ArticlePloS one2016
TCGA Expedition: A Data Acquisition and Management System for TCGA Data.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed.
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- CESCProg: a compact prognostic model and nomogram for cervical cancer based on miRNA biomarkers.PeerJ · 2023Article
- Integrative analysis of mitochondrial metabolic reprogramming in early-stage colon and liver cancer.Frontiers in oncology · 2023Article
- Identification of PTK2 as an adverse prognostic biomarker in breast cancer by integrated bioinformatics and experimental analyses.Frontiers in molecular biosciences · 2022Article
- Artificial intelligence unifies knowledge and actions in drug repositioning.Emerging topics in life sciences · 2021Review
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- Prediction of prognosis of patients with lung cancer in combination with the immune score.Bioscience reports · 2021Article
- Bioinformatics analysis of differentially expressed miRNAs in non-small cell lung cancer.Journal of clinical laboratory analysis · 2021Article
- Bioinformatics analysis identifies DYNC1I1 as prognosis marker in male patients with liver hepatocellular carcinoma.PloS one · 2021Article
- PD-1 Coexpression Gene Analysis and the Regulatory Network in Endometrial Cancer Based on Bioinformatics Analysis.BioMed research international · 2021Article
- Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer.BMC surgery · 2020Article
- Identification of a nine-gene prognostic signature for gastric carcinoma using integrated bioinformatics analyses.World journal of gastrointestinal oncology · 2020Article
- Downregulation of miR‑193a‑3p via targeting cyclin D1 in thyroid cancer.Molecular medicine reports · 2020Article
- A novel microRNA signature for pathological grading in lung adenocarcinoma based on TCGA and GEO data.International journal of molecular medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
backgroundThe Cancer Genome Atlas Project (TCGA) is a National Cancer Institute effort to profile at least 500 cases of 20 different tumor types using genomic platforms and to make these data, both raw and processed, available to all researchers. TCGA data are currently over 1.2 Petabyte in size and include whole genome sequence (WGS), whole exome sequence, methylation, RNA expression, proteomic, and clinical datasets. Publicly accessible TCGA data are released through public portals, but many challenges exist in navigating and using data obtained from these sites. We developed TCGA Expedition to support the research community focused on computational methods for cancer research. Data obtained, versioned, and archived using TCGA Expedition supports command line access at high-performance computing facilities as well as some functionality with third party tools. For a subset of TCGA data collected at University of Pittsburgh, we also re-associate TCGA data with de-identified data from the electronic health records. Here we describe the software as well as the architecture of our repository, methods for loading of TCGA data to multiple platforms, and security and regulatory controls that conform to federal best practices.
resultsTCGA Expedition software consists of a set of scripts written in Bash, Python and Java that download, extract, harmonize, version and store all TCGA data and metadata. The software generates a versioned, participant- and sample-centered, local TCGA data directory with metadata structures that directly reference the local data files as well as the original data files. The software supports flexible searches of the data via a web portal, user-centric data tracking tools, and data provenance tools. Using this software, we created a collaborative repository, the Pittsburgh Genome Resource Repository (PGRR) that enabled investigators at our institution to work with all TCGA data formats, and to interrogate these data with analysis pipelines, and associated tools. WGS data are especially challenging for individual investigators to use, due to issues with downloading, storage, and processing; having locally accessible WGS BAM files has proven invaluable.
conclusionOur open-source, freely available TCGA Expedition software can be used to create a local collaborative infrastructure for acquiring, managing, and analyzing TCGA data and other large public datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.