Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles)

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the United States National Cancer Institute convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: 1) an evolving list of comprehensive quality metrics and 2) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in the Journal of Proteome Research, Molecular and Cellular Proteomics, Proteomics, and Proteomics Clinical Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.     

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam principles)

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed upon two primary needs for the wide use of quality metrics: (i) an evolving list of comprehensive quality metrics and (ii) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in Proteomics, Proteomics Clinical Applications, Journal of Proteome Research, and Molecular and Cellular Proteomics, as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.     

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles)

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: (1) an evolving list of comprehensive quality metrics and (2) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in the Journal of Proteome Research, Molecular and Cellular Proteomics, Proteomics, and Proteomics Clinical Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.     

Recommendations on treatment for IPF

Patient management in Idiopathic Pulmonary Fibrosis (IPF) is largely based on societal guidelines and recommendations. A recent update by the American Thoracic Society (ATS), European Respiratory Society (ERS), Japanese Respiratory Society (JRS) and Latin American Thoracic Association (ALAT) provided updated guidance on the diagnosis and management of IPF, along with recommendations on pharmacologic and non-pharmacologic approaches to patient management. The treatment guidance is based on GRADE criteria, which rates the quality of evidence according to previously published methodology. Here we discuss how to interpret the recent guideline updates and the implications of this guidance for clinical practice. In addition we discuss the assessment and recommendations for a number of pharmacological agents that have been the focus of clinical trials over the past years. Although no single pharmacological agent was recommended by the guidelines committee, we discuss how since then, more recent data have resulted in the approval of pirfenidone in Europe, and preliminary negative findings regarding the safety of a triple therapy regimen consisting of prednisone, azathioprine and N-acetylcysteine have raised the question of whether it is no longer a treatment option. As clinicians, we must interpret the available guidance and recommendations as we consider each individual patient and as we discuss the available clinical data and the patient’s own preferences in our approach to the management of this disease.     

Effects of benzodiazepines on frog ERG

Effect of flurazepam (water-soluble benzodiazepine) on the amplitude and time course of ERG waves was investigated in superfused frog eyecups (Rana ridibunda). Flurazepam (50 and 100 microM) had inhibitory effect on the b- and d-wave amplitude, which was not accompanied with significant changes in their implicit time. Flurazepam potentiated the depressant effect of GABA (2.5 and 5 mM) on the b- and d-wave amplitude. The inhibitory effect of flurazepam was not blocked by 50 microM bicuculline (BCC), (GABA(A) antagonist), although the blocker markedly potentiated the b- and d-wave amplitude. The suppressive effect of flurazepam on the b- but not d-wave amplitude was blocked by 100 microM BCC. Our results indicate existence of functional benzodiazepine regulatory sites on GABA(A) receptors in distal frog retina.     

Neuroimaging evidence for processes underlying repetition of ignored stimuli

Prolonged response times are observed with targets having been presented as distractors immediately before, called negative priming effect. Among others, inhibitory and retrieval processes have been suggested underlying this behavioral effect. As those processes would involve different neural activation patterns, a functional magnetic resonance imaging (fMRI) study including 28 subjects was conducted. Two tasks were used to investigate stimulus repetition effects. One task focused on target location, the other on target identity. Both tasks are known to elicit the expected response time effects. However, there is less agreement about the relationship of those tasks with the explanatory accounts under consideration. Based on within-subject comparisons we found clear differences between the experimental repetition conditions and the neutral control condition on neural level for both tasks. Hemodynamic fronto-striatal activation patterns occurred for the location-based task favoring the selective inhibition account. Hippocampal activation found for the identity-based task suggests an assignment to the retrieval account; however, this task lacked a behavioral effect.     

Recommendations for the analysis of gene expression data to identify intrinsic differences between similar tissues

Identifying genes involved in functional differences between similar tissues from expression profiles is challenging, because the expected differences in expression levels are small. To exemplify this challenge, we studied the expression profiles of two skeletal muscles, deltoid and biceps, in healthy individuals. We provide a series of guides and recommendations for the analysis of this type of studies. These include how to account for batch effects and inter-individual differences to optimize the detection of gene signatures associated with tissue function. We provide guidance on the selection of optimal settings for constructing gene co-expression networks through parameter sweeps of settings and calculation of the overlap with an established knowledge network. Our main recommendation is to use a combination of the data-driven approaches, such as differential gene expression analysis and gene co-expression network analysis, and hypothesis-driven approaches, such as gene set connectivity analysis. Accordingly, we detected differences in metabolic gene expression between deltoid and biceps that were supported by both data- and hypothesis-driven approaches. Finally, we provide a bioinformatic framework that support the biological interpretation of expression profiles from related tissues from this combination of approaches, which is available at github.com/tabbassidaloii/AnalysisFrameworkSimilarTissues.