Technical Pitfalls and Biases in Molecular Biology
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biology".
Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 32829
Special Issue Editor
Interests: nerve injury and neuropathic pain; pain and aging; central adaptations to chronic pain; multiple sclerosis; neuroinflammation; neuro-immunologic communication; redox signaling; nitric oxide; endocannabinoids and other lipid signaling molecules; progranulin; autophagy
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Special Issue Information
Dear Colleagues,
The enormous progress of research techniques and tools and a steadily growing wealth of data increases the risk of putative technical errors, analytical biases, and erroneous analysis or mal-interpretation of research data. This Special Issue offers a platform to publish such errors and biases to increase the awareness and to improve the measures against such biases. For example, the off-target effects of gene manipulation or pharmacological interventions may produce unexpected effects, transgenic models may turn out to be something else, antibodies may un-specifically detect other proteins, alternative methods for normalization of omics data may yield controversial results, DNA/RNA extraction procedures may have a stronger effect on results than genotype or treatment, and differences between labs or day-to-day variability may be greater than the biological variability.
This Special Issue aims to gather a unique collection of original research articles and reviews, that address the occurrence, recognition, and avoidance of biases and errors and sharpen the awareness of experimental and data-analysis-based pitfalls. We welcome the submission of articles that cover, but are not limited to, the following topics:
- Analytical biases owing to sample handling or pre-analytical processing
- Impact of extraction procedures for DNA/RNA, proteins, metabolites, etc.
- Off-target effects of drugs or gene/protein manipulations
- Un-specificity of immune – or RNA-based detection and quantification methods
- Data-analysis based biases owing to normalization, data transformation, low expression genes/protein, low sample sizes, use of statistical methods, etc.
- Variability between labs or researchers versus biological variability
- Confounding influences of environmental factors
- Methods for visualizing biases
Prof. Dr. Irmgard Tegeder
Guest Editor
Manuscript Submission Information
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Keywords
- analytical and technical biases
- molecular biology
- reproducibility
- biological versus technical variability
- agreement of continuous variables
- artifacts
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