ArticleBMC research notes2014
Implementation of Cloud based next generation sequencing data analysis in a clinical laboratory.
Article in BMC research notes, 2014. 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
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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
9 citing papers in PubMed.
- Fluorescent optotracers for bacterial and biofilm detection and diagnostics.Science and technology of advanced materials · 2023Review
- Metagenomics in diagnosis and improved targeted treatment of UTI.World journal of urology · 2020Review
- Somatic Mosaicism in a Male Patient With X-linked Alport Syndrome.Kidney international reports · 2019Article
- Next generation sequencing for clinical diagnostics: Five year experience of an academic laboratory.Molecular genetics and metabolism reports · 2019Article
- Cloud computing applications for biomedical science: A perspective.PLoS computational biology · 2018Article
- A privacy-preserving solution for compressed storage and selective retrieval of genomic data.Genome research · 2016Article
- The long tail and rare disease research: the impact of next-generation sequencing for rare Mendelian disorders.Genetics research · 2015Review
- Bioregulatory systems medicine: an innovative approach to integrating the science of molecular networks, inflammation, and systems biology with the patient's autoregulatory capacity?Frontiers in physiology · 2015Article
- Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe introduction of next generation sequencing (NGS) has revolutionized molecular diagnostics, though several challenges remain limiting the widespread adoption of NGS testing into clinical practice. One such difficulty includes the development of a robust bioinformatics pipeline that can handle the volume of data generated by high-throughput sequencing in a cost-effective manner. Analysis of sequencing data typically requires a substantial level of computing power that is often cost-prohibitive to most clinical diagnostics laboratories.
findingsTo address this challenge, our institution has developed a Galaxy-based data analysis pipeline which relies on a web-based, cloud-computing infrastructure to process NGS data and identify genetic variants. It provides additional flexibility, needed to control storage costs, resulting in a pipeline that is cost-effective on a per-sample basis. It does not require the usage of EBS disk to run a sample.
conclusionsWe demonstrate the validation and feasibility of implementing this bioinformatics pipeline in a molecular diagnostics laboratory. Four samples were analyzed in duplicate pairs and showed 100% concordance in mutations identified. This pipeline is currently being used in the clinic and all identified pathogenic variants confirmed using Sanger sequencing further validating the software.
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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.