A uniform proteomics MS/MS analysis platform utilizing open XML file formats

Keller, Andrew; Eng, Jimmy K.; Zhang, Ning; Li, Xiaojun; Aebersold, Ruedi

doi:10.1038/msb4100024

Cited by 655 publications

(658 citation statements)

References 26 publications

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“…Due to its flexibility granted by semi-structured characteristics, XML represents the most popular resource for the exchange of both formats and data, and thus is well suited for biological data [43][44][45][46]. Defined XML schemata act as mediator, allowing multiple input formats to be subjected to a common data analysis pipeline (mzXML) [47]. Since then, several proteomics toolkits, such as OpenMS [48] or the software collection from the "Trans-Proteomic Pipeline" (http:// tools.proteomecenter.org/software.php) provide a set of computational tools for gel-free proteomics [47].…”

Section: Discussionmentioning

confidence: 99%

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

Nebrich¹,

Herrmann²,

Hartl³

et al. 2009

Proteomics

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In recent years proteomics became increasingly important to functional genomics. Although a large amount of data is generated by high throughput large-scale techniques, a connection of these mostly heterogeneous data from different analytical platforms and of different experiments is limited. Data mining procedures and algorithms are often insufficient to extract meaningful results from large datasets and therefore limit the exploitation of the generated biological information. In our proteomic core facility, which almost exclusively focuses on 2-DE/MS-based proteomics, we developed a proteomic database custom tailored to our needs aiming at connecting MS protein identification information to 2-DE derived protein expression profiles. The tools developed should not only enable an automatic evaluation of single experiments, but also link multiple 2-DE experiments with MS-data on different levels and thereby helping to create a comprehensive network of our proteomics data. Therefore the key feature of our "PROTEOMER" database is its high cross-referencing capacity, enabling integration of a wide range of experimental data. To illustrate the workflow and utility of the system, two practical examples are provided to demonstrate that proper data cross-referencing can transform information into biological knowledge.

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Section: Discussionmentioning

confidence: 99%

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

Nebrich¹,

Herrmann²,

Hartl³

et al. 2009

Proteomics

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show abstract

“…QTools, which are in-house developed visual basic macros (available from: www.dieter-wolf-lab.org/protocols) for automated spectral count analysis, were used to compute spectral counts of the proteins, using the PeptideProphet output from the trans-proteomic pipeline (TPP; Institute for Systems Biology, Seattle, WA; 30 ).…”

Section: Methodsmentioning

confidence: 99%

Systems analysis of the prostate tumor suppressor NKX3.1 supports roles in DNA repair and luminal cell differentiation

Yang

Chung²,

et al. 2014

F1000Res

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NKX3.1 is a homeobox transcription factor whose function as a prostate tumor suppressor remains insufficiently understood because neither the transcriptional program governed by NKX3.1, nor its interacting proteins have been fully revealed. Using affinity purification and mass spectrometry, we have established an extensive NKX3.1 interactome which contains the DNA repair proteins Ku70, Ku80, and PARP, thus providing a molecular underpinning to previous reports implicating NKX3.1 in DNA repair. Transcriptomic profiling of NKX3.1-negative prostate epithelial cells acutely expressing NKX3.1 revealed a rapid and complex response that is a near mirror image of the gene expression signature of human prostatic intraepithelial neoplasia (PIN). Pathway and network analyses suggested that NKX3.1 actuates a cellular reprogramming toward luminal cell differentiation characterized by suppression of pro-oncogenic c-MYC and interferon-STAT signaling and activation of tumor suppressor pathways. Consistently, ectopic expression of NKX3.1 conferred a growth arrest depending on TNFα and JNK signaling. We propose that the tumor suppressor function of NKX3.1 entails a transcriptional program that maintains the differentiation state of secretory luminal cells and that disruption of NKX3.1 contributes to prostate tumorigenesis by permitting luminal cell de-differentiation potentially augmented by defects in DNA repair.

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“…Especially, the tools from the Trans Proteomic Pipeline (TPP) [15] (Institute for Systems Biology, Seattle, WA, USA) are integrated, among them PeptideProphet [16] and ProteinProphet [17]. Quantitation is integrated through XPRESS [18] and Q3 [19].…”

Section: Installationmentioning

confidence: 99%

Using Laboratory Information Management Systems as central part of a proteomics data workflow

et al. 2010

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The organization and storage of proteomics data are challenging issues today and even more for the rising amount of information in the future. This review article describes the advantages of using Laboratory Information Management Systems (LIMS) in proteomics laboratories. Seven typical LIMS are explored in detail to describe their role in an even bigger interrelation. They are a central part of the proteomics data workflow, starting with data generation and ending with the publication in journals and repositories. Therefore, they enable community‐wide data utilization and further Systems Biology discoveries.

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A uniform proteomics MS/MS analysis platform utilizing open XML file formats

Cited by 655 publications

References 26 publications

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

Systems analysis of the prostate tumor suppressor NKX3.1 supports roles in DNA repair and luminal cell differentiation

Using Laboratory Information Management Systems as central part of a proteomics data workflow

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