In-Silico Analysis of Gene ALS2 Genetic Variants Identified in the Affected Horses and Humans With Motor Neuron Disease

Abstract Abstract Background: Low maximal oxygen uptake (VO2max) is a strong and independent risk factor for all-cause and cardiovascular disease (CVD) mortality. Although physical activity is a major determinant of VO2maxlevel, genetics contribution is estimated to be ∼50%. Methods We performed a genetic association study on 123.545 single-nucleotide polymorphisms (SNPs) and directly measured VO2max in 3470 individuals (exploration cohort). The candidate SNPs were subsequently analyzed in a separate cohort of 718 individuals (validation cohort), in addition to 7 wild-card SNPs previously associated with VO2max, but not included on the chip used in the exploration cohort. Sub-analyses were performed for each gender. In silico analysis and genotype-phenotype databases were used to predict physiological function of the SNPs. Results In the exploration cohort, 42 SNPs were associated with VO2max (p<5.0×10–4). Six of the candidate SNPs were also found to be associated with VO2max in the validation cohort (p<0.05, either in men, women or both), in addition to three wild-card SNPs. By using these nine SNPs we created a genetic score for inborn VO2max-level. Together, these nine SNPs explained ∼8% of the variation in VO2max, and discriminate individuals with inborn high versus low VO2max based on simultaneous carriage of multiple favorable alleles. The cumulative number of favorable SNPs correlated negatively with the presence of several CVDrisk factors, e.g. waist-circumference, visceral fat, fat %, cholesterol levels and BMI. In silico analysis indicated that several of the SNPs influence gene expression across multiple organs, including adipose tissue, skeletal muscle and heart. Conclusion We identified six novel genetic variants associated with VO2max, and validated three SNPs previously associated with fitness related traits. Acknowledgement/Funding K.G. Jebsen Foundation, the Norwegian Health Association, the Liaison Committee between the Central Norway Regional Health Authority (RHA) and NTNU

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A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

Seminars in Oncology ◽

10.1053/j.seminoncol.2020.02.012 ◽

2020 ◽

Vol 47 (6) ◽

pp. 398-408

Author(s):

Sonam Tulsyan ◽

Showket Hussain ◽

Balraj Mittal ◽

Sundeep Singh Saluja ◽

Pranay Tanwar ◽

...

Keyword(s):

Systematic Review ◽

Gallbladder Carcinoma ◽

In Silico ◽

In Silico Analysis ◽

Transcriptomic Profile ◽

Silico Analysis

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In Silico Analysis of Single Nucleotide Polymorphism in Human Prothrombin Gene

10.1055/s-0039-1680245 ◽

2019 ◽

Author(s):

I. Farah ◽

A. El-Mubark ◽

M. Osman ◽

A. Soliman ◽

F. Ali ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

In Silico ◽

In Silico Analysis ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Prothrombin Gene ◽

Silico Analysis

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Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

Current Medicinal Chemistry ◽

10.2174/0929867327666200727114410 ◽

2020 ◽

Vol 27 (38) ◽

pp. 6523-6535 ◽

Cited By ~ 3

Author(s):

Antreas Afantitis ◽

Andreas Tsoumanis ◽

Georgia Melagraki

Keyword(s):

Data Analysis ◽

Virtual Screening ◽

In Silico ◽

Model Development ◽

In Silico Analysis ◽

Material Design ◽

Efficient Solutions ◽

Biological Data ◽

Cost Efficient ◽

Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

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