ReviewThe AAPS journal2016
The FDA's Experience with Emerging Genomics Technologies-Past, Present, and Future.
Review in The AAPS journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed.
- Prospective target assessment and multimodal prediction of survival for personalized and risk-adapted treatment strategies in multiple myeloma in the GMMG-MM5 multicenter trial.Journal of hematology & oncology · 2019Trial
- Using synthetic chromosome controls to evaluate the sequencing of difficult regions within the human genome.Genome biology · 2022Article
- The Sequencing Quality Control 2 study: establishing community standards for sequencing in precision medicine.Genome biology · 2021Article
- Transcriptome from opaque cornea of Fanconi anemia patient uncovers fibrosis and two connected players.Molecular genetics and metabolism reports · 2021Article
- Utility of Extrapolating Human S1500+ Genes to the Whole Transcriptome: Tunicamycin Case Study.Bioinformatics and biology insights · 2020Article
- DNA methylation signatures as biomarkers of prior environmental exposures.Current epidemiology reports · 2019Article
- Enabling precision medicine via standard communication of HTS provenance, analysis, and results.PLoS biology · 2018Article
- Population-based dose-response analysis of liver transcriptional response to trichloroethylene in mouse.Mammalian genome : official journal of the International Mammalian Genome Society · 2018Article
- A generic Transcriptomics Reporting Framework (TRF) for 'omics data processing and analysis.Regulatory toxicology and pharmacology : RTP · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The rapid advancement of emerging genomics technologies and their application for assessing safety and efficacy of FDA-regulated products require a high standard of reliability and robustness supporting regulatory decision-making in the FDA. To facilitate the regulatory application, the FDA implemented a novel data submission program, Voluntary Genomics Data Submission (VGDS), and also to engage the stakeholders. As part of the endeavor, for the past 10 years, the FDA has led an international consortium of regulatory agencies, academia, pharmaceutical companies, and genomics platform providers, which was named MicroArray Quality Control Consortium (MAQC), to address issues such as reproducibility, precision, specificity/sensitivity, and data interpretation. Three projects have been completed so far assessing these genomics technologies: gene expression microarrays, whole genome genotyping arrays, and whole transcriptome sequencing (i.e., RNA-seq). The resultant studies provide the basic parameters for fit-for-purpose application of these new data streams in regulatory environments, and the solutions have been made available to the public through peer-reviewed publications. The latest MAQC project is also called the SEquencing Quality Control (SEQC) project focused on next-generation sequencing. Using reference samples with built-in controls, SEQC studies have demonstrated that relative gene expression can be measured accurately and reliably across laboratories and RNA-seq platforms. Besides prediction performance comparable to microarrays in clinical settings and safety assessments, RNA-seq is shown to have better sensitivity for low expression and reveal novel transcriptomic features. Future effort of MAQC will be focused on quality control of whole genome sequencing and targeted sequencing.
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Identifiers
What OpenQuestion holds
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.