaproteomics research. The study was conducted in 2 phases. In the first phase, participants were provided with mass spectrometry data files and were asked to identify the taxonomic composition and relative taxa abundances in the samples without supplying any protein sequence databases. The most challenging question asked of the participants was to postulate the nature of any biological phenomena that may have taken place in the samples, such as interactions among taxonomic species. In the second phase, participants were provided a protein sequence database composed of the species present in the sample and were asked to answer the same set of questions as for phase 1. In this report, we summarize the data processing methods and tools used by participants, including database searching and software tools used for taxonomic and functional analysis. This study provides insights into the status of metaproteomics bioinformatics in participating laboratories and core facilities.
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| 7. |
- LeDuc, Richard D., et al.
(författare)
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Proteomics Standards Initiative's ProForma 2.0 : Unifying the Encoding of Proteoforms and Peptidoforms br
- 2022
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 21:4, s. 1189-1195
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Tidskriftsartikel (refereegranskat)abstract
- It is important for the proteomics community to have a standardizedmanner to represent all possible variations of a protein or peptide primary sequence,including natural, chemically induced, and artifactual modifications. The HumanProteome Organization Proteomics Standards Initiative in collaboration with severalmembers of the Consortium for Top-Down Proteomics (CTDP) has developed astandard notation called ProForma 2.0, which is a substantial extension of the originalProForma notation developed by the CTDP. ProForma 2.0 aims to unify therepresentation of proteoforms and peptidoforms. ProForma 2.0 supports use casesneeded for bottom-up and middle-/top-down proteomics approaches and allows theencoding of highly modified proteins and peptides using a human- and machine-readable string. ProForma 2.0 can be used to represent protein modifications in a specified or ambiguous location, designated bymass shifts, chemical formulas, or controlled vocabulary terms, including cross-links (natural and chemical) and atomic isotopes.Notational conventions are based on public controlled vocabularies and ontologies. The most up-to-date full specification documentand information about software implementations are available athttp://psidev.info/proforma.
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| 8. |
- Luo, Xiyang, et al.
(författare)
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A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics
- 2022
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 21:6, s. 1566-1574
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Tidskriftsartikel (refereegranskat)abstract
- Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-duster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.
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| 9. |
- Moreno, Pablo, et al.
(författare)
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Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Making reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e.g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator.
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| 10. |
- Murillo, Jimmy Rodriguez, et al.
(författare)
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Mass spectrometry evaluation of a neuroblastoma SH-SY5Y cell culture protocol
- 2018
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Ingår i: Analytical Biochemistry. - : Elsevier BV. - 0003-2697. ; 559, s. 51-54
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Tidskriftsartikel (refereegranskat)abstract
- Cell line-based proteomics studies are susceptible to intrinsic biological variation that contributes to increasing false positive claims; most of the methods that follow these changes offer a limited understanding of the biological system. We applied a quantitative proteomic strategy (iTRAQ) to detect intrinsic protein variation across SH-SY5Y cell culture replicates. More than 95% of the quantified proteins presented a coefficient of variation (CV) < 20% between biological replicates and the variable proteins, which included cytoskeleton, cytoplasmic and housekeeping proteins, are widely reported in proteomic studies. We recommend this approach as an additional quality control before starting any proteomic experiment.
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