ReviewCancer informatics2015
An NGS Workflow Blueprint for DNA Sequencing Data and Its Application in Individualized Molecular Oncology.
Review in Cancer informatics, 2015. 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
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
10 citing papers in PubMed.
- Overview of exosomal non-coding RNAs in cardiovascular disease using high throughput sequencing.European journal of pharmacology · 2025Review
- Genetic Profiling of Acute and Chronic Leukemia via Next-Generation Sequencing: Current Insights and Future Perspectives.Hematology reports · 2025Review
- Molecular insights into regulatory RNAs in the cellular machinery.Experimental & molecular medicine · 2024Review
- Polymorphisms in NQO1 and MPO genes and risk for bladder cancer in Tunisian population.Molecular genetics & genomic medicine · 2021Article
- Pathogenicity and Virulence Factors ofMicroorganisms · 2020Review
- Gene Annotation in High Schools: Successful Student Pipeline and Teacher Professional Development in Bioscience Using GENI-ACT.Frontiers in microbiology · 2020Article
- Application of next-generation sequencing technology to precision medicine in cancer: joint consensus of the Tumor Biomarker Committee of the Chinese Society of Clinical Oncology.Cancer biology & medicine · 2019Article
- Promising clinical application of ctDNA in evaluating immunotherapy efficacy.American journal of cancer research · 2018Review
- Robust in-silico identification of cancer cell lines based on next generation sequencing.Oncotarget · 2017Article
- Methods and novel technology for microRNA quantification in colorectal cancer screening.Clinical epigenetics · 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
No grant is acknowledged in the PubMed record.
Abstract
Next-generation sequencing (NGS) technologies that have advanced rapidly in the past few years possess the potential to classify diseases, decipher the molecular code of related cell processes, identify targets for decision-making on targeted therapy or prevention strategies, and predict clinical treatment response. Thus, NGS is on its way to revolutionize oncology. With the help of NGS, we can draw a finer map for the genetic basis of diseases and can improve our understanding of diagnostic and prognostic applications and therapeutic methods. Despite these advantages and its potential, NGS is facing several critical challenges, including reduction of sequencing cost, enhancement of sequencing quality, improvement of technical simplicity and reliability, and development of semiautomated and integrated analysis workflow. In order to address these challenges, we conducted a literature research and summarized a four-stage NGS workflow for providing a systematic review on NGS-based analysis, explaining the strength and weakness of diverse NGS-based software tools, and elucidating its potential connection to individualized medicine. By presenting this four-stage NGS workflow, we try to provide a minimal structural layout required for NGS data storage and reproducibility.
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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.