scholarly journals Closing the gap in Genetic Data Analysis pipelines with ASPIREdb

2016 ◽  
Vol 37 (8) ◽  
pp. 717-717
Author(s):  
Jawahar Swaminathan
Keyword(s):  
Data Analysis ◽  
Genetic Data ◽  
Closing The Gap ◽  
Genetic Data Analysis

Genetic Data Analysis

1991 ◽  
Vol 84 (6) ◽  
pp. 639-639
Author(s):  
Louis C. Rutledge
Keyword(s):  
Data Analysis ◽  
Genetic Data ◽  
Genetic Data Analysis

Genetic Data Analysis

2021 ◽  
pp. 358-372
Author(s):  
M. Shamila ◽  
Amit Kumar Tyagi
Keyword(s):  
Data Analysis ◽  
Association Studies ◽  
Single Gene ◽  
Genetic Data ◽  
Building Blocks ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Human Beings ◽  
Genome Wide ◽  
Genetic Data Analysis

Genome-wide association studies (GWAS) or genetic data analysis is used to discover common genetic factors which influence the health of human beings and become a part of a disease. The concept of using genomics has increased in recent years, especially in e-healthcare. Today there is huge improvement required in this field or genomics. Note that the terms genomics and genetics are not similar terms here. Basically, the human genome is made up of DNA, which consists of four different chemical building blocks (called bases and abbreviated A, T, C, and G). Based on this, we differentiate each and every human being living on earth. The term ‘genetics' originated from the Greek word ‘genetikos'. It means ‘origin'. In simple terms, genetics can be defined as a branch of biology, which deals with the study of the functionalities and composition of a single gene in an organism. There are mainly three branches of genetics, which include classical genetics, molecular genetics, and population genetics.

Genetic data analysis II

1997 ◽  
Vol 13 (9) ◽  
pp. 379 ◽  
Author(s):  
Jurg Ott
Keyword(s):  
Data Analysis ◽  
Genetic Data ◽  
Genetic Data Analysis

Genetic Data Analysis II.

Biometrics ◽  
1997 ◽  
Vol 53 (1) ◽  
pp. 392 ◽  
Author(s):  
B. S. Weir
Keyword(s):  
Data Analysis ◽  
Genetic Data ◽  
Genetic Data Analysis

scholarly journals Polyglot Programming in Applications Used for Genetic Data Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Robert M. Nowak
Keyword(s):  
Data Analysis ◽  
User Interface ◽  
Data Processing ◽  
Programming Languages ◽  
High Performance ◽  
Genetic Data ◽  
Web Browser ◽  
Data Process ◽  
Genetic Data Analysis

Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.

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