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Topical Collection "Technical Pitfalls and Biases in Molecular Biology"

Editor

Prof. Dr. Irmgard Tegeder
E-Mail Website
Collection Editor
Pharmazentrum Frankfurt, Dept. of Clinical Pharmacology, Goethe-University of Frankfurt, Theodor Stern Kai 7, Bd. 74, 4th Fl, 60590 Frankfurt am Main, Germany
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
Special Issues and Collections in MDPI journals

Topical Collection 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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • analytical and technical biases
  • molecular biology
  • reproducibility
  • biological versus technical variability
  • agreement of continuous variables
  • artifacts

Published Papers (3 papers)

2021

Jump to: 2019

Communication
Evaluation of Stable LifeAct-mRuby2- and LAMP1-NeonGreen Expressing A549 Cell Lines for Investigation of Aspergillus fumigatus Interaction with Pulmonary Cells
Int. J. Mol. Sci. 2021, 22(11), 5965; https://doi.org/10.3390/ijms22115965 - 31 May 2021
Viewed by 464
Abstract
Inhaled Aspergillus fumigatus spores can be internalized by alveolar type II cells. Cell lines stably expressing fluorescently labeled components of endocytic pathway enable investigations of intracellular organization during conidia internalization and measurement of the process kinetics. The goal of this report was to [...] Read more.
Inhaled Aspergillus fumigatus spores can be internalized by alveolar type II cells. Cell lines stably expressing fluorescently labeled components of endocytic pathway enable investigations of intracellular organization during conidia internalization and measurement of the process kinetics. The goal of this report was to evaluate the methodological appliance of cell lines for studying fungal conidia internalization. We have generated A549 cell lines stably expressing fluorescently labeled actin (LifeAct-mRuby2) and late endosomal protein (LAMP1-NeonGreen) following an evaluation of cell-pathogen interactions in live and fixed cells. Our data show that the LAMP1-NeonGreen cell line can be used to visualize conidia co-localization with LAMP1 in live and fixed cells. However, caution is necessary when using LifeAct-mRuby2-cell lines as it may affect the conidia internalization dynamics. Full article
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Figure 1

2019

Jump to: 2021

Article
Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data
Int. J. Mol. Sci. 2020, 21(1), 79; https://doi.org/10.3390/ijms21010079 - 20 Dec 2019
Cited by 5 | Viewed by 743
Abstract
Advances in flow cytometry enable the acquisition of large and high-dimensional data sets per patient. Novel computational techniques allow the visualization of structures in these data and, finally, the identification of relevant subgroups. Correct data visualizations and projections from the high-dimensional space to [...] Read more.
Advances in flow cytometry enable the acquisition of large and high-dimensional data sets per patient. Novel computational techniques allow the visualization of structures in these data and, finally, the identification of relevant subgroups. Correct data visualizations and projections from the high-dimensional space to the visualization plane require the correct representation of the structures in the data. This work shows that frequently used techniques are unreliable in this respect. One of the most important methods for data projection in this area is the t-distributed stochastic neighbor embedding (t-SNE). We analyzed its performance on artificial and real biomedical data sets. t-SNE introduced a cluster structure for homogeneously distributed data that did not contain any subgroup structure. In other data sets, t-SNE occasionally suggested the wrong number of subgroups or projected data points belonging to different subgroups, as if belonging to the same subgroup. As an alternative approach, emergent self-organizing maps (ESOM) were used in combination with U-matrix methods. This approach allowed the correct identification of homogeneous data while in sets containing distance or density-based subgroups structures; the number of subgroups and data point assignments were correctly displayed. The results highlight possible pitfalls in the use of a currently widely applied algorithmic technique for the detection of subgroups in high dimensional cytometric data and suggest a robust alternative. Full article
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Article
Discrimination of DNA Methylation Signal from Background Variation for Clinical Diagnostics
Int. J. Mol. Sci. 2019, 20(21), 5343; https://doi.org/10.3390/ijms20215343 - 27 Oct 2019
Cited by 3 | Viewed by 1221
Abstract
Advances in the study of human DNA methylation variation offer a new avenue for the translation of epigenetic research results to clinical applications. Although current approaches to methylome analysis have been helpful in revealing an epigenetic influence in major human diseases, this type [...] Read more.
Advances in the study of human DNA methylation variation offer a new avenue for the translation of epigenetic research results to clinical applications. Although current approaches to methylome analysis have been helpful in revealing an epigenetic influence in major human diseases, this type of analysis has proven inadequate for the translation of these advances to clinical diagnostics. As in any clinical test, the use of a methylation signal for diagnostic purposes requires the estimation of an optimal cutoff value for the signal, which is necessary to discriminate a signal induced by a disease state from natural background variation. To address this issue, we propose the application of a fundamental signal detection theory and machine learning approaches. Simulation studies and tests of two available methylome datasets from autism and leukemia patients demonstrate the feasibility of this approach in clinical diagnostics, providing high discriminatory power for the methylation signal induced by disease, as well as high classification performance. Specifically, the analysis of whole biomarker genomic regions could suffice for a diagnostic, markedly decreasing its cost. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Current projection methods-induced biases at subgroup detection for machine-learning based data-analysis of biomedical data
Authors: Jörn Lötsch and Alfred Ultsch

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