TISIDB: an integrated repository portal for tumor–immune system interactions

2019 ◽  
Vol 35 (20) ◽  
pp. 4200-4202 ◽  
Author(s):  
Beibei Ru ◽  
Ching Ngar Wong ◽  
Yin Tong ◽  
Jia Yi Zhong ◽  
Sophia Shek Wa Zhong ◽  
...  
Keyword(s):  
Immune System ◽  
High Throughput ◽  
High Throughput Screening ◽  
Supplementary Information ◽  
Literature Mining ◽  
Comprehensive Investigation ◽  
High Throughput Data ◽  
Cancer Types ◽  
User Friendly ◽  
High Throughput Data Analysis

Abstract Summary The interaction between tumor and immune system plays a crucial role in both cancer development and treatment response. To facilitate comprehensive investigation of tumor–immune interactions, we have designed a user-friendly web portal TISIDB, which integrated multiple types of data resources in oncoimmunology. First, we manually curated 4176 records from 2530 publications, which reported 988 genes related to anti-tumor immunity. Second, genes associated with the resistance or sensitivity of tumor cells to T cell-mediated killing and immunotherapy were identified by analyzing high-throughput screening and genomic profiling data. Third, associations between any gene and immune features, such as lymphocytes, immunomodulators and chemokines, were pre-calculated for 30 TCGA cancer types. In TISIDB, biologists can cross-check a gene of interest about its role in tumor–immune interactions through literature mining and high-throughput data analysis, and generate testable hypotheses and high quality figures for publication. Availability and implementation http://cis.hku.hk/TISIDB Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Investigating Molecular Mechanisms of Immunotoxicity and the Utility of ToxCast for Immunotoxicity Screening of Chemicals Added to Food

2021 ◽  
Vol 18 (7) ◽  
pp. 3332
Author(s):  
Olga V. Naidenko ◽  
David Q. Andrews ◽  
Alexis M. Temkin ◽  
Tasha Stoiber ◽  
Uloma Igara Uche ◽  
...  
Keyword(s):  
Immune System ◽  
High Throughput ◽  
Environmental Protection Agency ◽  
High Throughput Screening ◽  
Molecular Mechanisms ◽  
Specific Activity ◽  
Laboratory Animals ◽  
In Vitro Assays ◽  
Toxicological Studies

The development of high-throughput screening methodologies may decrease the need for laboratory animals for toxicity testing. Here, we investigate the potential of assessing immunotoxicity with high-throughput screening data from the U.S. Environmental Protection Agency ToxCast program. As case studies, we analyzed the most common chemicals added to food as well as per- and polyfluoroalkyl substances (PFAS) shown to migrate to food from packaging materials or processing equipment. The antioxidant preservative tert-butylhydroquinone (TBHQ) showed activity both in ToxCast assays and in classical immunological assays, suggesting that it may affect the immune response in people. From the PFAS group, we identified eight substances that can migrate from food contact materials and have ToxCast data. In epidemiological and toxicological studies, PFAS suppress the immune system and decrease the response to vaccination. However, most PFAS show weak or no activity in immune-related ToxCast assays. This lack of concordance between toxicological and high-throughput data for common PFAS indicates the current limitations of in vitro screening for analyzing immunotoxicity. High-throughput in vitro assays show promise for providing mechanistic data relevant for immune risk assessment. In contrast, the lack of immune-specific activity in the existing high-throughput assays cannot validate the safety of a chemical for the immune system.

scholarly journals High-Throughput Analysis of Selected Urinary Hydroxy Polycyclic Aromatic Hydrocarbons by an Innovative Automated Solid-Phase Microextraction

Molecules ◽  
2018 ◽  
Vol 23 (8) ◽  
pp. 1869 ◽  
Author(s):  
Stefano Dugheri ◽  
Alessandro Bonari ◽  
Matteo Gentili ◽  
Giovanni Cappelli ◽  
Ilenia Pompilio ◽  
...  
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
High Throughput ◽  
Aromatic Hydrocarbons ◽  
High Throughput Screening ◽  
Solid Phase Microextraction ◽  
Solid Phase ◽  
User Friendliness ◽  
High Throughput Analysis ◽  
Polycyclic Aromatic ◽  
User Friendly

High-throughput screening of samples is the strategy of choice to detect occupational exposure biomarkers, yet it requires a user-friendly apparatus that gives relatively prompt results while ensuring high degrees of selectivity, precision, accuracy and automation, particularly in the preparation process. Miniaturization has attracted much attention in analytical chemistry and has driven solvent and sample savings as easier automation, the latter thanks to the introduction on the market of the three axis autosampler. In light of the above, this contribution describes a novel user-friendly solid-phase microextraction (SPME) off- and on-line platform coupled with gas chromatography and triple quadrupole-mass spectrometry to determine urinary metabolites of polycyclic aromatic hydrocarbons 1- and 2-hydroxy-naphthalene, 9-hydroxy-phenanthrene, 1-hydroxy-pyrene, 3- and 9-hydroxy-benzoantracene, and 3-hydroxy-benzo[a]pyrene. In this new procedure, chromatography’s sensitivity is combined with the user-friendliness of N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide on-fiber SPME derivatization using direct immersion sampling; moreover, specific isotope-labelled internal standards provide quantitative accuracy. The detection limits for the seven OH-PAHs ranged from 0.25 to 4.52 ng/L. Intra-(from 2.5 to 3.0%) and inter-session (from 2.4 to 3.9%) repeatability was also evaluated. This method serves to identify suitable risk-control strategies for occupational hygiene conservation programs.

scholarly journals Unraveling molecular mechanism underlying biomaterial and stem cells interaction during cell fate commitment using high throughput data analysis

2020 ◽  
Author(s):  
Erfan Sharifi ◽  
Niusha Khazaei ◽  
Nicholas Kieran ◽  
Sahel Jahangiri Esfahani ◽  
Abdulshakour Mohammadnia ◽  
...  
Keyword(s):  
Stem Cells ◽  
Data Analysis ◽  
Molecular Mechanism ◽  
Cell Fate ◽  
High Throughput ◽  
High Throughput Data ◽  
High Throughput Data Analysis

High-Throughput Data Analysis of Proteomic Mass Spectra on the SwissBioGrid

2011 ◽  
pp. 228-244
Author(s):  
Andreas Quandt ◽  
Sergio Maffioletti ◽  
Cesare Pautasso ◽  
Heinz Stockinger ◽  
Frederique Lisacek
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
High Throughput ◽  
Protein Function ◽  
Mass Spectra ◽  
High Throughput Data ◽  
Analysis Process ◽  
Speed Up ◽  
Grid Infrastructures ◽  
High Throughput Data Analysis

Proteomics is currently one of the most promising fields in bioinformatics as it provides important insights into the protein function of organisms. Mass spectrometry is one of the techniques to study the proteome, and several software tools exist for this purpose. The authors provide an extendable software platform called swissPIT that combines different existing tools and exploits Grid infrastructures to speed up the data analysis process for the proteomics pipeline.

scholarly journals Dissecting differential signals in high-throughput data from complex tissues

2019 ◽  
Vol 35 (20) ◽  
pp. 3898-3905 ◽  
Author(s):  
Ziyi Li ◽  
Zhijin Wu ◽  
Peng Jin ◽  
Hao Wu
Keyword(s):  
High Throughput ◽  
Cell Types ◽  
R Package ◽  
Supplementary Information ◽  
Simulation Studies ◽  
Clinical Practices ◽  
High Throughput Data ◽  
Heterogeneous Samples ◽  
Cell Type Specific ◽  
Different Cell Types

Abstract Motivation Samples from clinical practices are often mixtures of different cell types. The high-throughput data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis, and will lead to biased results if not properly accounted for. Results We develop a method to model the high-throughput data from mixed, heterogeneous samples, and to detect differential signals. Our method allows flexible statistical inference for detecting a variety of cell-type specific changes. Extensive simulation studies and analyses of two real datasets demonstrate the favorable performance of our proposed method compared with existing ones serving similar purpose. Availability and implementation The proposed method is implemented as an R package and is freely available on GitHub (https://github.com/ziyili20/TOAST). Supplementary information Supplementary data are available at Bioinformatics online.

Prioritizing hypothesis tests for high throughput data

2015 ◽  
Vol 32 (6) ◽  
pp. 850-858 ◽  
Author(s):  
Sangjin Kim ◽  
Paul Schliekelman
Keyword(s):  
High Throughput ◽  
Correction Factor ◽  
Type I Error ◽  
Supplementary Information ◽  
Type I ◽  
Hypothesis Tests ◽  
High Throughput Data ◽  
Independent Information ◽  
Discovery Probability

Abstract Motivation: The advent of high throughput data has led to a massive increase in the number of hypothesis tests conducted in many types of biological studies and a concomitant increase in stringency of significance thresholds. Filtering methods, which use independent information to eliminate less promising tests and thus reduce multiple testing, have been widely and successfully applied. However, key questions remain about how to best apply them: When is filtering beneficial and when is it detrimental? How good does the independent information need to be in order for filtering to be effective? How should one choose the filter cutoff that separates tests that pass the filter from those that don’t? Result: We quantify the effect of the quality of the filter information, the filter cutoff and other factors on the effectiveness of the filter and show a number of results: If the filter has a high probability (e.g. 70%) of ranking true positive features highly (e.g. top 10%), then filtering can lead to dramatic increase (e.g. 10-fold) in discovery probability when there is high redundancy in information between hypothesis tests. Filtering is less effective when there is low redundancy between hypothesis tests and its benefit decreases rapidly as the quality of the filter information decreases. Furthermore, the outcome is highly dependent on the choice of filter cutoff. Choosing the cutoff without reference to the data will often lead to a large loss in discovery probability. However, naïve optimization of the cutoff using the data will lead to inflated type I error. We introduce a data-based method for choosing the cutoff that maintains control of the family-wise error rate via a correction factor to the significance threshold. Application of this approach offers as much as a several-fold advantage in discovery probability relative to no filtering, while maintaining type I error control. We also introduce a closely related method of P-value weighting that further improves performance. Availability and implementation: R code for calculating the correction factor is available at http://www.stat.uga.edu/people/faculty/paul-schliekelman. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals Unraveling molecular mechanism underlying biomaterial and stem cells interaction during cell fate commitment using high throughput data analysis

Gene ◽  
2021 ◽  
pp. 146111
Author(s):  
Erfan Sharifi ◽  
Niusha Khazaei ◽  
Nicholas W. Kieran ◽  
Sahel Jahangiri Esfahani ◽  
Abdulshakour Mohammadnia ◽  
...  
Keyword(s):  
Stem Cells ◽  
Data Analysis ◽  
Molecular Mechanism ◽  
Cell Fate ◽  
High Throughput ◽  
High Throughput Data ◽  
High Throughput Data Analysis

Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies

2020 ◽  
Author(s):  
Xiaohua Douglas Zhang ◽  
Dandan Wang ◽  
Shixue Sun ◽  
Heping Zhang
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Screening ◽  
Theoretical Basis ◽  
Sampling Error ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Automation Technology ◽  
General Public License

Abstract Motivation High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. Results In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. Availability and implementation The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. Supplementary information Supplementary data are available at Bioinformatics online.

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