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Consequences of the discontinuation of the International Protein Index (IPI) database and its substitution by the UniProtKB "complete proteome" sets.

ABSTRACT: The International Protein Index (IPI) database has been one of the most widely used protein databases in MS proteomics approaches. Recently, the closure of IPI in September 2011 was announced. Its recommended replacement is the new UniProt Knowledgebase (UniProtKB) "complete proteome" sets, launched in May 2011. Here, we analyze the consequences of IPI's discontinuation for human and mouse data, and the effect of its substitution with UniProtKB on two levels: (i) data already produced and (ii) newly performed experiments. To estimate the effect on existing data, we investigated how well IPI identifiers map to UniProtKB accessions. We found that 21% of human and 10% of mouse identifiers do not map to UniProtKB and would thus be "lost." To investigate the impact on new experiments, we compared the theoretical search space (i.e. the tryptic peptides) of both resources and found that it is decreased by 14.0% for human and 8.9% for mouse data through IPI's closure. An analysis on the experimental evidence for these "lost" peptides showed that the vast majority has not been identified in experiments available in the major proteomics repositories. It thus seems likely that the search space provided by UniProtKB is of higher quality than the one currently provided by IPI.

SUBMITTER: Griss J

PROVIDER: S-EPMC3556690 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Consequences of the discontinuation of the International Protein Index (IPI) database and its substitution by the UniProtKB "complete proteome" sets.

Griss Johannes J Martín María M O'Donovan Claire C Apweiler Rolf R Hermjakob Henning H Vizcaíno Juan Antonio JA

Proteomics 20111017 22

The International Protein Index (IPI) database has been one of the most widely used protein databases in MS proteomics approaches. Recently, the closure of IPI in September 2011 was announced. Its recommended replacement is the new UniProt Knowledgebase (UniProtKB) "complete proteome" sets, launched in May 2011. Here, we analyze the consequences of IPI's discontinuation for human and mouse data, and the effect of its substitution with UniProtKB on two levels: (i) data already produced and (ii) n ...[more]

PMID: 21932440

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Project description:BackgroundSARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated.ObjectiveThe aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.MethodsWe followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia.ResultsThe internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68.ConclusionsOur results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

Dataset Information

Consequences of the discontinuation of the International Protein Index (IPI) database and its substitution by the UniProtKB "complete proteome" sets.

Publications

Consequences of the discontinuation of the International Protein Index (IPI) database and its substitution by the UniProtKB "complete proteome" sets.

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