| 1. |
- Apweiler, Rolf, et al.
(författare)
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Approaching clinical proteomics : current state and future fields of application in cellular proteomics
- 2009
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Ingår i: Cytometry. Part A : the journal of the International Society for Analytical Cytology. - : Wiley. - 1552-4922. ; 75A:10, s. 816-832
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Forskningsöversikt (refereegranskat)abstract
- Recent developments in proteomics technology offer new opportunities for clinical applications in hospital or specialized laboratories including the identification of novel biomarkers, monitoring of disease, detecting adverse effects of drugs, and environmental hazards. Advanced spectrometry technologies and the development of new protein array formats have brought these analyses to a standard, which now has the potential to be used in clinical diagnostics. Besides standardization of methodologies and distribution of proteomic data into public databases, the nature of the human body fluid proteome with its high dynamic range in protein concentrations, its quantitation problems, and its extreme complexity present enormous challenges. Molecular cell biology (cytomics) with its link to proteomics is a new fast moving scientific field, which addresses functional cell analysis and bioinformatic approaches to search for novel cellular proteomic biomarkers or their release products into body fluids that provide better insight into the enormous biocomplexity of disease processes and are suitable for patient stratification, therapeutic monitoring, and prediction of prognosis. Experience from studies of in vitro diagnostics and especially in clinical chemistry showed that the majority of errors occurs in the preanalytical phase and the setup of the diagnostic strategy. This is also true for clinical proteomics where similar preanalytical variables such as inter- and intra-assay variability due to biological variations or proteolytical activities in the sample will most likely also influence the results of proteomics studies. However, before complex proteomic analysis can be introduced at a broader level into the clinic, standardization of the preanalytical phase including patient preparation, sample collection, sample preparation, sample storage, measurement, and data analysis is another issue which has to be improved. In this report, we discuss the recent advances and applications that fulfill the criteria for clinical proteomics with the focus on cellular proteomics (cytoproteomics) as related to preanalytical and analytical standardization and to quality control measures required for effective implementation of these technologies and analytes into routine laboratory testing to generate novel actionable health information. It will then be crucial to design and carry out clinical studies that can eventually identify novel clinical diagnostic strategies based on these techniques and validate their impact on clinical decision making.
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| 2. |
- Apweiler, Rolf, et al.
(författare)
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Approaching clinical proteomics : current state and future fields of application in fluid proteomics
- 2009
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Ingår i: Clinical Chemistry and Laboratory Medicine. - 1434-6621 .- 1437-4331. ; 47:6, s. 724-744
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Forskningsöversikt (refereegranskat)abstract
- The field of clinical proteomics offers opportunities to identify new disease biomarkers in body fluids, cells and tissues. These biomarkers can be used in clinical applications for diagnosis, stratification of patients for specific treatment, or therapy monitoring. New protein array formats and improved spectrometry technologies have brought these analyses to a level with potential for use in clinical diagnostics. The nature of the human body fluid proteome with its large dynamic range of protein concentrations presents problems with quantitation. The extreme complexity of the proteome in body fluids presents enormous challenges and requires the establishment of standard operating procedures for handling of specimens, increasing sensitivity for detection and bioinformatical tools for distribution of proteomic data into the public domain. From studies of in vitro diagnostics, especially in clinical chemistry, it is evident that most errors occur in the preanalytical phase and during implementation of the diagnostic strategy. This is also true for clinical proteomics, and especially for fluid proteomics because of the multiple pretreatment processes. These processes include depletion of high-abundance proteins from plasma or enrichment processes for urine where biological variation or differences in proteolytic activities in the sample along with preanalytical variables such as inter- and intra-assay variability will likely influence the results of proteomics studies. However, before proteomic analysis can be introduced at a broader level into the clinical setting, standardization of the preanalytical phase including patient preparation, sample collection, sample preparation, sample storage, measurement and data analysis needs to be improved. In this review, we discuss the recent technological advances and applications that fulfil the criteria for clinical proteomics, with the focus on fluid proteomics. These advances relate to preanalytical factors, analytical standardization and quality-control measures required for effective implementation into routine laboratory testing in order to generate clinically useful information. With new disease biomarker candidates, it will be crucial to design and perform clinical studies that can identify novel diagnostic strategies based on these techniques, and to validate their impact on clinical decision-making.
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| 3. |
- 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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| 4. |
- Keshavan, Sandeep, et al.
(författare)
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Profiling of sub‐lethal in vitro effects of multi‐walled carbon nanotubes reveals changes in chemokines and chemokine receptors
- 2021
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Ingår i: Nanomaterials. - : MDPI AG. - 2079-4991. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- Engineered nanomaterials are potentially very useful for a variety of applications, but studies are needed to ascertain whether these materials pose a risk to human health. Here, we studied three benchmark nanomaterials (Ag nanoparticles, TiO2 nanoparticles, and multi‐walled carbon nanotubes, MWCNTs) procured from the nanomaterial repository at the Joint Research Centre of the European Commission. Having established a sub‐lethal concentration of these materials using two human cell lines representative of the immune system and the lungs, respectively, we performed RNA sequencing of the macrophage‐like cell line after exposure for 6, 12, and 24 h. Downstream analysis of the transcriptomics data revealed significant effects on chemokine signaling pathways. CCR2 was identified as the most significantly upregulated gene in MWCNT‐exposed cells. Using multiplex assays to evaluate cytokine and chemokine secretion, we could show significant effects of MWCNTs on several chemokines, including CCL2, a ligand of CCR2. The results demonstrate the importance of evaluating sub‐lethal concentrations of nanomaterials in relevant target cells.
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| 5. |
- Klau, Gunnar W., et al.
(författare)
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Integer linear programming approaches for non-unique probe selection
- 2007
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Ingår i: Discrete Applied Mathematics. - : Elsevier BV. - 0166-218X. ; 155, s. 840-856
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Tidskriftsartikel (refereegranskat)abstract
- In addition to their prevalent use for analyzing gene expression, DNA microarrays are an efficient tool for biological, medical, and industrial applications because of their ability to assess the presence or absence of biological agents, the targets, in a sample. Given a collection of genetic sequences of targets one faces the challenge of finding short oligonucleotides, the probes, which allow detection of targets in a sample by hybridization experiments. The experiments are conducted using either unique or non-unique probes, and the problem at hand is to compute a minimal design, i.e., a minimal set of probes that allows to infer the targets in the sample from the hybridization results. If we allow to test for more than one target in the sample, the design of the probe set becomes difficult in the case of non-unique probes. Building upon previous work on group testing for microarrays we describe the first approach to select a minimal probe set for the case of non-unique probes in the presence of a small number of multiple targets in the sample. The approach is based on an integer linear programming formulation and a branch-and-cut algorithm. Our implementation significantly reduces the number of probes needed while preserving the decoding capabilities of existing approaches. © 2006 Elsevier B.V. All rights reserved.
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| 6. |
- Klau, Gunnar W, et al.
(författare)
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Optimal robust non-unique probe selection using Integer Linear Programming.
- 2004
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Ingår i: Bioinformatics (Oxford, England). - : Oxford University Press (OUP). - 1367-4811 .- 1367-4803 .- 1460-2059. ; 20 Suppl 1
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Tidskriftsartikel (refereegranskat)abstract
- Besides their prevalent use for analyzing gene expression, microarrays are an efficient tool for biological, medical and industrial applications due to their ability to assess the presence or absence of biological agents, the targets, in a sample. Given a collection of genetic sequences of targets one faces the challenge of finding short oligonucleotides, the probes, which allow detection of targets in a sample. Each hybridization experiment determines whether the probe binds to its corresponding sequence in the target. Depending on the problem, the experiments are conducted using either unique or non-unique probes and usually assume that only one target is present in the sample. The problem at hand is to compute a design, i.e. a minimal set of probes that allows to infer the targets in the sample from the result of the hybridization experiment. If we allow to test for more than one target in the sample, the design of the probe set becomes difficult in the case of non-unique probes.Building upon previous work on group testing for microarrays, we describe the first approach to select a minimal probe set for the case of non-unique probes in the presence of a small number of multiple targets in the sample. The approach is based on an ILP formulation and a branch-and-cut algorithm. Our preliminary implementation greatly reduces the number of probes needed while preserving the decoding capabilities.http://www.inf.fu-berlin.de/inst/ag-bio
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| 7. |
- Marschall, Tobias, et al.
(författare)
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Computational pan-genomics : status, promises and challenges
- 2018
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; 19:1, s. 118-135
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Tidskriftsartikel (refereegranskat)abstract
- Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.
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