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Keller A.,Rosetta Biosoftware
Methods in molecular biology (Clifton, N.J.) | Year: 2011

The LC-MS/MS shotgun proteomics workflow is widely used to identify and quantify sample peptides and proteins. The technique, however, presents a number of challenges for large-scale use, including the diverse raw data file formats output by mass spectrometers, the large false positive rate among peptide assignments to MS/MS spectra, and the loss of connectivity between identified peptides and the sample proteins that gave rise to them. Here we describe the Trans-Proteomic Pipeline, a freely available open source software suite that provides uniform analysis of LC-MS/MS data from raw data to quantified sample proteins. In a straightforward manner, users can extract MS/MS information from raw data of many instrument formats, submit them to search engines for peptide identification, validate the results to remove false hits, combine together results of multiple search engines, infer sample proteins that gave rise to the identified peptides, and perform quantitation at the peptide and protein levels.


Paweletz C.P.,Merck And Co. | Wiener M.C.,Merck And Co. | Bondarenko A.Y.,Rosetta Biosoftware | Bondarenko A.Y.,Microsoft | And 19 more authors.
Journal of Proteome Research | Year: 2010

The rapid identification of protein biomarkers in biofluids is important to drug discovery and development. Here, we describe a general proteomic approach for the discovery and identification of proteins that exhibit a statistically significant difference in abundance in cerebrospinal fluid (CSF) before and after pharmacological intervention. This approach, differential mass spectrometry (dMS), is based on the analysis of full scan mass spectrometry data. The dMS workflow does not require complex mixing and pooling strategies, or isotope labeling techniques. Accordingly, clinical samples can be analyzed individually, allowing the use of longitudinal designs and within-subject data analysis in which each subject acts as its own control. As a proof of concept, we performed multifactorial dMS analyses on CSF samples drawn at 6 time points from n = 6 cisterna magna ported (CMP) rhesus monkeys treated with 2 potent gamma secretase inhibitors (GSI) or comparable vehicle in a 3-way crossover study that included a total of 108 individual CSF samples. Using analysis of variance and statistical filtering on the aligned and normalized LC-MS data sets, we detected 26 features that were significantly altered in CSF by drug treatment. Of those 26 features, which belong to 10 distinct isotopic distributions, 20 were identified by MS/MS as 7 peptides from CD99, a cell surface protein. Six features from the remaining 3 isotopic distributions were not identified. A subsequent analysis showed that the relative abundance of these 26 features showed the same temporal profile as the ELISA measured levels of CSF Abeta 42 peptide, a known pharmacodynamic marker for y-secretase inhibition. These data demonstrate that dMS is a promising approach for the discovery, quantification, and identification of candidate target engagement biomarkers in CSF. © 2010 American Chemical Society.


PubMed | Rosetta Biosoftware, University of Oxford and Merck And Co.
Type: Clinical Trial | Journal: PloS one | Year: 2015

Disease modifying treatments for Alzheimers disease (AD) constitute a major goal in medicine. Current trends suggest that biomarkers reflective of AD neuropathology and modifiable by treatment would provide supportive evidence for disease modification. Nevertheless, a lack of quantitative tools to assess disease modifying treatment effects remains a major hurdle. Cerebrospinal fluid (CSF) biochemical markers such as total tau, p-tau and Ab42 are well established markers of AD; however, global quantitative biochemical changes in CSF in AD disease progression remain largely uncharacterized. Here we applied a high resolution open discovery platform, dMS, to profile a cross-sectional cohort of lumbar CSF from post-mortem diagnosed AD patients versus those from non-AD/non-demented (control) patients. Multiple markers were identified to be statistically significant in the cohort tested. We selected two markers SME-1 (p<0.0001) and SME-2 (p = 0.0004) for evaluation in a second independent longitudinal cohort of human CSF from post-mortem diagnosed AD patients and age-matched and case-matched control patients. In cohort-2, SME-1, identified as neuronal secretory protein VGF, and SME-2, identified as neuronal pentraxin receptor-1 (NPTXR), in AD were 21% (p = 0.039) and 17% (p = 0.026) lower, at baseline, respectively, than in controls. Linear mixed model analysis in the longitudinal cohort estimate a decrease in the levels of VGF and NPTXR at the rate of 10.9% and 6.9% per year in the AD patients, whereas both markers increased in controls. Because these markers are detected by mass spectrometry without the need for antibody reagents, targeted MS based assays provide a clear translation path for evaluating selected AD disease-progression markers with high analytical precision in the clinic.


Zhang L.,Genomics Group | Yin S.,Genomics Group | Miclaus K.,SAS Institute | Chierici M.,Fondazione Bruno Kessler | And 6 more authors.
Pharmacogenomics Journal | Year: 2010

The robustness of genome-wide association study (GWAS) results depends on the genotyping algorithms used to establish the association. This paper initiated the assessment of the impact of the Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) genotyping quality on identifying real significant genes in a GWAS with large sample sizes. With microarray image data from the Wellcome Trust Case-Control Consortium (WTCCC), 1991 individuals with coronary artery disease (CAD) and 1500 controls, genetic associations were evaluated under various batch sizes and compositions. Experimental designs included different batch sizes of 250, 350, 500, 2000 samples with different distributions of cases and controls in each batch with either randomized or simply combined (4:3 case-control ratios) or separate case-control samples as well as whole 3491 samples. The separate composition could create 2-3% discordance in the single nucleotide polymorphism (SNP) results for quality control/statistical analysis and might contribute to the lack of reproducibility between GWAS. CRLMM shows high genotyping accuracy and stability to batch effects. According to the genotypic and allelic tests (P5.0 × 10 -7), nine significant signals on chromosome 9 were found consistently in all batch sizes with combined design. Our findings are critical to optimize the reproducibility of GWAS and confirm the genetic role in the pathophysiology of CAD. © 2010 Macmillan Publishers Limited. All rights reserved.


Chierici M.,Fondazione Bruno Kessler | Miclaus K.,SAS Institute | Vega S.,Rosetta Biosoftware | Furlanello C.,Fondazione Bruno Kessler
Pharmacogenomics Journal | Year: 2010

The discordance in results between independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. To identify the causes of variability underlying lack of reproducibility, here we present the results of a repeatability experiment on GWAS on a cohort of 1991 coronary artery disease individuals and 1500 controls (National Blood Service) provided by the Wellcome Trust Case Control Consortium. As part of the MicroArray Quality Control project, we identified quality control (QC) and association analysis steps with a major impact on the identification of candidate markers for possible classifiers. Different experimental conditions were used with the CHIAMO calling algorithm to assess the effects of batch size and case-control composition on downstream association analysis. Results showed that both composition and size create discordant single-nucleotide polymorphism (SNP) results for QC and statistical analysis and may contribute to the lack of reproducibility in GWAS. An interactive effect of batch size and composition contributes to discordant results in significantly associated loci. About 800 significant SNPs (Cochran-Armitage trend test, P5.0 × 10-7) were found for batches of 2000 samples with separated cases and controls, whereas only 14 significant markers were found with one batch of all samples. © 2010 Macmillan Publishers Limited. All rights reserved.


PubMed | Rosetta Biosoftware
Type: | Journal: Methods in molecular biology (Clifton, N.J.) | Year: 2010

The LC-MS/MS shotgun proteomics workflow is widely used to identify and quantify sample peptides and proteins. The technique, however, presents a number of challenges for large-scale use, including the diverse raw data file formats output by mass spectrometers, the large false positive rate among peptide assignments to MS/MS spectra, and the loss of connectivity between identified peptides and the sample proteins that gave rise to them. Here we describe the Trans-Proteomic Pipeline, a freely available open source software suite that provides uniform analysis of LC-MS/MS data from raw data to quantified sample proteins. In a straightforward manner, users can extract MS/MS information from raw data of many instrument formats, submit them to search engines for peptide identification, validate the results to remove false hits, combine together results of multiple search engines, infer sample proteins that gave rise to the identified peptides, and perform quantitation at the peptide and protein levels.


Rosetta Biosoftware | Entity website

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News Article | June 2, 2009
Site: www.cnet.com

Microsoft said on Monday it is buying the assets of Rosetta Biosoftware, a unit of Merck, as part of an effort to expand into the life sciences software arena. The Rosetta technology will be used to add genetic and genomic data management abilities to Microsoft's recently announced Amalga Life Sciences effort. As part of the deal, Merck will now become an Amalga customer, Microsoft said, Merck will also "provide strategic input to Microsoft on the direction and evolution of new solutions incorporating Rosetta Biosoftware technologies." "This agreement establishes a stable and sustainable platform for the Rosetta Biosoftware technology," Merck Research Laboratories VP Rupert Vessey said in a statement. Microsoft, which has a separate Amalga product family for hospitals, announced in April that it would offer Microsoft Amalga Life Sciences as an effort to help in the drug research software arena. The tools are designed to help manage and analyze the large amounts of data gathered in the process of designing new drugs. The Merck deal is expected to close at the end of June 2009, and the new Amalga Life Sciences platform incorporating Rosetta Biosoftware technologies should be available in early 2010, Microsoft said.


News Article | November 21, 2016
Site: www.newsmaker.com.au

WiseGuyReports.Com Publish a New Market Research Report On – “Bioinformatics 2016 Global Market Share,Growth,Trends & Forecast to 2021”. This report studies the global Bioinformatics market, analyzes and researches the Bioinformatics development status and forecast in United States, EU, Japan, China, India and Southeast Asia. This report focuses on the top players in global market, like  IBM Life Sciences  BIOVIA  Life Technologies Corporation  Agilent technologies  3rd Millennium  Celera Corporation  Affymetrix, BioWisdom  Rosetta Biosoftware For more information or any query mail at [email protected] Market segment by Type, Bioinformatics can be split into  Bio-content processing  Metabolomics  Molecular Phylogenetics Market segment by Application, Bioinformatics can be split into  Hospitals & Clinics  Healthcare institutions  Research & Clinical laboratories 1 Industry Overview      1.1 Bioinformatics Market Overview        1.1.1 Bioinformatics Product Scope        1.1.2 Market Status and Outlook      1.2 Global Bioinformatics Market Size and Analysis by Regions        1.2.1 United States        1.2.2 EU        1.2.3 Japan        1.2.4 China        1.2.5 India        1.2.6 Southeast Asia      1.3 Bioinformatics Market by Type        1.3.1 Genomics        1.3.2 Metabolomics        1.3.3 Molecular Phylogenetics        1.3.4 Chemoinformatics and Drug Designing        1.3.4 Proteomics        1.3.4 Transcriptomics      1.4 Bioinformatics Market by End Users/Application        1.4.1 Hospitals & Clinics        1.4.2 Healthcare institutions        1.4.3 Research & Clinical laboratories 2 Global Bioinformatics Competition Analysis by Players      2.1 Bioinformatics Market Size (Value) by Players (2015-2016)      2.2 Competitive Status and Trend        2.2.1 Market Concentration Rate        2.2.2 Product/Service Differences        2.2.3 New Entrants        2.2.4 The Technology Trends in Future 3 Company (Top Players) Profiles      3.1 IBM Life Sciences        3.1.1 Company Profile        3.1.2 Main Business/Business Overview        3.1.3 Products, Services and Solutions        3.1.4 Bioinformatics Revenue (Value) (2011-2016)        3.1.5 Recent Developments      3.2 BIOVIA        3.2.1 Company Profile        3.2.2 Main Business/Business Overview        3.2.3 Products, Services and Solutions        3.2.4 Bioinformatics Revenue (Value) (2011-2016)        3.2.5 Recent Developments      3.3 Life Technologies Corporation        3.3.1 Company Profile        3.3.2 Main Business/Business Overview        3.3.3 Products, Services and Solutions        3.3.4 Bioinformatics Revenue (Value) (2011-2016)        3.3.5 Recent Developments      3.4 Agilent technologies        3.4.1 Company Profile        3.4.2 Main Business/Business Overview        3.4.3 Products, Services and Solutions        3.4.4 Bioinformatics Revenue (Value) (2011-2016)        3.4.5 Recent Developments      3.5 3rd Millennium        3.5.1 Company Profile        3.5.2 Main Business/Business Overview        3.5.3 Products, Services and Solutions        3.5.4 Bioinformatics Revenue (Value) (2011-2016)        3.5.5 Recent Developments      3.6 Celera Corporation        3.6.1 Company Profile        3.6.2 Main Business/Business Overview        3.6.3 Products, Services and Solutions        3.6.4 Bioinformatics Revenue (Value) (2011-2016)        3.6.5 Recent Developments      3.7 Affymetrix, BioWisdom        3.7.1 Company Profile        3.7.2 Main Business/Business Overview        3.7.3 Products, Services and Solutions        3.7.4 Bioinformatics Revenue (Value) (2011-2016)        3.7.5 Recent Developments      3.8 Rosetta Biosoftware        3.8.1 Company Profile        3.8.2 Main Business/Business Overview        3.8.3 Products, Services and Solutions        3.8.4 Bioinformatics Revenue (Value) (2011-2016)        3.8.5 Recent Developments 4 Global Bioinformatics Market Size by Type and Application (2011-2016)      4.1 Global Bioinformatics Market Size by Type (2011-2016)      4.2 Global Bioinformatics Market Size by Application (2011-2016)      4.3 Potential Application of Bioinformatics in Future      4.4 Top Consumer / End Users of Bioinformatics 5 United States Bioinformatics Development Status and Outlook      5.1 United States Bioinformatics Market Size (2011-2016)      5.2 United States Bioinformatics Market Size and Market Share by Players (2015-2016) For more information or any query mail at [email protected] Wise Guy Reports is part of the Wise Guy Consultants Pvt. Ltd. and offers premium progressive statistical surveying, market research reports, analysis & forecast data for industries and governments around the globe. Wise Guy Reports features an exhaustive list of market research reports from hundreds of publishers worldwide. We boast a database spanning virtually every market category and an even more comprehensive collection of market research reports under these categories and sub-categories.

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