Role of in silico tools and text mining in the risk assessment of selected alkaloids

2018 ◽  
Vol 295 ◽  
pp. S159
Author(s):  
L. Sousselier ◽  
G. Raitano ◽  
M. Petoumenou ◽  
E. Benfenati ◽  
N. Nguyen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Text Mining ◽  
In Silico ◽  
In Silico Tools

Role of in silico tools and text mining in the safety assessment of selected plant coumarins

2017 ◽  
Vol 280 ◽  
pp. S96
Author(s):  
Giuseppa Raitano ◽  
Emilio Benfenati ◽  
Sophia Ananiadou ◽  
Xiao Fu ◽  
Quoc Tuan Do ◽  
...  
Keyword(s):  
Text Mining ◽  
In Silico ◽  
Safety Assessment ◽  
In Silico Tools

scholarly journals Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Linjun Zhou ◽  
Deling Fan ◽  
Wei Yin ◽  
Wen Gu ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Acute Toxicity ◽  
In Silico ◽  
Water Solubility ◽  
Expert Knowledge ◽  
Aquatic Toxicity ◽  
Predictive Ability ◽  
Validation Dataset ◽  
In Silico Models ◽  
In Silico Tools

Abstract Background A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset. Results In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools. Conclusion QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.

scholarly journals Role of Splicing Regulatory Elements and In Silico Tools Usage in the Identification of Deep Intronic Splicing Variants in Hereditary Breast/Ovarian Cancer Genes

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3341
Author(s):  
Alejandro Moles-Fernández ◽  
Joanna Domènech-Vivó ◽  
Anna Tenés ◽  
Judith Balmaña ◽  
Orland Diez ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
In Silico ◽  
Regulatory Elements ◽  
Cancer Genes ◽  
Splicing Variants ◽  
Splicing Regulatory Elements ◽  
Cryptic Splice Sites ◽  
Intronic Variants ◽  
In Silico Tools

The contribution of deep intronic splice-altering variants to hereditary breast and ovarian cancer (HBOC) is unknown. Current computational in silico tools to predict spliceogenic variants leading to pseudoexons have limited efficiency. We assessed the performance of the SpliceAI tool combined with ESRseq scores to identify spliceogenic deep intronic variants by affecting cryptic sites or splicing regulatory elements (SREs) using literature and experimental datasets. Our results with 233 published deep intronic variants showed that SpliceAI, with a 0.05 threshold, predicts spliceogenic deep intronic variants affecting cryptic splice sites, but is less effective in detecting those affecting SREs. Next, we characterized the SRE profiles using ESRseq, showing that pseudoexons are significantly enriched in SRE-enhancers compared to adjacent intronic regions. Although the combination of SpliceAI with ESRseq scores (considering ∆ESRseq and SRE landscape) showed higher sensitivity, the global performance did not improve because of the higher number of false positives. The combination of both tools was tested in a tumor RNA dataset with 207 intronic variants disrupting splicing, showing a sensitivity of 86%. Following the pipeline, five spliceogenic deep intronic variants were experimentally identified from 33 variants in HBOC genes. Overall, our results provide a framework to detect deep intronic variants disrupting splicing.

scholarly journals Computational SNP Analysis and Molecular Simulation Revealed the Most Deleterious Missense Variants in the NBD1 Domain of Human ABCA1 Transporter

2020 ◽  
Vol 21 (20) ◽  
pp. 7606 ◽  
Author(s):  
Raju Dash ◽  
Md. Chayan Ali ◽  
Md. Liton Rana ◽  
Yeasmin Akter Munni ◽  
Largess Barua ◽  
...  
Keyword(s):  
In Silico ◽  
Md Simulation ◽  
Atp Binding ◽  
Snp Analysis ◽  
Pathological Effect ◽  
Atp Binding Site ◽  
Functional Consequences ◽  
Membrane Bound ◽  
In Silico Tools

The ATP-binding cassette transporter A1 (ABCA1) is a membrane-bound exporter protein involved in regulating serum HDL level by exporting cholesterol and phospholipids to load up in lipid-poor ApoA-I and ApoE, which allows the formation of nascent HDL. Mutations in the ABCA1 gene, when presents in both alleles, disrupt the canonical function of ABCA1, which associates with many disorders related to lipid transport. Although many studies have reported the phenotypic effects of a large number of ABCA1 variants, the pathological effect of non-synonymous polymorphisms (nsSNPs) in ABCA1 remains elusive. Therefore, aiming at exploring the structural and functional consequences of nsSNPs in ABCA1, in this study, we employed an integrated computational approach consisting of nine well-known in silico tools to identify damaging SNPs and molecular dynamics (MD) simulation to get insights into the magnitudes of the damaging effects. In silico tools revealed four nsSNPs as being most deleterious, where the two SNPs (G1050V and S1067C) are identified as the highly conserved and functional disrupting mutations located in the NBD1 domain. MD simulation suggested that both SNPs, G1050V and S1067C, changed the overall structural flexibility and dynamics of NBD1, and induced substantial alteration in the structural organization of ATP binding site. Taken together, these findings direct future studies to get more insights into the role of these variants in the loss of the ABCA1 function.

(Eco)toxicological maps: A new risk assessment method integrating traditional and in silico tools and its application in the Ledra River (Italy)

2018 ◽  
Vol 119 ◽  
pp. 275-286 ◽  
Author(s):  
Giuseppa Raitano ◽  
Daniele Goi ◽  
Valentina Pieri ◽  
Alice Passoni ◽  
Michele Mattiussi ◽  
...  
Keyword(s):  
Risk Assessment ◽  
In Silico ◽  
Assessment Method ◽  
Risk Assessment Method ◽  
In Silico Tools

The role of omentin in inflammation and metabolic syndrome risk assessment in women with PCO-S

2019 ◽  
Author(s):  
Iwona Zieleń-Zynek ◽  
Joanna Kowalska ◽  
Nowak Justyna ◽  
Barbara Zubelewicz-Szkodzińska
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

2019 ◽  
pp. 1-4
Author(s):  
Tikam Chand ◽  
Tikam Chand
Keyword(s):  
Gene Regulation ◽  
In Silico ◽  
Mirna Target ◽  
Target Prediction ◽  
Differential Regulation ◽  
Cancer Development ◽  
Mirna Target Prediction ◽  
Cancer Types ◽  
In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

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