ArticleBMC systems biology2014
STATegra EMS: an Experiment Management System for complex next-generation omics experiments.
Article in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Data Integration Challenges for Machine Learning in Precision Medicine.Frontiers in medicine · 2021Review
- Computational Oncology in the Multi-Omics Era: State of the Art.Frontiers in oncology · 2020Review
- Making multi-omics data accessible to researchers.Scientific data · 2019Article
- STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse.Scientific data · 2019Article
- The metaRbolomics Toolbox in Bioconductor and beyond.Metabolites · 2019Review
- A feature selection strategy for gene expression time series experiments with hidden Markov models.PloS one · 2019Article
- The eBioKit, a stand-alone educational platform for bioinformatics.PLoS computational biology · 2017Article
- Computational challenges in modeling gene regulatory events.Transcription · 2016Article
- Ten Simple Rules for Experiments' Provenance.PLoS computational biology · 2015Article
- Data integration in the era of omics: current and future challenges.BMC systems biology · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput sequencing assays are now routinely used to study different aspects of genome organization. As decreasing costs and widespread availability of sequencing enable more laboratories to use sequencing assays in their research projects, the number of samples and replicates in these experiments can quickly grow to several dozens of samples and thus require standardized annotation, storage and management of preprocessing steps. As a part of the STATegra project, we have developed an Experiment Management System (EMS) for high throughput omics data that supports different types of sequencing-based assays such as RNA-seq, ChIP-seq, Methyl-seq, etc, as well as proteomics and metabolomics data. The STATegra EMS provides metadata annotation of experimental design, samples and processing pipelines, as well as storage of different types of data files, from raw data to ready-to-use measurements. The system has been developed to provide research laboratories with a freely-available, integrated system that offers a simple and effective way for experiment annotation and tracking of analysis procedures.
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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.