Sgdi: System for Genomic Data Integration

Carey, Vincent J.; Gentry, Jeff; Sarkar, Deepayan; Gentleman, Robert; Ramaswamy, Srini

doi:10.1142/9789812776136_0016

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…The process of downloading clinically annotated public genomic data and proceeding to a final computational analysis is, despite recent efforts (4, 5), still long and prone to errors. This is particularly true when the various data sets need to be comparable for meta-analyses, which requires a fully standardized annotation.…”

Section: Discussionmentioning

confidence: 99%

“…Two common problems of publicly available genomic data are the scarcity of clinical annotation and inconsistent definitions of clinical characteristics across independent data sets (5). In our review of original papers and curation of clinical annotations, we were however able to retain, in most studies, the clinical variables of proven importance: overall survival, age, optimal debulking surgery, tumour histology, grade and stage (Figure 2).…”

Section: Discussionmentioning

confidence: 99%

“…These are among the only databases that offer basics such as uniform gene identifiers to enable cross-study analysis, and then for only the most common microarray technologies. Carey et al (5) describe a framework for the curation, annotation and storage of microarray and high-throughput data in general. This framework allows, for example, institutions to provide researchers access to in-house and public data in a standardized and convenient fashion.…”

Section: Introductionmentioning

confidence: 99%

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curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome

et al. 2013

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This article introduces a manually curated data collection for gene expression meta-analysis of patients with ovarian cancer and software for reproducible preparation of similar databases. This resource provides uniformly prepared microarray data for 2970 patients from 23 studies with curated and documented clinical metadata. It allows users to efficiently identify studies and patient subgroups of interest for analysis and to perform meta-analysis immediately without the challenges posed by harmonizing heterogeneous microarray technologies, study designs, expression data processing methods and clinical data formats. We confirm that the recently proposed biomarker CXCL12 is associated with patient survival, independently of stage and optimal surgical debulking, which was possible only through meta-analysis owing to insufficient sample sizes of the individual studies. The database is implemented as the curatedOvarianData Bioconductor package for the R statistical computing language, providing a comprehensive and flexible resource for clinically oriented investigation of the ovarian cancer transcriptome. The package and pipeline for producing it are available from http://bcb.dfci.harvard.edu/ovariancancer.Database URL: http://bcb.dfci.harvard.edu/ovariancancer

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome

et al. 2013

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“…Currently, this package includes 49 TB transcriptomic datasets with well-annotated metadata, which serves as a curated resource that allows researchers to make efficient use of currently existing TB gene expression profiles. One of the most important outstanding problems for public transcriptomic TB datasets is that some of studies lack clinical information, and others may possess inconsistent definition of clinical features across different datasets (32). In our curation efforts, we were able to retrieve important clinical characteristics including age, gender, geographical region, TB status, etc., for most of the studies.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Cross-Study Replicability of Tuberculosis Gene Signatures Using 49 Curated Transcriptomic Datasets

Wang,

Harper,

Sinha

et al. 2023

Preprint

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BackgroundTuberculosis (TB) is the leading cause of infectious disease mortality worldwide. Numerous blood-based gene expression signatures have been proposed in the literature as alternative tools for diagnosing TB infection. Ongoing efforts are actively focused on developing additional signatures in other TB-related contexts. However, the generalizability of these signatures to different patient contexts is not well-characterized. There is a pressing need for a well-curated database of TB gene expression studies for the systematic assessment of existing and newly developed TB gene signatures.ResultsWe built the curatedTBData, a manually-curated database of 49 TB transcriptomic studies. This data resource is freely available through GitHub and as an R Bioconductor package that allows users to validate new and existing biomarkers without the challenges of harmonizing heterogeneous studies. We also demonstrate the use of this data resource with cross-study comparisons for 72 TB gene signatures. For the comparison of subjects with active TB from healthy controls, 19 gene signatures had weighted mean AUC of 0.90 or greater, with the highest result of 0.94. In active TB disease versus latent TB infection, 7 gene signatures had weighted mean AUC of 0.90 or greater, with a maximum of 0.93. We also explore ensembling methods for averaging predictions from multiple gene signatures to significantly improve diagnostic ability beyond any single signature.ConclusionsThe curatedTBData data package offers a comprehensive resource of curated gene expression and clinically annotated data. It could be used to identify robust new TB gene signatures, to perform comparative analysis of existing TB gene signatures, and to develop alternative gene set scoring or ensembling methods, among other things. This resource will also facilitate the development of new signatures that are generalizable across cohorts or more applicable to specific subsets of patients (e.g. with rare comorbid conditions, etc.). We demonstrated that these blood-based gene signatures could distinguish patients with distinct TB outcomes; moreover, the combination of multiple gene signatures could improve the overall predictive accuracy in differentiating these subtypes, which point out an important aspect for the translation of genomics to clinical implementation.

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Biomedical Informatics for Cancer Research

Ochs

Casagrande

Davuluri

2010

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Sgdi: System for Genomic Data Integration

Cited by 3 publications

References 12 publications

curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome

curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome

Analysis of the Cross-Study Replicability of Tuberculosis Gene Signatures Using 49 Curated Transcriptomic Datasets

Biomedical Informatics for Cancer Research

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